diff --git a/.gitignore b/.gitignore
index 05a8e1cf..3a9ad0b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,11 @@
node_modules
.rpt2_cache
.env*
+
tmp
proto
weights_uncompressed
-weights_unused
\ No newline at end of file
+weights_unused
+docs
+out
+build
\ No newline at end of file
diff --git a/.npmignore b/.npmignore
index 69b21017..48c03f67 100644
--- a/.npmignore
+++ b/.npmignore
@@ -6,5 +6,7 @@ proto
weights
weights_uncompressed
weights_unused
+src
test
-tools
\ No newline at end of file
+tools
+docs
\ No newline at end of file
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 00000000..70b26bae
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,25 @@
+sudo: required
+language: node_js
+node_js:
+ #- "node"
+ - "12"
+ - "11"
+ - "10"
+ - "8"
+ # node 6 is not compatible with tfjs-node
+ # - "6"
+services:
+ - xvfb
+env:
+ global:
+ - BACKEND_CPU=true EXCLUDE_UNCOMPRESSED=true
+ matrix:
+ - ENV=browser
+ - ENV=node
+addons:
+ chrome: stable
+install: npm install
+script:
+ - if [ $ENV == 'browser' ]; then npm run test-browser; fi
+ - if [ $ENV == 'node' ]; then npm run test-node; fi
+ - npm run build
\ No newline at end of file
diff --git a/README.md b/README.md
index 8d3ea09a..df98850e 100644
--- a/README.md
+++ b/README.md
@@ -1,177 +1,204 @@
# face-api.js
+[](https://travis-ci.org/justadudewhohacks/face-api.js)
[](https://slack.bri.im)
-**JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API ([tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core))**
+**JavaScript face recognition API for the browser and nodejs implemented on top of tensorflow.js core ([tensorflow/tfjs-core](https://github.com/tensorflow/tfjs))**
-Check out my face-api.js tutorials:
+
+
+## **[Click me for Live Demos!](https://justadudewhohacks.github.io/face-api.js/)**
+
+## Tutorials
* **[face-api.js — JavaScript API for Face Recognition in the Browser with tensorflow.js](https://itnext.io/face-api-js-javascript-api-for-face-recognition-in-the-browser-with-tensorflow-js-bcc2a6c4cf07)**
* **[Realtime JavaScript Face Tracking and Face Recognition using face-api.js’ MTCNN Face Detector](https://itnext.io/realtime-javascript-face-tracking-and-face-recognition-using-face-api-js-mtcnn-face-detector-d924dd8b5740)**
+* **[Realtime Webcam Face Detection And Emotion Recognition - Video](https://youtu.be/CVClHLwv-4I)**
+* **[Easy Face Recognition Tutorial With JavaScript - Video](https://youtu.be/AZ4PdALMqx0)**
+* **[Using face-api.js with Vue.js and Electron](https://medium.com/@andreas.schallwig/do-not-laugh-a-simple-ai-powered-game-3e22ad0f8166)**
+* **[Add Masks to People - Gant Laborde on Learn with Jason](https://www.learnwithjason.dev/fun-with-machine-learning-pt-2)**
-**Check out the live demos [here](https://justadudewhohacks.github.io/face-api.js/)!**
-
-Table of Contents:
+## Table of Contents
+* **[Features](#features)**
* **[Running the Examples](#running-the-examples)**
-* **[About the Package](#about-the-package)**
- * **[Face Detection - SSD Mobilenet v1](#about-face-detection-ssd)**
- * **[Face Detection - Tiny Yolo v2](#about-face-detection-yolo)**
- * **[Face Detection & 5 Point Face Landmarks - MTCNN](#about-face-detection-mtcnn)**
- * **[Face Recognition](#about-face-recognition)**
- * **[68 Point Face Landmark Detection](#about-face-landmark-detection)**
-* **[Usage](#usage)**
- * **[Loading the Models](#usage-load-models)**
- * **[Face Detection - SSD Mobilenet v1](#usage-face-detection-ssd)**
- * **[Face Detection - Tiny Yolo v2](#usage-face-detection-yolo)**
- * **[Face Detection & 5 Point Face Landmarks - MTCNN](#usage-face-detection-mtcnn)**
- * **[Face Recognition](#usage-face-recognition)**
- * **[68 Point Face Landmark Detection](#usage-face-landmark-detection)**
- * **[Shortcut Functions for Full Face Description](#shortcut-functions)**
-
-## Examples
+* **[face-api.js for the Browser](#face-api.js-for-the-browser)**
+* **[face-api.js for Nodejs](#face-api.js-for-nodejs)**
+* **[Usage](#getting-started)**
+ * **[Loading the Models](#getting-started-loading-models)**
+ * **[High Level API](#high-level-api)**
+ * **[Displaying Detection Results](#getting-started-displaying-detection-results)**
+ * **[Face Detection Options](#getting-started-face-detection-options)**
+ * **[Utility Classes](#getting-started-utility-classes)**
+ * **[Other Useful Utility](#other-useful-utility)**
+* **[Available Models](#models)**
+ * **[Face Detection](#models-face-detection)**
+ * **[Face Landmark Detection](#models-face-landmark-detection)**
+ * **[Face Recognition](#models-face-recognition)**
+ * **[Face Expression Recognition](#models-face-expression-recognition)**
+ * **[Age Estimation and Gender Recognition](#models-age-and-gender-recognition)**
+* **[API Documentation](https://justadudewhohacks.github.io/face-api.js/docs/globals.html)**
-### Face Recognition
+# Features
-
+## Face Recognition
-
+
-### Face Similarity
+## Face Landmark Detection
-
+
-### Face Landmarks
+## Face Expression Recognition
-
-
+
-
+## Age Estimation & Gender Recognition
-### Live Face Detection
+
-**SSD Mobilenet v1**
-
-
+
-**MTCNN**
+# Running the Examples
-
+Clone the repository:
-
+``` bash
+git clone https://github.com/justadudewhohacks/face-api.js.git
+```
-## Running the Examples
+## Running the Browser Examples
``` bash
-cd examples
+cd face-api.js/examples/examples-browser
npm i
npm start
```
Browse to http://localhost:3000/.
-
-
-## About the Package
+## Running the Nodejs Examples
-
+``` bash
+cd face-api.js/examples/examples-nodejs
+npm i
+```
-### Face Detection - SSD Mobilenet v1
+Now run one of the examples using ts-node:
-For face detection, this project implements a SSD (Single Shot Multibox Detector) based on MobileNetV1. The neural net will compute the locations of each face in an image and will return the bounding boxes together with it's probability for each face. This face detector is aiming towards obtaining high accuracy in detecting face bounding boxes instead of low inference time.
+``` bash
+ts-node faceDetection.ts
+```
-The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.
+Or simply compile and run them with node:
-
+``` bash
+tsc faceDetection.ts
+node faceDetection.js
+```
-### Face Detection - Tiny Yolo v2
+
-The Tiny Yolo v2 implementation is a very performant face detector, which can easily adapt to different input image sizes, thus can be used as an alternative to SSD Mobilenet v1 to trade off accuracy for performance (inference time). In general the models ability to locate smaller face bounding boxes is not as accurate as SSD Mobilenet v1.
+# face-api.js for the Browser
-The face detector has been trained on a custom dataset of ~10K images labeled with bounding boxes and uses depthwise separable convolutions instead of regular convolutions, which ensures very fast inference and allows to have a quantized model size of only 1.7MB making the model extremely mobile and web friendly. Thus, the Tiny Yolo v2 face detector should be your GO-TO face detector on mobile devices.
+Simply include the latest script from [dist/face-api.js](https://github.com/justadudewhohacks/face-api.js/tree/master/dist).
-
+Or install it via npm:
-### Face Detection & 5 Point Face Landmarks - MTCNN
+``` bash
+npm i face-api.js
+```
-MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative face detector to SSD Mobilenet v1 and Tiny Yolo v2, which offers much more room for configuration. By tuning the input parameters, MTCNN is able to detect a wide range of face bounding box sizes. MTCNN is a 3 stage cascaded CNN, which simultaneously returns 5 face landmark points along with the bounding boxes and scores for each face. By limiting the minimum size of faces expected in an image, MTCNN allows you to process frames from your webcam in realtime. Additionally with the model size is only 2MB.
+
-MTCNN has been presented in the paper [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf) by Zhang et al. and the model weights are provided in the official [repo](https://github.com/kpzhang93/MTCNN_face_detection_alignment) of the MTCNN implementation.
+# face-api.js for Nodejs
-
+We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.
-### Face Recognition
+Alternatively you can simply construct your own tensors from image data and pass tensors as inputs to the API.
-For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
+Furthermore you want to install @tensorflow/tfjs-node (not required, but highly recommended), which speeds things up drastically by compiling and binding to the native Tensorflow C++ library:
-The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.
+``` bash
+npm i face-api.js canvas @tensorflow/tfjs-node
+```
-
+Now we simply monkey patch the environment to use the polyfills:
-### 68 Point Face Landmark Detection
+``` javascript
+// import nodejs bindings to native tensorflow,
+// not required, but will speed up things drastically (python required)
+import '@tensorflow/tfjs-node';
-This package implements a very lightweight and fast, yet accurate 68 point face landmark detector. The default model has a size of only 350kb and the tiny model is only 80kb. Both models employ the ideas of depthwise separable convolutions as well as densely connected blocks. The models have been trained on a dataset of ~35k face images labeled with 68 face landmark points.
+// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
+import * as canvas from 'canvas';
-
+import * as faceapi from 'face-api.js';
-## Usage
+// patch nodejs environment, we need to provide an implementation of
+// HTMLCanvasElement and HTMLImageElement
+const { Canvas, Image, ImageData } = canvas
+faceapi.env.monkeyPatch({ Canvas, Image, ImageData })
+```
-Get the latest build from dist/face-api.js or dist/face-api.min.js and include the script:
+
-``` html
-
-```
+# Getting Started
-Or install the package:
+
-``` bash
-npm i face-api.js
-```
+## Loading the Models
-
+All global neural network instances are exported via faceapi.nets:
-### Loading the Models
+``` javascript
+console.log(faceapi.nets)
+// ageGenderNet
+// faceExpressionNet
+// faceLandmark68Net
+// faceLandmark68TinyNet
+// faceRecognitionNet
+// ssdMobilenetv1
+// tinyFaceDetector
+// tinyYolov2
+```
-To load a model, you have provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
+To load a model, you have to provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
Assuming the models reside in **public/models**:
``` javascript
-await faceapi.loadFaceDetectionModel('/models')
+await faceapi.nets.ssdMobilenetv1.loadFromUri('/models')
// accordingly for the other models:
-// await faceapi.loadFaceLandmarkModel('/models')
-// await faceapi.loadFaceLandmarkTinyModel('/models')
-// await faceapi.loadFaceRecognitionModel('/models')
-// await faceapi.loadMtcnnModel('/models')
-// await faceapi.loadTinyYolov2Model('/models')
+// await faceapi.nets.faceLandmark68Net.loadFromUri('/models')
+// await faceapi.nets.faceRecognitionNet.loadFromUri('/models')
+// ...
```
-As an alternative, you can also create instance of the neural nets:
+In a nodejs environment you can furthermore load the models directly from disk:
``` javascript
-const net = new faceapi.FaceDetectionNet()
-// accordingly for the other models:
-// const net = new faceapi.FaceLandmark68Net()
-// const net = new faceapi.FaceLandmark68TinyNet()
-// const net = new faceapi.FaceRecognitionNet()
-// const net = new faceapi.Mtcnn()
-// const net = new faceapi.TinyYolov2()
+await faceapi.nets.ssdMobilenetv1.loadFromDisk('./models')
+```
+
+You can also load the model from a tf.NamedTensorMap:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromWeightMap(weightMap)
+```
-await net.load('/models/face_detection_model-weights_manifest.json')
-// await net.load('/models/face_landmark_68_model-weights_manifest.json')
-// await net.load('/models/face_landmark_68_tiny_model-weights_manifest.json')
-// await net.load('/models/face_recognition_model-weights_manifest.json')
-// await net.load('/models/mtcnn_model-weights_manifest.json')
-// await net.load('/models/tiny_yolov2_separable_conv_model-weights_manifest.json')
+Alternatively, you can also create own instances of the neural nets:
+
+``` javascript
+const net = new faceapi.SsdMobilenetv1()
+await net.loadFromUri('/models')
```
-Using instances, you can also load the weights as a Float32Array (in case you want to use the uncompressed models):
+You can also load the weights as a Float32Array (in case you want to use the uncompressed models):
``` javascript
// using fetch
-const res = await fetch('/models/face_detection_model.weights')
-const weights = new Float32Array(await res.arrayBuffer())
-net.load(weights)
+net.load(await faceapi.fetchNetWeights('/models/face_detection_model.weights'))
// using axios
const res = await axios.get('/models/face_detection_model.weights', { responseType: 'arraybuffer' })
@@ -179,166 +206,521 @@ const weights = new Float32Array(res.data)
net.load(weights)
```
-
+
+
+## High Level API
-### Face Detection - SSD Mobilenet v1
+In the following **input** can be an HTML img, video or canvas element or the id of that element.
-Detect faces and get the bounding boxes and scores:
+``` html
+
+
+
+```
``` javascript
-// optional arguments
-const minConfidence = 0.8
-const maxResults = 10
+const input = document.getElementById('myImg')
+// const input = document.getElementById('myVideo')
+// const input = document.getElementById('myCanvas')
+// or simply:
+// const input = 'myImg'
+```
-// inputs can be html canvas, img or video element or their ids ...
-const myImg = document.getElementById('myImg')
-const detections = await faceapi.ssdMobilenetv1(myImg, minConfidence, maxResults)
+### Detecting Faces
+
+Detect all faces in an image. Returns **Array<[FaceDetection](#interface-face-detection)>**:
+
+``` javascript
+const detections = await faceapi.detectAllFaces(input)
+```
+
+Detect the face with the highest confidence score in an image. Returns **[FaceDetection](#interface-face-detection) | undefined**:
+
+``` javascript
+const detection = await faceapi.detectSingleFace(input)
+```
+
+By default **detectAllFaces** and **detectSingleFace** utilize the SSD Mobilenet V1 Face Detector. You can specify the face detector by passing the corresponding options object:
+
+``` javascript
+const detections1 = await faceapi.detectAllFaces(input, new faceapi.SsdMobilenetv1Options())
+const detections2 = await faceapi.detectAllFaces(input, new faceapi.TinyFaceDetectorOptions())
+```
+
+You can tune the options of each face detector as shown [here](#getting-started-face-detection-options).
+
+### Detecting 68 Face Landmark Points
+
+**After face detection, we can furthermore predict the facial landmarks for each detected face as follows:**
+
+Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceLandmarks>](#getting-started-utility-classes)>**:
+
+``` javascript
+const detectionsWithLandmarks = await faceapi.detectAllFaces(input).withFaceLandmarks()
+```
+
+Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks for that face. Returns **[WithFaceLandmarks>](#getting-started-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithLandmarks = await faceapi.detectSingleFace(input).withFaceLandmarks()
+```
+
+You can also specify to use the tiny model instead of the default model:
+
+``` javascript
+const useTinyModel = true
+const detectionsWithLandmarks = await faceapi.detectAllFaces(input).withFaceLandmarks(useTinyModel)
+```
+
+### Computing Face Descriptors
+
+**After face detection and facial landmark prediction the face descriptors for each face can be computed as follows:**
+
+Detect all faces in an image + compute 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceDescriptor>>](#getting-started-utility-classes)>**:
+
+``` javascript
+const results = await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceDescriptors()
+```
+
+Detect the face with the highest confidence score in an image + compute 68 Point Face Landmarks and face descriptor for that face. Returns **[WithFaceDescriptor>>](#getting-started-utility-classes) | undefined**:
+
+``` javascript
+const result = await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceDescriptor()
+```
+
+### Recognizing Face Expressions
+
+**Face expression recognition can be performed for detected faces as follows:**
+
+Detect all faces in an image + recognize face expressions of each face. Returns **Array<[WithFaceExpressions>>](#getting-started-utility-classes)>**:
+
+``` javascript
+const detectionsWithExpressions = await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions()
+```
+
+Detect the face with the highest confidence score in an image + recognize the face expressions for that face. Returns **[WithFaceExpressions>>](#getting-started-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithExpressions = await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions()
```
-Draw the detected faces to a canvas:
+**You can also skip .withFaceLandmarks(), which will skip the face alignment step (less stable accuracy):**
+
+Detect all faces without face alignment + recognize face expressions of each face. Returns **Array<[WithFaceExpressions>](#getting-started-utility-classes)>**:
``` javascript
-// resize the detected boxes in case your displayed image has a different size then the original
-const detectionsForSize = detections.map(det => det.forSize(myImg.width, myImg.height))
+const detectionsWithExpressions = await faceapi.detectAllFaces(input).withFaceExpressions()
+```
+
+Detect the face with the highest confidence score without face alignment + recognize the face expression for that face. Returns **[WithFaceExpressions>](#getting-started-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithExpressions = await faceapi.detectSingleFace(input).withFaceExpressions()
+```
+
+### Age Estimation and Gender Recognition
+
+**Age estimation and gender recognition from detected faces can be done as follows:**
+
+Detect all faces in an image + estimate age and recognize gender of each face. Returns **Array<[WithAge>>>](#getting-started-utility-classes)>**:
+
+``` javascript
+const detectionsWithAgeAndGender = await faceapi.detectAllFaces(input).withFaceLandmarks().withAgeAndGender()
+```
+
+Detect the face with the highest confidence score in an image + estimate age and recognize gender for that face. Returns **[WithAge>>>](#getting-started-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithAgeAndGender = await faceapi.detectSingleFace(input).withFaceLandmarks().withAgeAndGender()
+```
+
+**You can also skip .withFaceLandmarks(), which will skip the face alignment step (less stable accuracy):**
+
+Detect all faces without face alignment + estimate age and recognize gender of each face. Returns **Array<[WithAge>>](#getting-started-utility-classes)>**:
+
+``` javascript
+const detectionsWithAgeAndGender = await faceapi.detectAllFaces(input).withAgeAndGender()
+```
+
+Detect the face with the highest confidence score without face alignment + estimate age and recognize gender for that face. Returns **[WithAge>>](#getting-started-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithAgeAndGender = await faceapi.detectSingleFace(input).withAgeAndGender()
+```
+
+### Composition of Tasks
+
+**Tasks can be composed as follows:**
+
+``` javascript
+// all faces
+await faceapi.detectAllFaces(input)
+await faceapi.detectAllFaces(input).withFaceExpressions()
+await faceapi.detectAllFaces(input).withFaceLandmarks()
+await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions()
+await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions().withFaceDescriptors()
+await faceapi.detectAllFaces(input).withFaceLandmarks().withAgeAndGender().withFaceDescriptors()
+await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions().withAgeAndGender().withFaceDescriptors()
+
+// single face
+await faceapi.detectSingleFace(input)
+await faceapi.detectSingleFace(input).withFaceExpressions()
+await faceapi.detectSingleFace(input).withFaceLandmarks()
+await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions()
+await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions().withFaceDescriptor()
+await faceapi.detectSingleFace(input).withFaceLandmarks().withAgeAndGender().withFaceDescriptor()
+await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions().withAgeAndGender().withFaceDescriptor()
+```
+
+### Face Recognition by Matching Descriptors
+
+To perform face recognition, one can use faceapi.FaceMatcher to compare reference face descriptors to query face descriptors.
+
+First, we initialize the FaceMatcher with the reference data, for example we can simply detect faces in a **referenceImage** and match the descriptors of the detected faces to faces of subsequent images:
+
+``` javascript
+const results = await faceapi
+ .detectAllFaces(referenceImage)
+ .withFaceLandmarks()
+ .withFaceDescriptors()
+
+if (!results.length) {
+ return
+}
+
+// create FaceMatcher with automatically assigned labels
+// from the detection results for the reference image
+const faceMatcher = new faceapi.FaceMatcher(results)
+```
+
+Now we can recognize a persons face shown in **queryImage1**:
+
+``` javascript
+const singleResult = await faceapi
+ .detectSingleFace(queryImage1)
+ .withFaceLandmarks()
+ .withFaceDescriptor()
+
+if (singleResult) {
+ const bestMatch = faceMatcher.findBestMatch(singleResult.descriptor)
+ console.log(bestMatch.toString())
+}
+```
+
+Or we can recognize all faces shown in **queryImage2**:
+
+``` javascript
+const results = await faceapi
+ .detectAllFaces(queryImage2)
+ .withFaceLandmarks()
+ .withFaceDescriptors()
+
+results.forEach(fd => {
+ const bestMatch = faceMatcher.findBestMatch(fd.descriptor)
+ console.log(bestMatch.toString())
+})
+```
+
+You can also create labeled reference descriptors as follows:
+
+``` javascript
+const labeledDescriptors = [
+ new faceapi.LabeledFaceDescriptors(
+ 'obama',
+ [descriptorObama1, descriptorObama2]
+ ),
+ new faceapi.LabeledFaceDescriptors(
+ 'trump',
+ [descriptorTrump]
+ )
+]
+
+const faceMatcher = new faceapi.FaceMatcher(labeledDescriptors)
+```
+
+
+
+## Displaying Detection Results
+
+Preparing the overlay canvas:
+
+``` javascript
+const displaySize = { width: input.width, height: input.height }
+// resize the overlay canvas to the input dimensions
const canvas = document.getElementById('overlay')
-canvas.width = myImg.width
-canvas.height = myImg.height
-faceapi.drawDetection(canvas, detectionsForSize, { withScore: false })
+faceapi.matchDimensions(canvas, displaySize)
```
-You can also obtain the tensors of the unfiltered bounding boxes and scores for each image in the batch (tensors have to be disposed manually):
+face-api.js predefines some highlevel drawing functions, which you can utilize:
``` javascript
-const { boxes, scores } = await net.forward('myImg')
+/* Display detected face bounding boxes */
+const detections = await faceapi.detectAllFaces(input)
+// resize the detected boxes in case your displayed image has a different size than the original
+const resizedDetections = faceapi.resizeResults(detections, displaySize)
+// draw detections into the canvas
+faceapi.draw.drawDetections(canvas, resizedDetections)
+
+/* Display face landmarks */
+const detectionsWithLandmarks = await faceapi
+ .detectAllFaces(input)
+ .withFaceLandmarks()
+// resize the detected boxes and landmarks in case your displayed image has a different size than the original
+const resizedResults = faceapi.resizeResults(detectionsWithLandmarks, displaySize)
+// draw detections into the canvas
+faceapi.draw.drawDetections(canvas, resizedResults)
+// draw the landmarks into the canvas
+faceapi.draw.drawFaceLandmarks(canvas, resizedResults)
+
+
+/* Display face expression results */
+const detectionsWithExpressions = await faceapi
+ .detectAllFaces(input)
+ .withFaceLandmarks()
+ .withFaceExpressions()
+// resize the detected boxes and landmarks in case your displayed image has a different size than the original
+const resizedResults = faceapi.resizeResults(detectionsWithExpressions, displaySize)
+// draw detections into the canvas
+faceapi.draw.drawDetections(canvas, resizedResults)
+// draw a textbox displaying the face expressions with minimum probability into the canvas
+const minProbability = 0.05
+faceapi.draw.drawFaceExpressions(canvas, resizedResults, minProbability)
```
-
+You can also draw boxes with custom text ([DrawBox](https://github.com/justadudewhohacks/tfjs-image-recognition-base/blob/master/src/draw/DrawBox.ts)):
-### Face Detection - Tiny Yolo v2
+``` javascript
+const box = { x: 50, y: 50, width: 100, height: 100 }
+// see DrawBoxOptions below
+const drawOptions = {
+ label: 'Hello I am a box!',
+ lineWidth: 2
+}
+const drawBox = new faceapi.draw.DrawBox(box, drawOptions)
+drawBox.draw(document.getElementById('myCanvas'))
+```
-Detect faces and get the bounding boxes and scores:
+DrawBox drawing options:
``` javascript
-// defaults parameters shown:
-const forwardParams = {
- scoreThreshold: 0.5,
- // any number or one of the predefined sizes:
- // 'xs' (224 x 224) | 'sm' (320 x 320) | 'md' (416 x 416) | 'lg' (608 x 608)
- inputSize: 'md'
+export interface IDrawBoxOptions {
+ boxColor?: string
+ lineWidth?: number
+ drawLabelOptions?: IDrawTextFieldOptions
+ label?: string
}
+```
+
+Finally you can draw custom text fields ([DrawTextField](https://github.com/justadudewhohacks/tfjs-image-recognition-base/blob/master/src/draw/DrawTextField.ts)):
-const detections = await faceapi.tinyYolov2(document.getElementById('myImg'), forwardParams)
+``` javascript
+const text = [
+ 'This is a textline!',
+ 'This is another textline!'
+]
+const anchor = { x: 200, y: 200 }
+// see DrawTextField below
+const drawOptions = {
+ anchorPosition: 'TOP_LEFT',
+ backgroundColor: 'rgba(0, 0, 0, 0.5)'
+}
+const drawBox = new faceapi.draw.DrawTextField(text, anchor, drawOptions)
+drawBox.draw(document.getElementById('myCanvas'))
```
-
+DrawTextField drawing options:
-### Face Detection & 5 Point Face Landmarks - MTCNN
+``` javascript
+export interface IDrawTextFieldOptions {
+ anchorPosition?: AnchorPosition
+ backgroundColor?: string
+ fontColor?: string
+ fontSize?: number
+ fontStyle?: string
+ padding?: number
+}
+
+export enum AnchorPosition {
+ TOP_LEFT = 'TOP_LEFT',
+ TOP_RIGHT = 'TOP_RIGHT',
+ BOTTOM_LEFT = 'BOTTOM_LEFT',
+ BOTTOM_RIGHT = 'BOTTOM_RIGHT'
+}
+```
+
+
-Detect faces and get the bounding boxes and scores:
+## Face Detection Options
+
+### SsdMobilenetv1Options
``` javascript
-// defaults parameters shown:
-const forwardParams = {
- // number of scaled versions of the input image passed through the CNN
- // of the first stage, lower numbers will result in lower inference time,
- // but will also be less accurate
- maxNumScales: 10,
- // scale factor used to calculate the scale steps of the image
- // pyramid used in stage 1
- scaleFactor: 0.709,
- // the score threshold values used to filter the bounding
- // boxes of stage 1, 2 and 3
- scoreThresholds: [0.6, 0.7, 0.7],
- // minimum face size to expect, the higher the faster processing will be,
- // but smaller faces won't be detected
- minFaceSize: 20
+export interface ISsdMobilenetv1Options {
+ // minimum confidence threshold
+ // default: 0.5
+ minConfidence?: number
+
+ // maximum number of faces to return
+ // default: 100
+ maxResults?: number
}
-const results = await faceapi.mtcnn(document.getElementById('myImg'), forwardParams)
+// example
+const options = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.8 })
```
-Alternatively you can also specify the scale steps manually:
+### TinyFaceDetectorOptions
``` javascript
-const forwardParams = {
- scaleSteps: [0.4, 0.2, 0.1, 0.05]
+export interface ITinyFaceDetectorOptions {
+ // size at which image is processed, the smaller the faster,
+ // but less precise in detecting smaller faces, must be divisible
+ // by 32, common sizes are 128, 160, 224, 320, 416, 512, 608,
+ // for face tracking via webcam I would recommend using smaller sizes,
+ // e.g. 128, 160, for detecting smaller faces use larger sizes, e.g. 512, 608
+ // default: 416
+ inputSize?: number
+
+ // minimum confidence threshold
+ // default: 0.5
+ scoreThreshold?: number
}
-const results = await faceapi.mtcnn(document.getElementById('myImg'), forwardParams)
+// example
+const options = new faceapi.TinyFaceDetectorOptions({ inputSize: 320 })
```
-Finally you can draw the returned bounding boxes and 5 Point Face Landmarks into a canvas:
+
+
+## Utility Classes
+
+### IBox
``` javascript
-const minConfidence = 0.9
+export interface IBox {
+ x: number
+ y: number
+ width: number
+ height: number
+}
+```
+
+### IFaceDetection
-if (results) {
- results.forEach(({ faceDetection, faceLandmarks }) => {
+``` javascript
+export interface IFaceDetection {
+ score: number
+ box: Box
+}
+```
- // ignore results with low confidence score
- if (faceDetection.score < minConfidence) {
- return
- }
+### IFaceLandmarks
- faceapi.drawDetection('overlay', faceDetection)
- faceapi.drawLandmarks('overlay', faceLandmarks)
- })
+``` javascript
+export interface IFaceLandmarks {
+ positions: Point[]
+ shift: Point
}
```
-
+### WithFaceDetection
-### Face Recognition
+``` javascript
+export type WithFaceDetection = TSource & {
+ detection: FaceDetection
+}
+```
-Compute and compare the descriptors of two face images:
+### WithFaceLandmarks
``` javascript
-// inputs can be html canvas, img or video element or their ids ...
-const descriptor1 = await faceapi.computeFaceDescriptor('myImg')
-const descriptor2 = await faceapi.computeFaceDescriptor(document.getElementById('myCanvas'))
-const distance = faceapi.euclideanDistance(descriptor1, descriptor2)
+export type WithFaceLandmarks = TSource & {
+ unshiftedLandmarks: FaceLandmarks
+ landmarks: FaceLandmarks
+ alignedRect: FaceDetection
+}
+```
+
+### WithFaceDescriptor
-if (distance < 0.6)
- console.log('match')
-else
- console.log('no match')
+``` javascript
+export type WithFaceDescriptor = TSource & {
+ descriptor: Float32Array
+}
```
-Or simply obtain the tensor (tensor has to be disposed manually):
+### WithFaceExpressions
``` javascript
-const t = await net.forward('myImg')
+export type WithFaceExpressions = TSource & {
+ expressions: FaceExpressions
+}
```
-
+### WithAge
-### Face Landmark Detection
+``` javascript
+export type WithAge = TSource & {
+ age: number
+}
+```
-Detect face landmarks:
+### WithGender
``` javascript
-// inputs can be html canvas, img or video element or their ids ...
-const myImg = document.getElementById('myImg')
-const landmarks = await faceapi.detectLandmarks(myImg)
+export type WithGender = TSource & {
+ gender: Gender
+ genderProbability: number
+}
+
+export enum Gender {
+ FEMALE = 'female',
+ MALE = 'male'
+}
```
-Draw the detected face landmarks to a canvas:
+
+
+## Other Useful Utility
+
+### Using the Low Level API
+
+Instead of using the high level API, you can directly use the forward methods of each neural network:
``` javascript
-// adjust the landmark positions in case your displayed image has a different size then the original
-const landmarksForSize = landmarks.forSize(myImg.width, myImg.height)
-const canvas = document.getElementById('overlay')
-canvas.width = myImg.width
-canvas.height = myImg.height
-faceapi.drawLandmarks(canvas, landmarksForSize, { drawLines: true })
+const detections1 = await faceapi.ssdMobilenetv1(input, options)
+const detections2 = await faceapi.tinyFaceDetector(input, options)
+const landmarks1 = await faceapi.detectFaceLandmarks(faceImage)
+const landmarks2 = await faceapi.detectFaceLandmarksTiny(faceImage)
+const descriptor = await faceapi.computeFaceDescriptor(alignedFaceImage)
+```
+
+### Extracting a Canvas for an Image Region
+
+``` javascript
+const regionsToExtract = [
+ new faceapi.Rect(0, 0, 100, 100)
+]
+// actually extractFaces is meant to extract face regions from bounding boxes
+// but you can also use it to extract any other region
+const canvases = await faceapi.extractFaces(input, regionsToExtract)
+```
+
+### Euclidean Distance
+
+``` javascript
+// ment to be used for computing the euclidean distance between two face descriptors
+const dist = faceapi.euclideanDistance([0, 0], [0, 10])
+console.log(dist) // 10
```
-Retrieve the face landmark positions:
+### Retrieve the Face Landmark Points and Contours
``` javascript
-const landmarkPositions = landmarks.getPositions()
+const landmarkPositions = landmarks.positions
-// or get the positions of individual contours
+// or get the positions of individual contours,
+// only available for 68 point face ladnamrks (FaceLandmarks68)
const jawOutline = landmarks.getJawOutline()
const nose = landmarks.getNose()
const mouth = landmarks.getMouth()
@@ -348,48 +730,126 @@ const leftEyeBbrow = landmarks.getLeftEyeBrow()
const rightEyeBrow = landmarks.getRightEyeBrow()
```
-Compute the Face Landmarks for Detected Faces:
+### Fetch and Display Images from an URL
+
+``` html
+![]()
+```
``` javascript
-const detections = await faceapi.ssdMobilenetv1(input)
+const image = await faceapi.fetchImage('/images/example.png')
-// get the face tensors from the image (have to be disposed manually)
-const faceTensors = await faceapi.extractFaceTensors(input, detections)
-const landmarksByFace = await Promise.all(faceTensors.map(t => faceapi.detectLandmarks(t)))
+console.log(image instanceof HTMLImageElement) // true
-// free memory for face image tensors after we computed their descriptors
-faceTensors.forEach(t => t.dispose())
+// displaying the fetched image content
+const myImg = document.getElementById('myImg')
+myImg.src = image.src
```
-
+### Fetching JSON
-### Shortcut Functions for Full Face Description
+``` javascript
+const json = await faceapi.fetchJson('/files/example.json')
+```
-After face detection has been performed, I would recommend to align the bounding boxes of the detected faces before passing them to the face recognition net, which will make the computed face descriptor much more accurate. Fortunately, the api can do this for you under the hood by providing convenient shortcut functions. You can obtain the full face descriptions (location, landmarks and descriptor) of each face in an input image as follows.
+### Creating an Image Picker
-Using the SSD Mobilenet v1 face detector + 68 point face landmark detector:
+``` html
+![]()
+
+```
``` javascript
-const fullFaceDescriptions = await faceapi.allFacesSsdMobilenetv1(input, minConfidence)
+async function uploadImage() {
+ const imgFile = document.getElementById('myFileUpload').files[0]
+ // create an HTMLImageElement from a Blob
+ const img = await faceapi.bufferToImage(imgFile)
+ document.getElementById('myImg').src = img.src
+}
```
-Using the Tiny Yolo v2 face detector + 68 point face landmark detector:
+### Creating a Canvas Element from an Image or Video Element
-``` javascript
-const fullFaceDescriptions = await faceapi.allFacesTinyYolov2(input, { inputSize: 'md' })
+``` html
+
+
```
-Or with MTCNN face detection + 5 point face landmarks:
-
``` javascript
-const fullFaceDescriptions = await faceapi.allFacesMtcnn(input, { minFaceSize: 20 })
+const canvas1 = faceapi.createCanvasFromMedia(document.getElementById('myImg'))
+const canvas2 = faceapi.createCanvasFromMedia(document.getElementById('myVideo'))
```
-The shortcut functions return an array of FullFaceDescriptions:
+
-``` javascript
-const fullFaceDescription0 = fullFaceDescriptions[0]
-console.log(fullFaceDescription0.detection) // bounding box & score
-console.log(fullFaceDescription0.landmarks) // face landmarks
-console.log(fullFaceDescription0.descriptor) // face descriptor
-```
+# Available Models
+
+
+
+## Face Detection Models
+
+### SSD Mobilenet V1
+
+For face detection, this project implements a SSD (Single Shot Multibox Detector) based on MobileNetV1. The neural net will compute the locations of each face in an image and will return the bounding boxes together with it's probability for each face. This face detector is aiming towards obtaining high accuracy in detecting face bounding boxes instead of low inference time. The size of the quantized model is about 5.4 MB (**ssd_mobilenetv1_model**).
+
+The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.
+
+### Tiny Face Detector
+
+The Tiny Face Detector is a very performant, realtime face detector, which is much faster, smaller and less resource consuming compared to the SSD Mobilenet V1 face detector, in return it performs slightly less well on detecting small faces. This model is extremely mobile and web friendly, thus it should be your GO-TO face detector on mobile devices and resource limited clients. The size of the quantized model is only 190 KB (**tiny_face_detector_model**).
+
+The face detector has been trained on a custom dataset of ~14K images labeled with bounding boxes. Furthermore the model has been trained to predict bounding boxes, which entirely cover facial feature points, thus it in general produces better results in combination with subsequent face landmark detection than SSD Mobilenet V1.
+
+This model is basically an even tinier version of Tiny Yolo V2, replacing the regular convolutions of Yolo with depthwise separable convolutions. Yolo is fully convolutional, thus can easily adapt to different input image sizes to trade off accuracy for performance (inference time).
+
+
+
+## 68 Point Face Landmark Detection Models
+
+This package implements a very lightweight and fast, yet accurate 68 point face landmark detector. The default model has a size of only 350kb (**face_landmark_68_model**) and the tiny model is only 80kb (**face_landmark_68_tiny_model**). Both models employ the ideas of depthwise separable convolutions as well as densely connected blocks. The models have been trained on a dataset of ~35k face images labeled with 68 face landmark points.
+
+
+
+## Face Recognition Model
+
+For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
+
+The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.
+
+The size of the quantized model is roughly 6.2 MB (**face_recognition_model**).
+
+
+
+## Face Expression Recognition Model
+
+The face expression recognition model is lightweight, fast and provides reasonable accuracy. The model has a size of roughly 310kb and it employs depthwise separable convolutions and densely connected blocks. It has been trained on a variety of images from publicly available datasets as well as images scraped from the web. Note, that wearing glasses might decrease the accuracy of the prediction results.
+
+
+
+## Age and Gender Recognition Model
+
+The age and gender recognition model is a multitask network, which employs a feature extraction layer, an age regression layer and a gender classifier. The model has a size of roughly 420kb and the feature extractor employs a tinier but very similar architecture to Xception.
+
+This model has been trained and tested on the following databases with an 80/20 train/test split each: UTK, FGNET, Chalearn, Wiki, IMDB*, CACD*, MegaAge, MegaAge-Asian. The `*` indicates, that these databases have been algorithmically cleaned up, since the initial databases are very noisy.
+
+### Total Test Results
+
+Total MAE (Mean Age Error): **4.54**
+
+Total Gender Accuracy: **95%**
+
+### Test results for each database
+
+The `-` indicates, that there are no gender labels available for these databases.
+
+Database | UTK | FGNET | Chalearn | Wiki | IMDB* | CACD* | MegaAge | MegaAge-Asian |
+----------------|-------:|------:|---------:|-----:|------:|------:|--------:|--------------:|
+MAE | 5.25 | 4.23 | 6.24 | 6.54 | 3.63 | 3.20 | 6.23 | 4.21 |
+Gender Accuracy | 0.93 | - | 0.94 | 0.95 | - | 0.97 | - | - |
+
+### Test results for different age category groups
+
+Age Range | 0 - 3 | 4 - 8 | 9 - 18 | 19 - 28 | 29 - 40 | 41 - 60 | 60 - 80 | 80+ |
+----------------|-------:|------:|-------:|--------:|--------:|--------:|--------:|--------:|
+MAE | 1.52 | 3.06 | 4.82 | 4.99 | 5.43 | 4.94 | 6.17 | 9.91 |
+Gender Accuracy | 0.69 | 0.80 | 0.88 | 0.96 | 0.97 | 0.97 | 0.96 | 0.9 |
diff --git a/build/allFacesFactory.d.ts b/build/allFacesFactory.d.ts
deleted file mode 100644
index ee701d7e..00000000
--- a/build/allFacesFactory.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { TNetInput } from 'tfjs-image-recognition-base';
-import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
-import { TinyYolov2 } from '.';
-import { FullFaceDescription } from './classes/FullFaceDescription';
-import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
-import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
-import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
-import { Mtcnn } from './mtcnn/Mtcnn';
-import { MtcnnForwardParams } from './mtcnn/types';
-export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise;
-export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise;
-export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise;
diff --git a/build/allFacesFactory.js b/build/allFacesFactory.js
deleted file mode 100644
index f58e57b7..00000000
--- a/build/allFacesFactory.js
+++ /dev/null
@@ -1,129 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FullFaceDescription_1 = require("./classes/FullFaceDescription");
-var dom_1 = require("./dom");
-function computeDescriptorsFactory(recognitionNet) {
- return function (input, alignedFaceBoxes, useBatchProcessing) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var alignedFaceCanvases, descriptors, _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, dom_1.extractFaces(input, alignedFaceBoxes)];
- case 1:
- alignedFaceCanvases = _b.sent();
- if (!useBatchProcessing) return [3 /*break*/, 3];
- return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
- case 2:
- _a = _b.sent();
- return [3 /*break*/, 5];
- case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
- case 4:
- _a = _b.sent();
- _b.label = 5;
- case 5:
- descriptors = _a;
- return [2 /*return*/, descriptors];
- }
- });
- });
- };
-}
-function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
- var computeDescriptors = computeDescriptorsFactory(recognitionNet);
- return function (input, useBatchProcessing) {
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, detectFaces(input)];
- case 1:
- detections = _b.sent();
- return [4 /*yield*/, dom_1.extractFaces(input, detections)];
- case 2:
- faceCanvases = _b.sent();
- if (!useBatchProcessing) return [3 /*break*/, 4];
- return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
- case 3:
- _a = _b.sent();
- return [3 /*break*/, 6];
- case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
- case 5:
- _a = _b.sent();
- _b.label = 6;
- case 6:
- faceLandmarksByFace = _a;
- alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
- return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
- case 7:
- descriptors = _b.sent();
- return [2 /*return*/, detections.map(function (detection, i) {
- return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new tfjs_image_recognition_base_1.Point(detection.box.x, detection.box.y)), descriptors[i]);
- })];
- }
- });
- });
- };
-}
-function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
- return function (input, minConfidence, useBatchProcessing) {
- if (minConfidence === void 0) { minConfidence = 0.8; }
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var detectFaces, allFaces;
- return tslib_1.__generator(this, function (_a) {
- detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
- allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
- return [2 /*return*/, allFaces(input, useBatchProcessing)];
- });
- });
- };
-}
-exports.allFacesSsdMobilenetv1Factory = allFacesSsdMobilenetv1Factory;
-function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
- return function (input, forwardParams, useBatchProcessing) {
- if (forwardParams === void 0) { forwardParams = {}; }
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var detectFaces, allFaces;
- return tslib_1.__generator(this, function (_a) {
- detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
- allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
- return [2 /*return*/, allFaces(input, useBatchProcessing)];
- });
- });
- };
-}
-exports.allFacesTinyYolov2Factory = allFacesTinyYolov2Factory;
-function allFacesMtcnnFactory(mtcnn, recognitionNet) {
- var computeDescriptors = computeDescriptorsFactory(recognitionNet);
- return function (input, mtcnnForwardParams, useBatchProcessing) {
- if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var results, alignedFaceBoxes, descriptors;
- return tslib_1.__generator(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, mtcnn.forward(input, mtcnnForwardParams)];
- case 1:
- results = _a.sent();
- alignedFaceBoxes = results.map(function (_a) {
- var faceLandmarks = _a.faceLandmarks;
- return faceLandmarks.align();
- });
- return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
- case 2:
- descriptors = _a.sent();
- return [2 /*return*/, results.map(function (_a, i) {
- var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
- return new FullFaceDescription_1.FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
- })];
- }
- });
- });
- };
-}
-exports.allFacesMtcnnFactory = allFacesMtcnnFactory;
-//# sourceMappingURL=allFacesFactory.js.map
\ No newline at end of file
diff --git a/build/allFacesFactory.js.map b/build/allFacesFactory.js.map
deleted file mode 100644
index 79241605..00000000
--- a/build/allFacesFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/classes/FaceDetection.d.ts b/build/classes/FaceDetection.d.ts
deleted file mode 100644
index 7c870585..00000000
--- a/build/classes/FaceDetection.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { Dimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
-export declare class FaceDetection extends ObjectDetection {
- constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
-}
diff --git a/build/classes/FaceDetection.js b/build/classes/FaceDetection.js
deleted file mode 100644
index ac80fb00..00000000
--- a/build/classes/FaceDetection.js
+++ /dev/null
@@ -1,13 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceDetection = /** @class */ (function (_super) {
- tslib_1.__extends(FaceDetection, _super);
- function FaceDetection(score, relativeBox, imageDims) {
- return _super.call(this, score, score, '', relativeBox, imageDims) || this;
- }
- return FaceDetection;
-}(tfjs_image_recognition_base_1.ObjectDetection));
-exports.FaceDetection = FaceDetection;
-//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
diff --git a/build/classes/FaceDetection.js.map b/build/classes/FaceDetection.js.map
deleted file mode 100644
index 7e3e0990..00000000
--- a/build/classes/FaceDetection.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceDetection.js","sourceRoot":"","sources":["../../src/classes/FaceDetection.ts"],"names":[],"mappings":";;;AAAA,2EAAgF;AAEhF;IAAmC,yCAAe;IAChD,uBACE,KAAa,EACb,WAAiB,EACjB,SAAqB;eAErB,kBAAM,KAAK,EAAE,KAAK,EAAE,EAAE,EAAE,WAAW,EAAE,SAAS,CAAC;IACjD,CAAC;IACH,oBAAC;AAAD,CAAC,AARD,CAAmC,6CAAe,GAQjD;AARY,sCAAa"}
\ No newline at end of file
diff --git a/build/classes/FaceLandmarks.d.ts b/build/classes/FaceLandmarks.d.ts
deleted file mode 100644
index 398c0821..00000000
--- a/build/classes/FaceLandmarks.d.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import { Dimensions, Point, Rect } from 'tfjs-image-recognition-base';
-import { FaceDetection } from './FaceDetection';
-export declare class FaceLandmarks {
- protected _imageWidth: number;
- protected _imageHeight: number;
- protected _shift: Point;
- protected _faceLandmarks: Point[];
- constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
- getShift(): Point;
- getImageWidth(): number;
- getImageHeight(): number;
- getPositions(): Point[];
- getRelativePositions(): Point[];
- forSize(width: number, height: number): T;
- shift(x: number, y: number): T;
- shiftByPoint(pt: Point): T;
- /**
- * Aligns the face landmarks after face detection from the relative positions of the faces
- * bounding box, or it's current shift. This function should be used to align the face images
- * after face detection has been performed, before they are passed to the face recognition net.
- * This will make the computed face descriptor more accurate.
- *
- * @param detection (optional) The bounding box of the face or the face detection result. If
- * no argument was passed the position of the face landmarks are assumed to be relative to
- * it's current shift.
- * @returns The bounding box of the aligned face.
- */
- align(detection?: FaceDetection | Rect): Rect;
- protected getRefPointsForAlignment(): Point[];
-}
diff --git a/build/classes/FaceLandmarks.js b/build/classes/FaceLandmarks.js
deleted file mode 100644
index 3e6a53c9..00000000
--- a/build/classes/FaceLandmarks.js
+++ /dev/null
@@ -1,78 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceDetection_1 = require("./FaceDetection");
-// face alignment constants
-var relX = 0.5;
-var relY = 0.43;
-var relScale = 0.45;
-var FaceLandmarks = /** @class */ (function () {
- function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
- if (shift === void 0) { shift = new tfjs_image_recognition_base_1.Point(0, 0); }
- var width = imageDims.width, height = imageDims.height;
- this._imageWidth = width;
- this._imageHeight = height;
- this._shift = shift;
- this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new tfjs_image_recognition_base_1.Point(width, height)).add(shift); });
- }
- FaceLandmarks.prototype.getShift = function () {
- return new tfjs_image_recognition_base_1.Point(this._shift.x, this._shift.y);
- };
- FaceLandmarks.prototype.getImageWidth = function () {
- return this._imageWidth;
- };
- FaceLandmarks.prototype.getImageHeight = function () {
- return this._imageHeight;
- };
- FaceLandmarks.prototype.getPositions = function () {
- return this._faceLandmarks;
- };
- FaceLandmarks.prototype.getRelativePositions = function () {
- var _this = this;
- return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new tfjs_image_recognition_base_1.Point(_this._imageWidth, _this._imageHeight)); });
- };
- FaceLandmarks.prototype.forSize = function (width, height) {
- return new this.constructor(this.getRelativePositions(), { width: width, height: height });
- };
- FaceLandmarks.prototype.shift = function (x, y) {
- return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new tfjs_image_recognition_base_1.Point(x, y));
- };
- FaceLandmarks.prototype.shiftByPoint = function (pt) {
- return this.shift(pt.x, pt.y);
- };
- /**
- * Aligns the face landmarks after face detection from the relative positions of the faces
- * bounding box, or it's current shift. This function should be used to align the face images
- * after face detection has been performed, before they are passed to the face recognition net.
- * This will make the computed face descriptor more accurate.
- *
- * @param detection (optional) The bounding box of the face or the face detection result. If
- * no argument was passed the position of the face landmarks are assumed to be relative to
- * it's current shift.
- * @returns The bounding box of the aligned face.
- */
- FaceLandmarks.prototype.align = function (detection) {
- if (detection) {
- var box = detection instanceof FaceDetection_1.FaceDetection
- ? detection.getBox().floor()
- : detection;
- return this.shift(box.x, box.y).align();
- }
- var centers = this.getRefPointsForAlignment();
- var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
- var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
- var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
- var size = Math.floor(eyeToMouthDist / relScale);
- var refPoint = tfjs_image_recognition_base_1.getCenterPoint(centers);
- // TODO: pad in case rectangle is out of image bounds
- var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
- var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
- return new tfjs_image_recognition_base_1.Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
- };
- FaceLandmarks.prototype.getRefPointsForAlignment = function () {
- throw new Error('getRefPointsForAlignment not implemented by base class');
- };
- return FaceLandmarks;
-}());
-exports.FaceLandmarks = FaceLandmarks;
-//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
diff --git a/build/classes/FaceLandmarks.js.map b/build/classes/FaceLandmarks.js.map
deleted file mode 100644
index e96c8a9a..00000000
--- a/build/classes/FaceLandmarks.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmarks.js","sourceRoot":"","sources":["../../src/classes/FaceLandmarks.ts"],"names":[],"mappings":";;AAAA,2EAAsF;AAEtF,iDAAgD;AAEhD,2BAA2B;AAC3B,IAAM,IAAI,GAAG,GAAG,CAAA;AAChB,IAAM,IAAI,GAAG,IAAI,CAAA;AACjB,IAAM,QAAQ,GAAG,IAAI,CAAA;AAErB;IAME,uBACE,6BAAsC,EACtC,SAAqB,EACrB,KAA8B;QAA9B,sBAAA,EAAA,YAAmB,mCAAK,CAAC,CAAC,EAAE,CAAC,CAAC;QAEtB,IAAA,uBAAK,EAAE,yBAAM,CAAc;QACnC,IAAI,CAAC,WAAW,GAAG,KAAK,CAAA;QACxB,IAAI,CAAC,YAAY,GAAG,MAAM,CAAA;QAC1B,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,cAAc,GAAG,6BAA6B,CAAC,GAAG,CACrD,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,EAA3C,CAA2C,CAClD,CAAA;IACH,CAAC;IAEM,gCAAQ,GAAf;QACE,OAAO,IAAI,mCAAK,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,EAAE,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA;IAChD,CAAC;IAEM,qCAAa,GAApB;QACE,OAAO,IAAI,CAAC,WAAW,CAAA;IACzB,CAAC;IAEM,sCAAc,GAArB;QACE,OAAO,IAAI,CAAC,YAAY,CAAA;IAC1B,CAAC;IAEM,oCAAY,GAAnB;QACE,OAAO,IAAI,CAAC,cAAc,CAAA;IAC5B,CAAC;IAEM,4CAAoB,GAA3B;QAAA,iBAIC;QAHC,OAAO,IAAI,CAAC,cAAc,CAAC,GAAG,CAC5B,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,KAAI,CAAC,MAAM,CAAC,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,KAAI,CAAC,WAAW,EAAE,KAAI,CAAC,YAAY,CAAC,CAAC,EAAvE,CAAuE,CAC9E,CAAA;IACH,CAAC;IAEM,+BAAO,GAAd,UAAwC,KAAa,EAAE,MAAc;QACnE,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,oBAAoB,EAAE,EAC3B,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAClB,CAAA;IACH,CAAC;IAEM,6BAAK,GAAZ,UAAsC,CAAS,EAAE,CAAS;QACxD,OAAO,IAAK,IAAI,CAAC,WAAmB,CAClC,IAAI,CAAC,oBAAoB,EAAE,EAC3B,EAAE,KAAK,EAAE,IAAI,CAAC,WAAW,EAAE,MAAM,EAAE,IAAI,CAAC,YAAY,EAAE,EACtD,IAAI,mCAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAChB,CAAA;IACH,CAAC;IAEM,oCAAY,GAAnB,UAA6C,EAAS;QACpD,OAAO,IAAI,CAAC,KAAK,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;IAC/B,CAAC;IAED;;;;;;;;;;OAUG;IACI,6BAAK,GAAZ,UACE,SAAgC;QAEhC,IAAI,SAAS,EAAE;YACb,IAAM,GAAG,GAAG,SAAS,YAAY,6BAAa;gBAC5C,CAAC,CAAC,SAAS,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;gBAC5B,CAAC,CAAC,SAAS,CAAA;YAEb,OAAO,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAA;SACxC;QAED,IAAM,OAAO,GAAG,IAAI,CAAC,wBAAwB,EAAE,CAAA;QAExC,IAAA,0BAAa,EAAE,2BAAc,EAAE,wBAAW,CAAW;QAC5D,IAAM,WAAW,GAAG,UAAC,EAAS,IAAK,OAAA,WAAW,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,SAAS,EAAE,EAA/B,CAA+B,CAAA;QAClE,IAAM,cAAc,GAAG,CAAC,WAAW,CAAC,aAAa,CAAC,GAAG,WAAW,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,CAAA;QAErF,IAAM,IAAI,GAAG,IAAI,CAAC,KAAK,CAAC,cAAc,GAAG,QAAQ,CAAC,CAAA;QAElD,IAAM,QAAQ,GAAG,4CAAc,CAAC,OAAO,CAAC,CAAA;QACxC,qDAAqD;QACrD,IAAM,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC,GAAG,CAAC,IAAI,GAAG,IAAI,CAAC,CAAC,CAAC,CAAA;QAC7D,IAAM,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC,GAAG,CAAC,IAAI,GAAG,IAAI,CAAC,CAAC,CAAC,CAAA;QAE7D,OAAO,IAAI,kCAAI,CAAC,CAAC,EAAE,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,WAAW,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,YAAY,GAAG,CAAC,CAAC,CAAC,CAAA;IACpG,CAAC;IAES,gDAAwB,GAAlC;QACE,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;IAC3E,CAAC;IACH,oBAAC;AAAD,CAAC,AAtGD,IAsGC;AAtGY,sCAAa"}
\ No newline at end of file
diff --git a/build/classes/FaceLandmarks5.d.ts b/build/classes/FaceLandmarks5.d.ts
deleted file mode 100644
index 554ce2f1..00000000
--- a/build/classes/FaceLandmarks5.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { Point } from 'tfjs-image-recognition-base';
-import { FaceLandmarks } from './FaceLandmarks';
-export declare class FaceLandmarks5 extends FaceLandmarks {
- protected getRefPointsForAlignment(): Point[];
-}
diff --git a/build/classes/FaceLandmarks5.js b/build/classes/FaceLandmarks5.js
deleted file mode 100644
index d47a7dba..00000000
--- a/build/classes/FaceLandmarks5.js
+++ /dev/null
@@ -1,22 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceLandmarks_1 = require("./FaceLandmarks");
-var FaceLandmarks5 = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmarks5, _super);
- function FaceLandmarks5() {
- return _super !== null && _super.apply(this, arguments) || this;
- }
- FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
- var pts = this.getPositions();
- return [
- pts[0],
- pts[1],
- tfjs_image_recognition_base_1.getCenterPoint([pts[3], pts[4]])
- ];
- };
- return FaceLandmarks5;
-}(FaceLandmarks_1.FaceLandmarks));
-exports.FaceLandmarks5 = FaceLandmarks5;
-//# sourceMappingURL=FaceLandmarks5.js.map
\ No newline at end of file
diff --git a/build/classes/FaceLandmarks5.js.map b/build/classes/FaceLandmarks5.js.map
deleted file mode 100644
index 4fd38c31..00000000
--- a/build/classes/FaceLandmarks5.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmarks5.js","sourceRoot":"","sources":["../../src/classes/FaceLandmarks5.ts"],"names":[],"mappings":";;;AAAA,2EAAoE;AAEpE,iDAAgD;AAEhD;IAAoC,0CAAa;IAAjD;;IAUA,CAAC;IARW,iDAAwB,GAAlC;QACE,IAAM,GAAG,GAAG,IAAI,CAAC,YAAY,EAAE,CAAA;QAC/B,OAAO;YACL,GAAG,CAAC,CAAC,CAAC;YACN,GAAG,CAAC,CAAC,CAAC;YACN,4CAAc,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC;SACjC,CAAA;IACH,CAAC;IACH,qBAAC;AAAD,CAAC,AAVD,CAAoC,6BAAa,GAUhD;AAVY,wCAAc"}
\ No newline at end of file
diff --git a/build/classes/FaceLandmarks68.d.ts b/build/classes/FaceLandmarks68.d.ts
deleted file mode 100644
index 5ac3974e..00000000
--- a/build/classes/FaceLandmarks68.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { Point } from 'tfjs-image-recognition-base';
-import { FaceLandmarks } from '../classes/FaceLandmarks';
-export declare class FaceLandmarks68 extends FaceLandmarks {
- getJawOutline(): Point[];
- getLeftEyeBrow(): Point[];
- getRightEyeBrow(): Point[];
- getNose(): Point[];
- getLeftEye(): Point[];
- getRightEye(): Point[];
- getMouth(): Point[];
- protected getRefPointsForAlignment(): Point[];
-}
diff --git a/build/classes/FaceLandmarks68.js b/build/classes/FaceLandmarks68.js
deleted file mode 100644
index 4131c8e1..00000000
--- a/build/classes/FaceLandmarks68.js
+++ /dev/null
@@ -1,42 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceLandmarks_1 = require("../classes/FaceLandmarks");
-var FaceLandmarks68 = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmarks68, _super);
- function FaceLandmarks68() {
- return _super !== null && _super.apply(this, arguments) || this;
- }
- FaceLandmarks68.prototype.getJawOutline = function () {
- return this._faceLandmarks.slice(0, 17);
- };
- FaceLandmarks68.prototype.getLeftEyeBrow = function () {
- return this._faceLandmarks.slice(17, 22);
- };
- FaceLandmarks68.prototype.getRightEyeBrow = function () {
- return this._faceLandmarks.slice(22, 27);
- };
- FaceLandmarks68.prototype.getNose = function () {
- return this._faceLandmarks.slice(27, 36);
- };
- FaceLandmarks68.prototype.getLeftEye = function () {
- return this._faceLandmarks.slice(36, 42);
- };
- FaceLandmarks68.prototype.getRightEye = function () {
- return this._faceLandmarks.slice(42, 48);
- };
- FaceLandmarks68.prototype.getMouth = function () {
- return this._faceLandmarks.slice(48, 68);
- };
- FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
- return [
- this.getLeftEye(),
- this.getRightEye(),
- this.getMouth()
- ].map(tfjs_image_recognition_base_1.getCenterPoint);
- };
- return FaceLandmarks68;
-}(FaceLandmarks_1.FaceLandmarks));
-exports.FaceLandmarks68 = FaceLandmarks68;
-//# sourceMappingURL=FaceLandmarks68.js.map
\ No newline at end of file
diff --git a/build/classes/FaceLandmarks68.js.map b/build/classes/FaceLandmarks68.js.map
deleted file mode 100644
index c3b9886c..00000000
--- a/build/classes/FaceLandmarks68.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmarks68.js","sourceRoot":"","sources":["../../src/classes/FaceLandmarks68.ts"],"names":[],"mappings":";;;AAAA,2EAAoE;AAEpE,0DAAyD;AAGzD;IAAqC,2CAAa;IAAlD;;IAoCA,CAAC;IAnCQ,uCAAa,GAApB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,CAAA;IACzC,CAAC;IAEM,wCAAc,GAArB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,yCAAe,GAAtB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,iCAAO,GAAd;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,oCAAU,GAAjB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,qCAAW,GAAlB;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAEM,kCAAQ,GAAf;QACE,OAAO,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IAC1C,CAAC;IAES,kDAAwB,GAAlC;QACE,OAAO;YACL,IAAI,CAAC,UAAU,EAAE;YACjB,IAAI,CAAC,WAAW,EAAE;YAClB,IAAI,CAAC,QAAQ,EAAE;SAChB,CAAC,GAAG,CAAC,4CAAc,CAAC,CAAA;IACvB,CAAC;IACH,sBAAC;AAAD,CAAC,AApCD,CAAqC,6BAAa,GAoCjD;AApCY,0CAAe"}
\ No newline at end of file
diff --git a/build/classes/FullFaceDescription.d.ts b/build/classes/FullFaceDescription.d.ts
deleted file mode 100644
index 7309e266..00000000
--- a/build/classes/FullFaceDescription.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { FaceDetection } from './FaceDetection';
-import { FaceLandmarks } from './FaceLandmarks';
-export declare class FullFaceDescription {
- private _detection;
- private _landmarks;
- private _descriptor;
- constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
- readonly detection: FaceDetection;
- readonly landmarks: FaceLandmarks;
- readonly descriptor: Float32Array;
- forSize(width: number, height: number): FullFaceDescription;
-}
diff --git a/build/classes/FullFaceDescription.js b/build/classes/FullFaceDescription.js
deleted file mode 100644
index be002429..00000000
--- a/build/classes/FullFaceDescription.js
+++ /dev/null
@@ -1,36 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var FullFaceDescription = /** @class */ (function () {
- function FullFaceDescription(_detection, _landmarks, _descriptor) {
- this._detection = _detection;
- this._landmarks = _landmarks;
- this._descriptor = _descriptor;
- }
- Object.defineProperty(FullFaceDescription.prototype, "detection", {
- get: function () {
- return this._detection;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
- get: function () {
- return this._landmarks;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
- get: function () {
- return this._descriptor;
- },
- enumerable: true,
- configurable: true
- });
- FullFaceDescription.prototype.forSize = function (width, height) {
- return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
- };
- return FullFaceDescription;
-}());
-exports.FullFaceDescription = FullFaceDescription;
-//# sourceMappingURL=FullFaceDescription.js.map
\ No newline at end of file
diff --git a/build/classes/FullFaceDescription.js.map b/build/classes/FullFaceDescription.js.map
deleted file mode 100644
index 8ab4e771..00000000
--- a/build/classes/FullFaceDescription.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FullFaceDescription.js","sourceRoot":"","sources":["../../src/classes/FullFaceDescription.ts"],"names":[],"mappings":";;AAGA;IACE,6BACU,UAAyB,EACzB,UAAyB,EACzB,WAAyB;QAFzB,eAAU,GAAV,UAAU,CAAe;QACzB,eAAU,GAAV,UAAU,CAAe;QACzB,gBAAW,GAAX,WAAW,CAAc;IAChC,CAAC;IAEJ,sBAAW,0CAAS;aAApB;YACE,OAAO,IAAI,CAAC,UAAU,CAAA;QACxB,CAAC;;;OAAA;IAED,sBAAW,0CAAS;aAApB;YACE,OAAO,IAAI,CAAC,UAAU,CAAA;QACxB,CAAC;;;OAAA;IAED,sBAAW,2CAAU;aAArB;YACE,OAAO,IAAI,CAAC,WAAW,CAAA;QACzB,CAAC;;;OAAA;IAEM,qCAAO,GAAd,UAAe,KAAa,EAAE,MAAc;QAC1C,OAAO,IAAI,mBAAmB,CAC5B,IAAI,CAAC,UAAU,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,EACtC,IAAI,CAAC,UAAU,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,EACtC,IAAI,CAAC,WAAW,CACjB,CAAA;IACH,CAAC;IACH,0BAAC;AAAD,CAAC,AA1BD,IA0BC;AA1BY,kDAAmB"}
\ No newline at end of file
diff --git a/build/classes/index.d.ts b/build/classes/index.d.ts
deleted file mode 100644
index a2183b41..00000000
--- a/build/classes/index.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-export * from './FaceDetection';
-export * from './FaceLandmarks';
-export * from './FaceLandmarks5';
-export * from './FaceLandmarks68';
-export * from './FullFaceDescription';
diff --git a/build/classes/index.js b/build/classes/index.js
deleted file mode 100644
index cd716238..00000000
--- a/build/classes/index.js
+++ /dev/null
@@ -1,9 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-tslib_1.__exportStar(require("./FaceDetection"), exports);
-tslib_1.__exportStar(require("./FaceLandmarks"), exports);
-tslib_1.__exportStar(require("./FaceLandmarks5"), exports);
-tslib_1.__exportStar(require("./FaceLandmarks68"), exports);
-tslib_1.__exportStar(require("./FullFaceDescription"), exports);
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/classes/index.js.map b/build/classes/index.js.map
deleted file mode 100644
index 9de57d23..00000000
--- a/build/classes/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/classes/index.ts"],"names":[],"mappings":";;;AAAA,0DAAgC;AAChC,0DAAgC;AAChC,2DAAiC;AACjC,4DAAkC;AAClC,gEAAsC"}
\ No newline at end of file
diff --git a/build/dom/drawContour.d.ts b/build/dom/drawContour.d.ts
deleted file mode 100644
index 78664e6b..00000000
--- a/build/dom/drawContour.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Point } from 'tfjs-image-recognition-base';
-export declare function drawContour(ctx: CanvasRenderingContext2D, points: Point[], isClosed?: boolean): void;
diff --git a/build/dom/drawContour.js b/build/dom/drawContour.js
deleted file mode 100644
index 097275c3..00000000
--- a/build/dom/drawContour.js
+++ /dev/null
@@ -1,24 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function drawContour(ctx, points, isClosed) {
- if (isClosed === void 0) { isClosed = false; }
- ctx.beginPath();
- points.slice(1).forEach(function (_a, prevIdx) {
- var x = _a.x, y = _a.y;
- var from = points[prevIdx];
- ctx.moveTo(from.x, from.y);
- ctx.lineTo(x, y);
- });
- if (isClosed) {
- var from = points[points.length - 1];
- var to = points[0];
- if (!from || !to) {
- return;
- }
- ctx.moveTo(from.x, from.y);
- ctx.lineTo(to.x, to.y);
- }
- ctx.stroke();
-}
-exports.drawContour = drawContour;
-//# sourceMappingURL=drawContour.js.map
\ No newline at end of file
diff --git a/build/dom/drawContour.js.map b/build/dom/drawContour.js.map
deleted file mode 100644
index 9048df5c..00000000
--- a/build/dom/drawContour.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"drawContour.js","sourceRoot":"","sources":["../../src/dom/drawContour.ts"],"names":[],"mappings":";;AAEA,qBACE,GAA6B,EAC7B,MAAe,EACf,QAAyB;IAAzB,yBAAA,EAAA,gBAAyB;IAEzB,GAAG,CAAC,SAAS,EAAE,CAAA;IAEf,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,UAAC,EAAQ,EAAE,OAAO;YAAf,QAAC,EAAE,QAAC;QAC7B,IAAM,IAAI,GAAG,MAAM,CAAC,OAAO,CAAC,CAAA;QAC5B,GAAG,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAA;QAC1B,GAAG,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;IAClB,CAAC,CAAC,CAAA;IAEF,IAAI,QAAQ,EAAE;QACZ,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,CAAC,CAAA;QACtC,IAAM,EAAE,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;QACpB,IAAI,CAAC,IAAI,IAAI,CAAC,EAAE,EAAE;YAChB,OAAM;SACP;QAED,GAAG,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAA;QAC1B,GAAG,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;KACvB;IAED,GAAG,CAAC,MAAM,EAAE,CAAA;AACd,CAAC;AAzBD,kCAyBC"}
\ No newline at end of file
diff --git a/build/dom/drawLandmarks.d.ts b/build/dom/drawLandmarks.d.ts
deleted file mode 100644
index 4a372873..00000000
--- a/build/dom/drawLandmarks.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { FaceLandmarks } from '../classes/FaceLandmarks';
-import { DrawLandmarksOptions } from './types';
-export declare function drawLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: FaceLandmarks | FaceLandmarks[], options?: DrawLandmarksOptions): void;
diff --git a/build/dom/drawLandmarks.js b/build/dom/drawLandmarks.js
deleted file mode 100644
index e8c941c4..00000000
--- a/build/dom/drawLandmarks.js
+++ /dev/null
@@ -1,36 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
-var drawContour_1 = require("./drawContour");
-function drawLandmarks(canvasArg, faceLandmarks, options) {
- var canvas = tfjs_image_recognition_base_1.resolveInput(canvasArg);
- if (!(canvas instanceof HTMLCanvasElement)) {
- throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
- }
- var drawOptions = Object.assign(tfjs_image_recognition_base_1.getDefaultDrawOptions(options), (options || {}));
- var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
- var ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
- var lineWidth = drawOptions.lineWidth, _a = drawOptions.color, color = _a === void 0 ? 'blue' : _a;
- var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
- faceLandmarksArray.forEach(function (landmarks) {
- if (drawLines && landmarks instanceof FaceLandmarks68_1.FaceLandmarks68) {
- ctx.strokeStyle = color;
- ctx.lineWidth = lineWidth;
- drawContour_1.drawContour(ctx, landmarks.getJawOutline());
- drawContour_1.drawContour(ctx, landmarks.getLeftEyeBrow());
- drawContour_1.drawContour(ctx, landmarks.getRightEyeBrow());
- drawContour_1.drawContour(ctx, landmarks.getNose());
- drawContour_1.drawContour(ctx, landmarks.getLeftEye(), true);
- drawContour_1.drawContour(ctx, landmarks.getRightEye(), true);
- drawContour_1.drawContour(ctx, landmarks.getMouth(), true);
- return;
- }
- // else draw points
- var ptOffset = lineWidth / 2;
- ctx.fillStyle = color;
- landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
- });
-}
-exports.drawLandmarks = drawLandmarks;
-//# sourceMappingURL=drawLandmarks.js.map
\ No newline at end of file
diff --git a/build/dom/drawLandmarks.js.map b/build/dom/drawLandmarks.js.map
deleted file mode 100644
index 7e083d55..00000000
--- a/build/dom/drawLandmarks.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"drawLandmarks.js","sourceRoot":"","sources":["../../src/dom/drawLandmarks.ts"],"names":[],"mappings":";;AAAA,2EAAuG;AAGvG,8DAA6D;AAC7D,6CAA4C;AAG5C,uBACE,SAAqC,EACrC,aAA8C,EAC9C,OAA8B;IAE9B,IAAM,MAAM,GAAG,0CAAY,CAAC,SAAS,CAAC,CAAA;IACtC,IAAI,CAAC,CAAC,MAAM,YAAY,iBAAiB,CAAC,EAAE;QAC1C,MAAM,IAAI,KAAK,CAAC,kEAAkE,CAAC,CAAA;KACpF;IAED,IAAM,WAAW,GAAG,MAAM,CAAC,MAAM,CAC/B,mDAAqB,CAAC,OAAO,CAAC,EAC9B,CAAC,OAAO,IAAI,EAAE,CAAC,CAChB,CAAA;IAEO,IAAA,0EAAS,CAAyD;IAE1E,IAAM,GAAG,GAAG,iDAAmB,CAAC,MAAM,CAAC,CAAA;IAC/B,IAAA,iCAAS,EAAE,sBAAc,EAAd,mCAAc,CAAgB;IAEjD,IAAM,kBAAkB,GAAG,KAAK,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAA;IAEzF,kBAAkB,CAAC,OAAO,CAAC,UAAA,SAAS;QAClC,IAAI,SAAS,IAAI,SAAS,YAAY,iCAAe,EAAE;YACrD,GAAG,CAAC,WAAW,GAAG,KAAK,CAAA;YACvB,GAAG,CAAC,SAAS,GAAG,SAAS,CAAA;YACzB,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,aAAa,EAAE,CAAC,CAAA;YAC3C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,cAAc,EAAE,CAAC,CAAA;YAC5C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,eAAe,EAAE,CAAC,CAAA;YAC7C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,OAAO,EAAE,CAAC,CAAA;YACrC,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,UAAU,EAAE,EAAE,IAAI,CAAC,CAAA;YAC9C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,WAAW,EAAE,EAAE,IAAI,CAAC,CAAA;YAC/C,yBAAW,CAAC,GAAG,EAAE,SAAS,CAAC,QAAQ,EAAE,EAAE,IAAI,CAAC,CAAA;YAC5C,OAAM;SACP;QAED,mBAAmB;QACnB,IAAM,QAAQ,GAAG,SAAS,GAAG,CAAC,CAAA;QAC9B,GAAG,CAAC,SAAS,GAAG,KAAK,CAAA;QACrB,SAAS,CAAC,YAAY,EAAE,CAAC,OAAO,CAAC,UAAA,EAAE,IAAI,OAAA,GAAG,CAAC,QAAQ,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,EAAE,EAAE,CAAC,CAAC,GAAG,QAAQ,EAAE,SAAS,EAAE,SAAS,CAAC,EAApE,CAAoE,CAAC,CAAA;IAC9G,CAAC,CAAC,CAAA;AACJ,CAAC;AAzCD,sCAyCC"}
\ No newline at end of file
diff --git a/build/dom/extractFaceTensors.d.ts b/build/dom/extractFaceTensors.d.ts
deleted file mode 100644
index 429ea46e..00000000
--- a/build/dom/extractFaceTensors.d.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { Rect } from 'tfjs-image-recognition-base';
-import { FaceDetection } from '../classes/FaceDetection';
-/**
- * Extracts the tensors of the image regions containing the detected faces.
- * Useful if you want to compute the face descriptors for the face images.
- * Using this method is faster then extracting a canvas for each face and
- * converting them to tensors individually.
- *
- * @param imageTensor The image tensor that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns Tensors of the corresponding image region for each detected face.
- */
-export declare function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise;
diff --git a/build/dom/extractFaceTensors.js b/build/dom/extractFaceTensors.js
deleted file mode 100644
index 6b9b7882..00000000
--- a/build/dom/extractFaceTensors.js
+++ /dev/null
@@ -1,39 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceDetection_1 = require("../classes/FaceDetection");
-/**
- * Extracts the tensors of the image regions containing the detected faces.
- * Useful if you want to compute the face descriptors for the face images.
- * Using this method is faster then extracting a canvas for each face and
- * converting them to tensors individually.
- *
- * @param imageTensor The image tensor that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns Tensors of the corresponding image region for each detected face.
- */
-function extractFaceTensors(imageTensor, detections) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- return tslib_1.__generator(this, function (_a) {
- if (tfjs_image_recognition_base_1.isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
- throw new Error('extractFaceTensors - batchSize > 1 not supported');
- }
- return [2 /*return*/, tf.tidy(function () {
- var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
- var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
- ? det.forSize(imgWidth, imgHeight).getBox()
- : det; })
- .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
- var faceTensors = boxes.map(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
- });
- return faceTensors;
- })];
- });
- });
-}
-exports.extractFaceTensors = extractFaceTensors;
-//# sourceMappingURL=extractFaceTensors.js.map
\ No newline at end of file
diff --git a/build/dom/extractFaceTensors.js.map b/build/dom/extractFaceTensors.js.map
deleted file mode 100644
index e3333093..00000000
--- a/build/dom/extractFaceTensors.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA+D;AAE/D,0DAAyD;AAEzD;;;;;;;;;GASG;AACH,4BACE,WAAsC,EACtC,UAAuC;;;YAGvC,IAAI,wCAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;gBACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;aACpE;YAED,sBAAO,EAAE,CAAC,IAAI,CAAC;oBACP,IAAA,2FAA6F,EAA5F,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4D;oBAEnG,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,MAAM,EAAE;wBAC3C,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;oBAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;4BAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;wBAClD,OAAA,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;oBAAvG,CAAuG,CACxG,CAAA;oBAED,OAAO,WAAW,CAAA;gBACpB,CAAC,CAAC,EAAA;;;CACH;AAzBD,gDAyBC"}
\ No newline at end of file
diff --git a/build/dom/extractFaces.d.ts b/build/dom/extractFaces.d.ts
deleted file mode 100644
index d7bf0e60..00000000
--- a/build/dom/extractFaces.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import { Rect, TNetInput } from 'tfjs-image-recognition-base';
-import { FaceDetection } from '../classes/FaceDetection';
-/**
- * Extracts the image regions containing the detected faces.
- *
- * @param input The image that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns The Canvases of the corresponding image region for each detected face.
- */
-export declare function extractFaces(input: TNetInput, detections: Array): Promise;
diff --git a/build/dom/extractFaces.js b/build/dom/extractFaces.js
deleted file mode 100644
index cb10fee0..00000000
--- a/build/dom/extractFaces.js
+++ /dev/null
@@ -1,56 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceDetection_1 = require("../classes/FaceDetection");
-/**
- * Extracts the image regions containing the detected faces.
- *
- * @param input The image that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns The Canvases of the corresponding image region for each detected face.
- */
-function extractFaces(input, detections) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- canvas = input;
- if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1:
- netInput = _b.sent();
- if (netInput.batchSize > 1) {
- throw new Error('extractFaces - batchSize > 1 not supported');
- }
- tensorOrCanvas = netInput.getInput(0);
- if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
- _a = tensorOrCanvas;
- return [3 /*break*/, 4];
- case 2: return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(tensorOrCanvas)];
- case 3:
- _a = _b.sent();
- _b.label = 4;
- case 4:
- canvas = _a;
- _b.label = 5;
- case 5:
- ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
- boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
- ? det.forSize(canvas.width, canvas.height).getBox().floor()
- : det; })
- .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
- return [2 /*return*/, boxes.map(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- var faceImg = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
- tfjs_image_recognition_base_1.getContext2dOrThrow(faceImg)
- .putImageData(ctx.getImageData(x, y, width, height), 0, 0);
- return faceImg;
- })];
- }
- });
- });
-}
-exports.extractFaces = extractFaces;
-//# sourceMappingURL=extractFaces.js.map
\ No newline at end of file
diff --git a/build/dom/extractFaces.js.map b/build/dom/extractFaces.js.map
deleted file mode 100644
index b18ad2fe..00000000
--- a/build/dom/extractFaces.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../../src/dom/extractFaces.ts"],"names":[],"mappings":";;;AAAA,2EAOqC;AAErC,0DAAyD;AAEzD;;;;;;GAMG;AACH,sBACE,KAAgB,EAChB,UAAuC;;;;;;oBAGnC,MAAM,GAAG,KAA0B,CAAA;yBAEnC,CAAC,CAAC,KAAK,YAAY,iBAAiB,CAAC,EAArC,wBAAqC;oBACtB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;oBAAlC,QAAQ,GAAG,SAAuB;oBAExC,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEK,cAAc,GAAG,QAAQ,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;yBAClC,CAAA,cAAc,YAAY,iBAAiB,CAAA,EAA3C,wBAA2C;oBAChD,KAAA,cAAc,CAAA;;wBACd,qBAAM,iDAAmB,CAAC,cAAc,CAAC,EAAA;;oBAAzC,KAAA,SAAyC,CAAA;;;oBAF7C,MAAM,KAEuC,CAAA;;;oBAGzC,GAAG,GAAG,iDAAmB,CAAC,MAAM,CAAC,CAAA;oBACjC,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;wBAC3D,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,EAAnD,CAAmD,CAAC,CAAA;oBAElE,sBAAO,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;gCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;4BACrC,IAAM,OAAO,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;4BAC/C,iDAAmB,CAAC,OAAO,CAAC;iCACzB,YAAY,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;4BAC5D,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAC,EAAA;;;;CACH;AAlCD,oCAkCC"}
\ No newline at end of file
diff --git a/build/dom/index.d.ts b/build/dom/index.d.ts
deleted file mode 100644
index 2fdbb244..00000000
--- a/build/dom/index.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-export * from './drawContour';
-export * from './drawLandmarks';
-export * from './extractFaces';
-export * from './extractFaceTensors';
-export * from './types';
diff --git a/build/dom/index.js b/build/dom/index.js
deleted file mode 100644
index 0a57e0e2..00000000
--- a/build/dom/index.js
+++ /dev/null
@@ -1,8 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-tslib_1.__exportStar(require("./drawContour"), exports);
-tslib_1.__exportStar(require("./drawLandmarks"), exports);
-tslib_1.__exportStar(require("./extractFaces"), exports);
-tslib_1.__exportStar(require("./extractFaceTensors"), exports);
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/dom/index.js.map b/build/dom/index.js.map
deleted file mode 100644
index bb4d8ea5..00000000
--- a/build/dom/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/dom/index.ts"],"names":[],"mappings":";;;AAAA,wDAA6B;AAC7B,0DAA+B;AAC/B,yDAA8B;AAC9B,+DAAoC"}
\ No newline at end of file
diff --git a/build/dom/types.d.ts b/build/dom/types.d.ts
deleted file mode 100644
index cd664596..00000000
--- a/build/dom/types.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-export declare type DrawLandmarksOptions = {
- lineWidth?: number;
- color?: string;
- drawLines?: boolean;
-};
diff --git a/build/dom/types.js b/build/dom/types.js
deleted file mode 100644
index 11e638d1..00000000
--- a/build/dom/types.js
+++ /dev/null
@@ -1,3 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/dom/types.js.map b/build/dom/types.js.map
deleted file mode 100644
index 3c420806..00000000
--- a/build/dom/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/dom/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/euclideanDistance.d.ts b/build/euclideanDistance.d.ts
deleted file mode 100644
index d555de27..00000000
--- a/build/euclideanDistance.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array): number;
diff --git a/build/euclideanDistance.js b/build/euclideanDistance.js
deleted file mode 100644
index e263610d..00000000
--- a/build/euclideanDistance.js
+++ /dev/null
@@ -1,13 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function euclideanDistance(arr1, arr2) {
- if (arr1.length !== arr2.length)
- throw new Error('euclideanDistance: arr1.length !== arr2.length');
- var desc1 = Array.from(arr1);
- var desc2 = Array.from(arr2);
- return Math.sqrt(desc1
- .map(function (val, i) { return val - desc2[i]; })
- .reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
-}
-exports.euclideanDistance = euclideanDistance;
-//# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file
diff --git a/build/euclideanDistance.js.map b/build/euclideanDistance.js.map
deleted file mode 100644
index ad1f8cf0..00000000
--- a/build/euclideanDistance.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"euclideanDistance.js","sourceRoot":"","sources":["../src/euclideanDistance.ts"],"names":[],"mappings":";;AAAA,2BAAkC,IAA6B,EAAE,IAA6B;IAC5F,IAAI,IAAI,CAAC,MAAM,KAAK,IAAI,CAAC,MAAM;QAC7B,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;IAEnE,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;IAC9B,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;IAE9B,OAAO,IAAI,CAAC,IAAI,CACd,KAAK;SACF,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,GAAG,GAAG,KAAK,CAAC,CAAC,CAAC,EAAd,CAAc,CAAC;SAC/B,MAAM,CAAC,UAAC,GAAG,EAAE,IAAI,IAAK,OAAA,GAAG,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,CAAC,CAAC,EAAvB,CAAuB,EAAE,CAAC,CAAC,CACrD,CAAA;AACH,CAAC;AAZD,8CAYC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/FaceDetectionNet.d.ts b/build/faceDetectionNet/FaceDetectionNet.d.ts
deleted file mode 100644
index 4115ec04..00000000
--- a/build/faceDetectionNet/FaceDetectionNet.d.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
-import { FaceDetection } from '../classes/FaceDetection';
-import { NetParams } from './types';
-export declare class FaceDetectionNet extends NeuralNetwork {
- constructor();
- forwardInput(input: NetInput): {
- boxes: tf.Tensor[];
- scores: tf.Tensor[];
- };
- forward(input: TNetInput): Promise<{
- boxes: tf.Tensor[];
- scores: tf.Tensor[];
- }>;
- locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise;
- protected loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- }>;
- protected extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- };
-}
diff --git a/build/faceDetectionNet/FaceDetectionNet.js b/build/faceDetectionNet/FaceDetectionNet.js
deleted file mode 100644
index 8ac29b7d..00000000
--- a/build/faceDetectionNet/FaceDetectionNet.js
+++ /dev/null
@@ -1,102 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceDetection_1 = require("../classes/FaceDetection");
-var extractParams_1 = require("./extractParams");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-var mobileNetV1_1 = require("./mobileNetV1");
-var nonMaxSuppression_1 = require("./nonMaxSuppression");
-var outputLayer_1 = require("./outputLayer");
-var predictionLayer_1 = require("./predictionLayer");
-var FaceDetectionNet = /** @class */ (function (_super) {
- tslib_1.__extends(FaceDetectionNet, _super);
- function FaceDetectionNet() {
- return _super.call(this, 'FaceDetectionNet') || this;
- }
- FaceDetectionNet.prototype.forwardInput = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceDetectionNet - load model before inference');
- }
- return tf.tidy(function () {
- var batchTensor = input.toBatchTensor(512, false).toFloat();
- var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
- var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
- var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
- return outputLayer_1.outputLayer(boxPredictions, classPredictions, params.output_layer);
- });
- };
- FaceDetectionNet.prototype.forward = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
- if (minConfidence === void 0) { minConfidence = 0.8; }
- if (maxResults === void 0) { maxResults = 100; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
- return tslib_1.__generator(this, function (_d) {
- switch (_d.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1:
- netInput = _d.sent();
- _a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
- boxes = _boxes[0];
- scores = _scores[0];
- for (i = 1; i < _boxes.length; i++) {
- _boxes[i].dispose();
- _scores[i].dispose();
- }
- _c = (_b = Array).from;
- return [4 /*yield*/, scores.data()];
- case 2:
- scoresData = _c.apply(_b, [_d.sent()]);
- iouThreshold = 0.5;
- indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
- reshapedDims = netInput.getReshapedInputDimensions(0);
- inputSize = netInput.inputSize;
- padX = inputSize / reshapedDims.width;
- padY = inputSize / reshapedDims.height;
- results = indices
- .map(function (idx) {
- var _a = [
- Math.max(0, boxes.get(idx, 0)),
- Math.min(1.0, boxes.get(idx, 2))
- ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
- var _b = [
- Math.max(0, boxes.get(idx, 1)),
- Math.min(1.0, boxes.get(idx, 3))
- ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
- return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
- height: netInput.getInputHeight(0),
- width: netInput.getInputWidth(0)
- });
- });
- boxes.dispose();
- scores.dispose();
- return [2 /*return*/, results];
- }
- });
- });
- };
- FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams_1.loadQuantizedParams(uri);
- };
- FaceDetectionNet.prototype.extractParams = function (weights) {
- return extractParams_1.extractParams(weights);
- };
- return FaceDetectionNet;
-}(tfjs_image_recognition_base_1.NeuralNetwork));
-exports.FaceDetectionNet = FaceDetectionNet;
-//# sourceMappingURL=FaceDetectionNet.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/FaceDetectionNet.js.map b/build/faceDetectionNet/FaceDetectionNet.js.map
deleted file mode 100644
index 014f400d..00000000
--- a/build/faceDetectionNet/FaceDetectionNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/faceDetectionNet/boxPredictionLayer.d.ts b/build/faceDetectionNet/boxPredictionLayer.d.ts
deleted file mode 100644
index bed8cd15..00000000
--- a/build/faceDetectionNet/boxPredictionLayer.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { BoxPredictionParams } from './types';
-export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): {
- boxPredictionEncoding: tf.Tensor;
- classPrediction: tf.Tensor;
-};
diff --git a/build/faceDetectionNet/boxPredictionLayer.js b/build/faceDetectionNet/boxPredictionLayer.js
deleted file mode 100644
index f5acb02e..00000000
--- a/build/faceDetectionNet/boxPredictionLayer.js
+++ /dev/null
@@ -1,17 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-function boxPredictionLayer(x, params) {
- return tf.tidy(function () {
- var batchSize = x.shape[0];
- var boxPredictionEncoding = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
- var classPrediction = tf.reshape(tfjs_tiny_yolov2_1.convLayer(x, params.class_predictor), [batchSize, -1, 3]);
- return {
- boxPredictionEncoding: boxPredictionEncoding,
- classPrediction: classPrediction
- };
- });
-}
-exports.boxPredictionLayer = boxPredictionLayer;
-//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/boxPredictionLayer.js.map b/build/faceDetectionNet/boxPredictionLayer.js.map
deleted file mode 100644
index fa957e02..00000000
--- a/build/faceDetectionNet/boxPredictionLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"boxPredictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/boxPredictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,qDAA6C;AAK7C,4BACE,CAAc,EACd,MAA2B;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAE5B,IAAM,qBAAqB,GAAG,EAAE,CAAC,OAAO,CACtC,4BAAS,CAAC,CAAC,EAAE,MAAM,CAAC,sBAAsB,CAAC,EAC3C,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACtB,CAAA;QACD,IAAM,eAAe,GAAG,EAAE,CAAC,OAAO,CAChC,4BAAS,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,EACpC,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACnB,CAAA;QAED,OAAO;YACL,qBAAqB,uBAAA;YACrB,eAAe,iBAAA;SAChB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AAtBD,gDAsBC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/extractParams.d.ts b/build/faceDetectionNet/extractParams.d.ts
deleted file mode 100644
index 40295777..00000000
--- a/build/faceDetectionNet/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: ParamMapping[];
-};
diff --git a/build/faceDetectionNet/extractParams.js b/build/faceDetectionNet/extractParams.js
deleted file mode 100644
index ccf07792..00000000
--- a/build/faceDetectionNet/extractParams.js
+++ /dev/null
@@ -1,162 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-function extractorsFactory(extractWeights, paramMappings) {
- function extractDepthwiseConvParams(numChannels, mappedPrefix) {
- var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
- var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
- var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
- var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
- var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
- return {
- filters: filters,
- batch_norm_scale: batch_norm_scale,
- batch_norm_offset: batch_norm_offset,
- batch_norm_mean: batch_norm_mean,
- batch_norm_variance: batch_norm_variance
- };
- }
- function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
- var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
- var bias = tf.tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
- return { filters: filters, bias: bias };
- }
- function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
- var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
- return {
- filters: filters,
- batch_norm_offset: bias
- };
- }
- function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
- var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
- var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
- return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
- }
- function extractMobilenetV1Params() {
- var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
- var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
- var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
- var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
- var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
- var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
- var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
- var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
- var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
- var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
- var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
- var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
- var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
- var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
- return {
- conv_0: conv_0,
- conv_1: conv_1,
- conv_2: conv_2,
- conv_3: conv_3,
- conv_4: conv_4,
- conv_5: conv_5,
- conv_6: conv_6,
- conv_7: conv_7,
- conv_8: conv_8,
- conv_9: conv_9,
- conv_10: conv_10,
- conv_11: conv_11,
- conv_12: conv_12,
- conv_13: conv_13
- };
- }
- function extractPredictionLayerParams() {
- var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
- var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
- var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
- var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
- var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
- var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
- var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
- var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
- var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
- var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
- var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
- var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
- var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
- var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
- var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
- var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
- var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
- var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
- var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
- var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
- var box_predictor_0 = {
- box_encoding_predictor: box_encoding_0_predictor,
- class_predictor: class_predictor_0
- };
- var box_predictor_1 = {
- box_encoding_predictor: box_encoding_1_predictor,
- class_predictor: class_predictor_1
- };
- var box_predictor_2 = {
- box_encoding_predictor: box_encoding_2_predictor,
- class_predictor: class_predictor_2
- };
- var box_predictor_3 = {
- box_encoding_predictor: box_encoding_3_predictor,
- class_predictor: class_predictor_3
- };
- var box_predictor_4 = {
- box_encoding_predictor: box_encoding_4_predictor,
- class_predictor: class_predictor_4
- };
- var box_predictor_5 = {
- box_encoding_predictor: box_encoding_5_predictor,
- class_predictor: class_predictor_5
- };
- return {
- conv_0: conv_0,
- conv_1: conv_1,
- conv_2: conv_2,
- conv_3: conv_3,
- conv_4: conv_4,
- conv_5: conv_5,
- conv_6: conv_6,
- conv_7: conv_7,
- box_predictor_0: box_predictor_0,
- box_predictor_1: box_predictor_1,
- box_predictor_2: box_predictor_2,
- box_predictor_3: box_predictor_3,
- box_predictor_4: box_predictor_4,
- box_predictor_5: box_predictor_5
- };
- }
- return {
- extractMobilenetV1Params: extractMobilenetV1Params,
- extractPredictionLayerParams: extractPredictionLayerParams
- };
-}
-function extractParams(weights) {
- var paramMappings = [];
- var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
- var mobilenetv1 = extractMobilenetV1Params();
- var prediction_layer = extractPredictionLayerParams();
- var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
- var output_layer = {
- extra_dim: extra_dim
- };
- paramMappings.push({ paramPath: 'output_layer/extra_dim' });
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return {
- params: {
- mobilenetv1: mobilenetv1,
- prediction_layer: prediction_layer,
- output_layer: output_layer
- },
- paramMappings: paramMappings
- };
-}
-exports.extractParams = extractParams;
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/extractParams.js.map b/build/faceDetectionNet/extractParams.js.map
deleted file mode 100644
index 5abc7306..00000000
--- a/build/faceDetectionNet/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/faceDetectionNet/index.d.ts b/build/faceDetectionNet/index.d.ts
deleted file mode 100644
index 226c613c..00000000
--- a/build/faceDetectionNet/index.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { FaceDetectionNet } from './FaceDetectionNet';
-export * from './FaceDetectionNet';
-export declare function createFaceDetectionNet(weights: Float32Array): FaceDetectionNet;
-export declare function faceDetectionNet(weights: Float32Array): FaceDetectionNet;
diff --git a/build/faceDetectionNet/index.js b/build/faceDetectionNet/index.js
deleted file mode 100644
index f881ab9b..00000000
--- a/build/faceDetectionNet/index.js
+++ /dev/null
@@ -1,17 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var FaceDetectionNet_1 = require("./FaceDetectionNet");
-tslib_1.__exportStar(require("./FaceDetectionNet"), exports);
-function createFaceDetectionNet(weights) {
- var net = new FaceDetectionNet_1.FaceDetectionNet();
- net.extractWeights(weights);
- return net;
-}
-exports.createFaceDetectionNet = createFaceDetectionNet;
-function faceDetectionNet(weights) {
- console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
- return createFaceDetectionNet(weights);
-}
-exports.faceDetectionNet = faceDetectionNet;
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/index.js.map b/build/faceDetectionNet/index.js.map
deleted file mode 100644
index e1539f02..00000000
--- a/build/faceDetectionNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceDetectionNet/index.ts"],"names":[],"mappings":";;;AAAA,uDAAsD;AAEtD,6DAAmC;AAEnC,gCAAuC,OAAqB;IAC1D,IAAM,GAAG,GAAG,IAAI,mCAAgB,EAAE,CAAA;IAClC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,wDAIC;AAED,0BAAiC,OAAqB;IACpD,OAAO,CAAC,IAAI,CAAC,0GAA0G,CAAC,CAAA;IACxH,OAAO,sBAAsB,CAAC,OAAO,CAAC,CAAA;AACxC,CAAC;AAHD,4CAGC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/loadQuantizedParams.d.ts b/build/faceDetectionNet/loadQuantizedParams.d.ts
deleted file mode 100644
index c5e1dc44..00000000
--- a/build/faceDetectionNet/loadQuantizedParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: ParamMapping[];
-}>;
diff --git a/build/faceDetectionNet/loadQuantizedParams.js b/build/faceDetectionNet/loadQuantizedParams.js
deleted file mode 100644
index c16b7d7e..00000000
--- a/build/faceDetectionNet/loadQuantizedParams.js
+++ /dev/null
@@ -1,114 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
-function extractorsFactory(weightMap, paramMappings) {
- var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
- function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
- var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
- var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
- return { filters: filters, batch_norm_offset: batch_norm_offset };
- }
- function extractConvPairParams(idx) {
- var mappedPrefix = "mobilenetv1/conv_" + idx;
- var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
- var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
- var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
- var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
- var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
- var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
- var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
- var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
- return {
- depthwise_conv: {
- filters: filters,
- batch_norm_scale: batch_norm_scale,
- batch_norm_offset: batch_norm_offset,
- batch_norm_mean: batch_norm_mean,
- batch_norm_variance: batch_norm_variance
- },
- pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
- };
- }
- function extractMobilenetV1Params() {
- return {
- conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
- conv_1: extractConvPairParams(1),
- conv_2: extractConvPairParams(2),
- conv_3: extractConvPairParams(3),
- conv_4: extractConvPairParams(4),
- conv_5: extractConvPairParams(5),
- conv_6: extractConvPairParams(6),
- conv_7: extractConvPairParams(7),
- conv_8: extractConvPairParams(8),
- conv_9: extractConvPairParams(9),
- conv_10: extractConvPairParams(10),
- conv_11: extractConvPairParams(11),
- conv_12: extractConvPairParams(12),
- conv_13: extractConvPairParams(13)
- };
- }
- function extractConvParams(prefix, mappedPrefix) {
- var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
- var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
- return { filters: filters, bias: bias };
- }
- function extractBoxPredictorParams(idx) {
- var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
- var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
- return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
- }
- function extractPredictionLayerParams() {
- return {
- conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
- conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
- conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
- conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
- conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
- conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
- conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
- conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
- box_predictor_0: extractBoxPredictorParams(0),
- box_predictor_1: extractBoxPredictorParams(1),
- box_predictor_2: extractBoxPredictorParams(2),
- box_predictor_3: extractBoxPredictorParams(3),
- box_predictor_4: extractBoxPredictorParams(4),
- box_predictor_5: extractBoxPredictorParams(5)
- };
- }
- return {
- extractMobilenetV1Params: extractMobilenetV1Params,
- extractPredictionLayerParams: extractPredictionLayerParams
- };
-}
-function loadQuantizedParams(uri) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
- extra_dim = weightMap['Output/extra_dim'];
- paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
- if (!tfjs_image_recognition_base_1.isTensor3D(extra_dim)) {
- throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
- }
- params = {
- mobilenetv1: extractMobilenetV1Params(),
- prediction_layer: extractPredictionLayerParams(),
- output_layer: {
- extra_dim: extra_dim
- }
- };
- tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
-}
-exports.loadQuantizedParams = loadQuantizedParams;
-//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/loadQuantizedParams.js.map b/build/faceDetectionNet/loadQuantizedParams.js.map
deleted file mode 100644
index a362d15e..00000000
--- a/build/faceDetectionNet/loadQuantizedParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/faceDetectionNet/mobileNetV1.d.ts b/build/faceDetectionNet/mobileNetV1.d.ts
deleted file mode 100644
index 05f5c5fe..00000000
--- a/build/faceDetectionNet/mobileNetV1.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { MobileNetV1 } from './types';
-export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): {
- out: tf.Tensor;
- conv11: any;
-};
diff --git a/build/faceDetectionNet/mobileNetV1.js b/build/faceDetectionNet/mobileNetV1.js
deleted file mode 100644
index e73ca54e..00000000
--- a/build/faceDetectionNet/mobileNetV1.js
+++ /dev/null
@@ -1,54 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
-var epsilon = 0.0010000000474974513;
-function depthwiseConvLayer(x, params, strides) {
- return tf.tidy(function () {
- var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
- out = tf.batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
- return tf.clipByValue(out, 0, 6);
- });
-}
-function getStridesForLayerIdx(layerIdx) {
- return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
-}
-function mobileNetV1(x, params) {
- return tf.tidy(function () {
- var conv11 = null;
- var out = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [2, 2]);
- var convPairParams = [
- params.conv_1,
- params.conv_2,
- params.conv_3,
- params.conv_4,
- params.conv_5,
- params.conv_6,
- params.conv_7,
- params.conv_8,
- params.conv_9,
- params.conv_10,
- params.conv_11,
- params.conv_12,
- params.conv_13
- ];
- convPairParams.forEach(function (param, i) {
- var layerIdx = i + 1;
- var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
- out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
- out = pointwiseConvLayer_1.pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
- if (layerIdx === 11) {
- conv11 = out;
- }
- });
- if (conv11 === null) {
- throw new Error('mobileNetV1 - output of conv layer 11 is null');
- }
- return {
- out: out,
- conv11: conv11
- };
- });
-}
-exports.mobileNetV1 = mobileNetV1;
-//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/mobileNetV1.js.map b/build/faceDetectionNet/mobileNetV1.js.map
deleted file mode 100644
index b5404c74..00000000
--- a/build/faceDetectionNet/mobileNetV1.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"mobileNetV1.js","sourceRoot":"","sources":["../../src/faceDetectionNet/mobileNetV1.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAG1D,IAAM,OAAO,GAAG,qBAAqB,CAAA;AAErC,4BACE,CAAc,EACd,MAAuC,EACvC,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QAChE,GAAG,GAAG,EAAE,CAAC,kBAAkB,CACzB,GAAG,EACH,MAAM,CAAC,eAAe,EACtB,MAAM,CAAC,mBAAmB,EAC1B,OAAO,EACP,MAAM,CAAC,gBAAgB,EACvB,MAAM,CAAC,iBAAiB,CACzB,CAAA;QACD,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,+BAA+B,QAAgB;IAC7C,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,IAAI,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,KAAK,QAAQ,EAAhB,CAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;AACtE,CAAC;AAED,qBAA4B,CAAc,EAAE,MAA0B;IACpE,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,MAAM,GAAG,IAAI,CAAA;QACjB,IAAI,GAAG,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEtD,IAAM,cAAc,GAAG;YACrB,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,MAAM;YACb,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;YACd,MAAM,CAAC,OAAO;SACf,CAAA;QAED,cAAc,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;YAC9B,IAAM,QAAQ,GAAG,CAAC,GAAG,CAAC,CAAA;YACtB,IAAM,oBAAoB,GAAG,qBAAqB,CAAC,QAAQ,CAAC,CAAA;YAC5D,GAAG,GAAG,kBAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,cAAc,EAAE,oBAAoB,CAAC,CAAA;YACzE,GAAG,GAAG,uCAAkB,CAAC,GAAG,EAAE,KAAK,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC3D,IAAI,QAAQ,KAAK,EAAE,EAAE;gBACnB,MAAM,GAAG,GAAG,CAAA;aACb;QACH,CAAC,CAAC,CAAA;QAEF,IAAI,MAAM,KAAK,IAAI,EAAE;YACnB,MAAM,IAAI,KAAK,CAAC,+CAA+C,CAAC,CAAA;SACjE;QAED,OAAO;YACL,GAAG,KAAA;YACH,MAAM,EAAE,MAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC;AA1CD,kCA0CC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/nonMaxSuppression.d.ts b/build/faceDetectionNet/nonMaxSuppression.d.ts
deleted file mode 100644
index 6b215a26..00000000
--- a/build/faceDetectionNet/nonMaxSuppression.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];
diff --git a/build/faceDetectionNet/nonMaxSuppression.js b/build/faceDetectionNet/nonMaxSuppression.js
deleted file mode 100644
index 65e75e87..00000000
--- a/build/faceDetectionNet/nonMaxSuppression.js
+++ /dev/null
@@ -1,56 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
- var numBoxes = boxes.shape[0];
- var outputSize = Math.min(maxOutputSize, numBoxes);
- var candidates = scores
- .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
- .filter(function (c) { return c.score > scoreThreshold; })
- .sort(function (c1, c2) { return c2.score - c1.score; });
- var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
- var selected = [];
- candidates.forEach(function (c) {
- if (selected.length >= outputSize) {
- return;
- }
- var originalScore = c.score;
- for (var j = selected.length - 1; j >= 0; --j) {
- var iou = IOU(boxes, c.boxIndex, selected[j]);
- if (iou === 0.0) {
- continue;
- }
- c.score *= suppressFunc(iou);
- if (c.score <= scoreThreshold) {
- break;
- }
- }
- if (originalScore === c.score) {
- selected.push(c.boxIndex);
- }
- });
- return selected;
-}
-exports.nonMaxSuppression = nonMaxSuppression;
-function IOU(boxes, i, j) {
- var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
- var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
- var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
- var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
- var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
- var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
- var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
- var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
- var areaI = (ymaxI - yminI) * (xmaxI - xminI);
- var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
- if (areaI <= 0 || areaJ <= 0) {
- return 0.0;
- }
- var intersectionYmin = Math.max(yminI, yminJ);
- var intersectionXmin = Math.max(xminI, xminJ);
- var intersectionYmax = Math.min(ymaxI, ymaxJ);
- var intersectionXmax = Math.min(xmaxI, xmaxJ);
- var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
- Math.max(intersectionXmax - intersectionXmin, 0.0);
- return intersectionArea / (areaI + areaJ - intersectionArea);
-}
-//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/nonMaxSuppression.js.map b/build/faceDetectionNet/nonMaxSuppression.js.map
deleted file mode 100644
index a5241d47..00000000
--- a/build/faceDetectionNet/nonMaxSuppression.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"nonMaxSuppression.js","sourceRoot":"","sources":["../../src/faceDetectionNet/nonMaxSuppression.ts"],"names":[],"mappings":";;AAEA,2BACE,KAAkB,EAClB,MAAgB,EAChB,aAAqB,EACrB,YAAoB,EACpB,cAAsB;IAGtB,IAAM,QAAQ,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAC/B,IAAM,UAAU,GAAG,IAAI,CAAC,GAAG,CACzB,aAAa,EACb,QAAQ,CACT,CAAA;IAED,IAAM,UAAU,GAAG,MAAM;SACtB,GAAG,CAAC,UAAC,KAAK,EAAE,QAAQ,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,QAAQ,UAAA,EAAE,CAAC,EAArB,CAAqB,CAAC;SAC/C,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;SACrC,IAAI,CAAC,UAAC,EAAE,EAAE,EAAE,IAAK,OAAA,EAAE,CAAC,KAAK,GAAG,EAAE,CAAC,KAAK,EAAnB,CAAmB,CAAC,CAAA;IAExC,IAAM,YAAY,GAAG,UAAC,CAAS,IAAK,OAAA,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAzB,CAAyB,CAAA;IAE7D,IAAM,QAAQ,GAAa,EAAE,CAAA;IAE7B,UAAU,CAAC,OAAO,CAAC,UAAA,CAAC;QAClB,IAAI,QAAQ,CAAC,MAAM,IAAI,UAAU,EAAE;YACjC,OAAM;SACP;QACD,IAAM,aAAa,GAAG,CAAC,CAAC,KAAK,CAAA;QAE7B,KAAK,IAAI,CAAC,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,EAAE,CAAC,EAAE;YAC7C,IAAM,GAAG,GAAG,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/C,IAAI,GAAG,KAAK,GAAG,EAAE;gBACf,SAAQ;aACT;YACD,CAAC,CAAC,KAAK,IAAI,YAAY,CAAC,GAAG,CAAC,CAAA;YAC5B,IAAI,CAAC,CAAC,KAAK,IAAI,cAAc,EAAE;gBAC7B,MAAK;aACN;SACF;QAED,IAAI,aAAa,KAAK,CAAC,CAAC,KAAK,EAAE;YAC7B,QAAQ,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAA;SAC1B;IACH,CAAC,CAAC,CAAA;IAEF,OAAO,QAAQ,CAAA;AACjB,CAAC;AA9CD,8CA8CC;AAED,aAAa,KAAkB,EAAE,CAAS,EAAE,CAAS;IACnD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACxD,IAAM,KAAK,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,CAAA;IAC/C,IAAM,KAAK,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,CAAA;IAC/C,IAAI,KAAK,IAAI,CAAC,IAAI,KAAK,IAAI,CAAC,EAAE;QAC5B,OAAO,GAAG,CAAA;KACX;IACD,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,IAAM,gBAAgB,GAClB,IAAI,CAAC,GAAG,CAAC,gBAAgB,GAAG,gBAAgB,EAAE,GAAG,CAAC;QAClD,IAAI,CAAC,GAAG,CAAC,gBAAgB,GAAG,gBAAgB,EAAE,GAAG,CAAC,CAAA;IACtD,OAAO,gBAAgB,GAAG,CAAC,KAAK,GAAG,KAAK,GAAG,gBAAgB,CAAC,CAAA;AAC9D,CAAC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/outputLayer.d.ts b/build/faceDetectionNet/outputLayer.d.ts
deleted file mode 100644
index 9ce8d135..00000000
--- a/build/faceDetectionNet/outputLayer.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { OutputLayerParams } from './types';
-export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): {
- boxes: tf.Tensor[];
- scores: tf.Tensor[];
-};
diff --git a/build/faceDetectionNet/outputLayer.js b/build/faceDetectionNet/outputLayer.js
deleted file mode 100644
index 63ec147c..00000000
--- a/build/faceDetectionNet/outputLayer.js
+++ /dev/null
@@ -1,50 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function getCenterCoordinatesAndSizesLayer(x) {
- var vec = tf.unstack(tf.transpose(x, [1, 0]));
- var sizes = [
- tf.sub(vec[2], vec[0]),
- tf.sub(vec[3], vec[1])
- ];
- var centers = [
- tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
- tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
- ];
- return {
- sizes: sizes,
- centers: centers
- };
-}
-function decodeBoxesLayer(x0, x1) {
- var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
- var vec = tf.unstack(tf.transpose(x1, [1, 0]));
- var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
- var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
- var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
- var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
- return tf.transpose(tf.stack([
- tf.sub(add0_out, div0_out),
- tf.sub(add1_out, div1_out),
- tf.add(add0_out, div0_out),
- tf.add(add1_out, div1_out)
- ]), [1, 0]);
-}
-function outputLayer(boxPredictions, classPredictions, params) {
- return tf.tidy(function () {
- var batchSize = boxPredictions.shape[0];
- var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
- boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
- var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
- var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
- scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
- var boxesByBatch = tf.unstack(boxes);
- var scoresByBatch = tf.unstack(scores);
- return {
- boxes: boxesByBatch,
- scores: scoresByBatch
- };
- });
-}
-exports.outputLayer = outputLayer;
-//# sourceMappingURL=outputLayer.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/outputLayer.js.map b/build/faceDetectionNet/outputLayer.js.map
deleted file mode 100644
index 943f4510..00000000
--- a/build/faceDetectionNet/outputLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"outputLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/outputLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAK5C,2CAA2C,CAAc;IACvD,IAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAE/C,IAAM,KAAK,GAAG;QACZ,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;KACvB,CAAA;IAED,IAAM,OAAO,GAAG;QACd,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC9C,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/C,CAAA;IAED,OAAO;QACL,KAAK,OAAA;QACL,OAAO,SAAA;KACR,CAAA;AACH,CAAC;AAED,0BAA0B,EAAe,EAAE,EAAe;IAClD,IAAA,0CAGmC,EAFvC,gBAAK,EACL,oBAAO,CACgC;IAEzC,IAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAEhD,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,OAAO,EAAE,CAAC,SAAS,CACjB,EAAE,CAAC,KAAK,CAAC;QACP,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;KAC3B,CAAC,EACF,CAAC,CAAC,EAAE,CAAC,CAAC,CACP,CAAA;AACH,CAAC;AAED,qBACE,cAA2B,EAC3B,gBAA6B,EAC7B,MAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAEzC,IAAI,KAAK,GAAG,gBAAgB,CAC1B,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,SAAS,EAAE,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,EAChF,EAAE,CAAC,OAAO,CAAC,cAAc,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CACnD,CAAA;QACD,KAAK,GAAG,EAAE,CAAC,OAAO,CAChB,KAAK,EACL,CAAC,SAAS,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,EAAE,CAAC,CAAC,CAC7C,CAAA;QAED,IAAM,gBAAgB,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACxF,IAAI,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAc,CAAA;QAE5E,MAAM,GAAG,EAAE,CAAC,OAAO,CACjB,MAAM,EACN,CAAC,SAAS,EAAE,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAC7B,CAAA;QAED,IAAM,YAAY,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,CAAkB,CAAA;QACvD,IAAM,aAAa,GAAG,EAAE,CAAC,OAAO,CAAC,MAAM,CAAkB,CAAA;QAEzD,OAAO;YACL,KAAK,EAAE,YAAY;YACnB,MAAM,EAAE,aAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC;AAnCD,kCAmCC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/pointwiseConvLayer.d.ts b/build/faceDetectionNet/pointwiseConvLayer.d.ts
deleted file mode 100644
index f7a92d55..00000000
--- a/build/faceDetectionNet/pointwiseConvLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { PointwiseConvParams } from './types';
-export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): tf.Tensor;
diff --git a/build/faceDetectionNet/pointwiseConvLayer.js b/build/faceDetectionNet/pointwiseConvLayer.js
deleted file mode 100644
index 71ace7c1..00000000
--- a/build/faceDetectionNet/pointwiseConvLayer.js
+++ /dev/null
@@ -1,12 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function pointwiseConvLayer(x, params, strides) {
- return tf.tidy(function () {
- var out = tf.conv2d(x, params.filters, strides, 'same');
- out = tf.add(out, params.batch_norm_offset);
- return tf.clipByValue(out, 0, 6);
- });
-}
-exports.pointwiseConvLayer = pointwiseConvLayer;
-//# sourceMappingURL=pointwiseConvLayer.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/pointwiseConvLayer.js.map b/build/faceDetectionNet/pointwiseConvLayer.js.map
deleted file mode 100644
index 177e7d97..00000000
--- a/build/faceDetectionNet/pointwiseConvLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/pointwiseConvLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C,4BACE,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC;AAZD,gDAYC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/predictionLayer.d.ts b/build/faceDetectionNet/predictionLayer.d.ts
deleted file mode 100644
index 3f136062..00000000
--- a/build/faceDetectionNet/predictionLayer.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { PredictionLayerParams } from './types';
-export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): {
- boxPredictions: tf.Tensor;
- classPredictions: tf.Tensor;
-};
diff --git a/build/faceDetectionNet/predictionLayer.js b/build/faceDetectionNet/predictionLayer.js
deleted file mode 100644
index 33847b68..00000000
--- a/build/faceDetectionNet/predictionLayer.js
+++ /dev/null
@@ -1,45 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var boxPredictionLayer_1 = require("./boxPredictionLayer");
-var pointwiseConvLayer_1 = require("./pointwiseConvLayer");
-function predictionLayer(x, conv11, params) {
- return tf.tidy(function () {
- var conv0 = pointwiseConvLayer_1.pointwiseConvLayer(x, params.conv_0, [1, 1]);
- var conv1 = pointwiseConvLayer_1.pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
- var conv2 = pointwiseConvLayer_1.pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
- var conv3 = pointwiseConvLayer_1.pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
- var conv4 = pointwiseConvLayer_1.pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
- var conv5 = pointwiseConvLayer_1.pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
- var conv6 = pointwiseConvLayer_1.pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
- var conv7 = pointwiseConvLayer_1.pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
- var boxPrediction0 = boxPredictionLayer_1.boxPredictionLayer(conv11, params.box_predictor_0);
- var boxPrediction1 = boxPredictionLayer_1.boxPredictionLayer(x, params.box_predictor_1);
- var boxPrediction2 = boxPredictionLayer_1.boxPredictionLayer(conv1, params.box_predictor_2);
- var boxPrediction3 = boxPredictionLayer_1.boxPredictionLayer(conv3, params.box_predictor_3);
- var boxPrediction4 = boxPredictionLayer_1.boxPredictionLayer(conv5, params.box_predictor_4);
- var boxPrediction5 = boxPredictionLayer_1.boxPredictionLayer(conv7, params.box_predictor_5);
- var boxPredictions = tf.concat([
- boxPrediction0.boxPredictionEncoding,
- boxPrediction1.boxPredictionEncoding,
- boxPrediction2.boxPredictionEncoding,
- boxPrediction3.boxPredictionEncoding,
- boxPrediction4.boxPredictionEncoding,
- boxPrediction5.boxPredictionEncoding
- ], 1);
- var classPredictions = tf.concat([
- boxPrediction0.classPrediction,
- boxPrediction1.classPrediction,
- boxPrediction2.classPrediction,
- boxPrediction3.classPrediction,
- boxPrediction4.classPrediction,
- boxPrediction5.classPrediction
- ], 1);
- return {
- boxPredictions: boxPredictions,
- classPredictions: classPredictions
- };
- });
-}
-exports.predictionLayer = predictionLayer;
-//# sourceMappingURL=predictionLayer.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/predictionLayer.js.map b/build/faceDetectionNet/predictionLayer.js.map
deleted file mode 100644
index b2cb37ba..00000000
--- a/build/faceDetectionNet/predictionLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../src/faceDetectionNet/predictionLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2DAA0D;AAC1D,2DAA0D;AAG1D,yBACE,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,KAAK,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC1D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,IAAM,KAAK,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE9D,IAAM,cAAc,GAAG,uCAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACzE,IAAM,cAAc,GAAG,uCAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACpE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,IAAM,cAAc,GAAG,uCAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QAExE,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,IAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc,gBAAA;YACd,gBAAgB,kBAAA;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC;AA9CD,0CA8CC"}
\ No newline at end of file
diff --git a/build/faceDetectionNet/types.d.ts b/build/faceDetectionNet/types.d.ts
deleted file mode 100644
index f0eed191..00000000
--- a/build/faceDetectionNet/types.d.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams } from 'tfjs-tiny-yolov2';
-export declare type PointwiseConvParams = {
- filters: tf.Tensor4D;
- batch_norm_offset: tf.Tensor1D;
-};
-export declare namespace MobileNetV1 {
- type DepthwiseConvParams = {
- filters: tf.Tensor4D;
- batch_norm_scale: tf.Tensor1D;
- batch_norm_offset: tf.Tensor1D;
- batch_norm_mean: tf.Tensor1D;
- batch_norm_variance: tf.Tensor1D;
- };
- type ConvPairParams = {
- depthwise_conv: DepthwiseConvParams;
- pointwise_conv: PointwiseConvParams;
- };
- type Params = {
- conv_0: PointwiseConvParams;
- conv_1: ConvPairParams;
- conv_2: ConvPairParams;
- conv_3: ConvPairParams;
- conv_4: ConvPairParams;
- conv_5: ConvPairParams;
- conv_6: ConvPairParams;
- conv_7: ConvPairParams;
- conv_8: ConvPairParams;
- conv_9: ConvPairParams;
- conv_10: ConvPairParams;
- conv_11: ConvPairParams;
- conv_12: ConvPairParams;
- conv_13: ConvPairParams;
- };
-}
-export declare type BoxPredictionParams = {
- box_encoding_predictor: ConvParams;
- class_predictor: ConvParams;
-};
-export declare type PredictionLayerParams = {
- conv_0: PointwiseConvParams;
- conv_1: PointwiseConvParams;
- conv_2: PointwiseConvParams;
- conv_3: PointwiseConvParams;
- conv_4: PointwiseConvParams;
- conv_5: PointwiseConvParams;
- conv_6: PointwiseConvParams;
- conv_7: PointwiseConvParams;
- box_predictor_0: BoxPredictionParams;
- box_predictor_1: BoxPredictionParams;
- box_predictor_2: BoxPredictionParams;
- box_predictor_3: BoxPredictionParams;
- box_predictor_4: BoxPredictionParams;
- box_predictor_5: BoxPredictionParams;
-};
-export declare type OutputLayerParams = {
- extra_dim: tf.Tensor3D;
-};
-export declare type NetParams = {
- mobilenetv1: MobileNetV1.Params;
- prediction_layer: PredictionLayerParams;
- output_layer: OutputLayerParams;
-};
diff --git a/build/faceDetectionNet/types.js b/build/faceDetectionNet/types.js
deleted file mode 100644
index 11e638d1..00000000
--- a/build/faceDetectionNet/types.js
+++ /dev/null
@@ -1,3 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/faceDetectionNet/types.js.map b/build/faceDetectionNet/types.js.map
deleted file mode 100644
index 8e7195f9..00000000
--- a/build/faceDetectionNet/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceDetectionNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmark68Net.d.ts b/build/faceLandmarkNet/FaceLandmark68Net.d.ts
deleted file mode 100644
index 93d5106a..00000000
--- a/build/faceLandmarkNet/FaceLandmark68Net.d.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput } from 'tfjs-image-recognition-base';
-import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
-import { NetParams } from './types';
-export declare class FaceLandmark68Net extends FaceLandmark68NetBase {
- constructor();
- runNet(input: NetInput): tf.Tensor2D;
- protected loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- }>;
- protected extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- };
-}
diff --git a/build/faceLandmarkNet/FaceLandmark68Net.js b/build/faceLandmarkNet/FaceLandmark68Net.js
deleted file mode 100644
index d6a72f98..00000000
--- a/build/faceLandmarkNet/FaceLandmark68Net.js
+++ /dev/null
@@ -1,56 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var depthwiseSeparableConv_1 = require("./depthwiseSeparableConv");
-var extractParams_1 = require("./extractParams");
-var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
-var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-function denseBlock(x, denseBlockParams, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- return tf.tidy(function () {
- var out1 = tf.relu(isFirstLayer
- ? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
- : depthwiseSeparableConv_1.depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
- var out2 = depthwiseSeparableConv_1.depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
- var in3 = tf.relu(tf.add(out1, out2));
- var out3 = depthwiseSeparableConv_1.depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
- var in4 = tf.relu(tf.add(out1, tf.add(out2, out3)));
- var out4 = depthwiseSeparableConv_1.depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
- return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4))));
- });
-}
-var FaceLandmark68Net = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmark68Net, _super);
- function FaceLandmark68Net() {
- return _super.call(this, 'FaceLandmark68LargeNet') || this;
- }
- FaceLandmark68Net.prototype.runNet = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceLandmark68LargeNet - load model before inference');
- }
- return tf.tidy(function () {
- var batchTensor = input.toBatchTensor(112, true);
- var meanRgb = [122.782, 117.001, 104.298];
- var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
- var out = denseBlock(normalized, params.dense0, true);
- out = denseBlock(out, params.dense1);
- out = denseBlock(out, params.dense2);
- out = denseBlock(out, params.dense3);
- out = tf.avgPool(out, [7, 7], [2, 2], 'valid');
- return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
- });
- };
- FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams_1.loadQuantizedParams(uri);
- };
- FaceLandmark68Net.prototype.extractParams = function (weights) {
- return extractParams_1.extractParams(weights);
- };
- return FaceLandmark68Net;
-}(FaceLandmark68NetBase_1.FaceLandmark68NetBase));
-exports.FaceLandmark68Net = FaceLandmark68Net;
-//# sourceMappingURL=FaceLandmark68Net.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmark68Net.js.map b/build/faceLandmarkNet/FaceLandmark68Net.js.map
deleted file mode 100644
index 120700f6..00000000
--- a/build/faceLandmarkNet/FaceLandmark68Net.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmark68Net.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmark68Net.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAkE;AAIlE,mEAAkE;AAClE,iDAAgD;AAChD,iEAAgE;AAChE,6DAA4D;AAC5D,6DAA4D;AAG5D,oBACE,CAAc,EACd,gBAAmC,EACnC,YAA6B;IAA7B,6BAAA,EAAA,oBAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EAC5E,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,+CAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CACtE,CAAA;QAChB,IAAM,IAAI,GAAG,+CAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,IAAM,IAAI,GAAG,+CAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;QACpE,IAAM,IAAI,GAAG,+CAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC,CAAgB,CAAA;IAC/E,CAAC,CAAC,CAAA;AACJ,CAAC;AAED;IAAuC,6CAAgC;IAErE;eACE,kBAAM,wBAAwB,CAAC;IACjC,CAAC;IAEM,kCAAM,GAAb,UAAc,KAAe;QAEnB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,sDAAsD,CAAC,CAAA;SACxE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,IAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,IAAM,UAAU,GAAG,uCAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,UAAU,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACrD,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACpC,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACpC,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACpC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,yCAAmB,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;QACnE,CAAC,CAAC,CAAA;IACJ,CAAC;IAES,+CAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAGS,yCAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,wBAAC;AAAD,CAAC,AArCD,CAAuC,6CAAqB,GAqC3D;AArCY,8CAAiB"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmark68NetBase.d.ts b/build/faceLandmarkNet/FaceLandmark68NetBase.d.ts
deleted file mode 100644
index 01e1e9f2..00000000
--- a/build/faceLandmarkNet/FaceLandmark68NetBase.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, NeuralNetwork, TNetInput, Dimensions } from 'tfjs-image-recognition-base';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-export declare class FaceLandmark68NetBase extends NeuralNetwork {
- private __name;
- constructor(_name: string);
- runNet(_: NetInput): tf.Tensor2D;
- postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D;
- forwardInput(input: NetInput): tf.Tensor2D;
- forward(input: TNetInput): Promise;
- detectLandmarks(input: TNetInput): Promise;
-}
diff --git a/build/faceLandmarkNet/FaceLandmark68NetBase.js b/build/faceLandmarkNet/FaceLandmark68NetBase.js
deleted file mode 100644
index 9183847c..00000000
--- a/build/faceLandmarkNet/FaceLandmark68NetBase.js
+++ /dev/null
@@ -1,115 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
-var FaceLandmark68NetBase = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmark68NetBase, _super);
- function FaceLandmark68NetBase(_name) {
- var _this = _super.call(this, _name) || this;
- _this.__name = _name;
- return _this;
- }
- FaceLandmark68NetBase.prototype.runNet = function (_) {
- throw new Error(this.__name + " - runNet not implemented");
- };
- FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
- var inputDimensions = originalDimensions.map(function (_a) {
- var width = _a.width, height = _a.height;
- var scale = inputSize / Math.max(height, width);
- return {
- width: width * scale,
- height: height * scale
- };
- });
- var batchSize = inputDimensions.length;
- return tf.tidy(function () {
- var createInterleavedTensor = function (fillX, fillY) {
- return tf.stack([
- tf.fill([68], fillX),
- tf.fill([68], fillY)
- ], 1).as2D(1, 136).as1D();
- };
- var getPadding = function (batchIdx, cond) {
- var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
- return cond(width, height) ? Math.abs(width - height) / 2 : 0;
- };
- var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
- var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
- var landmarkTensors = output
- .mul(tf.fill([batchSize, 136], inputSize))
- .sub(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
- return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
- })))
- .div(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
- return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
- })));
- return landmarkTensors;
- });
- };
- FaceLandmark68NetBase.prototype.forwardInput = function (input) {
- var _this = this;
- return tf.tidy(function () {
- var out = _this.runNet(input);
- return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
- var height = _a[0], width = _a[1];
- return ({ height: height, width: width });
- }));
- });
- };
- FaceLandmark68NetBase.prototype.forward = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _this = this;
- var netInput, landmarkTensors, landmarksForBatch;
- return tslib_1.__generator(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1:
- netInput = _a.sent();
- landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
- return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
- var landmarksArray, _a, _b, xCoords, yCoords;
- return tslib_1.__generator(this, function (_c) {
- switch (_c.label) {
- case 0:
- _b = (_a = Array).from;
- return [4 /*yield*/, landmarkTensor.data()];
- case 1:
- landmarksArray = _b.apply(_a, [_c.sent()]);
- xCoords = landmarksArray.filter(function (_, i) { return tfjs_image_recognition_base_1.isEven(i); });
- yCoords = landmarksArray.filter(function (_, i) { return !tfjs_image_recognition_base_1.isEven(i); });
- return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new tfjs_image_recognition_base_1.Point(xCoords[i], yCoords[i]); }), {
- height: netInput.getInputHeight(batchIdx),
- width: netInput.getInputWidth(batchIdx),
- })];
- }
- });
- }); }))];
- case 2:
- landmarksForBatch = _a.sent();
- landmarkTensors.forEach(function (t) { return t.dispose(); });
- return [2 /*return*/, netInput.isBatchInput
- ? landmarksForBatch
- : landmarksForBatch[0]];
- }
- });
- });
- };
- return FaceLandmark68NetBase;
-}(tfjs_image_recognition_base_1.NeuralNetwork));
-exports.FaceLandmark68NetBase = FaceLandmark68NetBase;
-//# sourceMappingURL=FaceLandmark68NetBase.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmark68NetBase.js.map b/build/faceLandmarkNet/FaceLandmark68NetBase.js.map
deleted file mode 100644
index 7434c376..00000000
--- a/build/faceLandmarkNet/FaceLandmark68NetBase.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAwH;AAExH,8DAA6D;AAE7D;IAAsD,iDAAwB;IAK5E,+BAAY,KAAa;QAAzB,YACE,kBAAM,KAAK,CAAC,SAEb;QADC,KAAI,CAAC,MAAM,GAAG,KAAK,CAAA;;IACrB,CAAC;IAEM,sCAAM,GAAb,UAAc,CAAW;QACvB,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,MAAM,8BAA2B,CAAC,CAAA;IAC5D,CAAC;IAEM,2CAAW,GAAlB,UAAmB,MAAmB,EAAE,SAAiB,EAAE,kBAAgC;QAEzF,IAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,UAAC,EAAiB;gBAAf,gBAAK,EAAE,kBAAM;YAC7D,IAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,IAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,uBAAuB,GAAG,UAAC,KAAa,EAAE,KAAa;gBAC3D,OAAA,EAAE,CAAC,KAAK,CAAC;oBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;oBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;iBACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE;YAHzB,CAGyB,CAAA;YAE3B,IAAM,UAAU,GAAG,UAAC,QAAgB,EAAE,IAAuC;gBACrE,IAAA,8BAA6C,EAA3C,gBAAK,EAAE,kBAAM,CAA8B;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAC/E,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAE/E,IAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;iBACzC,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC;YAHD,CAGC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,4CAAY,GAAnB,UAAoB,KAAe;QAAnC,iBASC;QARC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,GAAG,GAAG,KAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,KAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,UAAC,EAAe;oBAAd,cAAM,EAAE,aAAK;gBAAM,OAAA,CAAC,EAAE,MAAM,QAAA,EAAE,KAAK,OAAA,EAAE,CAAC;YAAnB,CAAmB,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,uCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,+CAAe,GAA5B,UAA6B,KAAgB;;;;;;4BAC1B,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAClC,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAEyB,qBAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,UAAO,cAAc,EAAE,QAAQ;;;;;4CACN,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;4CAAC,qBAAM,cAAc,CAAC,IAAI,EAAE,EAAA;;4CAAvD,cAAc,GAAG,cAAW,SAA2B,EAAC;4CACxD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,oCAAM,CAAC,CAAC,CAAC,EAAT,CAAS,CAAC,CAAA;4CACpD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,oCAAM,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC,CAAA;4CAE3D,sBAAO,IAAI,iCAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,IAAI,mCAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,EAAjC,CAAiC,CAAC,EAClE;oDACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;oDACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;iDACzC,CACF,EAAA;;;iCACF,CACF,CAAC,EAAA;;wBAdI,iBAAiB,GAAG,SAcxB;wBAEF,eAAe,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAEzC,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,iBAAiB;gCACnB,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,EAAA;;;;KACzB;IACH,4BAAC;AAAD,CAAC,AAtGD,CAAsD,2CAAa,GAsGlE;AAtGY,sDAAqB"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmark68TinyNet.d.ts b/build/faceLandmarkNet/FaceLandmark68TinyNet.d.ts
deleted file mode 100644
index 774b1109..00000000
--- a/build/faceLandmarkNet/FaceLandmark68TinyNet.d.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput } from 'tfjs-image-recognition-base';
-import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
-import { TinyNetParams } from './types';
-export declare class FaceLandmark68TinyNet extends FaceLandmark68NetBase {
- constructor();
- runNet(input: NetInput): tf.Tensor2D;
- protected loadQuantizedParams(uri: string | undefined): Promise<{
- params: TinyNetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- }>;
- protected extractParams(weights: Float32Array): {
- params: TinyNetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- };
-}
diff --git a/build/faceLandmarkNet/FaceLandmark68TinyNet.js b/build/faceLandmarkNet/FaceLandmark68TinyNet.js
deleted file mode 100644
index ef919986..00000000
--- a/build/faceLandmarkNet/FaceLandmark68TinyNet.js
+++ /dev/null
@@ -1,53 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var depthwiseSeparableConv_1 = require("./depthwiseSeparableConv");
-var extractParamsTiny_1 = require("./extractParamsTiny");
-var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
-var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
-var loadQuantizedParamsTiny_1 = require("./loadQuantizedParamsTiny");
-function denseBlock(x, denseBlockParams, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- return tf.tidy(function () {
- var out1 = tf.relu(isFirstLayer
- ? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
- : depthwiseSeparableConv_1.depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
- var out2 = depthwiseSeparableConv_1.depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
- var in3 = tf.relu(tf.add(out1, out2));
- var out3 = depthwiseSeparableConv_1.depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
- return tf.relu(tf.add(out1, tf.add(out2, out3)));
- });
-}
-var FaceLandmark68TinyNet = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmark68TinyNet, _super);
- function FaceLandmark68TinyNet() {
- return _super.call(this, 'FaceLandmark68TinyNet') || this;
- }
- FaceLandmark68TinyNet.prototype.runNet = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceLandmark68TinyNet - load model before inference');
- }
- return tf.tidy(function () {
- var batchTensor = input.toBatchTensor(112, true);
- var meanRgb = [122.782, 117.001, 104.298];
- var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(255));
- var out = denseBlock(normalized, params.dense0, true);
- out = denseBlock(out, params.dense1);
- out = denseBlock(out, params.dense2);
- out = tf.avgPool(out, [14, 14], [2, 2], 'valid');
- return fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
- });
- };
- FaceLandmark68TinyNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParamsTiny_1.loadQuantizedParamsTiny(uri);
- };
- FaceLandmark68TinyNet.prototype.extractParams = function (weights) {
- return extractParamsTiny_1.extractParamsTiny(weights);
- };
- return FaceLandmark68TinyNet;
-}(FaceLandmark68NetBase_1.FaceLandmark68NetBase));
-exports.FaceLandmark68TinyNet = FaceLandmark68TinyNet;
-//# sourceMappingURL=FaceLandmark68TinyNet.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmark68TinyNet.js.map b/build/faceLandmarkNet/FaceLandmark68TinyNet.js.map
deleted file mode 100644
index 2b24ef4b..00000000
--- a/build/faceLandmarkNet/FaceLandmark68TinyNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmark68TinyNet.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmark68TinyNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAkE;AAIlE,mEAAkE;AAClE,yDAAwD;AACxD,iEAAgE;AAChE,6DAA4D;AAC5D,qEAAoE;AAGpE,oBACE,CAAc,EACd,gBAAmC,EACnC,YAA6B;IAA7B,6BAAA,EAAA,oBAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EAC5E,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,+CAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CACtE,CAAA;QAChB,IAAM,IAAI,GAAG,+CAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,IAAM,IAAI,GAAG,+CAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;IACjE,CAAC,CAAC,CAAA;AACJ,CAAC;AAED;IAA2C,iDAAoC;IAE7E;eACE,kBAAM,uBAAuB,CAAC;IAChC,CAAC;IAEM,sCAAM,GAAb,UAAc,KAAe;QAEnB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,qDAAqD,CAAC,CAAA;SACvE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,IAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,IAAM,UAAU,GAAG,uCAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,UAAU,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACrD,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACpC,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACpC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,yCAAmB,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;QACnE,CAAC,CAAC,CAAA;IACJ,CAAC;IAES,mDAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,iDAAuB,CAAC,GAAG,CAAC,CAAA;IACrC,CAAC;IAES,6CAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,qCAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;IACH,4BAAC;AAAD,CAAC,AAnCD,CAA2C,6CAAqB,GAmC/D;AAnCY,sDAAqB"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmarkNet.d.ts b/build/faceLandmarkNet/FaceLandmarkNet.d.ts
deleted file mode 100644
index 25ad34ad..00000000
--- a/build/faceLandmarkNet/FaceLandmarkNet.d.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { NetParams } from './types';
-export declare class FaceLandmarkNet extends NeuralNetwork {
- constructor();
- forwardInput(input: NetInput): tf.Tensor2D;
- forward(input: TNetInput): Promise;
- detectLandmarks(input: TNetInput): Promise;
- protected loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- }>;
- protected extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- };
-}
diff --git a/build/faceLandmarkNet/FaceLandmarkNet.js b/build/faceLandmarkNet/FaceLandmarkNet.js
deleted file mode 100644
index a23d0ffc..00000000
--- a/build/faceLandmarkNet/FaceLandmarkNet.js
+++ /dev/null
@@ -1,127 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
-var extractParams_1 = require("./extractParams");
-var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-function conv(x, params) {
- return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true);
-}
-function maxPool(x, strides) {
- if (strides === void 0) { strides = [2, 2]; }
- return tf.maxPool(x, [2, 2], strides, 'valid');
-}
-var FaceLandmarkNet = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmarkNet, _super);
- function FaceLandmarkNet() {
- return _super.call(this, 'FaceLandmarkNet') || this;
- }
- FaceLandmarkNet.prototype.forwardInput = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceLandmarkNet - load model before inference');
- }
- return tf.tidy(function () {
- var batchTensor = input.toBatchTensor(128, true);
- var out = conv(batchTensor, params.conv0);
- out = maxPool(out);
- out = conv(out, params.conv1);
- out = conv(out, params.conv2);
- out = maxPool(out);
- out = conv(out, params.conv3);
- out = conv(out, params.conv4);
- out = maxPool(out);
- out = conv(out, params.conv5);
- out = conv(out, params.conv6);
- out = maxPool(out, [1, 1]);
- out = conv(out, params.conv7);
- var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
- var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
- var createInterleavedTensor = function (fillX, fillY) {
- return tf.stack([
- tf.fill([68], fillX),
- tf.fill([68], fillY)
- ], 1).as2D(1, 136).as1D();
- };
- /* shift coordinates back, to undo centered padding
- x = ((x * widthAfterPadding) - shiftX) / width
- y = ((y * heightAfterPadding) - shiftY) / height
- */
- var landmarkTensors = fc1
- .mul(tf.stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
- return createInterleavedTensor(input.getPaddings(batchIdx).x + input.getInputWidth(batchIdx), input.getPaddings(batchIdx).y + input.getInputHeight(batchIdx));
- })))
- .sub(tf.stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
- return createInterleavedTensor(Math.floor(input.getPaddings(batchIdx).x / 2), Math.floor(input.getPaddings(batchIdx).y / 2));
- })))
- .div(tf.stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
- return createInterleavedTensor(input.getInputWidth(batchIdx), input.getInputHeight(batchIdx));
- })));
- return landmarkTensors;
- });
- };
- FaceLandmarkNet.prototype.forward = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceLandmarkNet.prototype.detectLandmarks = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _this = this;
- var netInput, landmarkTensors, landmarksForBatch;
- return tslib_1.__generator(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
- case 1:
- netInput = _a.sent();
- landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
- return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
- var landmarksArray, _a, _b, xCoords, yCoords;
- return tslib_1.__generator(this, function (_c) {
- switch (_c.label) {
- case 0:
- _b = (_a = Array).from;
- return [4 /*yield*/, landmarkTensor.data()];
- case 1:
- landmarksArray = _b.apply(_a, [_c.sent()]);
- xCoords = landmarksArray.filter(function (_, i) { return tfjs_image_recognition_base_1.isEven(i); });
- yCoords = landmarksArray.filter(function (_, i) { return !tfjs_image_recognition_base_1.isEven(i); });
- return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new tfjs_image_recognition_base_1.Point(xCoords[i], yCoords[i]); }), {
- height: netInput.getInputHeight(batchIdx),
- width: netInput.getInputWidth(batchIdx),
- })];
- }
- });
- }); }))];
- case 2:
- landmarksForBatch = _a.sent();
- landmarkTensors.forEach(function (t) { return t.dispose(); });
- return [2 /*return*/, netInput.isBatchInput
- ? landmarksForBatch
- : landmarksForBatch[0]];
- }
- });
- });
- };
- FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams_1.loadQuantizedParams(uri);
- };
- FaceLandmarkNet.prototype.extractParams = function (weights) {
- return extractParams_1.extractParams(weights);
- };
- return FaceLandmarkNet;
-}(tfjs_image_recognition_base_1.NeuralNetwork));
-exports.FaceLandmarkNet = FaceLandmarkNet;
-//# sourceMappingURL=FaceLandmarkNet.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/FaceLandmarkNet.js.map b/build/faceLandmarkNet/FaceLandmarkNet.js.map
deleted file mode 100644
index 5935f936..00000000
--- a/build/faceLandmarkNet/FaceLandmarkNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/faceLandmarkNet/depthwiseSeparableConv.d.ts b/build/faceLandmarkNet/depthwiseSeparableConv.d.ts
deleted file mode 100644
index cc1ad50a..00000000
--- a/build/faceLandmarkNet/depthwiseSeparableConv.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
-export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams, stride: [number, number]): tf.Tensor4D;
diff --git a/build/faceLandmarkNet/depthwiseSeparableConv.js b/build/faceLandmarkNet/depthwiseSeparableConv.js
deleted file mode 100644
index 969f2522..00000000
--- a/build/faceLandmarkNet/depthwiseSeparableConv.js
+++ /dev/null
@@ -1,12 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function depthwiseSeparableConv(x, params, stride) {
- return tf.tidy(function () {
- var out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
- out = tf.add(out, params.bias);
- return out;
- });
-}
-exports.depthwiseSeparableConv = depthwiseSeparableConv;
-//# sourceMappingURL=depthwiseSeparableConv.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/depthwiseSeparableConv.js.map b/build/faceLandmarkNet/depthwiseSeparableConv.js.map
deleted file mode 100644
index 4e425cf4..00000000
--- a/build/faceLandmarkNet/depthwiseSeparableConv.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/depthwiseSeparableConv.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAG5C,gCACE,CAAc,EACd,MAA2B,EAC3B,MAAwB;IAExB,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,EAAE,MAAM,CAAC,CAAA;QACjG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAC9B,OAAO,GAAG,CAAA;IACZ,CAAC,CAAC,CAAA;AACJ,CAAC;AAVD,wDAUC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/extractParams.d.ts b/build/faceLandmarkNet/extractParams.d.ts
deleted file mode 100644
index 40295777..00000000
--- a/build/faceLandmarkNet/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: ParamMapping[];
-};
diff --git a/build/faceLandmarkNet/extractParams.js b/build/faceLandmarkNet/extractParams.js
deleted file mode 100644
index 1b2bf57a..00000000
--- a/build/faceLandmarkNet/extractParams.js
+++ /dev/null
@@ -1,23 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var extractorsFactory_1 = require("./extractorsFactory");
-function extractParams(weights) {
- var paramMappings = [];
- var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var _b = extractorsFactory_1.extractorsFactory(extractWeights, paramMappings), extractDenseBlock4Params = _b.extractDenseBlock4Params, extractFCParams = _b.extractFCParams;
- var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
- var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
- var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
- var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
- var fc = extractFCParams(256, 136, 'fc');
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return {
- paramMappings: paramMappings,
- params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3, fc: fc }
- };
-}
-exports.extractParams = extractParams;
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/extractParams.js.map b/build/faceLandmarkNet/extractParams.js.map
deleted file mode 100644
index c732fc31..00000000
--- a/build/faceLandmarkNet/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/extractParams.ts"],"names":[],"mappings":";;AAAA,2EAAkF;AAElF,yDAAwD;AAGxD,uBAA8B,OAAqB;IAEjD,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,iEAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAE5B,IAAA,yEAG8C,EAFlD,sDAAwB,EACxB,oCAAe,CACmC;IAEpD,IAAM,MAAM,GAAG,wBAAwB,CAAC,CAAC,EAAE,EAAE,EAAE,QAAQ,EAAE,IAAI,CAAC,CAAA;IAC9D,IAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,EAAE,EAAE,QAAQ,CAAC,CAAA;IACzD,IAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAA;IAC1D,IAAM,MAAM,GAAG,wBAAwB,CAAC,GAAG,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAA;IAC3D,IAAM,EAAE,GAAG,eAAe,CAAC,GAAG,EAAE,GAAG,EAAE,IAAI,CAAC,CAAA;IAE1C,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa,eAAA;QACb,MAAM,EAAE,EAAE,MAAM,QAAA,EAAE,MAAM,QAAA,EAAE,MAAM,QAAA,EAAE,MAAM,QAAA,EAAE,EAAE,IAAA,EAAE;KAC/C,CAAA;AACH,CAAC;AA5BD,sCA4BC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/extractParamsTiny.d.ts b/build/faceLandmarkNet/extractParamsTiny.d.ts
deleted file mode 100644
index ef9c65b1..00000000
--- a/build/faceLandmarkNet/extractParamsTiny.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { TinyNetParams } from './types';
-export declare function extractParamsTiny(weights: Float32Array): {
- params: TinyNetParams;
- paramMappings: ParamMapping[];
-};
diff --git a/build/faceLandmarkNet/extractParamsTiny.js b/build/faceLandmarkNet/extractParamsTiny.js
deleted file mode 100644
index de267340..00000000
--- a/build/faceLandmarkNet/extractParamsTiny.js
+++ /dev/null
@@ -1,22 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var extractorsFactory_1 = require("./extractorsFactory");
-function extractParamsTiny(weights) {
- var paramMappings = [];
- var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var _b = extractorsFactory_1.extractorsFactory(extractWeights, paramMappings), extractDenseBlock3Params = _b.extractDenseBlock3Params, extractFCParams = _b.extractFCParams;
- var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);
- var dense1 = extractDenseBlock3Params(32, 64, 'dense1');
- var dense2 = extractDenseBlock3Params(64, 128, 'dense2');
- var fc = extractFCParams(128, 136, 'fc');
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return {
- paramMappings: paramMappings,
- params: { dense0: dense0, dense1: dense1, dense2: dense2, fc: fc }
- };
-}
-exports.extractParamsTiny = extractParamsTiny;
-//# sourceMappingURL=extractParamsTiny.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/extractParamsTiny.js.map b/build/faceLandmarkNet/extractParamsTiny.js.map
deleted file mode 100644
index 15b730fe..00000000
--- a/build/faceLandmarkNet/extractParamsTiny.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsTiny.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/extractParamsTiny.ts"],"names":[],"mappings":";;AAAA,2EAAkF;AAElF,yDAAwD;AAGxD,2BAAkC,OAAqB;IAErD,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,iEAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAE5B,IAAA,yEAG8C,EAFlD,sDAAwB,EACxB,oCAAe,CACmC;IAEpD,IAAM,MAAM,GAAG,wBAAwB,CAAC,CAAC,EAAE,EAAE,EAAE,QAAQ,EAAE,IAAI,CAAC,CAAA;IAC9D,IAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,EAAE,EAAE,QAAQ,CAAC,CAAA;IACzD,IAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAA;IAC1D,IAAM,EAAE,GAAG,eAAe,CAAC,GAAG,EAAE,GAAG,EAAE,IAAI,CAAC,CAAA;IAE1C,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa,eAAA;QACb,MAAM,EAAE,EAAE,MAAM,QAAA,EAAE,MAAM,QAAA,EAAE,MAAM,QAAA,EAAE,EAAE,IAAA,EAAE;KACvC,CAAA;AACH,CAAC;AA3BD,8CA2BC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/extractorsFactory.d.ts b/build/faceLandmarkNet/extractorsFactory.d.ts
deleted file mode 100644
index 247236f3..00000000
--- a/build/faceLandmarkNet/extractorsFactory.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
-import { FCParams } from 'tfjs-tiny-yolov2';
-import { DenseBlock3Params, DenseBlock4Params } from './types';
-export declare function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): {
- extractDenseBlock3Params: (channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer?: boolean) => DenseBlock3Params;
- extractDenseBlock4Params: (channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer?: boolean) => DenseBlock4Params;
- extractFCParams: (channelsIn: number, channelsOut: number, mappedPrefix: string) => FCParams;
-};
diff --git a/build/faceLandmarkNet/extractorsFactory.js b/build/faceLandmarkNet/extractorsFactory.js
deleted file mode 100644
index e1daa63f..00000000
--- a/build/faceLandmarkNet/extractorsFactory.js
+++ /dev/null
@@ -1,46 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-var types_1 = require("tfjs-tiny-yolov2/build/tinyYolov2/types");
-function extractorsFactory(extractWeights, paramMappings) {
- function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
- var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
- var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
- var bias = tf.tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
- return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
- }
- function extractFCParams(channelsIn, channelsOut, mappedPrefix) {
- var weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
- var bias = tf.tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
- return {
- weights: weights,
- bias: bias
- };
- }
- var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
- function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var conv0 = isFirstLayer
- ? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv0")
- : extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/conv0");
- var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv1");
- var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv2");
- return { conv0: conv0, conv1: conv1, conv2: conv2 };
- }
- function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;
- var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv3");
- return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
- }
- return {
- extractDenseBlock3Params: extractDenseBlock3Params,
- extractDenseBlock4Params: extractDenseBlock4Params,
- extractFCParams: extractFCParams
- };
-}
-exports.extractorsFactory = extractorsFactory;
-//# sourceMappingURL=extractorsFactory.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/extractorsFactory.js.map b/build/faceLandmarkNet/extractorsFactory.js.map
deleted file mode 100644
index 71e0335b..00000000
--- a/build/faceLandmarkNet/extractorsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractorsFactory.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/extractorsFactory.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,qDAAsE;AACtE,iEAA8E;AAI9E,2BAAkC,cAAsC,EAAE,aAA6B;IAErG,oCAAoC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAC/F,IAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,UAAU,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,CAAC,CAAC,CAAC,CAAA;QAC/F,IAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QAC/G,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,sBAAmB,EAAE,EACjD,EAAE,SAAS,EAAK,YAAY,sBAAmB,EAAE,EACjD,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO,IAAI,2BAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC;IAED,yBAAyB,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QACpF,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QAChG,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO;YACL,OAAO,SAAA;YACP,IAAI,MAAA;SACL,CAAA;IACH,CAAC;IAED,IAAM,iBAAiB,GAAG,2CAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,kCAAkC,UAAkB,EAAE,WAAmB,EAAE,YAAoB,EAAE,YAA6B;QAA7B,6BAAA,EAAA,oBAA6B;QAE5H,IAAM,KAAK,GAAG,YAAY;YACxB,CAAC,CAAC,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAK,YAAY,WAAQ,CAAC;YACxE,CAAC,CAAC,0BAA0B,CAAC,UAAU,EAAE,WAAW,EAAK,YAAY,WAAQ,CAAC,CAAA;QAChF,IAAM,KAAK,GAAG,0BAA0B,CAAC,WAAW,EAAE,WAAW,EAAK,YAAY,WAAQ,CAAC,CAAA;QAC3F,IAAM,KAAK,GAAG,0BAA0B,CAAC,WAAW,EAAE,WAAW,EAAK,YAAY,WAAQ,CAAC,CAAA;QAE3F,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IAChC,CAAC;IAED,kCAAkC,UAAkB,EAAE,WAAmB,EAAE,YAAoB,EAAE,YAA6B;QAA7B,6BAAA,EAAA,oBAA6B;QAEtH,IAAA,kFAAuG,EAArG,gBAAK,EAAE,gBAAK,EAAE,gBAAK,CAAkF;QAC7G,IAAM,KAAK,GAAG,0BAA0B,CAAC,WAAW,EAAE,WAAW,EAAK,YAAY,WAAQ,CAAC,CAAA;QAE3F,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACvC,CAAC;IAED,OAAO;QACL,wBAAwB,0BAAA;QACxB,wBAAwB,0BAAA;QACxB,eAAe,iBAAA;KAChB,CAAA;AAEH,CAAC;AA9DD,8CA8DC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/fullyConnectedLayer.d.ts b/build/faceLandmarkNet/fullyConnectedLayer.d.ts
deleted file mode 100644
index f9f052aa..00000000
--- a/build/faceLandmarkNet/fullyConnectedLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { FCParams } from 'tfjs-tiny-yolov2';
-export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FCParams): tf.Tensor2D;
diff --git a/build/faceLandmarkNet/fullyConnectedLayer.js b/build/faceLandmarkNet/fullyConnectedLayer.js
deleted file mode 100644
index 624c1d5e..00000000
--- a/build/faceLandmarkNet/fullyConnectedLayer.js
+++ /dev/null
@@ -1,10 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function fullyConnectedLayer(x, params) {
- return tf.tidy(function () {
- return tf.add(tf.matMul(x, params.weights), params.bias);
- });
-}
-exports.fullyConnectedLayer = fullyConnectedLayer;
-//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/fullyConnectedLayer.js.map b/build/faceLandmarkNet/fullyConnectedLayer.js.map
deleted file mode 100644
index bc2ea575..00000000
--- a/build/faceLandmarkNet/fullyConnectedLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"fullyConnectedLayer.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/fullyConnectedLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAG5C,6BACE,CAAc,EACd,MAAgB;IAEhB,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,OAAA,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAC5B,MAAM,CAAC,IAAI,CACZ;IAHD,CAGC,CACF,CAAA;AACH,CAAC;AAVD,kDAUC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/index.d.ts b/build/faceLandmarkNet/index.d.ts
deleted file mode 100644
index 2f124a94..00000000
--- a/build/faceLandmarkNet/index.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { FaceLandmark68Net } from './FaceLandmark68Net';
-export * from './FaceLandmark68Net';
-export * from './FaceLandmark68TinyNet';
-export declare class FaceLandmarkNet extends FaceLandmark68Net {
-}
-export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
-export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
diff --git a/build/faceLandmarkNet/index.js b/build/faceLandmarkNet/index.js
deleted file mode 100644
index d1a64d00..00000000
--- a/build/faceLandmarkNet/index.js
+++ /dev/null
@@ -1,26 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var FaceLandmark68Net_1 = require("./FaceLandmark68Net");
-tslib_1.__exportStar(require("./FaceLandmark68Net"), exports);
-tslib_1.__exportStar(require("./FaceLandmark68TinyNet"), exports);
-var FaceLandmarkNet = /** @class */ (function (_super) {
- tslib_1.__extends(FaceLandmarkNet, _super);
- function FaceLandmarkNet() {
- return _super !== null && _super.apply(this, arguments) || this;
- }
- return FaceLandmarkNet;
-}(FaceLandmark68Net_1.FaceLandmark68Net));
-exports.FaceLandmarkNet = FaceLandmarkNet;
-function createFaceLandmarkNet(weights) {
- var net = new FaceLandmarkNet();
- net.extractWeights(weights);
- return net;
-}
-exports.createFaceLandmarkNet = createFaceLandmarkNet;
-function faceLandmarkNet(weights) {
- console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
- return createFaceLandmarkNet(weights);
-}
-exports.faceLandmarkNet = faceLandmarkNet;
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/index.js.map b/build/faceLandmarkNet/index.js.map
deleted file mode 100644
index 3e700abb..00000000
--- a/build/faceLandmarkNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";;;AAAA,yDAAwD;AAExD,8DAAoC;AACpC,kEAAwC;AAExC;IAAqC,2CAAiB;IAAtD;;IAAwD,CAAC;IAAD,sBAAC;AAAD,CAAC,AAAzD,CAAqC,qCAAiB,GAAG;AAA5C,0CAAe;AAE5B,+BAAsC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,eAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,sDAIC;AAED,yBAAgC,OAAqB;IACnD,OAAO,CAAC,IAAI,CAAC,wGAAwG,CAAC,CAAA;IACtH,OAAO,qBAAqB,CAAC,OAAO,CAAC,CAAA;AACvC,CAAC;AAHD,0CAGC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/loadParamsFactory.d.ts b/build/faceLandmarkNet/loadParamsFactory.d.ts
deleted file mode 100644
index f4c2f6ad..00000000
--- a/build/faceLandmarkNet/loadParamsFactory.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { FCParams } from 'tfjs-tiny-yolov2';
-import { DenseBlock3Params, DenseBlock4Params } from './types';
-export declare function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]): {
- extractDenseBlock3Params: (prefix: string, isFirstLayer?: boolean) => DenseBlock3Params;
- extractDenseBlock4Params: (prefix: string, isFirstLayer?: boolean) => DenseBlock4Params;
- extractFcParams: (prefix: string) => FCParams;
-};
diff --git a/build/faceLandmarkNet/loadParamsFactory.js b/build/faceLandmarkNet/loadParamsFactory.js
deleted file mode 100644
index 08cd6f25..00000000
--- a/build/faceLandmarkNet/loadParamsFactory.js
+++ /dev/null
@@ -1,49 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var types_1 = require("tfjs-tiny-yolov2/build/tinyYolov2/types");
-function loadParamsFactory(weightMap, paramMappings) {
- var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
- function extractConvParams(prefix) {
- var filters = extractWeightEntry(prefix + "/filters", 4);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { filters: filters, bias: bias };
- }
- function extractSeparableConvParams(prefix) {
- var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
- var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return new types_1.SeparableConvParams(depthwise_filter, pointwise_filter, bias);
- }
- function extractDenseBlock3Params(prefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var conv0 = isFirstLayer
- ? extractConvParams(prefix + "/conv0")
- : extractSeparableConvParams(prefix + "/conv0");
- var conv1 = extractSeparableConvParams(prefix + "/conv1");
- var conv2 = extractSeparableConvParams(prefix + "/conv2");
- return { conv0: conv0, conv1: conv1, conv2: conv2 };
- }
- function extractDenseBlock4Params(prefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var conv0 = isFirstLayer
- ? extractConvParams(prefix + "/conv0")
- : extractSeparableConvParams(prefix + "/conv0");
- var conv1 = extractSeparableConvParams(prefix + "/conv1");
- var conv2 = extractSeparableConvParams(prefix + "/conv2");
- var conv3 = extractSeparableConvParams(prefix + "/conv3");
- return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
- }
- function extractFcParams(prefix) {
- var weights = extractWeightEntry(prefix + "/weights", 2);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { weights: weights, bias: bias };
- }
- return {
- extractDenseBlock3Params: extractDenseBlock3Params,
- extractDenseBlock4Params: extractDenseBlock4Params,
- extractFcParams: extractFcParams
- };
-}
-exports.loadParamsFactory = loadParamsFactory;
-//# sourceMappingURL=loadParamsFactory.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/loadParamsFactory.js.map b/build/faceLandmarkNet/loadParamsFactory.js.map
deleted file mode 100644
index ed9eef1c..00000000
--- a/build/faceLandmarkNet/loadParamsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadParamsFactory.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/loadParamsFactory.ts"],"names":[],"mappings":";;AACA,2EAAsF;AAEtF,iEAA8E;AAI9E,2BAAkC,SAAc,EAAE,aAA6B;IAE7E,IAAM,kBAAkB,GAAG,uDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,2BAA2B,MAAc;QACvC,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,CAAC,CAAA;QACvE,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,oCAAoC,MAAc;QAChD,IAAM,gBAAgB,GAAG,kBAAkB,CAAiB,MAAM,sBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,IAAM,gBAAgB,GAAG,kBAAkB,CAAiB,MAAM,sBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,IAAI,2BAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC;IAED,kCAAkC,MAAc,EAAE,YAA6B;QAA7B,6BAAA,EAAA,oBAA6B;QAC7E,IAAM,KAAK,GAAG,YAAY;YACxB,CAAC,CAAC,iBAAiB,CAAI,MAAM,WAAQ,CAAC;YACtC,CAAC,CAAC,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QACjD,IAAM,KAAK,GAAG,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QAE3D,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IAChC,CAAC;IAED,kCAAkC,MAAc,EAAE,YAA6B;QAA7B,6BAAA,EAAA,oBAA6B;QAC7E,IAAM,KAAK,GAAG,YAAY;YACxB,CAAC,CAAC,iBAAiB,CAAI,MAAM,WAAQ,CAAC;YACtC,CAAC,CAAC,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QACjD,IAAM,KAAK,GAAG,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,0BAA0B,CAAI,MAAM,WAAQ,CAAC,CAAA;QAE3D,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACvC,CAAC;IAED,yBAAyB,MAAc;QACrC,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,CAAC,CAAA;QACvE,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,OAAO;QACL,wBAAwB,0BAAA;QACxB,wBAAwB,0BAAA;QACxB,eAAe,iBAAA;KAChB,CAAA;AACH,CAAC;AAxDD,8CAwDC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/loadQuantizedParams.d.ts b/build/faceLandmarkNet/loadQuantizedParams.d.ts
deleted file mode 100644
index c5e1dc44..00000000
--- a/build/faceLandmarkNet/loadQuantizedParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: ParamMapping[];
-}>;
diff --git a/build/faceLandmarkNet/loadQuantizedParams.js b/build/faceLandmarkNet/loadQuantizedParams.js
deleted file mode 100644
index bea29a7a..00000000
--- a/build/faceLandmarkNet/loadQuantizedParams.js
+++ /dev/null
@@ -1,31 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var loadParamsFactory_1 = require("./loadParamsFactory");
-var DEFAULT_MODEL_NAME = 'face_landmark_68_model';
-function loadQuantizedParams(uri) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractDenseBlock4Params, extractFcParams, params;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
- params = {
- dense0: extractDenseBlock4Params('dense0', true),
- dense1: extractDenseBlock4Params('dense1'),
- dense2: extractDenseBlock4Params('dense2'),
- dense3: extractDenseBlock4Params('dense3'),
- fc: extractFcParams('fc')
- };
- tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
-}
-exports.loadQuantizedParams = loadQuantizedParams;
-//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/loadQuantizedParams.js.map b/build/faceLandmarkNet/loadQuantizedParams.js.map
deleted file mode 100644
index 5c1a32c9..00000000
--- a/build/faceLandmarkNet/loadQuantizedParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AAAA,2EAAsG;AAEtG,yDAAwD;AAGxD,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,6BACE,GAAuB;;;;;wBAGL,qBAAM,2CAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,qCAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,wBAAwB,8BAAA,EACxB,eAAe,qBAAA,CAC8B;oBAEzC,MAAM,GAAG;wBACb,MAAM,EAAE,wBAAwB,CAAC,QAAQ,EAAE,IAAI,CAAC;wBAChD,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;wBAC1C,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;wBAC1C,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;wBAC1C,EAAE,EAAE,eAAe,CAAC,IAAI,CAAC;qBAC1B,CAAA;oBAED,wDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AAvBD,kDAuBC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/loadQuantizedParamsTiny.d.ts b/build/faceLandmarkNet/loadQuantizedParamsTiny.d.ts
deleted file mode 100644
index 36f4ea62..00000000
--- a/build/faceLandmarkNet/loadQuantizedParamsTiny.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { TinyNetParams } from './types';
-export declare function loadQuantizedParamsTiny(uri: string | undefined): Promise<{
- params: TinyNetParams;
- paramMappings: ParamMapping[];
-}>;
diff --git a/build/faceLandmarkNet/loadQuantizedParamsTiny.js b/build/faceLandmarkNet/loadQuantizedParamsTiny.js
deleted file mode 100644
index cee78ef9..00000000
--- a/build/faceLandmarkNet/loadQuantizedParamsTiny.js
+++ /dev/null
@@ -1,30 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var loadParamsFactory_1 = require("./loadParamsFactory");
-var DEFAULT_MODEL_NAME = 'face_landmark_68_tiny_model';
-function loadQuantizedParamsTiny(uri) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractDenseBlock3Params, extractFcParams, params;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = loadParamsFactory_1.loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
- params = {
- dense0: extractDenseBlock3Params('dense0', true),
- dense1: extractDenseBlock3Params('dense1'),
- dense2: extractDenseBlock3Params('dense2'),
- fc: extractFcParams('fc')
- };
- tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
-}
-exports.loadQuantizedParamsTiny = loadQuantizedParamsTiny;
-//# sourceMappingURL=loadQuantizedParamsTiny.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/loadQuantizedParamsTiny.js.map b/build/faceLandmarkNet/loadQuantizedParamsTiny.js.map
deleted file mode 100644
index fbbd01c8..00000000
--- a/build/faceLandmarkNet/loadQuantizedParamsTiny.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadQuantizedParamsTiny.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/loadQuantizedParamsTiny.ts"],"names":[],"mappings":";;;AAAA,2EAAsG;AAEtG,yDAAwD;AAGxD,IAAM,kBAAkB,GAAG,6BAA6B,CAAA;AAExD,iCACE,GAAuB;;;;;wBAGL,qBAAM,2CAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,qCAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,wBAAwB,8BAAA,EACxB,eAAe,qBAAA,CAC8B;oBAEzC,MAAM,GAAG;wBACb,MAAM,EAAE,wBAAwB,CAAC,QAAQ,EAAE,IAAI,CAAC;wBAChD,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;wBAC1C,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;wBAC1C,EAAE,EAAE,eAAe,CAAC,IAAI,CAAC;qBAC1B,CAAA;oBAED,wDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AAtBD,0DAsBC"}
\ No newline at end of file
diff --git a/build/faceLandmarkNet/types.d.ts b/build/faceLandmarkNet/types.d.ts
deleted file mode 100644
index cbd3ad3b..00000000
--- a/build/faceLandmarkNet/types.d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
-import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
-export declare type ConvWithBatchNormParams = BatchNormParams & {
- filter: tf.Tensor4D;
-};
-export declare type BatchNormParams = {
- mean: tf.Tensor1D;
- variance: tf.Tensor1D;
- scale: tf.Tensor1D;
- offset: tf.Tensor1D;
-};
-export declare type SeparableConvWithBatchNormParams = {
- depthwise: ConvWithBatchNormParams;
- pointwise: ConvWithBatchNormParams;
-};
-export declare type FCWithBatchNormParams = BatchNormParams & {
- weights: tf.Tensor2D;
-};
-export declare type DenseBlock3Params = {
- conv0: SeparableConvParams | ConvParams;
- conv1: SeparableConvParams;
- conv2: SeparableConvParams;
-};
-export declare type DenseBlock4Params = DenseBlock3Params & {
- conv3: SeparableConvParams;
-};
-export declare type TinyNetParams = {
- dense0: DenseBlock3Params;
- dense1: DenseBlock3Params;
- dense2: DenseBlock3Params;
- fc: FCParams;
-};
-export declare type NetParams = {
- dense0: DenseBlock4Params;
- dense1: DenseBlock4Params;
- dense2: DenseBlock4Params;
- dense3: DenseBlock4Params;
- fc: FCParams;
-};
diff --git a/build/faceLandmarkNet/types.js b/build/faceLandmarkNet/types.js
deleted file mode 100644
index 11e638d1..00000000
--- a/build/faceLandmarkNet/types.js
+++ /dev/null
@@ -1,3 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/faceLandmarkNet/types.js.map b/build/faceLandmarkNet/types.js.map
deleted file mode 100644
index ab1ed4d0..00000000
--- a/build/faceLandmarkNet/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/FaceRecognitionNet.d.ts b/build/faceRecognitionNet/FaceRecognitionNet.d.ts
deleted file mode 100644
index f34abc0c..00000000
--- a/build/faceRecognitionNet/FaceRecognitionNet.d.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare class FaceRecognitionNet extends NeuralNetwork {
- constructor();
- forwardInput(input: NetInput): tf.Tensor2D;
- forward(input: TNetInput): Promise;
- computeFaceDescriptor(input: TNetInput): Promise;
- protected loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- }>;
- protected extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- };
-}
diff --git a/build/faceRecognitionNet/FaceRecognitionNet.js b/build/faceRecognitionNet/FaceRecognitionNet.js
deleted file mode 100644
index 55d18628..00000000
--- a/build/faceRecognitionNet/FaceRecognitionNet.js
+++ /dev/null
@@ -1,88 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var convLayer_1 = require("./convLayer");
-var extractParams_1 = require("./extractParams");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-var residualLayer_1 = require("./residualLayer");
-var FaceRecognitionNet = /** @class */ (function (_super) {
- tslib_1.__extends(FaceRecognitionNet, _super);
- function FaceRecognitionNet() {
- return _super.call(this, 'FaceRecognitionNet') || this;
- }
- FaceRecognitionNet.prototype.forwardInput = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceRecognitionNet - load model before inference');
- }
- return tf.tidy(function () {
- var batchTensor = input.toBatchTensor(150, true).toFloat();
- var meanRgb = [122.782, 117.001, 104.298];
- var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
- var out = convLayer_1.convDown(normalized, params.conv32_down);
- out = tf.maxPool(out, 3, 2, 'valid');
- out = residualLayer_1.residual(out, params.conv32_1);
- out = residualLayer_1.residual(out, params.conv32_2);
- out = residualLayer_1.residual(out, params.conv32_3);
- out = residualLayer_1.residualDown(out, params.conv64_down);
- out = residualLayer_1.residual(out, params.conv64_1);
- out = residualLayer_1.residual(out, params.conv64_2);
- out = residualLayer_1.residual(out, params.conv64_3);
- out = residualLayer_1.residualDown(out, params.conv128_down);
- out = residualLayer_1.residual(out, params.conv128_1);
- out = residualLayer_1.residual(out, params.conv128_2);
- out = residualLayer_1.residualDown(out, params.conv256_down);
- out = residualLayer_1.residual(out, params.conv256_1);
- out = residualLayer_1.residual(out, params.conv256_2);
- out = residualLayer_1.residualDown(out, params.conv256_down_out);
- var globalAvg = out.mean([1, 2]);
- var fullyConnected = tf.matMul(globalAvg, params.fc);
- return fullyConnected;
- });
- };
- FaceRecognitionNet.prototype.forward = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _this = this;
- var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
- return tslib_1.__generator(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1:
- netInput = _a.sent();
- faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
- return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
- case 2:
- faceDescriptorsForBatch = _a.sent();
- faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
- return [2 /*return*/, netInput.isBatchInput
- ? faceDescriptorsForBatch
- : faceDescriptorsForBatch[0]];
- }
- });
- });
- };
- FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams_1.loadQuantizedParams(uri);
- };
- FaceRecognitionNet.prototype.extractParams = function (weights) {
- return extractParams_1.extractParams(weights);
- };
- return FaceRecognitionNet;
-}(tfjs_image_recognition_base_1.NeuralNetwork));
-exports.FaceRecognitionNet = FaceRecognitionNet;
-//# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/FaceRecognitionNet.js.map b/build/faceRecognitionNet/FaceRecognitionNet.js.map
deleted file mode 100644
index 36e820fa..00000000
--- a/build/faceRecognitionNet/FaceRecognitionNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAwG;AAExG,yCAAuC;AACvC,iDAAgD;AAChD,6DAA4D;AAC5D,iDAAyD;AAIzD;IAAwC,8CAAwB;IAE9D;eACE,kBAAM,oBAAoB,CAAC;IAC7B,CAAC;IAEM,yCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,OAAO,EAAE,CAAA;YAE5D,IAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,IAAM,UAAU,GAAG,uCAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,oBAAQ,CAAC,UAAU,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YAErC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,CAAC,CAAA;YAEhD,IAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;YAEtD,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,oCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,kDAAqB,GAAlC,UAAmC,KAAgB;;;;;;4BAChC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAElC,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAE+B,qBAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,EAAE,EAAR,CAAQ,CACd,CAAC,EAAA;;wBAFI,uBAAuB,GAAG,SAEZ;wBAEpB,qBAAqB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/C,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,uBAAuB;gCACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,EAAA;;;;KAC/B;IAES,gDAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,0CAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,yBAAC;AAAD,CAAC,AA7ED,CAAwC,2CAAa,GA6EpD;AA7EY,gDAAkB"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/convLayer.d.ts b/build/faceRecognitionNet/convLayer.d.ts
deleted file mode 100644
index a750f6c4..00000000
--- a/build/faceRecognitionNet/convLayer.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ConvLayerParams } from './types';
-export declare function conv(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor;
-export declare function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor;
-export declare function convDown(x: tf.Tensor4D, params: ConvLayerParams): tf.Tensor;
diff --git a/build/faceRecognitionNet/convLayer.js b/build/faceRecognitionNet/convLayer.js
deleted file mode 100644
index 4c4a2fcf..00000000
--- a/build/faceRecognitionNet/convLayer.js
+++ /dev/null
@@ -1,25 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var scaleLayer_1 = require("./scaleLayer");
-function convLayer(x, params, strides, withRelu, padding) {
- if (padding === void 0) { padding = 'same'; }
- var _a = params.conv, filters = _a.filters, bias = _a.bias;
- var out = tf.conv2d(x, filters, strides, padding);
- out = tf.add(out, bias);
- out = scaleLayer_1.scale(out, params.scale);
- return withRelu ? tf.relu(out) : out;
-}
-function conv(x, params) {
- return convLayer(x, params, [1, 1], true);
-}
-exports.conv = conv;
-function convNoRelu(x, params) {
- return convLayer(x, params, [1, 1], false);
-}
-exports.convNoRelu = convNoRelu;
-function convDown(x, params) {
- return convLayer(x, params, [2, 2], true, 'valid');
-}
-exports.convDown = convDown;
-//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/convLayer.js.map b/build/faceRecognitionNet/convLayer.js.map
deleted file mode 100644
index a159eb95..00000000
--- a/build/faceRecognitionNet/convLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,2CAAqC;AAIrC,mBACE,CAAc,EACd,MAAuB,EACvB,OAAyB,EACzB,QAAiB,EACjB,OAAkC;IAAlC,wBAAA,EAAA,gBAAkC;IAE5B,IAAA,gBAA+B,EAA7B,oBAAO,EAAE,cAAI,CAAgB;IAErC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;IACjD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IACvB,GAAG,GAAG,kBAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,cAAqB,CAAc,EAAE,MAAuB;IAC1D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAA;AAC3C,CAAC;AAFD,oBAEC;AAED,oBAA2B,CAAc,EAAE,MAAuB;IAChE,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAA;AAC5C,CAAC;AAFD,gCAEC;AAED,kBAAyB,CAAc,EAAE,MAAuB;IAC9D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AACpD,CAAC;AAFD,4BAEC"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/extractParams.d.ts b/build/faceRecognitionNet/extractParams.d.ts
deleted file mode 100644
index 40295777..00000000
--- a/build/faceRecognitionNet/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: ParamMapping[];
-};
diff --git a/build/faceRecognitionNet/extractParams.js b/build/faceRecognitionNet/extractParams.js
deleted file mode 100644
index 46740854..00000000
--- a/build/faceRecognitionNet/extractParams.js
+++ /dev/null
@@ -1,90 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-function extractorsFactory(extractWeights, paramMappings) {
- function extractFilterValues(numFilterValues, numFilters, filterSize) {
- var weights = extractWeights(numFilterValues);
- var depth = weights.length / (numFilters * filterSize * filterSize);
- if (tfjs_image_recognition_base_1.isFloat(depth)) {
- throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
- }
- return tf.tidy(function () { return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
- }
- function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
- var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
- var bias = tf.tensor1d(extractWeights(numFilters));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
- return { filters: filters, bias: bias };
- }
- function extractScaleLayerParams(numWeights, mappedPrefix) {
- var weights = tf.tensor1d(extractWeights(numWeights));
- var biases = tf.tensor1d(extractWeights(numWeights));
- paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
- return {
- weights: weights,
- biases: biases
- };
- }
- function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
- var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
- var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
- return { conv: conv, scale: scale };
- }
- function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
- if (isDown === void 0) { isDown = false; }
- var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
- var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
- return { conv1: conv1, conv2: conv2 };
- }
- return {
- extractConvLayerParams: extractConvLayerParams,
- extractResidualLayerParams: extractResidualLayerParams
- };
-}
-function extractParams(weights) {
- var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var paramMappings = [];
- var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
- var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
- var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
- var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
- var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
- var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
- var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
- var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
- var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
- var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
- var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
- var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
- var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
- var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
- var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
- var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
- var fc = tf.tidy(function () { return tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });
- paramMappings.push({ paramPath: "fc" });
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- var params = {
- conv32_down: conv32_down,
- conv32_1: conv32_1,
- conv32_2: conv32_2,
- conv32_3: conv32_3,
- conv64_down: conv64_down,
- conv64_1: conv64_1,
- conv64_2: conv64_2,
- conv64_3: conv64_3,
- conv128_down: conv128_down,
- conv128_1: conv128_1,
- conv128_2: conv128_2,
- conv256_down: conv256_down,
- conv256_1: conv256_1,
- conv256_2: conv256_2,
- conv256_down_out: conv256_down_out,
- fc: fc
- };
- return { params: params, paramMappings: paramMappings };
-}
-exports.extractParams = extractParams;
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/extractParams.js.map b/build/faceRecognitionNet/extractParams.js.map
deleted file mode 100644
index 01bc36db..00000000
--- a/build/faceRecognitionNet/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/extractParams.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,2EAAmH;AAKnH,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,6BAA6B,eAAuB,EAAE,UAAkB,EAAE,UAAkB;QAC1F,IAAM,OAAO,GAAG,cAAc,CAAC,eAAe,CAAC,CAAA;QAC/C,IAAM,KAAK,GAAG,OAAO,CAAC,MAAM,GAAG,CAAC,UAAU,GAAG,UAAU,GAAG,UAAU,CAAC,CAAA;QAErE,IAAI,qCAAO,CAAC,KAAK,CAAC,EAAE;YAClB,MAAM,IAAI,KAAK,CAAC,iCAA+B,KAAK,0BAAqB,OAAO,CAAC,MAAM,sBAAiB,UAAU,sBAAiB,UAAY,CAAC,CAAA;SACjJ;QAED,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,SAAS,CAChB,EAAE,CAAC,QAAQ,CAAC,OAAO,EAAE,CAAC,UAAU,EAAE,KAAK,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,EACjE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,EAHK,CAGL,CACF,CAAA;IACH,CAAC;IAED,2BACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,OAAO,GAAG,mBAAmB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,CAAC,CAAA;QAC5E,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QAEpD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,UAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,iCAAiC,UAAkB,EAAE,YAAoB;QAEvE,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAA;QAEtD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAK,YAAY,aAAU,EAAE,EACxC,EAAE,SAAS,EAAK,YAAY,YAAS,EAAE,CACxC,CAAA;QAED,OAAO;YACL,OAAO,SAAA;YACP,MAAM,QAAA;SACP,CAAA;IACH,CAAC;IAED,gCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB;QAGpB,IAAM,IAAI,GAAG,iBAAiB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,UAAO,CAAC,CAAA;QAC/F,IAAM,KAAK,GAAG,uBAAuB,CAAC,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAE1E,OAAO,EAAE,IAAI,MAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACxB,CAAC;IAED,oCACE,eAAuB,EACvB,UAAkB,EAClB,UAAkB,EAClB,YAAoB,EACpB,MAAuB;QAAvB,uBAAA,EAAA,cAAuB;QAGvB,IAAM,KAAK,GAAG,sBAAsB,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAC3H,IAAM,KAAK,GAAG,sBAAsB,CAAC,eAAe,EAAE,UAAU,EAAE,UAAU,EAAK,YAAY,WAAQ,CAAC,CAAA;QAEtG,OAAO,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,CAAA;IACzB,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAE3C,IAAA,iEAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAG8C,EAFlD,kDAAsB,EACtB,0DAA0B,CACwB;IAEpD,IAAM,WAAW,GAAG,sBAAsB,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,aAAa,CAAC,CAAA;IACtE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACpE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACpE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,IAAI,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IAEpE,IAAM,WAAW,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,aAAa,EAAE,IAAI,CAAC,CAAA;IACjF,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACrE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IACrE,IAAM,QAAQ,GAAG,0BAA0B,CAAC,KAAK,EAAE,EAAE,EAAE,CAAC,EAAE,UAAU,CAAC,CAAA;IAErE,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,EAAE,IAAI,CAAC,CAAA;IACrF,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IAEzE,IAAM,YAAY,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,cAAc,EAAE,IAAI,CAAC,CAAA;IACrF,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,SAAS,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IACzE,IAAM,gBAAgB,GAAG,0BAA0B,CAAC,MAAM,EAAE,GAAG,EAAE,CAAC,EAAE,kBAAkB,CAAC,CAAA;IAEvF,IAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAChB,cAAM,OAAA,EAAE,CAAC,SAAS,CAAC,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,GAAG,GAAG,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAxE,CAAwE,CAC/E,CAAA;IACD,aAAa,CAAC,IAAI,CAAC,EAAE,SAAS,EAAE,IAAI,EAAE,CAAC,CAAA;IAEvC,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,IAAM,MAAM,GAAG;QACb,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,WAAW,aAAA;QACX,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,QAAQ,UAAA;QACR,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,YAAY,cAAA;QACZ,SAAS,WAAA;QACT,SAAS,WAAA;QACT,gBAAgB,kBAAA;QAChB,EAAE,IAAA;KACH,CAAA;IAED,OAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,CAAA;AAClC,CAAC;AA9DD,sCA8DC"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/index.d.ts b/build/faceRecognitionNet/index.d.ts
deleted file mode 100644
index dd325fef..00000000
--- a/build/faceRecognitionNet/index.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { FaceRecognitionNet } from './FaceRecognitionNet';
-export * from './FaceRecognitionNet';
-export declare function createFaceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
-export declare function faceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
diff --git a/build/faceRecognitionNet/index.js b/build/faceRecognitionNet/index.js
deleted file mode 100644
index 8544422a..00000000
--- a/build/faceRecognitionNet/index.js
+++ /dev/null
@@ -1,17 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var FaceRecognitionNet_1 = require("./FaceRecognitionNet");
-tslib_1.__exportStar(require("./FaceRecognitionNet"), exports);
-function createFaceRecognitionNet(weights) {
- var net = new FaceRecognitionNet_1.FaceRecognitionNet();
- net.extractWeights(weights);
- return net;
-}
-exports.createFaceRecognitionNet = createFaceRecognitionNet;
-function faceRecognitionNet(weights) {
- console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
- return createFaceRecognitionNet(weights);
-}
-exports.faceRecognitionNet = faceRecognitionNet;
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/index.js.map b/build/faceRecognitionNet/index.js.map
deleted file mode 100644
index 3cbcc672..00000000
--- a/build/faceRecognitionNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/index.ts"],"names":[],"mappings":";;;AAAA,2DAA0D;AAE1D,+DAAqC;AAErC,kCAAyC,OAAqB;IAC5D,IAAM,GAAG,GAAG,IAAI,uCAAkB,EAAE,CAAA;IACpC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4DAIC;AAED,4BAAmC,OAAqB;IACtD,OAAO,CAAC,IAAI,CAAC,8GAA8G,CAAC,CAAA;IAC5H,OAAO,wBAAwB,CAAC,OAAO,CAAC,CAAA;AAC1C,CAAC;AAHD,gDAGC"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/loadQuantizedParams.d.ts b/build/faceRecognitionNet/loadQuantizedParams.d.ts
deleted file mode 100644
index c5e1dc44..00000000
--- a/build/faceRecognitionNet/loadQuantizedParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: ParamMapping[];
-}>;
diff --git a/build/faceRecognitionNet/loadQuantizedParams.js b/build/faceRecognitionNet/loadQuantizedParams.js
deleted file mode 100644
index e726e443..00000000
--- a/build/faceRecognitionNet/loadQuantizedParams.js
+++ /dev/null
@@ -1,85 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var DEFAULT_MODEL_NAME = 'face_recognition_model';
-function extractorsFactory(weightMap, paramMappings) {
- var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
- function extractScaleLayerParams(prefix) {
- var weights = extractWeightEntry(prefix + "/scale/weights", 1);
- var biases = extractWeightEntry(prefix + "/scale/biases", 1);
- return { weights: weights, biases: biases };
- }
- function extractConvLayerParams(prefix) {
- var filters = extractWeightEntry(prefix + "/conv/filters", 4);
- var bias = extractWeightEntry(prefix + "/conv/bias", 1);
- var scale = extractScaleLayerParams(prefix);
- return { conv: { filters: filters, bias: bias }, scale: scale };
- }
- function extractResidualLayerParams(prefix) {
- return {
- conv1: extractConvLayerParams(prefix + "/conv1"),
- conv2: extractConvLayerParams(prefix + "/conv2")
- };
- }
- return {
- extractConvLayerParams: extractConvLayerParams,
- extractResidualLayerParams: extractResidualLayerParams
- };
-}
-function loadQuantizedParams(uri) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc, params;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
- conv32_down = extractConvLayerParams('conv32_down');
- conv32_1 = extractResidualLayerParams('conv32_1');
- conv32_2 = extractResidualLayerParams('conv32_2');
- conv32_3 = extractResidualLayerParams('conv32_3');
- conv64_down = extractResidualLayerParams('conv64_down');
- conv64_1 = extractResidualLayerParams('conv64_1');
- conv64_2 = extractResidualLayerParams('conv64_2');
- conv64_3 = extractResidualLayerParams('conv64_3');
- conv128_down = extractResidualLayerParams('conv128_down');
- conv128_1 = extractResidualLayerParams('conv128_1');
- conv128_2 = extractResidualLayerParams('conv128_2');
- conv256_down = extractResidualLayerParams('conv256_down');
- conv256_1 = extractResidualLayerParams('conv256_1');
- conv256_2 = extractResidualLayerParams('conv256_2');
- conv256_down_out = extractResidualLayerParams('conv256_down_out');
- fc = weightMap['fc'];
- paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
- if (!tfjs_image_recognition_base_1.isTensor2D(fc)) {
- throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
- }
- params = {
- conv32_down: conv32_down,
- conv32_1: conv32_1,
- conv32_2: conv32_2,
- conv32_3: conv32_3,
- conv64_down: conv64_down,
- conv64_1: conv64_1,
- conv64_2: conv64_2,
- conv64_3: conv64_3,
- conv128_down: conv128_down,
- conv128_1: conv128_1,
- conv128_2: conv128_2,
- conv256_down: conv256_down,
- conv256_1: conv256_1,
- conv256_2: conv256_2,
- conv256_down_out: conv256_down_out,
- fc: fc
- };
- tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
-}
-exports.loadQuantizedParams = loadQuantizedParams;
-//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/loadQuantizedParams.js.map b/build/faceRecognitionNet/loadQuantizedParams.js.map
deleted file mode 100644
index d5895052..00000000
--- a/build/faceRecognitionNet/loadQuantizedParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/loadQuantizedParams.ts"],"names":[],"mappings":";;;AACA,2EAMqC;AAIrC,IAAM,kBAAkB,GAAG,wBAAwB,CAAA;AAEnD,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,uDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,iCAAiC,MAAc;QAE7C,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,mBAAgB,EAAE,CAAC,CAAC,CAAA;QAC7E,IAAM,MAAM,GAAG,kBAAkB,CAAiB,MAAM,kBAAe,EAAE,CAAC,CAAC,CAAA;QAE3E,OAAO,EAAE,OAAO,SAAA,EAAE,MAAM,QAAA,EAAE,CAAA;IAC5B,CAAC;IAED,gCAAgC,MAAc;QAE5C,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,kBAAe,EAAE,CAAC,CAAC,CAAA;QAC5E,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,eAAY,EAAE,CAAC,CAAC,CAAA;QACtE,IAAM,KAAK,GAAG,uBAAuB,CAAC,MAAM,CAAC,CAAA;QAE7C,OAAO,EAAE,IAAI,EAAE,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,EAAE,KAAK,OAAA,EAAE,CAAA;IAC3C,CAAC;IAED,oCAAoC,MAAc;QAChD,OAAO;YACL,KAAK,EAAE,sBAAsB,CAAI,MAAM,WAAQ,CAAC;YAChD,KAAK,EAAE,sBAAsB,CAAI,MAAM,WAAQ,CAAC;SACjD,CAAA;IACH,CAAC;IAED,OAAO;QACL,sBAAsB,wBAAA;QACtB,0BAA0B,4BAAA;KAC3B,CAAA;AAEH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,2CAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAGF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAF7C,sBAAsB,4BAAA,EACtB,0BAA0B,gCAAA,CACmB;oBAEzC,WAAW,GAAG,sBAAsB,CAAC,aAAa,CAAC,CAAA;oBACnD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBAEjD,WAAW,GAAG,0BAA0B,CAAC,aAAa,CAAC,CAAA;oBACvD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBACjD,QAAQ,GAAG,0BAA0B,CAAC,UAAU,CAAC,CAAA;oBAEjD,YAAY,GAAG,0BAA0B,CAAC,cAAc,CAAC,CAAA;oBACzD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBAEnD,YAAY,GAAG,0BAA0B,CAAC,cAAc,CAAC,CAAA;oBACzD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,SAAS,GAAG,0BAA0B,CAAC,WAAW,CAAC,CAAA;oBACnD,gBAAgB,GAAG,0BAA0B,CAAC,kBAAkB,CAAC,CAAA;oBAEjE,EAAE,GAAG,SAAS,CAAC,IAAI,CAAC,CAAA;oBAC1B,aAAa,CAAC,IAAI,CAAC,EAAE,YAAY,EAAE,IAAI,EAAE,SAAS,EAAE,IAAI,EAAE,CAAC,CAAA;oBAE3D,IAAI,CAAC,wCAAU,CAAC,EAAE,CAAC,EAAE;wBACnB,MAAM,IAAI,KAAK,CAAC,2DAAyD,EAAI,CAAC,CAAA;qBAC/E;oBAEK,MAAM,GAAG;wBACb,WAAW,aAAA;wBACX,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,WAAW,aAAA;wBACX,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,QAAQ,UAAA;wBACR,YAAY,cAAA;wBACZ,SAAS,WAAA;wBACT,SAAS,WAAA;wBACT,YAAY,cAAA;wBACZ,SAAS,WAAA;wBACT,SAAS,WAAA;wBACT,gBAAgB,kBAAA;wBAChB,EAAE,IAAA;qBACH,CAAA;oBAED,wDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,QAAA,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACjC;AA5DD,kDA4DC"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/residualLayer.d.ts b/build/faceRecognitionNet/residualLayer.d.ts
deleted file mode 100644
index 1656e0a0..00000000
--- a/build/faceRecognitionNet/residualLayer.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ResidualLayerParams } from './types';
-export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
-export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
diff --git a/build/faceRecognitionNet/residualLayer.js b/build/faceRecognitionNet/residualLayer.js
deleted file mode 100644
index 04c60b23..00000000
--- a/build/faceRecognitionNet/residualLayer.js
+++ /dev/null
@@ -1,36 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var convLayer_1 = require("./convLayer");
-function residual(x, params) {
- var out = convLayer_1.conv(x, params.conv1);
- out = convLayer_1.convNoRelu(out, params.conv2);
- out = tf.add(out, x);
- out = tf.relu(out);
- return out;
-}
-exports.residual = residual;
-function residualDown(x, params) {
- var out = convLayer_1.convDown(x, params.conv1);
- out = convLayer_1.convNoRelu(out, params.conv2);
- var pooled = tf.avgPool(x, 2, 2, 'valid');
- var zeros = tf.zeros(pooled.shape);
- var isPad = pooled.shape[3] !== out.shape[3];
- var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
- if (isAdjustShape) {
- var padShapeX = out.shape.slice();
- padShapeX[1] = 1;
- var zerosW = tf.zeros(padShapeX);
- out = tf.concat([out, zerosW], 1);
- var padShapeY = out.shape.slice();
- padShapeY[2] = 1;
- var zerosH = tf.zeros(padShapeY);
- out = tf.concat([out, zerosH], 2);
- }
- pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;
- out = tf.add(pooled, out);
- out = tf.relu(out);
- return out;
-}
-exports.residualDown = residualDown;
-//# sourceMappingURL=residualLayer.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/residualLayer.js.map b/build/faceRecognitionNet/residualLayer.js.map
deleted file mode 100644
index b49c7a5b..00000000
--- a/build/faceRecognitionNet/residualLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"residualLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/residualLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,yCAAyD;AAGzD,kBAAyB,CAAc,EAAE,MAA2B;IAClE,IAAI,GAAG,GAAG,gBAAI,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC/B,GAAG,GAAG,sBAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC;AAND,4BAMC;AAED,sBAA6B,CAAc,EAAE,MAA2B;IACtE,IAAI,GAAG,GAAG,oBAAQ,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,sBAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAEnC,IAAI,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAgB,CAAA;IACxD,IAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAa,MAAM,CAAC,KAAK,CAAC,CAAA;IAChD,IAAM,KAAK,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAC9C,IAAM,aAAa,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAE1F,IAAI,aAAa,EAAE;QACjB,IAAM,SAAS,GAAO,GAAG,CAAC,KAAK,QAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,IAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;QAEjC,IAAM,SAAS,GAAO,GAAG,CAAC,KAAK,QAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,IAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;KAClC;IAED,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;IACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAgB,CAAA;IAExC,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC;AA1BD,oCA0BC"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/scaleLayer.d.ts b/build/faceRecognitionNet/scaleLayer.d.ts
deleted file mode 100644
index 07a866ea..00000000
--- a/build/faceRecognitionNet/scaleLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ScaleLayerParams } from './types';
-export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;
diff --git a/build/faceRecognitionNet/scaleLayer.js b/build/faceRecognitionNet/scaleLayer.js
deleted file mode 100644
index e00e7958..00000000
--- a/build/faceRecognitionNet/scaleLayer.js
+++ /dev/null
@@ -1,8 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function scale(x, params) {
- return tf.add(tf.mul(x, params.weights), params.biases);
-}
-exports.scale = scale;
-//# sourceMappingURL=scaleLayer.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/scaleLayer.js.map b/build/faceRecognitionNet/scaleLayer.js.map
deleted file mode 100644
index 570affc6..00000000
--- a/build/faceRecognitionNet/scaleLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAI5C,eAAsB,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC;AAFD,sBAEC"}
\ No newline at end of file
diff --git a/build/faceRecognitionNet/types.d.ts b/build/faceRecognitionNet/types.d.ts
deleted file mode 100644
index 8f3446d4..00000000
--- a/build/faceRecognitionNet/types.d.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams } from 'tfjs-tiny-yolov2';
-export declare type ScaleLayerParams = {
- weights: tf.Tensor1D;
- biases: tf.Tensor1D;
-};
-export declare type ResidualLayerParams = {
- conv1: ConvLayerParams;
- conv2: ConvLayerParams;
-};
-export declare type ConvLayerParams = {
- conv: ConvParams;
- scale: ScaleLayerParams;
-};
-export declare type NetParams = {
- conv32_down: ConvLayerParams;
- conv32_1: ResidualLayerParams;
- conv32_2: ResidualLayerParams;
- conv32_3: ResidualLayerParams;
- conv64_down: ResidualLayerParams;
- conv64_1: ResidualLayerParams;
- conv64_2: ResidualLayerParams;
- conv64_3: ResidualLayerParams;
- conv128_down: ResidualLayerParams;
- conv128_1: ResidualLayerParams;
- conv128_2: ResidualLayerParams;
- conv256_down: ResidualLayerParams;
- conv256_1: ResidualLayerParams;
- conv256_2: ResidualLayerParams;
- conv256_down_out: ResidualLayerParams;
- fc: tf.Tensor2D;
-};
diff --git a/build/faceRecognitionNet/types.js b/build/faceRecognitionNet/types.js
deleted file mode 100644
index 11e638d1..00000000
--- a/build/faceRecognitionNet/types.js
+++ /dev/null
@@ -1,3 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/faceRecognitionNet/types.js.map b/build/faceRecognitionNet/types.js.map
deleted file mode 100644
index 08c56157..00000000
--- a/build/faceRecognitionNet/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/globalApi.d.ts b/build/globalApi.d.ts
deleted file mode 100644
index 0bf3a75a..00000000
--- a/build/globalApi.d.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { NetInput, TNetInput } from 'tfjs-image-recognition-base';
-import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
-import { FaceDetection } from './classes/FaceDetection';
-import { FaceLandmarks68 } from './classes/FaceLandmarks68';
-import { FullFaceDescription } from './classes/FullFaceDescription';
-import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
-import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
-import { FaceLandmark68TinyNet } from './faceLandmarkNet/FaceLandmark68TinyNet';
-import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
-import { Mtcnn } from './mtcnn/Mtcnn';
-import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
-import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
-export declare const detectionNet: FaceDetectionNet;
-export declare const landmarkNet: FaceLandmark68Net;
-export declare const recognitionNet: FaceRecognitionNet;
-export declare const nets: {
- ssdMobilenetv1: FaceDetectionNet;
- faceLandmark68Net: FaceLandmark68Net;
- faceLandmark68TinyNet: FaceLandmark68TinyNet;
- faceRecognitionNet: FaceRecognitionNet;
- mtcnn: Mtcnn;
- tinyYolov2: TinyYolov2;
-};
-export declare function loadSsdMobilenetv1Model(url: string): Promise;
-export declare function loadFaceLandmarkModel(url: string): Promise;
-export declare function loadFaceLandmarkTinyModel(url: string): Promise;
-export declare function loadFaceRecognitionModel(url: string): Promise;
-export declare function loadMtcnnModel(url: string): Promise;
-export declare function loadTinyYolov2Model(url: string): Promise;
-export declare function loadFaceDetectionModel(url: string): Promise;
-export declare function loadModels(url: string): Promise<[void, void, void, void, void]>;
-export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise;
-export declare const ssdMobilenetv1: typeof locateFaces;
-export declare function detectLandmarks(input: TNetInput): Promise;
-export declare function detectLandmarksTiny(input: TNetInput): Promise;
-export declare function computeFaceDescriptor(input: TNetInput): Promise;
-export declare function mtcnn(input: TNetInput, forwardParams: MtcnnForwardParams): Promise;
-export declare function tinyYolov2(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise;
-export declare type allFacesSsdMobilenetv1Function = (input: tf.Tensor | NetInput | TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise;
-export declare const allFacesSsdMobilenetv1: allFacesSsdMobilenetv1Function;
-export declare type allFacesTinyYolov2Function = (input: tf.Tensor | NetInput | TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise;
-export declare const allFacesTinyYolov2: allFacesTinyYolov2Function;
-export declare type allFacesMtcnnFunction = (input: tf.Tensor | NetInput | TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise;
-export declare const allFacesMtcnn: allFacesMtcnnFunction;
-export declare const allFaces: allFacesSsdMobilenetv1Function;
diff --git a/build/globalApi.js b/build/globalApi.js
deleted file mode 100644
index 3b52596c..00000000
--- a/build/globalApi.js
+++ /dev/null
@@ -1,91 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var allFacesFactory_1 = require("./allFacesFactory");
-var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
-var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net");
-var FaceLandmark68TinyNet_1 = require("./faceLandmarkNet/FaceLandmark68TinyNet");
-var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
-var Mtcnn_1 = require("./mtcnn/Mtcnn");
-var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
-exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
-exports.landmarkNet = new FaceLandmark68Net_1.FaceLandmark68Net();
-exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
-// nets need more specific names, to avoid ambiguity in future
-// when alternative net implementations are provided
-exports.nets = {
- ssdMobilenetv1: exports.detectionNet,
- faceLandmark68Net: exports.landmarkNet,
- faceLandmark68TinyNet: new FaceLandmark68TinyNet_1.FaceLandmark68TinyNet(),
- faceRecognitionNet: exports.recognitionNet,
- mtcnn: new Mtcnn_1.Mtcnn(),
- tinyYolov2: new TinyYolov2_1.TinyYolov2()
-};
-function loadSsdMobilenetv1Model(url) {
- return exports.nets.ssdMobilenetv1.load(url);
-}
-exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
-function loadFaceLandmarkModel(url) {
- return exports.nets.faceLandmark68Net.load(url);
-}
-exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
-function loadFaceLandmarkTinyModel(url) {
- return exports.nets.faceLandmark68TinyNet.load(url);
-}
-exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
-function loadFaceRecognitionModel(url) {
- return exports.nets.faceRecognitionNet.load(url);
-}
-exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
-function loadMtcnnModel(url) {
- return exports.nets.mtcnn.load(url);
-}
-exports.loadMtcnnModel = loadMtcnnModel;
-function loadTinyYolov2Model(url) {
- return exports.nets.tinyYolov2.load(url);
-}
-exports.loadTinyYolov2Model = loadTinyYolov2Model;
-function loadFaceDetectionModel(url) {
- return loadSsdMobilenetv1Model(url);
-}
-exports.loadFaceDetectionModel = loadFaceDetectionModel;
-function loadModels(url) {
- console.warn('loadModels will be deprecated in future');
- return Promise.all([
- loadSsdMobilenetv1Model(url),
- loadFaceLandmarkModel(url),
- loadFaceRecognitionModel(url),
- loadMtcnnModel(url),
- loadTinyYolov2Model(url)
- ]);
-}
-exports.loadModels = loadModels;
-function locateFaces(input, minConfidence, maxResults) {
- return exports.nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
-}
-exports.locateFaces = locateFaces;
-exports.ssdMobilenetv1 = locateFaces;
-function detectLandmarks(input) {
- return exports.nets.faceLandmark68Net.detectLandmarks(input);
-}
-exports.detectLandmarks = detectLandmarks;
-function detectLandmarksTiny(input) {
- return exports.nets.faceLandmark68TinyNet.detectLandmarks(input);
-}
-exports.detectLandmarksTiny = detectLandmarksTiny;
-function computeFaceDescriptor(input) {
- return exports.nets.faceRecognitionNet.computeFaceDescriptor(input);
-}
-exports.computeFaceDescriptor = computeFaceDescriptor;
-function mtcnn(input, forwardParams) {
- return exports.nets.mtcnn.forward(input, forwardParams);
-}
-exports.mtcnn = mtcnn;
-function tinyYolov2(input, forwardParams) {
- return exports.nets.tinyYolov2.locateFaces(input, forwardParams);
-}
-exports.tinyYolov2 = tinyYolov2;
-exports.allFacesSsdMobilenetv1 = allFacesFactory_1.allFacesSsdMobilenetv1Factory(exports.nets.ssdMobilenetv1, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
-exports.allFacesTinyYolov2 = allFacesFactory_1.allFacesTinyYolov2Factory(exports.nets.tinyYolov2, exports.nets.faceLandmark68Net, exports.nets.faceRecognitionNet);
-exports.allFacesMtcnn = allFacesFactory_1.allFacesMtcnnFactory(exports.nets.mtcnn, exports.nets.faceRecognitionNet);
-exports.allFaces = exports.allFacesSsdMobilenetv1;
-//# sourceMappingURL=globalApi.js.map
\ No newline at end of file
diff --git a/build/globalApi.js.map b/build/globalApi.js.map
deleted file mode 100644
index 83d077d3..00000000
--- a/build/globalApi.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/index.d.ts b/build/index.d.ts
deleted file mode 100644
index e8beba8b..00000000
--- a/build/index.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-export { tf };
-export * from 'tfjs-image-recognition-base';
-export * from './classes';
-export * from './dom';
-export * from './euclideanDistance';
-export * from './faceDetectionNet';
-export * from './faceLandmarkNet';
-export * from './faceRecognitionNet';
-export * from './globalApi';
-export * from './mtcnn';
-export * from './tinyYolov2';
diff --git a/build/index.js b/build/index.js
deleted file mode 100644
index 9760f10b..00000000
--- a/build/index.js
+++ /dev/null
@@ -1,16 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-exports.tf = tf;
-tslib_1.__exportStar(require("tfjs-image-recognition-base"), exports);
-tslib_1.__exportStar(require("./classes"), exports);
-tslib_1.__exportStar(require("./dom"), exports);
-tslib_1.__exportStar(require("./euclideanDistance"), exports);
-tslib_1.__exportStar(require("./faceDetectionNet"), exports);
-tslib_1.__exportStar(require("./faceLandmarkNet"), exports);
-tslib_1.__exportStar(require("./faceRecognitionNet"), exports);
-tslib_1.__exportStar(require("./globalApi"), exports);
-tslib_1.__exportStar(require("./mtcnn"), exports);
-tslib_1.__exportStar(require("./tinyYolov2"), exports);
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/index.js.map b/build/index.js.map
deleted file mode 100644
index 6021586f..00000000
--- a/build/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAG1C,gBAAE;AAGJ,sEAA4C;AAE5C,oDAA0B;AAE1B,gDAAqB;AACrB,8DAAoC;AACpC,6DAAmC;AACnC,4DAAkC;AAClC,+DAAqC;AACrC,sDAA4B;AAC5B,kDAAwB;AACxB,uDAA6B"}
\ No newline at end of file
diff --git a/build/mtcnn/Mtcnn.d.ts b/build/mtcnn/Mtcnn.d.ts
deleted file mode 100644
index 78588b1a..00000000
--- a/build/mtcnn/Mtcnn.d.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
-import { MtcnnForwardParams, MtcnnResult, NetParams } from './types';
-export declare class Mtcnn extends NeuralNetwork {
- constructor();
- forwardInput(input: NetInput, forwardParams?: MtcnnForwardParams): Promise<{
- results: MtcnnResult[];
- stats: any;
- }>;
- forward(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise;
- forwardWithStats(input: TNetInput, forwardParams?: MtcnnForwardParams): Promise<{
- results: MtcnnResult[];
- stats: any;
- }>;
- protected loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- }>;
- protected extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: {
- originalPath?: string | undefined;
- paramPath: string;
- }[];
- };
-}
diff --git a/build/mtcnn/Mtcnn.js b/build/mtcnn/Mtcnn.js
deleted file mode 100644
index 9df0e28f..00000000
--- a/build/mtcnn/Mtcnn.js
+++ /dev/null
@@ -1,137 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var FaceDetection_1 = require("../classes/FaceDetection");
-var FaceLandmarks5_1 = require("../classes/FaceLandmarks5");
-var bgrToRgbTensor_1 = require("./bgrToRgbTensor");
-var config_1 = require("./config");
-var extractParams_1 = require("./extractParams");
-var getDefaultMtcnnForwardParams_1 = require("./getDefaultMtcnnForwardParams");
-var getSizesForScale_1 = require("./getSizesForScale");
-var loadQuantizedParams_1 = require("./loadQuantizedParams");
-var pyramidDown_1 = require("./pyramidDown");
-var stage1_1 = require("./stage1");
-var stage2_1 = require("./stage2");
-var stage3_1 = require("./stage3");
-var Mtcnn = /** @class */ (function (_super) {
- tslib_1.__extends(Mtcnn, _super);
- function Mtcnn() {
- return _super.call(this, 'Mtcnn') || this;
- }
- Mtcnn.prototype.forwardInput = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
- return tslib_1.__generator(this, function (_c) {
- switch (_c.label) {
- case 0:
- params = this.params;
- if (!params) {
- throw new Error('Mtcnn - load model before inference');
- }
- inputCanvas = input.canvases[0];
- if (!inputCanvas) {
- throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
- }
- stats = {};
- tsTotal = Date.now();
- imgTensor = tf.tidy(function () {
- return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat());
- });
- onReturn = function (results) {
- // dispose tensors on return
- imgTensor.dispose();
- stats.total = Date.now() - tsTotal;
- return results;
- };
- _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
- _b = Object.assign({}, getDefaultMtcnnForwardParams_1.getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
- scales = (scaleSteps || pyramidDown_1.pyramidDown(minFaceSize, scaleFactor, [height, width]))
- .filter(function (scale) {
- var sizes = getSizesForScale_1.getSizesForScale(scale, [height, width]);
- return Math.min(sizes.width, sizes.height) > config_1.CELL_SIZE;
- })
- .slice(0, maxNumScales);
- stats.scales = scales;
- stats.pyramid = scales.map(function (scale) { return getSizesForScale_1.getSizesForScale(scale, [height, width]); });
- ts = Date.now();
- return [4 /*yield*/, stage1_1.stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
- case 1:
- out1 = _c.sent();
- stats.total_stage1 = Date.now() - ts;
- if (!out1.boxes.length) {
- return [2 /*return*/, onReturn({ results: [], stats: stats })];
- }
- stats.stage2_numInputBoxes = out1.boxes.length;
- // using the inputCanvas to extract and resize the image patches, since it is faster
- // than doing this on the gpu
- ts = Date.now();
- return [4 /*yield*/, stage2_1.stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
- case 2:
- out2 = _c.sent();
- stats.total_stage2 = Date.now() - ts;
- if (!out2.boxes.length) {
- return [2 /*return*/, onReturn({ results: [], stats: stats })];
- }
- stats.stage3_numInputBoxes = out2.boxes.length;
- ts = Date.now();
- return [4 /*yield*/, stage3_1.stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
- case 3:
- out3 = _c.sent();
- stats.total_stage3 = Date.now() - ts;
- results = out3.boxes.map(function (box, idx) { return ({
- faceDetection: new FaceDetection_1.FaceDetection(out3.scores[idx], new tfjs_image_recognition_base_1.Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
- height: height,
- width: width
- }),
- faceLandmarks: new FaceLandmarks5_1.FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new tfjs_image_recognition_base_1.Point(width, height)); }), { width: width, height: height })
- }); });
- return [2 /*return*/, onReturn({ results: results, stats: stats })];
- }
- });
- });
- };
- Mtcnn.prototype.forward = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
- forwardParams])];
- case 2: return [2 /*return*/, (_b.sent()).results];
- }
- });
- });
- };
- Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _a;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
- forwardParams])];
- }
- });
- });
- };
- // none of the param tensors are quantized yet
- Mtcnn.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams_1.loadQuantizedParams(uri);
- };
- Mtcnn.prototype.extractParams = function (weights) {
- return extractParams_1.extractParams(weights);
- };
- return Mtcnn;
-}(tfjs_image_recognition_base_1.NeuralNetwork));
-exports.Mtcnn = Mtcnn;
-//# sourceMappingURL=Mtcnn.js.map
\ No newline at end of file
diff --git a/build/mtcnn/Mtcnn.js.map b/build/mtcnn/Mtcnn.js.map
deleted file mode 100644
index 9515ffd9..00000000
--- a/build/mtcnn/Mtcnn.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/mtcnn/MtcnnBox.d.ts b/build/mtcnn/MtcnnBox.d.ts
deleted file mode 100644
index e61cf2f0..00000000
--- a/build/mtcnn/MtcnnBox.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { Box } from 'tfjs-image-recognition-base';
-export declare class MtcnnBox extends Box {
- constructor(left: number, top: number, right: number, bottom: number);
-}
diff --git a/build/mtcnn/MtcnnBox.js b/build/mtcnn/MtcnnBox.js
deleted file mode 100644
index 1759d29b..00000000
--- a/build/mtcnn/MtcnnBox.js
+++ /dev/null
@@ -1,13 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var MtcnnBox = /** @class */ (function (_super) {
- tslib_1.__extends(MtcnnBox, _super);
- function MtcnnBox(left, top, right, bottom) {
- return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
- }
- return MtcnnBox;
-}(tfjs_image_recognition_base_1.Box));
-exports.MtcnnBox = MtcnnBox;
-//# sourceMappingURL=MtcnnBox.js.map
\ No newline at end of file
diff --git a/build/mtcnn/MtcnnBox.js.map b/build/mtcnn/MtcnnBox.js.map
deleted file mode 100644
index 50615247..00000000
--- a/build/mtcnn/MtcnnBox.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/mtcnn/ONet.d.ts b/build/mtcnn/ONet.d.ts
deleted file mode 100644
index dfc7f113..00000000
--- a/build/mtcnn/ONet.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ONetParams } from './types';
-export declare function ONet(x: tf.Tensor4D, params: ONetParams): {
- scores: tf.Tensor1D;
- regions: tf.Tensor2D;
- points: tf.Tensor2D;
-};
diff --git a/build/mtcnn/ONet.js b/build/mtcnn/ONet.js
deleted file mode 100644
index 50ace1cf..00000000
--- a/build/mtcnn/ONet.js
+++ /dev/null
@@ -1,27 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
-var prelu_1 = require("./prelu");
-var sharedLayers_1 = require("./sharedLayers");
-function ONet(x, params) {
- return tf.tidy(function () {
- var out = sharedLayers_1.sharedLayer(x, params);
- out = tf.maxPool(out, [2, 2], [2, 2], 'same');
- out = tfjs_tiny_yolov2_1.convLayer(out, params.conv4, 'valid');
- out = prelu_1.prelu(out, params.prelu4_alpha);
- var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
- var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
- var prelu5 = prelu_1.prelu(fc1, params.prelu5_alpha);
- var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_1);
- var max = tf.expandDims(tf.max(fc2_1, 1), 1);
- var prob = tf.softmax(tf.sub(fc2_1, max), 1);
- var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_2);
- var points = fullyConnectedLayer_1.fullyConnectedLayer(prelu5, params.fc2_3);
- var scores = tf.unstack(prob, 1)[1];
- return { scores: scores, regions: regions, points: points };
- });
-}
-exports.ONet = ONet;
-//# sourceMappingURL=ONet.js.map
\ No newline at end of file
diff --git a/build/mtcnn/ONet.js.map b/build/mtcnn/ONet.js.map
deleted file mode 100644
index 99deb135..00000000
--- a/build/mtcnn/ONet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"ONet.js","sourceRoot":"","sources":["../../src/mtcnn/ONet.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,qDAA6C;AAE7C,8EAA6E;AAC7E,iCAAgC;AAChC,+CAA6C;AAG7C,cAAqB,CAAc,EAAE,MAAkB;IACrD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,0BAAW,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;QAChC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;QAC7C,GAAG,GAAG,4BAAS,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,EAAE,OAAO,CAAC,CAAA;QAC3C,GAAG,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAElD,IAAM,UAAU,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QAC9F,IAAM,GAAG,GAAG,yCAAmB,CAAC,UAAU,EAAE,MAAM,CAAC,GAAG,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QACvD,IAAM,GAAG,GAAG,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QAE9C,IAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,EAAE,CAAC,CAAgB,CAAA;QAC7D,IAAM,OAAO,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QACzD,IAAM,MAAM,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QAExD,IAAM,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACpD,OAAO,EAAE,MAAM,QAAA,EAAE,OAAO,SAAA,EAAE,MAAM,QAAA,EAAE,CAAA;IACpC,CAAC,CAAC,CAAA;AACJ,CAAC;AArBD,oBAqBC"}
\ No newline at end of file
diff --git a/build/mtcnn/PNet.d.ts b/build/mtcnn/PNet.d.ts
deleted file mode 100644
index 74e2c841..00000000
--- a/build/mtcnn/PNet.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { PNetParams } from './types';
-export declare function PNet(x: tf.Tensor4D, params: PNetParams): {
- prob: tf.Tensor4D;
- regions: tf.Tensor4D;
-};
diff --git a/build/mtcnn/PNet.js b/build/mtcnn/PNet.js
deleted file mode 100644
index 696fd4ec..00000000
--- a/build/mtcnn/PNet.js
+++ /dev/null
@@ -1,17 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-var sharedLayers_1 = require("./sharedLayers");
-function PNet(x, params) {
- return tf.tidy(function () {
- var out = sharedLayers_1.sharedLayer(x, params, true);
- var conv = tfjs_tiny_yolov2_1.convLayer(out, params.conv4_1, 'valid');
- var max = tf.expandDims(tf.max(conv, 3), 3);
- var prob = tf.softmax(tf.sub(conv, max), 3);
- var regions = tfjs_tiny_yolov2_1.convLayer(out, params.conv4_2, 'valid');
- return { prob: prob, regions: regions };
- });
-}
-exports.PNet = PNet;
-//# sourceMappingURL=PNet.js.map
\ No newline at end of file
diff --git a/build/mtcnn/PNet.js.map b/build/mtcnn/PNet.js.map
deleted file mode 100644
index 2cbf5676..00000000
--- a/build/mtcnn/PNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"PNet.js","sourceRoot":"","sources":["../../src/mtcnn/PNet.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,qDAA6C;AAE7C,+CAA6C;AAG7C,cAAqB,CAAc,EAAE,MAAkB;IACrD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,0BAAW,CAAC,CAAC,EAAE,MAAM,EAAE,IAAI,CAAC,CAAA;QACtC,IAAM,IAAI,GAAG,4BAAS,CAAC,GAAG,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,CAAC,CAAA;QACpD,IAAM,GAAG,GAAG,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QAC7C,IAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,GAAG,CAAC,EAAE,CAAC,CAAgB,CAAA;QAC5D,IAAM,OAAO,GAAG,4BAAS,CAAC,GAAG,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,CAAC,CAAA;QAEvD,OAAO,EAAE,IAAI,MAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IAC1B,CAAC,CAAC,CAAA;AACJ,CAAC;AAXD,oBAWC"}
\ No newline at end of file
diff --git a/build/mtcnn/RNet.d.ts b/build/mtcnn/RNet.d.ts
deleted file mode 100644
index c6883ff7..00000000
--- a/build/mtcnn/RNet.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { RNetParams } from './types';
-export declare function RNet(x: tf.Tensor4D, params: RNetParams): {
- scores: tf.Tensor1D;
- regions: tf.Tensor2D;
-};
diff --git a/build/mtcnn/RNet.js b/build/mtcnn/RNet.js
deleted file mode 100644
index 2f975438..00000000
--- a/build/mtcnn/RNet.js
+++ /dev/null
@@ -1,22 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var fullyConnectedLayer_1 = require("../faceLandmarkNet/fullyConnectedLayer");
-var prelu_1 = require("./prelu");
-var sharedLayers_1 = require("./sharedLayers");
-function RNet(x, params) {
- return tf.tidy(function () {
- var convOut = sharedLayers_1.sharedLayer(x, params);
- var vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
- var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
- var prelu4 = prelu_1.prelu(fc1, params.prelu4_alpha);
- var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_1);
- var max = tf.expandDims(tf.max(fc2_1, 1), 1);
- var prob = tf.softmax(tf.sub(fc2_1, max), 1);
- var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_2);
- var scores = tf.unstack(prob, 1)[1];
- return { scores: scores, regions: regions };
- });
-}
-exports.RNet = RNet;
-//# sourceMappingURL=RNet.js.map
\ No newline at end of file
diff --git a/build/mtcnn/RNet.js.map b/build/mtcnn/RNet.js.map
deleted file mode 100644
index 1cce89bf..00000000
--- a/build/mtcnn/RNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"RNet.js","sourceRoot":"","sources":["../../src/mtcnn/RNet.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,8EAA6E;AAC7E,iCAAgC;AAChC,+CAA6C;AAG7C,cAAqB,CAAc,EAAE,MAAkB;IACrD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAM,OAAO,GAAG,0BAAW,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;QACtC,IAAM,UAAU,GAAG,EAAE,CAAC,OAAO,CAAC,OAAO,EAAE,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACtG,IAAM,GAAG,GAAG,yCAAmB,CAAC,UAAU,EAAE,MAAM,CAAC,GAAG,CAAC,CAAA;QACvD,IAAM,MAAM,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3D,IAAM,KAAK,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QACvD,IAAM,GAAG,GAAG,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QAC9C,IAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,EAAE,CAAC,CAAgB,CAAA;QAC7D,IAAM,OAAO,GAAG,yCAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;QAEzD,IAAM,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACpD,OAAO,EAAE,MAAM,QAAA,EAAE,OAAO,SAAA,EAAE,CAAA;IAC5B,CAAC,CAAC,CAAA;AACJ,CAAC;AAfD,oBAeC"}
\ No newline at end of file
diff --git a/build/mtcnn/bgrToRgbTensor.d.ts b/build/mtcnn/bgrToRgbTensor.d.ts
deleted file mode 100644
index 7b544bef..00000000
--- a/build/mtcnn/bgrToRgbTensor.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-export declare function bgrToRgbTensor(tensor: tf.Tensor4D): tf.Tensor4D;
diff --git a/build/mtcnn/bgrToRgbTensor.js b/build/mtcnn/bgrToRgbTensor.js
deleted file mode 100644
index 262ce6dd..00000000
--- a/build/mtcnn/bgrToRgbTensor.js
+++ /dev/null
@@ -1,8 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function bgrToRgbTensor(tensor) {
- return tf.tidy(function () { return tf.stack(tf.unstack(tensor, 3).reverse(), 3); });
-}
-exports.bgrToRgbTensor = bgrToRgbTensor;
-//# sourceMappingURL=bgrToRgbTensor.js.map
\ No newline at end of file
diff --git a/build/mtcnn/bgrToRgbTensor.js.map b/build/mtcnn/bgrToRgbTensor.js.map
deleted file mode 100644
index 86757569..00000000
--- a/build/mtcnn/bgrToRgbTensor.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"bgrToRgbTensor.js","sourceRoot":"","sources":["../../src/mtcnn/bgrToRgbTensor.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,wBAA+B,MAAmB;IAChD,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,OAAO,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,EAAE,CAAC,CAAC,EAA5C,CAA4C,CACpC,CAAA;AAClB,CAAC;AAJD,wCAIC"}
\ No newline at end of file
diff --git a/build/mtcnn/config.d.ts b/build/mtcnn/config.d.ts
deleted file mode 100644
index 05a91437..00000000
--- a/build/mtcnn/config.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export declare const CELL_STRIDE = 2;
-export declare const CELL_SIZE = 12;
diff --git a/build/mtcnn/config.js b/build/mtcnn/config.js
deleted file mode 100644
index fa6824e6..00000000
--- a/build/mtcnn/config.js
+++ /dev/null
@@ -1,5 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.CELL_STRIDE = 2;
-exports.CELL_SIZE = 12;
-//# sourceMappingURL=config.js.map
\ No newline at end of file
diff --git a/build/mtcnn/config.js.map b/build/mtcnn/config.js.map
deleted file mode 100644
index 8f408967..00000000
--- a/build/mtcnn/config.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"config.js","sourceRoot":"","sources":["../../src/mtcnn/config.ts"],"names":[],"mappings":";;AAAa,QAAA,WAAW,GAAG,CAAC,CAAA;AACf,QAAA,SAAS,GAAG,EAAE,CAAA"}
\ No newline at end of file
diff --git a/build/mtcnn/extractImagePatches.d.ts b/build/mtcnn/extractImagePatches.d.ts
deleted file mode 100644
index ea6f6111..00000000
--- a/build/mtcnn/extractImagePatches.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { Box, Dimensions } from 'tfjs-image-recognition-base';
-export declare function extractImagePatches(img: HTMLCanvasElement, boxes: Box[], {width, height}: Dimensions): Promise;
diff --git a/build/mtcnn/extractImagePatches.js b/build/mtcnn/extractImagePatches.js
deleted file mode 100644
index 8498f1b8..00000000
--- a/build/mtcnn/extractImagePatches.js
+++ /dev/null
@@ -1,55 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var normalize_1 = require("./normalize");
-function extractImagePatches(img, boxes, _a) {
- var width = _a.width, height = _a.height;
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var _this = this;
- var imgCtx, bitmaps, imagePatchesDatas;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0:
- imgCtx = tfjs_image_recognition_base_1.getContext2dOrThrow(img);
- return [4 /*yield*/, Promise.all(boxes.map(function (box) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
- var _a, y, ey, x, ex, fromX, fromY, imgData;
- return tslib_1.__generator(this, function (_b) {
- _a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;
- fromX = x - 1;
- fromY = y - 1;
- imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
- return [2 /*return*/, createImageBitmap(imgData)];
- });
- }); }))];
- case 1:
- bitmaps = _b.sent();
- imagePatchesDatas = [];
- bitmaps.forEach(function (bmp) {
- var patch = tfjs_image_recognition_base_1.createCanvas({ width: width, height: height });
- var patchCtx = tfjs_image_recognition_base_1.getContext2dOrThrow(patch);
- patchCtx.drawImage(bmp, 0, 0, width, height);
- var data = patchCtx.getImageData(0, 0, width, height).data;
- var currData = [];
- // RGBA -> BGR
- for (var i = 0; i < data.length; i += 4) {
- currData.push(data[i + 2]);
- currData.push(data[i + 1]);
- currData.push(data[i]);
- }
- imagePatchesDatas.push(currData);
- });
- return [2 /*return*/, imagePatchesDatas.map(function (data) {
- var t = tf.tidy(function () {
- var imagePatchTensor = tf.transpose(tf.tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
- return normalize_1.normalize(imagePatchTensor);
- });
- return t;
- })];
- }
- });
- });
-}
-exports.extractImagePatches = extractImagePatches;
-//# sourceMappingURL=extractImagePatches.js.map
\ No newline at end of file
diff --git a/build/mtcnn/extractImagePatches.js.map b/build/mtcnn/extractImagePatches.js.map
deleted file mode 100644
index b65aefd7..00000000
--- a/build/mtcnn/extractImagePatches.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractImagePatches.js","sourceRoot":"","sources":["../../src/mtcnn/extractImagePatches.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAiG;AAEjG,yCAAwC;AAExC,6BACE,GAAsB,EACtB,KAAY,EACZ,EAA6B;QAA3B,gBAAK,EAAE,kBAAM;;;;;;;oBAIT,MAAM,GAAG,iDAAmB,CAAC,GAAG,CAAC,CAAA;oBAEvB,qBAAM,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,UAAM,GAAG;;;gCAE7C,KAAmB,GAAG,CAAC,YAAY,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,KAAK,CAAC,EAAxD,CAAC,OAAA,EAAE,EAAE,QAAA,EAAE,CAAC,OAAA,EAAE,EAAE,QAAA,CAA4C;gCAE1D,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,OAAO,GAAG,MAAM,CAAC,YAAY,CAAC,KAAK,EAAE,KAAK,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,CAAC,CAAA;gCAE7E,sBAAO,iBAAiB,CAAC,OAAO,CAAC,EAAA;;6BAClC,CAAC,CAAC,EAAA;;oBATG,OAAO,GAAG,SASb;oBAEG,iBAAiB,GAAe,EAAE,CAAA;oBAExC,OAAO,CAAC,OAAO,CAAC,UAAA,GAAG;wBACjB,IAAM,KAAK,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;wBAC7C,IAAM,QAAQ,GAAG,iDAAmB,CAAC,KAAK,CAAC,CAAA;wBAC3C,QAAQ,CAAC,SAAS,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;wBACpC,IAAA,sDAAI,CAA+C;wBAE3D,IAAM,QAAQ,GAAG,EAAE,CAAA;wBACnB,cAAc;wBACd,KAAI,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,IAAE,CAAC,EAAE;4BACpC,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA;yBACvB;wBACD,iBAAiB,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAA;oBAClC,CAAC,CAAC,CAAA;oBAGF,sBAAO,iBAAiB,CAAC,GAAG,CAAC,UAAA,IAAI;4BAC/B,IAAM,CAAC,GAAG,EAAE,CAAC,IAAI,CAAC;gCAChB,IAAM,gBAAgB,GAAG,EAAE,CAAC,SAAS,CACnC,EAAE,CAAC,QAAQ,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,CAAC,CAAC,EACxC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,CAAC,OAAO,EAAiB,CAAA;gCAE1B,OAAO,qBAAS,CAAC,gBAAgB,CAAC,CAAA;4BACpC,CAAC,CAAC,CAAA;4BACF,OAAO,CAAC,CAAA;wBACV,CAAC,CAAC,EAAA;;;;CACH;AAlDD,kDAkDC"}
\ No newline at end of file
diff --git a/build/mtcnn/extractParams.d.ts b/build/mtcnn/extractParams.d.ts
deleted file mode 100644
index 40295777..00000000
--- a/build/mtcnn/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
- params: NetParams;
- paramMappings: ParamMapping[];
-};
diff --git a/build/mtcnn/extractParams.js b/build/mtcnn/extractParams.js
deleted file mode 100644
index 31a8189b..00000000
--- a/build/mtcnn/extractParams.js
+++ /dev/null
@@ -1,69 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-function extractorsFactory(extractWeights, paramMappings) {
- var extractConvParams = tfjs_tiny_yolov2_1.extractConvParamsFactory(extractWeights, paramMappings);
- var extractFCParams = tfjs_tiny_yolov2_1.extractFCParamsFactory(extractWeights, paramMappings);
- function extractPReluParams(size, paramPath) {
- var alpha = tf.tensor1d(extractWeights(size));
- paramMappings.push({ paramPath: paramPath });
- return alpha;
- }
- function extractSharedParams(numFilters, mappedPrefix, isRnet) {
- if (isRnet === void 0) { isRnet = false; }
- var conv1 = extractConvParams(numFilters[0], numFilters[1], 3, mappedPrefix + "/conv1");
- var prelu1_alpha = extractPReluParams(numFilters[1], mappedPrefix + "/prelu1_alpha");
- var conv2 = extractConvParams(numFilters[1], numFilters[2], 3, mappedPrefix + "/conv2");
- var prelu2_alpha = extractPReluParams(numFilters[2], mappedPrefix + "/prelu2_alpha");
- var conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, mappedPrefix + "/conv3");
- var prelu3_alpha = extractPReluParams(numFilters[3], mappedPrefix + "/prelu3_alpha");
- return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
- }
- function extractPNetParams() {
- var sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet');
- var conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1');
- var conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2');
- return tslib_1.__assign({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
- }
- function extractRNetParams() {
- var sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true);
- var fc1 = extractFCParams(576, 128, 'rnet/fc1');
- var prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha');
- var fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1');
- var fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2');
- return tslib_1.__assign({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
- }
- function extractONetParams() {
- var sharedParams = extractSharedParams([3, 32, 64, 64], 'onet');
- var conv4 = extractConvParams(64, 128, 2, 'onet/conv4');
- var prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha');
- var fc1 = extractFCParams(1152, 256, 'onet/fc1');
- var prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha');
- var fc2_1 = extractFCParams(256, 2, 'onet/fc2_1');
- var fc2_2 = extractFCParams(256, 4, 'onet/fc2_2');
- var fc2_3 = extractFCParams(256, 10, 'onet/fc2_3');
- return tslib_1.__assign({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
- }
- return {
- extractPNetParams: extractPNetParams,
- extractRNetParams: extractRNetParams,
- extractONetParams: extractONetParams
- };
-}
-function extractParams(weights) {
- var _a = tfjs_image_recognition_base_1.extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var paramMappings = [];
- var _b = extractorsFactory(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
- var pnet = extractPNetParams();
- var rnet = extractRNetParams();
- var onet = extractONetParams();
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
-}
-exports.extractParams = extractParams;
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/mtcnn/extractParams.js.map b/build/mtcnn/extractParams.js.map
deleted file mode 100644
index 59e68fb6..00000000
--- a/build/mtcnn/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../src/mtcnn/extractParams.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA0G;AAC1G,qDAAoF;AAIpF,2BAA2B,cAAsC,EAAE,aAA6B;IAE9F,IAAM,iBAAiB,GAAG,2CAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IACjF,IAAM,eAAe,GAAG,yCAAsB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAE7E,4BAA4B,IAAY,EAAE,SAAiB;QACzD,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC/C,aAAa,CAAC,IAAI,CAAC,EAAE,SAAS,WAAA,EAAE,CAAC,CAAA;QACjC,OAAO,KAAK,CAAA;IACd,CAAC;IAED,6BAA6B,UAAoB,EAAE,YAAoB,EAAE,MAAuB;QAAvB,uBAAA,EAAA,cAAuB;QAE9F,IAAM,KAAK,GAAG,iBAAiB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,EAAK,YAAY,WAAQ,CAAC,CAAA;QACzF,IAAM,YAAY,GAAG,kBAAkB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAK,YAAY,kBAAe,CAAC,CAAA;QACtF,IAAM,KAAK,GAAG,iBAAiB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,EAAK,YAAY,WAAQ,CAAC,CAAA;QACzF,IAAM,YAAY,GAAG,kBAAkB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAK,YAAY,kBAAe,CAAC,CAAA;QACtF,IAAM,KAAK,GAAG,iBAAiB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAK,YAAY,WAAQ,CAAC,CAAA;QACtG,IAAM,YAAY,GAAG,kBAAkB,CAAC,UAAU,CAAC,CAAC,CAAC,EAAK,YAAY,kBAAe,CAAC,CAAA;QAEtF,OAAO,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,CAAA;IAC1E,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,MAAM,CAAC,CAAA;QACjE,IAAM,OAAO,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;QAC3D,IAAM,OAAO,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,cAAc,CAAC,CAAA;QAE3D,4BAAY,YAAY,IAAE,OAAO,SAAA,EAAE,OAAO,SAAA,IAAE;IAC9C,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,MAAM,EAAE,IAAI,CAAC,CAAA;QACvE,IAAM,GAAG,GAAG,eAAe,CAAC,GAAG,EAAE,GAAG,EAAE,UAAU,CAAC,CAAA;QACjD,IAAM,YAAY,GAAG,kBAAkB,CAAC,GAAG,EAAE,mBAAmB,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACnD,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QAEnD,4BAAY,YAAY,IAAE,GAAG,KAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,IAAE;IAC7D,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,MAAM,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACzD,IAAM,YAAY,GAAG,kBAAkB,CAAC,GAAG,EAAE,mBAAmB,CAAC,CAAA;QACjE,IAAM,GAAG,GAAG,eAAe,CAAC,IAAI,EAAE,GAAG,EAAE,UAAU,CAAC,CAAA;QAClD,IAAM,YAAY,GAAG,kBAAkB,CAAC,GAAG,EAAE,mBAAmB,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACnD,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,YAAY,CAAC,CAAA;QACnD,IAAM,KAAK,GAAG,eAAe,CAAC,GAAG,EAAE,EAAE,EAAE,YAAY,CAAC,CAAA;QAEpD,4BAAY,YAAY,IAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,GAAG,KAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,IAAE;IACzF,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,iBAAiB,mBAAA;QACjB,iBAAiB,mBAAA;KAClB,CAAA;AAEH,CAAC;AAED,uBAA8B,OAAqB;IAE3C,IAAA,iEAG4B,EAFhC,kCAAc,EACd,4CAAmB,CACa;IAElC,IAAM,aAAa,GAAmB,EAAE,CAAA;IAElC,IAAA,qDAI8C,EAHlD,wCAAiB,EACjB,wCAAiB,EACjB,wCAAiB,CACiC;IAEpD,IAAM,IAAI,GAAG,iBAAiB,EAAE,CAAA;IAChC,IAAM,IAAI,GAAG,iBAAiB,EAAE,CAAA;IAChC,IAAM,IAAI,GAAG,iBAAiB,EAAE,CAAA;IAEhC,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,oCAAkC,mBAAmB,EAAE,CAAC,MAAQ,CAAC,CAAA;KAClF;IAED,OAAO,EAAE,MAAM,EAAE,EAAE,IAAI,MAAA,EAAE,IAAI,MAAA,EAAE,IAAI,MAAA,EAAE,EAAE,aAAa,eAAA,EAAE,CAAA;AACxD,CAAC;AAxBD,sCAwBC"}
\ No newline at end of file
diff --git a/build/mtcnn/getDefaultMtcnnForwardParams.d.ts b/build/mtcnn/getDefaultMtcnnForwardParams.d.ts
deleted file mode 100644
index af2d2abf..00000000
--- a/build/mtcnn/getDefaultMtcnnForwardParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export declare function getDefaultMtcnnForwardParams(): {
- minFaceSize: number;
- scaleFactor: number;
- maxNumScales: number;
- scoreThresholds: number[];
-};
diff --git a/build/mtcnn/getDefaultMtcnnForwardParams.js b/build/mtcnn/getDefaultMtcnnForwardParams.js
deleted file mode 100644
index a26c2f47..00000000
--- a/build/mtcnn/getDefaultMtcnnForwardParams.js
+++ /dev/null
@@ -1,12 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function getDefaultMtcnnForwardParams() {
- return {
- minFaceSize: 20,
- scaleFactor: 0.709,
- maxNumScales: 10,
- scoreThresholds: [0.6, 0.7, 0.7]
- };
-}
-exports.getDefaultMtcnnForwardParams = getDefaultMtcnnForwardParams;
-//# sourceMappingURL=getDefaultMtcnnForwardParams.js.map
\ No newline at end of file
diff --git a/build/mtcnn/getDefaultMtcnnForwardParams.js.map b/build/mtcnn/getDefaultMtcnnForwardParams.js.map
deleted file mode 100644
index 665b88de..00000000
--- a/build/mtcnn/getDefaultMtcnnForwardParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"getDefaultMtcnnForwardParams.js","sourceRoot":"","sources":["../../src/mtcnn/getDefaultMtcnnForwardParams.ts"],"names":[],"mappings":";;AAAA;IACE,OAAO;QACL,WAAW,EAAE,EAAE;QACf,WAAW,EAAE,KAAK;QAClB,YAAY,EAAE,EAAE;QAChB,eAAe,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC;KACjC,CAAA;AACH,CAAC;AAPD,oEAOC"}
\ No newline at end of file
diff --git a/build/mtcnn/getSizesForScale.d.ts b/build/mtcnn/getSizesForScale.d.ts
deleted file mode 100644
index 4ab739a5..00000000
--- a/build/mtcnn/getSizesForScale.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export declare function getSizesForScale(scale: number, [height, width]: number[]): {
- height: number;
- width: number;
-};
diff --git a/build/mtcnn/getSizesForScale.js b/build/mtcnn/getSizesForScale.js
deleted file mode 100644
index b2cd4cee..00000000
--- a/build/mtcnn/getSizesForScale.js
+++ /dev/null
@@ -1,11 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-function getSizesForScale(scale, _a) {
- var height = _a[0], width = _a[1];
- return {
- height: Math.floor(height * scale),
- width: Math.floor(width * scale)
- };
-}
-exports.getSizesForScale = getSizesForScale;
-//# sourceMappingURL=getSizesForScale.js.map
\ No newline at end of file
diff --git a/build/mtcnn/getSizesForScale.js.map b/build/mtcnn/getSizesForScale.js.map
deleted file mode 100644
index a6b9b64c..00000000
--- a/build/mtcnn/getSizesForScale.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"getSizesForScale.js","sourceRoot":"","sources":["../../src/mtcnn/getSizesForScale.ts"],"names":[],"mappings":";;AAAA,0BAAiC,KAAa,EAAE,EAAyB;QAAxB,cAAM,EAAE,aAAK;IAC5D,OAAO;QACL,MAAM,EAAE,IAAI,CAAC,KAAK,CAAC,MAAM,GAAG,KAAK,CAAC;QAClC,KAAK,EAAE,IAAI,CAAC,KAAK,CAAC,KAAK,GAAG,KAAK,CAAC;KACjC,CAAA;AACH,CAAC;AALD,4CAKC"}
\ No newline at end of file
diff --git a/build/mtcnn/index.d.ts b/build/mtcnn/index.d.ts
deleted file mode 100644
index 9f8eb6ad..00000000
--- a/build/mtcnn/index.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { Mtcnn } from './Mtcnn';
-export * from './Mtcnn';
-export declare function createMtcnn(weights: Float32Array): Mtcnn;
diff --git a/build/mtcnn/index.js b/build/mtcnn/index.js
deleted file mode 100644
index e84c9aa9..00000000
--- a/build/mtcnn/index.js
+++ /dev/null
@@ -1,12 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var Mtcnn_1 = require("./Mtcnn");
-tslib_1.__exportStar(require("./Mtcnn"), exports);
-function createMtcnn(weights) {
- var net = new Mtcnn_1.Mtcnn();
- net.extractWeights(weights);
- return net;
-}
-exports.createMtcnn = createMtcnn;
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/mtcnn/index.js.map b/build/mtcnn/index.js.map
deleted file mode 100644
index de5a178a..00000000
--- a/build/mtcnn/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/mtcnn/index.ts"],"names":[],"mappings":";;;AAAA,iCAAgC;AAEhC,kDAAwB;AAExB,qBAA4B,OAAqB;IAC/C,IAAM,GAAG,GAAG,IAAI,aAAK,EAAE,CAAA;IACvB,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,kCAIC"}
\ No newline at end of file
diff --git a/build/mtcnn/loadQuantizedParams.d.ts b/build/mtcnn/loadQuantizedParams.d.ts
deleted file mode 100644
index c5e1dc44..00000000
--- a/build/mtcnn/loadQuantizedParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from 'tfjs-image-recognition-base';
-import { NetParams } from './types';
-export declare function loadQuantizedParams(uri: string | undefined): Promise<{
- params: NetParams;
- paramMappings: ParamMapping[];
-}>;
diff --git a/build/mtcnn/loadQuantizedParams.js b/build/mtcnn/loadQuantizedParams.js
deleted file mode 100644
index 65f271ee..00000000
--- a/build/mtcnn/loadQuantizedParams.js
+++ /dev/null
@@ -1,81 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var DEFAULT_MODEL_NAME = 'mtcnn_model';
-function extractorsFactory(weightMap, paramMappings) {
- var extractWeightEntry = tfjs_image_recognition_base_1.extractWeightEntryFactory(weightMap, paramMappings);
- function extractConvParams(prefix) {
- var filters = extractWeightEntry(prefix + "/weights", 4, prefix + "/filters");
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { filters: filters, bias: bias };
- }
- function extractFCParams(prefix) {
- var weights = extractWeightEntry(prefix + "/weights", 2);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { weights: weights, bias: bias };
- }
- function extractPReluParams(paramPath) {
- return extractWeightEntry(paramPath, 1);
- }
- function extractSharedParams(prefix) {
- var conv1 = extractConvParams(prefix + "/conv1");
- var prelu1_alpha = extractPReluParams(prefix + "/prelu1_alpha");
- var conv2 = extractConvParams(prefix + "/conv2");
- var prelu2_alpha = extractPReluParams(prefix + "/prelu2_alpha");
- var conv3 = extractConvParams(prefix + "/conv3");
- var prelu3_alpha = extractPReluParams(prefix + "/prelu3_alpha");
- return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
- }
- function extractPNetParams() {
- var sharedParams = extractSharedParams('pnet');
- var conv4_1 = extractConvParams('pnet/conv4_1');
- var conv4_2 = extractConvParams('pnet/conv4_2');
- return tslib_1.__assign({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
- }
- function extractRNetParams() {
- var sharedParams = extractSharedParams('rnet');
- var fc1 = extractFCParams('rnet/fc1');
- var prelu4_alpha = extractPReluParams('rnet/prelu4_alpha');
- var fc2_1 = extractFCParams('rnet/fc2_1');
- var fc2_2 = extractFCParams('rnet/fc2_2');
- return tslib_1.__assign({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
- }
- function extractONetParams() {
- var sharedParams = extractSharedParams('onet');
- var conv4 = extractConvParams('onet/conv4');
- var prelu4_alpha = extractPReluParams('onet/prelu4_alpha');
- var fc1 = extractFCParams('onet/fc1');
- var prelu5_alpha = extractPReluParams('onet/prelu5_alpha');
- var fc2_1 = extractFCParams('onet/fc2_1');
- var fc2_2 = extractFCParams('onet/fc2_2');
- var fc2_3 = extractFCParams('onet/fc2_3');
- return tslib_1.__assign({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
- }
- return {
- extractPNetParams: extractPNetParams,
- extractRNetParams: extractRNetParams,
- extractONetParams: extractONetParams
- };
-}
-function loadQuantizedParams(uri) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet;
- return tslib_1.__generator(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.loadWeightMap(uri, DEFAULT_MODEL_NAME)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;
- pnet = extractPNetParams();
- rnet = extractRNetParams();
- onet = extractONetParams();
- tfjs_image_recognition_base_1.disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings }];
- }
- });
- });
-}
-exports.loadQuantizedParams = loadQuantizedParams;
-//# sourceMappingURL=loadQuantizedParams.js.map
\ No newline at end of file
diff --git a/build/mtcnn/loadQuantizedParams.js.map b/build/mtcnn/loadQuantizedParams.js.map
deleted file mode 100644
index 248cb3cc..00000000
--- a/build/mtcnn/loadQuantizedParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadQuantizedParams.js","sourceRoot":"","sources":["../../src/mtcnn/loadQuantizedParams.ts"],"names":[],"mappings":";;;AACA,2EAKqC;AAKrC,IAAM,kBAAkB,GAAG,aAAa,CAAA;AAExC,2BAA2B,SAAc,EAAE,aAA6B;IAEtE,IAAM,kBAAkB,GAAG,uDAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,2BAA2B,MAAc;QACvC,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,EAAK,MAAM,aAAU,CAAC,CAAA;QAC5F,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,yBAAyB,MAAc;QACrC,IAAM,OAAO,GAAG,kBAAkB,CAAiB,MAAM,aAAU,EAAE,CAAC,CAAC,CAAA;QACvE,IAAM,IAAI,GAAG,kBAAkB,CAAiB,MAAM,UAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,EAAE,OAAO,SAAA,EAAE,IAAI,MAAA,EAAE,CAAA;IAC1B,CAAC;IAED,4BAA4B,SAAiB;QAC3C,OAAO,kBAAkB,CAAc,SAAS,EAAE,CAAC,CAAC,CAAA;IACtD,CAAC;IAED,6BAA6B,MAAc;QAEzC,IAAM,KAAK,GAAG,iBAAiB,CAAI,MAAM,WAAQ,CAAC,CAAA;QAClD,IAAM,YAAY,GAAG,kBAAkB,CAAI,MAAM,kBAAe,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,iBAAiB,CAAI,MAAM,WAAQ,CAAC,CAAA;QAClD,IAAM,YAAY,GAAG,kBAAkB,CAAI,MAAM,kBAAe,CAAC,CAAA;QACjE,IAAM,KAAK,GAAG,iBAAiB,CAAI,MAAM,WAAQ,CAAC,CAAA;QAClD,IAAM,YAAY,GAAG,kBAAkB,CAAI,MAAM,kBAAe,CAAC,CAAA;QAEjE,OAAO,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,CAAA;IAC1E,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;QAChD,IAAM,OAAO,GAAG,iBAAiB,CAAC,cAAc,CAAC,CAAA;QACjD,IAAM,OAAO,GAAG,iBAAiB,CAAC,cAAc,CAAC,CAAA;QAEjD,4BAAY,YAAY,IAAE,OAAO,SAAA,EAAE,OAAO,SAAA,IAAE;IAC9C,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;QAChD,IAAM,GAAG,GAAG,eAAe,CAAC,UAAU,CAAC,CAAA;QACvC,IAAM,YAAY,GAAG,kBAAkB,CAAC,mBAAmB,CAAC,CAAA;QAC5D,IAAM,KAAK,GAAG,eAAe,CAAC,YAAY,CAAC,CAAA;QAC3C,IAAM,KAAK,GAAG,eAAe,CAAC,YAAY,CAAC,CAAA;QAE3C,4BAAY,YAAY,IAAE,GAAG,KAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,IAAE;IAC7D,CAAC;IAED;QAEE,IAAM,YAAY,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;QAChD,IAAM,KAAK,GAAG,iBAAiB,CAAC,YAAY,CAAC,CAAA;QAC7C,IAAM,YAAY,GAAG,kBAAkB,CAAC,mBAAmB,CAAC,CAAA;QAC5D,IAAM,GAAG,GAAG,eAAe,CAAC,UAAU,CAAC,CAAA;QACvC,IAAM,YAAY,GAAG,kBAAkB,CAAC,mBAAmB,CAAC,CAAA;QAC5D,IAAM,KAAK,GAAG,eAAe,CAAC,YAAY,CAAC,CAAA;QAC3C,IAAM,KAAK,GAAG,eAAe,CAAC,YAAY,CAAC,CAAA;QAC3C,IAAM,KAAK,GAAG,eAAe,CAAC,YAAY,CAAC,CAAA;QAE3C,4BAAY,YAAY,IAAE,KAAK,OAAA,EAAE,YAAY,cAAA,EAAE,GAAG,KAAA,EAAE,YAAY,cAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,EAAE,KAAK,OAAA,IAAE;IACzF,CAAC;IAED,OAAO;QACL,iBAAiB,mBAAA;QACjB,iBAAiB,mBAAA;QACjB,iBAAiB,mBAAA;KAClB,CAAA;AAEH,CAAC;AAED,6BACE,GAAuB;;;;;wBAGL,qBAAM,2CAAa,CAAC,GAAG,EAAE,kBAAkB,CAAC,EAAA;;oBAAxD,SAAS,GAAG,SAA4C;oBACxD,aAAa,GAAmB,EAAE,CAAA;oBAElC,KAIF,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,EAH7C,iBAAiB,uBAAA,EACjB,iBAAiB,uBAAA,EACjB,iBAAiB,uBAAA,CAC4B;oBAEzC,IAAI,GAAG,iBAAiB,EAAE,CAAA;oBAC1B,IAAI,GAAG,iBAAiB,EAAE,CAAA;oBAC1B,IAAI,GAAG,iBAAiB,EAAE,CAAA;oBAEhC,wDAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;oBAEpD,sBAAO,EAAE,MAAM,EAAE,EAAE,IAAI,MAAA,EAAE,IAAI,MAAA,EAAE,IAAI,MAAA,EAAE,EAAE,aAAa,eAAA,EAAE,EAAA;;;;CACvD;AApBD,kDAoBC"}
\ No newline at end of file
diff --git a/build/mtcnn/normalize.d.ts b/build/mtcnn/normalize.d.ts
deleted file mode 100644
index 1a14045d..00000000
--- a/build/mtcnn/normalize.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-export declare function normalize(x: tf.Tensor4D): tf.Tensor4D;
diff --git a/build/mtcnn/normalize.js b/build/mtcnn/normalize.js
deleted file mode 100644
index e0cff3f7..00000000
--- a/build/mtcnn/normalize.js
+++ /dev/null
@@ -1,8 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function normalize(x) {
- return tf.tidy(function () { return tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125)); });
-}
-exports.normalize = normalize;
-//# sourceMappingURL=normalize.js.map
\ No newline at end of file
diff --git a/build/mtcnn/normalize.js.map b/build/mtcnn/normalize.js.map
deleted file mode 100644
index 5f905cdd..00000000
--- a/build/mtcnn/normalize.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../src/mtcnn/normalize.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,mBAA0B,CAAc;IACtC,OAAO,EAAE,CAAC,IAAI,CACZ,cAAM,OAAA,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,SAAS,CAAC,CAAC,EAAzD,CAAyD,CAChE,CAAA;AACH,CAAC;AAJD,8BAIC"}
\ No newline at end of file
diff --git a/build/mtcnn/prelu.d.ts b/build/mtcnn/prelu.d.ts
deleted file mode 100644
index 3019168f..00000000
--- a/build/mtcnn/prelu.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-export declare function prelu(x: T, alpha: tf.Tensor1D): T;
diff --git a/build/mtcnn/prelu.js b/build/mtcnn/prelu.js
deleted file mode 100644
index c6c2e457..00000000
--- a/build/mtcnn/prelu.js
+++ /dev/null
@@ -1,10 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-function prelu(x, alpha) {
- return tf.tidy(function () {
- return tf.add(tf.relu(x), tf.mul(alpha, tf.neg(tf.relu(tf.neg(x)))));
- });
-}
-exports.prelu = prelu;
-//# sourceMappingURL=prelu.js.map
\ No newline at end of file
diff --git a/build/mtcnn/prelu.js.map b/build/mtcnn/prelu.js.map
deleted file mode 100644
index a954d4de..00000000
--- a/build/mtcnn/prelu.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"prelu.js","sourceRoot":"","sources":["../../src/mtcnn/prelu.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,eAA2C,CAAI,EAAE,KAAkB;IACjE,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,OAAA,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EACV,EAAE,CAAC,GAAG,CAAC,KAAK,EAAE,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAC1C;IAHD,CAGC,CACF,CAAA;AACH,CAAC;AAPD,sBAOC"}
\ No newline at end of file
diff --git a/build/mtcnn/pyramidDown.d.ts b/build/mtcnn/pyramidDown.d.ts
deleted file mode 100644
index 58073049..00000000
--- a/build/mtcnn/pyramidDown.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function pyramidDown(minFaceSize: number, scaleFactor: number, dims: number[]): number[];
diff --git a/build/mtcnn/pyramidDown.js b/build/mtcnn/pyramidDown.js
deleted file mode 100644
index 44ab651a..00000000
--- a/build/mtcnn/pyramidDown.js
+++ /dev/null
@@ -1,18 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var config_1 = require("./config");
-function pyramidDown(minFaceSize, scaleFactor, dims) {
- var height = dims[0], width = dims[1];
- var m = config_1.CELL_SIZE / minFaceSize;
- var scales = [];
- var minLayer = Math.min(height, width) * m;
- var exp = 0;
- while (minLayer >= 12) {
- scales.push(m * Math.pow(scaleFactor, exp));
- minLayer = minLayer * scaleFactor;
- exp += 1;
- }
- return scales;
-}
-exports.pyramidDown = pyramidDown;
-//# sourceMappingURL=pyramidDown.js.map
\ No newline at end of file
diff --git a/build/mtcnn/pyramidDown.js.map b/build/mtcnn/pyramidDown.js.map
deleted file mode 100644
index f8bec617..00000000
--- a/build/mtcnn/pyramidDown.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"pyramidDown.js","sourceRoot":"","sources":["../../src/mtcnn/pyramidDown.ts"],"names":[],"mappings":";;AAAA,mCAAqC;AAErC,qBAA4B,WAAmB,EAAE,WAAmB,EAAE,IAAc;IAE3E,IAAA,gBAAM,EAAE,eAAK,CAAQ;IAC5B,IAAM,CAAC,GAAG,kBAAS,GAAG,WAAW,CAAA;IAEjC,IAAM,MAAM,GAAG,EAAE,CAAA;IAEjB,IAAI,QAAQ,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,CAAC,CAAA;IAC1C,IAAI,GAAG,GAAG,CAAC,CAAA;IACX,OAAO,QAAQ,IAAI,EAAE,EAAE;QACrB,MAAM,CAAC,IAAI,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,WAAW,EAAE,GAAG,CAAC,CAAC,CAAA;QAC3C,QAAQ,GAAG,QAAQ,GAAG,WAAW,CAAA;QACjC,GAAG,IAAI,CAAC,CAAA;KACT;IAED,OAAO,MAAM,CAAA;AACf,CAAC;AAhBD,kCAgBC"}
\ No newline at end of file
diff --git a/build/mtcnn/sharedLayers.d.ts b/build/mtcnn/sharedLayers.d.ts
deleted file mode 100644
index afc63db2..00000000
--- a/build/mtcnn/sharedLayers.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { SharedParams } from './types';
-export declare function sharedLayer(x: tf.Tensor4D, params: SharedParams, isPnet?: boolean): tf.Tensor;
diff --git a/build/mtcnn/sharedLayers.js b/build/mtcnn/sharedLayers.js
deleted file mode 100644
index 4fbda0e7..00000000
--- a/build/mtcnn/sharedLayers.js
+++ /dev/null
@@ -1,21 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-var prelu_1 = require("./prelu");
-function sharedLayer(x, params, isPnet) {
- if (isPnet === void 0) { isPnet = false; }
- return tf.tidy(function () {
- var out = tfjs_tiny_yolov2_1.convLayer(x, params.conv1, 'valid');
- out = prelu_1.prelu(out, params.prelu1_alpha);
- out = tf.maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');
- out = tfjs_tiny_yolov2_1.convLayer(out, params.conv2, 'valid');
- out = prelu_1.prelu(out, params.prelu2_alpha);
- out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid');
- out = tfjs_tiny_yolov2_1.convLayer(out, params.conv3, 'valid');
- out = prelu_1.prelu(out, params.prelu3_alpha);
- return out;
- });
-}
-exports.sharedLayer = sharedLayer;
-//# sourceMappingURL=sharedLayers.js.map
\ No newline at end of file
diff --git a/build/mtcnn/sharedLayers.js.map b/build/mtcnn/sharedLayers.js.map
deleted file mode 100644
index 0c673b3d..00000000
--- a/build/mtcnn/sharedLayers.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"sharedLayers.js","sourceRoot":"","sources":["../../src/mtcnn/sharedLayers.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,qDAA6C;AAE7C,iCAAgC;AAGhC,qBAA4B,CAAc,EAAE,MAAoB,EAAE,MAAuB;IAAvB,uBAAA,EAAA,cAAuB;IACvF,OAAO,EAAE,CAAC,IAAI,CAAC;QAEb,IAAI,GAAG,GAAG,4BAAS,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,EAAE,OAAO,CAAC,CAAA;QAC7C,GAAG,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;QAC9D,GAAG,GAAG,4BAAS,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,EAAE,OAAO,CAAC,CAAA;QAC3C,GAAG,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAClD,GAAG,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAC7D,GAAG,GAAG,4BAAS,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,EAAE,OAAO,CAAC,CAAA;QAC3C,GAAG,GAAG,aAAK,CAAc,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAElD,OAAO,GAAG,CAAA;IACZ,CAAC,CAAC,CAAA;AACJ,CAAC;AAdD,kCAcC"}
\ No newline at end of file
diff --git a/build/mtcnn/stage1.d.ts b/build/mtcnn/stage1.d.ts
deleted file mode 100644
index f096cea1..00000000
--- a/build/mtcnn/stage1.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { BoundingBox } from 'tfjs-image-recognition-base';
-import { PNetParams } from './types';
-export declare function stage1(imgTensor: tf.Tensor4D, scales: number[], scoreThreshold: number, params: PNetParams, stats: any): {
- boxes: BoundingBox[];
- scores: number[];
-};
diff --git a/build/mtcnn/stage1.js b/build/mtcnn/stage1.js
deleted file mode 100644
index 14ad0297..00000000
--- a/build/mtcnn/stage1.js
+++ /dev/null
@@ -1,94 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var config_1 = require("./config");
-var getSizesForScale_1 = require("./getSizesForScale");
-var MtcnnBox_1 = require("./MtcnnBox");
-var normalize_1 = require("./normalize");
-var PNet_1 = require("./PNet");
-function rescaleAndNormalize(x, scale) {
- return tf.tidy(function () {
- var _a = getSizesForScale_1.getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
- var resized = tf.image.resizeBilinear(x, [height, width]);
- var normalized = normalize_1.normalize(resized);
- return tf.transpose(normalized, [0, 2, 1, 3]);
- });
-}
-function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold) {
- // TODO: fix this!, maybe better to use tf.gather here
- var indices = [];
- for (var y = 0; y < scoresTensor.shape[0]; y++) {
- for (var x = 0; x < scoresTensor.shape[1]; x++) {
- if (scoresTensor.get(y, x) >= scoreThreshold) {
- indices.push(new tfjs_image_recognition_base_1.Point(x, y));
- }
- }
- }
- var boundingBoxes = indices.map(function (idx) {
- var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
- var score = scoresTensor.get(idx.y, idx.x);
- var region = new MtcnnBox_1.MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
- return {
- cell: cell,
- score: score,
- region: region
- };
- });
- return boundingBoxes;
-}
-function stage1(imgTensor, scales, scoreThreshold, params, stats) {
- stats.stage1 = [];
- var pnetOutputs = scales.map(function (scale) { return tf.tidy(function () {
- var statsForScale = { scale: scale };
- var resized = rescaleAndNormalize(imgTensor, scale);
- var ts = Date.now();
- var _a = PNet_1.PNet(resized, params), prob = _a.prob, regions = _a.regions;
- statsForScale.pnet = Date.now() - ts;
- var scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0];
- var regionsTensor = tf.unstack(regions)[0];
- return {
- scoresTensor: scoresTensor,
- regionsTensor: regionsTensor,
- scale: scale,
- statsForScale: statsForScale
- };
- }); });
- var boxesForScale = pnetOutputs.map(function (_a) {
- var scoresTensor = _a.scoresTensor, regionsTensor = _a.regionsTensor, scale = _a.scale, statsForScale = _a.statsForScale;
- var boundingBoxes = extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold);
- scoresTensor.dispose();
- regionsTensor.dispose();
- if (!boundingBoxes.length) {
- stats.stage1.push(statsForScale);
- return [];
- }
- var ts = Date.now();
- var indices = tfjs_image_recognition_base_1.nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
- statsForScale.nms = Date.now() - ts;
- statsForScale.numBoxes = indices.length;
- stats.stage1.push(statsForScale);
- return indices.map(function (boxIdx) { return boundingBoxes[boxIdx]; });
- });
- var allBoxes = boxesForScale.reduce(function (all, boxes) { return all.concat(boxes); }, []);
- var finalBoxes = [];
- var finalScores = [];
- if (allBoxes.length > 0) {
- var ts = Date.now();
- var indices = tfjs_image_recognition_base_1.nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
- stats.stage1_nms = Date.now() - ts;
- finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
- finalBoxes = indices
- .map(function (idx) { return allBoxes[idx]; })
- .map(function (_a) {
- var cell = _a.cell, region = _a.region;
- return new tfjs_image_recognition_base_1.BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
- });
- }
- return {
- boxes: finalBoxes,
- scores: finalScores
- };
-}
-exports.stage1 = stage1;
-//# sourceMappingURL=stage1.js.map
\ No newline at end of file
diff --git a/build/mtcnn/stage1.js.map b/build/mtcnn/stage1.js.map
deleted file mode 100644
index 02dcf3b1..00000000
--- a/build/mtcnn/stage1.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/mtcnn/stage2.d.ts b/build/mtcnn/stage2.d.ts
deleted file mode 100644
index de3a4c61..00000000
--- a/build/mtcnn/stage2.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { Box } from 'tfjs-image-recognition-base';
-import { RNetParams } from './types';
-export declare function stage2(img: HTMLCanvasElement, inputBoxes: Box[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
- boxes: Box[];
- scores: number[];
-}>;
diff --git a/build/mtcnn/stage2.js b/build/mtcnn/stage2.js
deleted file mode 100644
index 72375ab1..00000000
--- a/build/mtcnn/stage2.js
+++ /dev/null
@@ -1,69 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var extractImagePatches_1 = require("./extractImagePatches");
-var MtcnnBox_1 = require("./MtcnnBox");
-var RNet_1 = require("./RNet");
-function stage2(img, inputBoxes, scoreThreshold, params, stats) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;
- return tslib_1.__generator(this, function (_c) {
- switch (_c.label) {
- case 0:
- ts = Date.now();
- return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
- case 1:
- rnetInputs = _c.sent();
- stats.stage2_extractImagePatches = Date.now() - ts;
- ts = Date.now();
- rnetOuts = rnetInputs.map(function (rnetInput) {
- var out = RNet_1.RNet(rnetInput, params);
- rnetInput.dispose();
- return out;
- });
- stats.stage2_rnet = Date.now() - ts;
- scoresTensor = rnetOuts.length > 1
- ? tf.concat(rnetOuts.map(function (out) { return out.scores; }))
- : rnetOuts[0].scores;
- _b = (_a = Array).from;
- return [4 /*yield*/, scoresTensor.data()];
- case 2:
- scores = _b.apply(_a, [_c.sent()]);
- scoresTensor.dispose();
- indices = scores
- .map(function (score, idx) { return ({ score: score, idx: idx }); })
- .filter(function (c) { return c.score > scoreThreshold; })
- .map(function (_a) {
- var idx = _a.idx;
- return idx;
- });
- filteredBoxes = indices.map(function (idx) { return inputBoxes[idx]; });
- filteredScores = indices.map(function (idx) { return scores[idx]; });
- finalBoxes = [];
- finalScores = [];
- if (filteredBoxes.length > 0) {
- ts = Date.now();
- indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
- stats.stage2_nms = Date.now() - ts;
- regions_1 = indicesNms.map(function (idx) {
- return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
- });
- finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
- finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
- }
- rnetOuts.forEach(function (t) {
- t.regions.dispose();
- t.scores.dispose();
- });
- return [2 /*return*/, {
- boxes: finalBoxes,
- scores: finalScores
- }];
- }
- });
- });
-}
-exports.stage2 = stage2;
-//# sourceMappingURL=stage2.js.map
\ No newline at end of file
diff --git a/build/mtcnn/stage2.js.map b/build/mtcnn/stage2.js.map
deleted file mode 100644
index 031174f0..00000000
--- a/build/mtcnn/stage2.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"stage2.js","sourceRoot":"","sources":["../../src/mtcnn/stage2.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAqE;AAErE,6DAA4D;AAC5D,uCAAsC;AACtC,+BAA8B;AAG9B,gBACE,GAAsB,EACtB,UAAiB,EACjB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,aAAa,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,UAAU,CAAC,GAAG,CAAC,EAAf,CAAe,CAAC,CAAA;oBACnD,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAU,EAAE,CAAA;oBACtB,WAAW,GAAa,EAAE,CAAA;oBAE9B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAC5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,CACJ,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE5B,YAAU,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG;4BAChC,OAAA,IAAI,mBAAQ,CACV,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CACzC;wBALD,CAKC,CACF,CAAA;wBAED,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,aAAa,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,SAAO,CAAC,CAAC,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;qBAClF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;yBACpB,EAAA;;;;CACF;AAtED,wBAsEC"}
\ No newline at end of file
diff --git a/build/mtcnn/stage3.d.ts b/build/mtcnn/stage3.d.ts
deleted file mode 100644
index 607d3047..00000000
--- a/build/mtcnn/stage3.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { BoundingBox, Box, Point } from 'tfjs-image-recognition-base';
-import { ONetParams } from './types';
-export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
- boxes: Box[];
- scores: number[];
- points: Point[][];
-}>;
diff --git a/build/mtcnn/stage3.js b/build/mtcnn/stage3.js
deleted file mode 100644
index 09dd0b17..00000000
--- a/build/mtcnn/stage3.js
+++ /dev/null
@@ -1,76 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tf = require("@tensorflow/tfjs-core");
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-var extractImagePatches_1 = require("./extractImagePatches");
-var MtcnnBox_1 = require("./MtcnnBox");
-var ONet_1 = require("./ONet");
-function stage3(img, inputBoxes, scoreThreshold, params, stats) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
- return tslib_1.__generator(this, function (_c) {
- switch (_c.label) {
- case 0:
- ts = Date.now();
- return [4 /*yield*/, extractImagePatches_1.extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
- case 1:
- onetInputs = _c.sent();
- stats.stage3_extractImagePatches = Date.now() - ts;
- ts = Date.now();
- onetOuts = onetInputs.map(function (onetInput) {
- var out = ONet_1.ONet(onetInput, params);
- onetInput.dispose();
- return out;
- });
- stats.stage3_onet = Date.now() - ts;
- scoresTensor = onetOuts.length > 1
- ? tf.concat(onetOuts.map(function (out) { return out.scores; }))
- : onetOuts[0].scores;
- _b = (_a = Array).from;
- return [4 /*yield*/, scoresTensor.data()];
- case 2:
- scores = _b.apply(_a, [_c.sent()]);
- scoresTensor.dispose();
- indices = scores
- .map(function (score, idx) { return ({ score: score, idx: idx }); })
- .filter(function (c) { return c.score > scoreThreshold; })
- .map(function (_a) {
- var idx = _a.idx;
- return idx;
- });
- filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
- filteredBoxes = indices
- .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
- filteredScores = indices.map(function (idx) { return scores[idx]; });
- finalBoxes = [];
- finalScores = [];
- points = [];
- if (filteredBoxes.length > 0) {
- ts = Date.now();
- indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);
- stats.stage3_nms = Date.now() - ts;
- finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
- finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
- points = indicesNms.map(function (idx, i) {
- return Array(5).fill(0).map(function (_, ptIdx) {
- return new tfjs_image_recognition_base_1.Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
- });
- });
- }
- onetOuts.forEach(function (t) {
- t.regions.dispose();
- t.scores.dispose();
- t.points.dispose();
- });
- return [2 /*return*/, {
- boxes: finalBoxes,
- scores: finalScores,
- points: points
- }];
- }
- });
- });
-}
-exports.stage3 = stage3;
-//# sourceMappingURL=stage3.js.map
\ No newline at end of file
diff --git a/build/mtcnn/stage3.js.map b/build/mtcnn/stage3.js.map
deleted file mode 100644
index c61a5807..00000000
--- a/build/mtcnn/stage3.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"stage3.js","sourceRoot":"","sources":["../../src/mtcnn/stage3.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAyF;AAEzF,6DAA4D;AAC5D,uCAAsC;AACtC,+BAA8B;AAG9B,gBACE,GAAsB,EACtB,UAAyB,EACzB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,eAAe,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,IAAI,mBAAQ,CACrD,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAChC,EAL0C,CAK1C,CAAC,CAAA;oBACI,aAAa,GAAG,OAAO;yBAC1B,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,UAAU,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAA7C,CAA6C,CAAC,CAAA;oBAC3D,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAU,EAAE,CAAA;oBACtB,WAAW,GAAa,EAAE,CAAA;oBAC1B,MAAM,GAAc,EAAE,CAAA;oBAE1B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAE5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,EACH,KAAK,CACN,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAElC,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,aAAa,CAAC,GAAG,CAAC,EAAlB,CAAkB,CAAC,CAAA;wBACtD,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,MAAM,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC;4BAC7B,OAAA,KAAK,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,KAAK;gCAC5B,OAAA,IAAI,mCAAK,CACP,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EACvF,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,GAAG,CAAC,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAC5F;4BAHD,CAGC,CACF;wBALD,CAKC,CACF,CAAA;qBACF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;wBAClB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;4BACnB,MAAM,QAAA;yBACP,EAAA;;;;CAEF;AAlFD,wBAkFC"}
\ No newline at end of file
diff --git a/build/mtcnn/types.d.ts b/build/mtcnn/types.d.ts
deleted file mode 100644
index d19f281b..00000000
--- a/build/mtcnn/types.d.ts
+++ /dev/null
@@ -1,47 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
-import { FaceDetection } from '../classes/FaceDetection';
-import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
-export declare type SharedParams = {
- conv1: ConvParams;
- prelu1_alpha: tf.Tensor1D;
- conv2: ConvParams;
- prelu2_alpha: tf.Tensor1D;
- conv3: ConvParams;
- prelu3_alpha: tf.Tensor1D;
-};
-export declare type PNetParams = SharedParams & {
- conv4_1: ConvParams;
- conv4_2: ConvParams;
-};
-export declare type RNetParams = SharedParams & {
- fc1: FCParams;
- prelu4_alpha: tf.Tensor1D;
- fc2_1: FCParams;
- fc2_2: FCParams;
-};
-export declare type ONetParams = SharedParams & {
- conv4: ConvParams;
- prelu4_alpha: tf.Tensor1D;
- fc1: FCParams;
- prelu5_alpha: tf.Tensor1D;
- fc2_1: FCParams;
- fc2_2: FCParams;
- fc2_3: FCParams;
-};
-export declare type NetParams = {
- pnet: PNetParams;
- rnet: RNetParams;
- onet: ONetParams;
-};
-export declare type MtcnnResult = {
- faceDetection: FaceDetection;
- faceLandmarks: FaceLandmarks5;
-};
-export declare type MtcnnForwardParams = {
- minFaceSize?: number;
- scaleFactor?: number;
- maxNumScales?: number;
- scoreThresholds?: number[];
- scaleSteps?: number[];
-};
diff --git a/build/mtcnn/types.js b/build/mtcnn/types.js
deleted file mode 100644
index 11e638d1..00000000
--- a/build/mtcnn/types.js
+++ /dev/null
@@ -1,3 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/mtcnn/types.js.map b/build/mtcnn/types.js.map
deleted file mode 100644
index 1b46c285..00000000
--- a/build/mtcnn/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/mtcnn/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/tinyYolov2/TinyYolov2.d.ts b/build/tinyYolov2/TinyYolov2.d.ts
deleted file mode 100644
index 6273e242..00000000
--- a/build/tinyYolov2/TinyYolov2.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import { Point, TNetInput } from 'tfjs-image-recognition-base';
-import { TinyYolov2 as TinyYolov2Base, TinyYolov2Types } from 'tfjs-tiny-yolov2';
-import { FaceDetection } from '../classes';
-export declare class TinyYolov2 extends TinyYolov2Base {
- constructor(withSeparableConvs?: boolean);
- readonly withSeparableConvs: boolean;
- readonly anchors: Point[];
- locateFaces(input: TNetInput, forwardParams: TinyYolov2Types.TinyYolov2ForwardParams): Promise;
- protected loadQuantizedParams(modelUri: string | undefined): any;
-}
diff --git a/build/tinyYolov2/TinyYolov2.js b/build/tinyYolov2/TinyYolov2.js
deleted file mode 100644
index d0ffa925..00000000
--- a/build/tinyYolov2/TinyYolov2.js
+++ /dev/null
@@ -1,62 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
-var classes_1 = require("../classes");
-var const_1 = require("./const");
-var TinyYolov2 = /** @class */ (function (_super) {
- tslib_1.__extends(TinyYolov2, _super);
- function TinyYolov2(withSeparableConvs) {
- if (withSeparableConvs === void 0) { withSeparableConvs = true; }
- var _this = this;
- var config = Object.assign({}, {
- withSeparableConvs: withSeparableConvs,
- iouThreshold: const_1.IOU_THRESHOLD,
- classes: ['face']
- }, withSeparableConvs
- ? {
- anchors: const_1.BOX_ANCHORS_SEPARABLE,
- meanRgb: const_1.MEAN_RGB_SEPARABLE
- }
- : {
- anchors: const_1.BOX_ANCHORS,
- withClassScores: true
- });
- _this = _super.call(this, config) || this;
- return _this;
- }
- Object.defineProperty(TinyYolov2.prototype, "withSeparableConvs", {
- get: function () {
- return this.config.withSeparableConvs;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2.prototype, "anchors", {
- get: function () {
- return this.config.anchors;
- },
- enumerable: true,
- configurable: true
- });
- TinyYolov2.prototype.locateFaces = function (input, forwardParams) {
- return tslib_1.__awaiter(this, void 0, void 0, function () {
- var objectDetections;
- return tslib_1.__generator(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
- case 1:
- objectDetections = _a.sent();
- return [2 /*return*/, objectDetections.map(function (det) { return new classes_1.FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
- }
- });
- });
- };
- TinyYolov2.prototype.loadQuantizedParams = function (modelUri) {
- var defaultModelName = this.withSeparableConvs ? const_1.DEFAULT_MODEL_NAME_SEPARABLE_CONV : const_1.DEFAULT_MODEL_NAME;
- return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
- };
- return TinyYolov2;
-}(tfjs_tiny_yolov2_1.TinyYolov2));
-exports.TinyYolov2 = TinyYolov2;
-//# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file
diff --git a/build/tinyYolov2/TinyYolov2.js.map b/build/tinyYolov2/TinyYolov2.js.map
deleted file mode 100644
index ed5bd570..00000000
--- a/build/tinyYolov2/TinyYolov2.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"TinyYolov2.js","sourceRoot":"","sources":["../../src/tinyYolov2/TinyYolov2.ts"],"names":[],"mappings":";;;AACA,qDAAiF;AAEjF,sCAA2C;AAC3C,iCAOiB;AAEjB;IAAgC,sCAAc;IAE5C,oBAAY,kBAAkC;QAAlC,mCAAA,EAAA,yBAAkC;QAA9C,iBAkBC;QAjBC,IAAM,MAAM,GAAG,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE;YAC/B,kBAAkB,oBAAA;YAClB,YAAY,EAAE,qBAAa;YAC3B,OAAO,EAAE,CAAC,MAAM,CAAC;SAClB,EACD,kBAAkB;YAChB,CAAC,CAAC;gBACA,OAAO,EAAE,6BAAqB;gBAC9B,OAAO,EAAE,0BAAkB;aAC5B;YACD,CAAC,CAAC;gBACA,OAAO,EAAE,mBAAW;gBACpB,eAAe,EAAE,IAAI;aACtB,CACF,CAAA;QAED,QAAA,kBAAM,MAAM,CAAC,SAAA;;IACf,CAAC;IAED,sBAAW,0CAAkB;aAA7B;YACE,OAAO,IAAI,CAAC,MAAM,CAAC,kBAAkB,CAAA;QACvC,CAAC;;;OAAA;IAED,sBAAW,+BAAO;aAAlB;YACE,OAAO,IAAI,CAAC,MAAM,CAAC,OAAO,CAAA;QAC5B,CAAC;;;OAAA;IAEY,gCAAW,GAAxB,UAAyB,KAAgB,EAAE,aAAsD;;;;;4BACtE,qBAAM,IAAI,CAAC,MAAM,CAAC,KAAK,EAAE,aAAa,CAAC,EAAA;;wBAA1D,gBAAgB,GAAG,SAAuC;wBAChE,sBAAO,gBAAgB,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,IAAI,uBAAa,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,WAAW,EAAE,EAAE,KAAK,EAAE,GAAG,CAAC,UAAU,EAAE,MAAM,EAAE,GAAG,CAAC,WAAW,EAAE,CAAC,EAAjG,CAAiG,CAAC,EAAA;;;;KACtI;IAES,wCAAmB,GAA7B,UAA8B,QAA4B;QACxD,IAAM,gBAAgB,GAAG,IAAI,CAAC,kBAAkB,CAAC,CAAC,CAAC,yCAAiC,CAAC,CAAC,CAAC,0BAAkB,CAAA;QACzG,OAAO,iBAAM,mBAAmB,YAAC,QAAQ,EAAE,gBAAgB,CAAQ,CAAA;IACrE,CAAC;IACH,iBAAC;AAAD,CAAC,AAvCD,CAAgC,6BAAc,GAuC7C;AAvCY,gCAAU"}
\ No newline at end of file
diff --git a/build/tinyYolov2/const.d.ts b/build/tinyYolov2/const.d.ts
deleted file mode 100644
index e64e2478..00000000
--- a/build/tinyYolov2/const.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { Point } from 'tfjs-image-recognition-base';
-export declare const IOU_THRESHOLD = 0.4;
-export declare const BOX_ANCHORS: Point[];
-export declare const BOX_ANCHORS_SEPARABLE: Point[];
-export declare const MEAN_RGB_SEPARABLE: [number, number, number];
-export declare const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-export declare const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
diff --git a/build/tinyYolov2/const.js b/build/tinyYolov2/const.js
deleted file mode 100644
index 72df0e30..00000000
--- a/build/tinyYolov2/const.js
+++ /dev/null
@@ -1,22 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
-exports.IOU_THRESHOLD = 0.4;
-exports.BOX_ANCHORS = [
- new tfjs_image_recognition_base_1.Point(0.738768, 0.874946),
- new tfjs_image_recognition_base_1.Point(2.42204, 2.65704),
- new tfjs_image_recognition_base_1.Point(4.30971, 7.04493),
- new tfjs_image_recognition_base_1.Point(10.246, 4.59428),
- new tfjs_image_recognition_base_1.Point(12.6868, 11.8741)
-];
-exports.BOX_ANCHORS_SEPARABLE = [
- new tfjs_image_recognition_base_1.Point(1.603231, 2.094468),
- new tfjs_image_recognition_base_1.Point(6.041143, 7.080126),
- new tfjs_image_recognition_base_1.Point(2.882459, 3.518061),
- new tfjs_image_recognition_base_1.Point(4.266906, 5.178857),
- new tfjs_image_recognition_base_1.Point(9.041765, 10.66308)
-];
-exports.MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-exports.DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
-exports.DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
-//# sourceMappingURL=const.js.map
\ No newline at end of file
diff --git a/build/tinyYolov2/const.js.map b/build/tinyYolov2/const.js.map
deleted file mode 100644
index 8affa9e1..00000000
--- a/build/tinyYolov2/const.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"const.js","sourceRoot":"","sources":["../../src/tinyYolov2/const.ts"],"names":[],"mappings":";;AAAA,2EAAoD;AAEvC,QAAA,aAAa,GAAG,GAAG,CAAA;AAEnB,QAAA,WAAW,GAAG;IACzB,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,mCAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,mCAAK,CAAC,MAAM,EAAE,OAAO,CAAC;IAC1B,IAAI,mCAAK,CAAC,OAAO,EAAE,OAAO,CAAC;CAC5B,CAAA;AAEY,QAAA,qBAAqB,GAAG;IACnC,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,mCAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;CAC9B,CAAA;AAEY,QAAA,kBAAkB,GAA6B,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;AAEzE,QAAA,kBAAkB,GAAG,mBAAmB,CAAA;AACxC,QAAA,iCAAiC,GAAG,kCAAkC,CAAA"}
\ No newline at end of file
diff --git a/build/tinyYolov2/index.d.ts b/build/tinyYolov2/index.d.ts
deleted file mode 100644
index 37d04441..00000000
--- a/build/tinyYolov2/index.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { TinyYolov2 } from './TinyYolov2';
-export * from './TinyYolov2';
-export declare function createTinyYolov2(weights: Float32Array, withSeparableConvs?: boolean): TinyYolov2;
diff --git a/build/tinyYolov2/index.js b/build/tinyYolov2/index.js
deleted file mode 100644
index 2a274368..00000000
--- a/build/tinyYolov2/index.js
+++ /dev/null
@@ -1,13 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-var tslib_1 = require("tslib");
-var TinyYolov2_1 = require("./TinyYolov2");
-tslib_1.__exportStar(require("./TinyYolov2"), exports);
-function createTinyYolov2(weights, withSeparableConvs) {
- if (withSeparableConvs === void 0) { withSeparableConvs = true; }
- var net = new TinyYolov2_1.TinyYolov2(withSeparableConvs);
- net.extractWeights(weights);
- return net;
-}
-exports.createTinyYolov2 = createTinyYolov2;
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/tinyYolov2/index.js.map b/build/tinyYolov2/index.js.map
deleted file mode 100644
index 31cc08dd..00000000
--- a/build/tinyYolov2/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/tinyYolov2/index.ts"],"names":[],"mappings":";;;AAAA,2CAA0C;AAE1C,uDAA6B;AAE7B,0BAAiC,OAAqB,EAAE,kBAAkC;IAAlC,mCAAA,EAAA,yBAAkC;IACxF,IAAM,GAAG,GAAG,IAAI,uBAAU,CAAC,kBAAkB,CAAC,CAAA;IAC9C,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,4CAIC"}
\ No newline at end of file
diff --git a/dist/face-api.js b/dist/face-api.js
index 79c1fcfc..a4373418 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -1,4861 +1,6568 @@
(function (global, factory) {
- typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
- typeof define === 'function' && define.amd ? define(['exports'], factory) :
- (factory((global.faceapi = global.faceapi || {})));
+ typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
+ typeof define === 'function' && define.amd ? define(['exports'], factory) :
+ (global = global || self, factory(global.faceapi = global.faceapi || {}));
}(this, (function (exports) { 'use strict';
- /**
- * @license
- * Copyright 2018 Google LLC. All Rights Reserved.
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- * =============================================================================
- */
- var extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t;}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n]);};function __extends(e,t){function n(){this.constructor=e;}extendStatics(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n);}var __assign=Object.assign||function(e){for(var t,n=1,r=arguments.length;n0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]0;)r=Math.random()*t|0,n=e[--t],e[t]=e[r],e[r]=n;}function clamp(e,t,n){return Math.max(e,Math.min(t,n))}function randUniform(e,t){return Math.random()*(t-e)+e}function distSquared(e,t){for(var n=0,r=0;r0,function(){return "Element arr["+n.join("][")+"] should be a primitive, but is an array of "+e.length+" elements"}),assert(e.length===t[0],function(){return "Element arr["+n.join("][")+"] should have "+t[0]+" elements, but has "+e.length+" elements"});for(var r=t.slice(1),o=0;o1;--t)if(e%t==0)return [t,e/t];return [1,e]}function createShuffledIndices(e){for(var t=new Uint32Array(e),n=0;n=n?o():setTimeout(i,s);}};i();})}function inferFromImplicitShape(e,t){for(var n=1,r=-1,o=0;o=0)n*=e[o];else if(-1===e[o]){if(-1!==r)throw Error("Shapes can only have 1 implicit size. Found -1 at dim "+r+" and dim "+o);r=o;}else if(e[o]<0)throw Error("Shapes can not be < 0. Found "+e[o]+" at dim "+o);if(-1===r){if(t>0&&t!==n)throw Error("Size("+t+") must match the product of shape "+e);return e}if(0===n)throw Error("Cannot infer the missing size in ["+e+"] when there are 0 elements");if(t%n!=0)throw Error("The implicit shape can't be a fractional number. Got "+t+" / "+n);var a=e.slice();return a[r]=t/n,a}function squeezeShape(e,t){for(var n=[],r=[],o=0,a=0;aa)&&1===e[a]&&(n.push(e[a]),r.push(a)),t[o]<=a&&o++;}1!==e[a]&&(n.push(e[a]),r.push(a));}return {newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else{if("bool"!==e)throw new Error("Unknown data type "+e);n=new Uint8Array(t);}return n}function checkComputationForNaN(e,t,n){if("float32"===t)for(var r=0;r=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){return noConversionNeeded(e,t)?e:(Array.isArray(e)&&(e=flatten(e)),copyTypedArray(e,t,n))}function noConversionNeeded(e,t){return e instanceof Float32Array&&"float32"===t||e instanceof Int32Array&&"int32"===t||e instanceof Uint8Array&&"bool"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r=0;a--){i=(m=e[a]).inputs;var h=[];h.push(m.output);for(l=0;l=0;n--){var r=t[n],o=e[r.output.id];if(null==r.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+r.name+".");var a=r.gradient(o);for(var i in r.inputs){if(!(i in a))throw new Error("Cannot backprop through input "+i+". Available gradients found: "+Object.keys(a)+".");var s=a[i](),u=r.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error("Error in gradient for op "+r.name+". The gradient of input '"+i+"' has shape '"+s.shape+"', which does not match the shape of the input '"+u.shape+"'");if(null==e[u.id])e[u.id]=s;else{var c=e[u.id];e[u.id]=c.add(s),c.dispose();}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,o),i=t.length,s=subTensorToString(e,t,o,a),u=["Tensor"];return r&&(u.push(" dtype: "+n),u.push(" rank: "+i),u.push(" shape: ["+t+"]"),u.push(" values:")),u.push(s.map(function(e){return " "+e}).join("\n")),u.join("\n")}function computeMaxSizePerColumn(e,t,n){var r=sizeFromShape(t),o=n[n.length-1],a=new Array(o).fill(0);if(t.length>1)for(var i=0;iFORMAT_LIMIT_NUM_VALS){var s=Array.from(e.subarray(0,FORMAT_NUM_FIRST_LAST_VALS)),u=Array.from(e.subarray(a-FORMAT_NUM_FIRST_LAST_VALS,a));return ["["+s.map(function(e,t){return valToString(e,r[t])}).join(", ")+", ..., "+u.map(function(e,t){return valToString(e,r[a-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(", ")+"]"]}return ["["+Array.from(e).map(function(e,t){return valToString(e,r[t])}).join(", ")+"]"]}var c=t.slice(1),l=n.slice(1),p=n[0],d=[];if(a>FORMAT_LIMIT_NUM_VALS){for(var h=0;h {...}) to avoid memory leaks.");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:"unnamed scope"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n;},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return r.add(e.id)});for(var a=0;a0,"gradients() received an empty list of xs."),this.tidy("gradients",function(){var a=e();assert(a instanceof Tensor,"The result y returned by f() must be a tensor.");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");var s={};return s[a.id]=null==n?ones(a.shape):n,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),"The f passed in customGrad(f) must be a function."),function(){for(var n,r,o=[],a=0;a {op();...}); to avoid memory leaks.");return this.activeScope.track.push(e),e},e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),"float32");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]="NUMBER",e[e.BOOLEAN=1]="BOOLEAN",e[e.STRING=2]="STRING";}(Type||(Type={}));var URL_PROPERTIES=[{name:"DEBUG",type:Type.BOOLEAN},{name:"IS_BROWSER",type:Type.BOOLEAN},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",type:Type.NUMBER},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",type:Type.BOOLEAN},{name:"WEBGL_VERSION",type:Type.NUMBER},{name:"WEBGL_RENDER_FLOAT32_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DOWNLOAD_FLOAT_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_FENCE_API_ENABLED",type:Type.BOOLEAN},{name:"BACKEND",type:Type.STRING},{name:"EPSILON",type:Type.NUMBER}];function isWebGLVersionEnabled(e,t){var n;try{n=getWebGLRenderingContext(e,t);}catch(e){return !1}return null!=n&&(loseContext(n),!0)}function getWebGLDisjointQueryTimerVersion(e,t){if(0===e)return 0;var n,r=getWebGLRenderingContext(e,t);return n=hasExtension(r,"EXT_disjoint_timer_query_webgl2")&&2===e?2:hasExtension(r,"EXT_disjoint_timer_query")?1:0,null!=r&&loseContext(r),n}function isRenderToFloatTextureEnabled(e,t){if(0===e)return !1;var n=getWebGLRenderingContext(e,t);if(1===e){if(!hasExtension(n,"OES_texture_float"))return !1}else if(!hasExtension(n,"EXT_color_buffer_float"))return !1;var r=createFloatTextureAndBindToFramebuffer(n,e);return loseContext(n),r}function isDownloadFloatTextureEnabled(e,t){if(0===e)return !1;var n=getWebGLRenderingContext(e,t);if(1===e){if(!hasExtension(n,"OES_texture_float"))return !1;if(!hasExtension(n,"WEBGL_color_buffer_float"))return !1}else if(!hasExtension(n,"EXT_color_buffer_float"))return !1;var r=createFloatTextureAndBindToFramebuffer(n,e);return loseContext(n),r}function isWebGLFenceEnabled(e,t){if(2!==e)return !1;var n=getWebGLRenderingContext(e,t),r=null!=n.fenceSync;return loseContext(n),r}function isChrome(){return "undefined"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX="tfjsflags";function getFeaturesFromURL(){var e={};if("undefined"==typeof window||void 0===window.location)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(function(e){var t=e.split(":"),r=t[0],o=t[1];n[r]=o;}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log("Setting feature override from URL "+t.name+": "+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]="true"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn("Unknown URL param: "+t.name+"."));});}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function getWebGLRenderingContext(e,t){if(0===e||!t)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");var n=document.createElement("canvas");return 1===e?n.getContext("webgl")||n.getContext("experimental-webgl"):n.getContext("webgl2")}function loseContext(e){if(null!=e){var t=e.getExtension("WEBGL_lose_context");if(null==t)throw new Error("Extension WEBGL_lose_context not supported on this browser.");t.loseContext();}}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r0?0:getWebGLDisjointQueryTimerVersion(t,this.get("IS_BROWSER"))}if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE"===e)return this.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!isMobile();if("HAS_WEBGL"===e)return this.get("WEBGL_VERSION")>0;if("WEBGL_VERSION"===e)return isWebGLVersionEnabled(2,this.get("IS_BROWSER"))?2:isWebGLVersionEnabled(1,this.get("IS_BROWSER"))?1:0;if("WEBGL_RENDER_FLOAT32_ENABLED"===e)return isRenderToFloatTextureEnabled(this.get("WEBGL_VERSION"),this.get("IS_BROWSER"));if("WEBGL_DOWNLOAD_FLOAT_ENABLED"===e)return isDownloadFloatTextureEnabled(this.get("WEBGL_VERSION"),this.get("IS_BROWSER"));if("WEBGL_FENCE_API_ENABLED"===e)return isWebGLFenceEnabled(this.get("WEBGL_VERSION"),this.get("IS_BROWSER"));if("TEST_EPSILON"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if("EPSILON"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;throw new Error("Unknown feature "+e+".")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e);},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null);},e.prototype.initBackend=function(e,t){var n=this;void 0===t&&(t=!1),this.backendName=e;var r=this.findBackend(e);this.globalEngine=new Engine(r,t,function(){return n.get("DEBUG")});},Object.defineProperty(e.prototype,"backend",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,n,r){var o=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+" backend was already registered. Reusing existing backend"),null!=r&&r(function(){return o.engine}),!1;try{var a=t();return this.registry[e]={backend:a,priority:n},!0}catch(t){return console.warn("Registration of backend "+e+" failed"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+" backend not found in registry");this.registry[e].backend.dispose(),delete this.registry[e];},Object.defineProperty(e.prototype,"engine",{get:function(){return this.initDefaultBackend(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initDefaultBackend=function(){null==this.globalEngine&&this.initBackend(this.get("BACKEND"),!1);},e}();function getGlobalNamespace(){var e;if("undefined"!=typeof window)e=window;else{if("undefined"==typeof process)throw new Error("Could not find a global object");e=process;}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),"The f passed in grad(f) must be a function"),function(t,n){return assert(t instanceof Tensor,"The x passed in grad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in grad(f)(x, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),"The f passed in grads(f) must be a function"),function(t,n){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in grads(f)(args) must be an array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in grads(f)(args, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),"The f passed in valueAndGrad(f) must be a function"),function(t,n){assert(t instanceof Tensor,"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),"The f passed in valueAndGrads(f) must be a function"),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),"The f passed in variableGrads(f) must be a function"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),"The varList passed in variableGrads(f, varList) must be an array of variables"),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,"variableGrads() expects at least one of the input variables to be trainable, but none of the "+r+" variables is trainable.");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(0===a.rank,"The f passed in variableGrads(f) must return a scalar, but it returned a rank-"+a.rank+" tensor");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t]);}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n the f you passed encloses all operations that lead from x to y.")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time;function warn(){for(var e=[],t=0;t=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i);}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a=-n&&et||n===e){r=!0;break}n=nearestDivisor(e,n+1);}return n}function computeOutShape(e,t,n){for(var r=[],o=e.length,a=0;a0?o>=l[t]:o<=l[t]);o+=r[t])n+=1;return n}),[c,h,p]}function startForAxis(e,t,n,r,o){var a=t[o];e&1<0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,n,r,o){var a=t[o];e&1<0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=n[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function convertToTensor(e,t,n,r){if(void 0===r&&(r="float32"),r=r||"float32",e instanceof Tensor)return e;if(!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e)throw new Error("Argument '"+t+"' passed to '"+n+"' must be a Tensor or TensorLike, but got "+e.constructor.name);var o=inferShape(e);return isTypedArray(e)||Array.isArray(e)||(e=[e]),Tensor.make(o,{values:toTypedArray(e,r,ENV.get("DEBUG"))},r)}function convertToTensorArray(e,t,n){if(!Array.isArray(e))throw new Error("Argument "+t+" passed to "+n+" must be a `Tensor[]` or `TensorLike[]`");return e.map(function(e,r){return convertToTensor(e,t+"["+r+"]",n)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error("Please provide an object with a single key (operation name) mapping to a function. Got an object with "+t.length+" keys.");var n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);to}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s=0;--d){if(intersectionOverUnion(e,l,i[d])>=r){p=!0;break}}if(!p&&(i.push(l),i.length>=n))break}return tensor1d(i,"int32")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),c=Math.min(o[0],o[2]),l=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-c)*(d-l);if(h<=0||f<=0)return 0;var m=Math.max(a,c),g=Math.max(i,l),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],c=getTypedArrayFromDType(n,s*r),l=getTypedArrayFromDType("int32",s*r),p=0;p":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * "+r+";\n\n int bestIndex = 0;\n float bestValue = getA(batch, inOffset);\n\n for (int i = 0; i < "+r+"; i++) {\n int inIdx = "+u+";\n float candidate = getA(batch, inIdx);\n if (candidate "+s+" bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n ";}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=1/(t*n);this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n const float avgMultiplier = float("+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+t+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n ";}}();function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o1&&1===i&&r.unshift(a);}return r}function getReductionAxes(e,t){for(var n=[],r=0;r1)&&n.unshift(a);}return n}function broadcastDimsAreOuter(e){for(var t=0;t=0&&n= "+e.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+t+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+t+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * "+a+" + dm;\n\n float dotProd = 0.0;\n\n // TODO: Vec4 over the batch size\n for (int b = 0; b < "+e.batchSize+"; b++) {\n for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n int xR = wR + yR * "+t+" - "+r+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < "+t+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+t+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n // TODO: Vec4 over the channelMul\n for (int dm = 0; dm < "+s+"; dm++) {\n int d2 = d1 * "+s+" + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),Conv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,c=4*Math.floor(e.inChannels/4),l=e.inChannels%4;this.userCode="\n const ivec2 strides = ivec2("+r+", "+o+");\n const ivec2 pads = ivec2("+t+", "+n+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+s+"; wR++) {\n int xR = xRCorner + wR * "+a+";\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+u+"; wC++) {\n int xC = xCCorner + wC * "+i+";\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+c+"; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if ("+(1===l)+") {\n dotProd +=\n getX(batch, xR, xC, "+c+") *\n getW(wR, wC, "+c+", d2);\n } else if ("+(2===l)+") {\n vec2 xValues = vec2(\n getX(batch, xR, xC, "+c+"),\n getX(batch, xR, xC, "+c+" + 1)\n );\n vec2 wValues = vec2(\n getW(wR, wC, "+c+", d2),\n getW(wR, wC, "+c+" + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if ("+(3===l)+") {\n vec3 xValues = vec3(\n getX(batch, xR, xC, "+c+"),\n getX(batch, xR, xC, "+c+" + 1),\n getX(batch, xR, xC, "+c+" + 2)\n );\n vec3 wValues = vec3(\n getW(wR, wC, "+c+", d2),\n getW(wR, wC, "+c+" + 1, d2),\n getW(wR, wC, "+c+" + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n setOutput(dotProd);\n }\n ";}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode="\n const ivec2 strides = ivec2("+a+", "+i+");\n const ivec2 pads = ivec2("+r+", "+o+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / "+p+";\n int q = d2 - d1 * "+p+";\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < "+c+"; wR++) {\n int xR = xRCorner + wR * "+s+";\n\n if (xR < 0 || xR >= "+t+") {\n continue;\n }\n\n for (int wC = 0; wC < "+l+"; wC++) {\n int xC = xCCorner + wC * "+u+";\n\n if (xC < 0 || xC >= "+n+") {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n setOutput(dotProd);\n }\n ";}}();function makeShader(e,t,n,r){var o=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?"uniform float "+e.name+(t>1?"["+t+"]":"")+";":"uniform sampler2D "+e.name+";"});o=o.join("\n");var a=e.map(function(e){return getInputSamplingSnippet(e,t,r)}).join("\n"),i=t.texShape,s=getOutputSamplingSnippet(t.logicalShape,i);return [SHADER_PREFIX,FLOAT_TEXTURE_SAMPLE_SNIPPET,FLOAT_TEXTURE_SETOUTPUT_SNIPPET,o,s,a,n].join("\n")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+"-D input sampling is not yet supported")}}function getInputSamplingSnippet(e,t,n){var r=getSamplerFlat(e);return r+=getSamplerFromInInfo(e),(n||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(r+=getSamplerAtOutputCoords(e,t,n)),r}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+"-D output sampling is not yet supported")}}var SAMPLE_1D_SNIPPET="\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_2D_SNIPPET="\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n int index = row * numC + col;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_3D_SNIPPET="\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n int stride1, int row, int col, int depth) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * stride0 + col * stride1 + depth;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_4D_SNIPPET="\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n int stride1, int stride2, int row, int col, int depth,\n int depth2) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_5D_SNIPPET="\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n int stride1, int stride2, int stride3, int row, int col, int depth,\n int depth2, int depth3) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * stride0 + col * stride1 +\n depth * stride2 + depth2 * stride3 + depth3;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_6D_SNIPPET="\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n int stride1, int stride2, int stride3, int stride4,\n int row, int col, int depth, int depth2, int depth3, int depth4) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n stride3 + depth3 * stride4 + depth4;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",FLOAT_TEXTURE_SAMPLE_SNIPPET="\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return texture2D(textureSampler, uv).r;\n }\n",FLOAT_TEXTURE_SETOUTPUT_SNIPPET="\n void setOutput(float val) {\n gl_FragColor = vec4(val, 0, 0, 0);\n }\n",SHADER_PREFIX="\n precision highp float;\n precision highp int;\n varying vec2 resultUV;\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n bool isNaN(float val) {\n return (val < 0.0 || 0.0 < val || val == 0.0) ? false : true;\n }\n\n bool hasNaN(vec4 values) {\n vec4 v1 = values * values;\n vec4 v2 = values * values;\n return any(notEqual(v1, v2));\n }\n\n float getNaN(vec4 values) {\n return dot(vec4(1), values);\n }\n\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n "+SAMPLE_1D_SNIPPET+"\n "+SAMPLE_2D_SNIPPET+"\n "+SAMPLE_3D_SNIPPET+"\n "+SAMPLE_4D_SNIPPET+"\n "+SAMPLE_5D_SNIPPET+"\n "+SAMPLE_6D_SNIPPET+"\n";function getOutputScalarCoords(){return "\n int getOutputCoords() {\n return 0;\n }\n "}function getOutput1DCoords(e,t){return 1===t[0]?"\n int getOutputCoords() {\n return int(resultUV.x * "+t[1]+".0);\n }\n ":1===t[1]?"\n int getOutputCoords() {\n return int(resultUV.y * "+t[0]+".0);\n }\n ":"\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n return resTexRC.x * "+t[1]+" + resTexRC.y;\n }\n "}function getOutput3DCoords(e,t){var n=e[1]*e[2],r=e[2];return "\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n int r = index / "+n+";\n index -= r * "+n+";\n int c = index / "+r+";\n int d = index - c * "+r+";\n return ivec3(r, c, d);\n }\n "}function getOutput4DCoords(e,t){var n=e[3],r=e[2]*n,o=e[1]*r;return "\n ivec4 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n int r = index / "+o+";\n index -= r * "+o+";\n\n int c = index / "+r+";\n index -= c * "+r+";\n\n int d = index / "+n+";\n int d2 = index - d * "+n+";\n\n return ivec4(r, c, d, d2);\n }\n "}function getOutput5DCoords(e,t){var n=e[4],r=e[3]*n,o=e[2]*r,a=e[1]*o;return "\n ivec5 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2("+t[0]+",\n "+t[1]+"));\n\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n int r = index / "+a+";\n index -= r * "+a+";\n\n int c = index / "+o+";\n index -= c * "+o+";\n\n int d = index / "+r+";\n index -= d * "+r+";\n\n int d2 = index / "+n+";\n int d3 = index - d2 * "+n+";\n\n ivec5 outShape = ivec5(r, c, d, d2, d3);\n return outShape;\n }\n "}function getOutput6DCoords(e,t){var n=e[5],r=e[4]*n,o=e[3]*r,a=e[2]*o,i=e[1]*a;return "\n ivec6 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n int r = index / "+i+";\n index -= r * "+i+";\n\n int c = index / "+a+";\n index -= c * "+a+";\n\n int d = index / "+o+";\n index -= d * "+o+";\n\n int d2 = index / "+r+";\n index -= d2 * "+r+";\n\n int d3 = index / "+n+";\n int d4 = index - d3 * "+n+";\n\n ivec6 result = ivec6(r, c, d, d2, d3, d4);\n return result;\n }\n "}function getOutput2DCoords(e,t){return arraysEqual(e,t)?"\n ivec2 getOutputCoords() {\n return ivec2(resultUV.yx * vec2("+t[0]+", "+t[1]+"));\n }\n ":1===e[1]?"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n return ivec2(index, 0);\n }\n ":1===e[0]?"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n return ivec2(0, index);\n }\n ":"\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+t[0]+", "+t[1]+"));\n int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n int r = index / "+e[1]+";\n int c = index - r * "+e[1]+";\n return ivec2(r, c);\n }\n "}function getSamplerScalar(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?"float "+n+"() {return "+t+";}":"\n float "+n+"() {\n return sampleTexture("+t+", halfCR);\n }\n "}function getSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return "\n float "+n+"(int index) {\n return "+n+"Flat(index);\n }\n "}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return "\n float "+r+"(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+a+".0);\n return sampleTexture("+n+", uv);\n }\n "}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,c=s;if(c.length=1?"coords = 0;":s.map(function(e){return "coords["+(e+u)+"] = 0;"}).join("\n"))+"\n return get"+n+"("+(a<2&&o>0?"coords":e.shapeInfo.logicalShape.map(function(e,t){return "coords["+(t+u)+"]"}).join(", "))+");\n }\n "}function getSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a="get"+o+"AtOutCoords",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,c=n&&(u>s||i.length>0),l=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(c&&!l)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h="";c&&l&&(h="\n int mainPart = index / "+d+";\n index -= mainPart * "+d+";\n ");var f=t.texShape;if(p)return 1===d?"float "+a+"() {return "+r+";}":"\n float "+a+"() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+f[0]+", "+f[1]+"));\n int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n "+h+"\n return get"+o+"Flat(index);\n }\n ";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?"\n float "+a+"() {\n return sampleTexture("+r+", resultUV);\n }\n ":"\n float "+a+"() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+f[0]+", "+f[1]+"));\n int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n "+h+"\n int texR = index / "+m[1]+";\n int texC = index - texR * "+m[1]+";\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2("+m[1]+".0, "+m[0]+".0);\n\n return sampleTexture("+r+", uv);\n }\n "}function getCoordsDataType(e){if(e<=1)return "int";if(2===e)return "ivec2";if(3===e)return "ivec3";if(4===e)return "ivec4";if(5===e)return "ivec5";if(6===e)return "ivec6";throw Error("GPU for rank "+e+" is not yet supported")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(", ")}var CumSumProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?"<":">";this.userCode="\n int getIndex(int i) {\n "+(n?"return "+o+" -i - 1;":"return i;")+"\n }\n\n void main() {\n "+getCoordsDataType(r)+" coords = getOutputCoords();\n int end = "+getFinalCoord(r,"coords")+";\n float val = 0.0;\n for (int i = "+o+" - 1; i >= 0; i -= 1) {\n int idx = getIndex(i);\n if (idx "+a+" end) {\n continue;\n }\n if (idx == end && "+t+") {\n continue;\n }\n "+getFinalCoord(r,"coords")+" = idx;\n val += getX("+getCoords(r,"coords")+");\n }\n setOutput(val);\n }\n ";}}();function getCoords(e,t){if(1===e)return ""+t;if(2===e)return t+".x, "+t+".y";if(3===e)return t+".x, "+t+".y, "+t+".z";if(4===e)return t+".x, "+t+".y, "+t+".z, "+t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}function getFinalCoord(e,t){if(1===e)return ""+t;if(2===e)return t+".y";if(3===e)return t+".z";if(4===e)return t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}var TextureUsage,PhysicalTextureType,EncodeFloatProgram=function(){return function(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isNaN(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n\n void main() {\n float x = getAAtOutCoords();\n gl_FragColor = encode_float(x);\n }\n ";}}(),FromPixelsProgram=function(){return function(e){this.variableNames=["A"];var t=e[0],n=e[1];this.outputShape=e,this.userCode="\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+t+".0);\n\n vec4 values = texture2D(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n ";}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=["A","indices"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,n);this.userCode="\n void main() {\n "+o+" resRC = getOutputCoords();\n setOutput(getA("+a+"));\n }\n ";}}();function getSourceCoords(e,t){var n=e.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return "int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],a=0;a= "+r);for(var o=0,a=0;a= "+r);for(var o=0,a=0;a= "+o);for(var a=getPackedMatrixTextureShapeWidthHeight(t,n),i=a[0],s=a[1],u=n%2==1,c=t%2==1,l=Math.floor(n/2),p=Math.floor(t/2),d=u?4:0,h=n,f=0,m=0;m= "+o);for(var a=n%2==1,i=t%2==1,s=Math.floor(n/2),u=Math.floor(t/2),c=getPackedMatrixTextureShapeWidthHeight(t,n),l=c[0],p=c[1],d=a?4:0,h=n+(a?1:0),f=0,m=0,g=n,v=0;vr||n>r){o="["+t+"x"+n+"]";throw new Error("Requested texture size "+o+" greater than WebGL maximum on this browser / GPU "+("["+r+"x"+r+"]")+".")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i){var s=e.getAttribLocation(t,n);return -1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)});}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function getProgramUniformLocationOrThrow(e,t,n){return throwIfNull(e,function(){return e.getUniformLocation(t,n)},'uniform "'+n+'" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o){callAndCheck(e,function(){return bindTextureUnit(e,n,o)}),callAndCheck(e,function(){return e.uniform1i(r,o)});}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)});}function bindColorTextureToFramebuffer(e,t,n){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)});}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)});}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return "FRAMEBUFFER_UNSUPPORTED";default:return "unknown error "+t}}function throwIfNull(e,t,n){var r=callAndCheck(e,function(){return t()});if(null==r)throw new Error(n);return r}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(rn)throw new Error("textureUnit must be in "+("[gl.TEXTURE0, gl.TEXTURE"+n+"]")+".")}function getTextureShapeFromLogicalShape(e,t){2!==t.length&&(t=squeezeShape(t).newShape);var n=queryMaxTextureSize(e),r=sizeFromShape(t);return t.length<=1&&r<=n?[r,1]:2===t.length&&t[0]<=n&&t[1]<=n?t:3===t.length&&t[0]<=n&&t[1]*t[2]<=n?[t[0],t[1]*t[2]]:4===t.length&&t[0]<=n&&t[1]*t[2]*t[3]<=n?[t[0],t[1]*t[2]*t[3]]:sizeToSquarishShape(r)}var webgl_util=Object.freeze({createWebGLRenderingContext:createWebGLRenderingContext,createWebGLRenderingContextFromCanvas:createWebGLRenderingContextFromCanvas,callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,queryMaxTextureSize:queryMaxTextureSize,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape});function getWebGLContextAttributes(){return {alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0}}function createWebGLContext(e){var t,n=getWebGLContextAttributes();return callAndCheck(t=null!=e?createWebGLRenderingContextFromCanvas(e,n):createWebGLRenderingContext(n),function(){return t.disable(t.DEPTH_TEST)}),callAndCheck(t,function(){return t.disable(t.STENCIL_TEST)}),callAndCheck(t,function(){return t.disable(t.BLEND)}),callAndCheck(t,function(){return t.disable(t.DITHER)}),callAndCheck(t,function(){return t.disable(t.POLYGON_OFFSET_FILL)}),callAndCheck(t,function(){return t.disable(t.SAMPLE_COVERAGE)}),callAndCheck(t,function(){return t.enable(t.SCISSOR_TEST)}),callAndCheck(t,function(){return t.enable(t.CULL_FACE)}),callAndCheck(t,function(){return t.cullFace(t.BACK)}),t}function createVertexShader$1(e){return createVertexShader(e,"\n precision highp float;\n attribute vec3 clipSpacePos;\n attribute vec2 uv;\n varying vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,c=e;return 2===ENV.get("WEBGL_VERSION")?(n=c.R32F,r=c.R16F,o=c.RGBA32F,a=c.RED,i=4,s=1,u=c.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=c.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,n,r,o,a){validateTextureSize(e,t,n);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,r,t,n,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,r.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function bindVertexProgramAttributeStreams(e,t,n){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),bindVertexBufferToProgramAttribute(e,t,"clipSpacePos",n,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,"uv",n,2,20,12)}function uploadPixelDataToTexture(e,t,n){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadDataToTexture(e,t,n,r,o,a){validateTextureSize(e,n,r),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadMatrixToTexture(e,t,n,r,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(n,r),c=u[0],l=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,c,l,s,i.textureFormatFloat);}function uploadMatrixToPackedTexture(e,t,n,r,o,a){var i=getPackedMatrixTextureShapeWidthHeight(n,r),s=i[0],u=i[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(n,r));encodeMatrixToPackedRGBA(o,n,r,c),uploadDataToTexture(e,t,s,u,c,e.RGBA);}function maybeCreateBufferFromOutputTexture(e,t,n,r,o){var a=t;if(2===ENV.get("WEBGL_VERSION")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,r,n,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s;}return a}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*n,r.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromUnpackedArray(s,u,r.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*n,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadMatrixFromPackedOutputTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(t,n));callAndCheck(e,function(){return e.readPixels(0,0,a,i,e.RGBA,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromPackedRGBA(s,t,n,u)}var gpgpu_util=Object.freeze({getWebGLContextAttributes:getWebGLContextAttributes,createWebGLContext:createWebGLContext,createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:createWebGLContext(),1===ENV.get("WEBGL_VERSION")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_float"),this.colorBufferFloatExtension=this.gl.getExtension("WEBGL_color_buffer_float"),ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_half_float"),this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,"EXT_color_buffer_float"),this.loseContextExtension=getExtensionOrThrow(this.gl,"WEBGL_lose_context"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension);}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.vertexBuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.loseContextExtension.loseContext(),this.disposed=!0;}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e);},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t);},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)});},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,n,r,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get("WEBGL_FENCE_API_ENABLED")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a;}else ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return !0};return {query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,e),r=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,r)}),callAndCheck(t,function(){return t.attachShader(o,n)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)});},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)});},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,n);},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t);},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a);},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t);},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl);},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)});},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()});},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT);}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){switch(n.label){case 0:return [4,repeatedTry(function(){return t.isQueryAvailable(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return !0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()});})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0, this.itemsToPoll[t].resolveFn)();}this.itemsToPoll=this.itemsToPoll.slice(e+1);},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length});},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl);},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer);},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,function(){return r.scissor(0,0,t,n)});},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,n,r)});},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},e}();function binSearchLastTrue(e){for(var t=0,n=e.length-1,r=-1;t<=n;){var o=t+n>>1;e[o]()?(r=o,t=o+1):n=o-1;}return r}function compileProgram(e,t,n,r){for(var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.tensor.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform};return {name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.tensor.shape,texShape:r.texData.texShape,isUniform:!1},u=makeShader(a,s,o,!0===t.supportsBroadcasting),c=e.createProgram(u),l={},p=0;p= 0 && idx <= "+s+") {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * "+a+";\n setOutput(val);\n }\n ";}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < "+this.depth+"; ++d) {\n int depthBegin = int(max(0.0, float(d - "+t+")));\n int depthEnd = int(min(float("+this.depth+"),\n float(d + "+t+" + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = "+this.depth+";\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float("+r+") * norm + float("+n+");\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float("+r+")\n * float("+o+")\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * "+o+");\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n ";}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=t*n-1;this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+t+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = "+s+" - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * "+n+" + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n ";}}(),MatMulProgram=function(){return function(e,t,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=n?e[1]:e[0],a=r?t[0]:t[1],i=n?e[0]:e[1];this.outputShape=[o,a];var s=function(e,t){return n?t+" + "+e+", aRow":"aRow, "+t+" + "+e},u=function(e,t){return r?"bCol, "+t+" + "+e:t+" + "+e+", bCol"},c=4*Math.floor(i/4),l=i%4;this.userCode=" float dotARowBCol(int aRow, int bCol) {\n float result = 0.0;\n for (int i = 0; i < "+c+"; i += 4) {\n vec4 a = vec4(\n getMatrixA("+s(0,"i")+"),\n getMatrixA("+s(1,"i")+"),\n getMatrixA("+s(2,"i")+"),\n getMatrixA("+s(3,"i")+")\n );\n vec4 b = vec4(\n getMatrixB("+u(0,"i")+"),\n getMatrixB("+u(1,"i")+"),\n getMatrixB("+u(2,"i")+"),\n getMatrixB("+u(3,"i")+")\n );\n\n result += dot(a, b);\n }\n\n if ("+(1===l)+") {\n result += getMatrixA("+s(0,c)+") *\n getMatrixB("+u(0,c)+");\n } else if ("+(2===l)+") {\n vec2 a = vec2(\n getMatrixA("+s(0,c)+"),\n getMatrixA("+s(1,c)+")\n );\n vec2 b = vec2(\n getMatrixB("+u(0,c)+"),\n getMatrixB("+u(1,c)+")\n );\n result += dot(a, b);\n } else if ("+(3===l)+") {\n vec3 a = vec3(\n getMatrixA("+s(0,c)+"),\n getMatrixA("+s(1,c)+"),\n getMatrixA("+s(2,c)+")\n );\n vec3 b = vec3(\n getMatrixB("+u(0,c)+"),\n getMatrixB("+u(1,c)+"),\n getMatrixB("+u(2,c)+")\n );\n result += dot(a, b);\n }\n\n return result;\n }\n\n void main() {\n ivec2 resRC = getOutputCoords();\n setOutput(dotARowBCol(resRC.x, resRC.y));\n }\n ";}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode="\n uniform float seed;\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < "+(t-1)+"; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float("+(t-1)+"));\n }\n ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(t.seedLoc,e);}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float("+r+"), float("+n+"),\n float(index == coords.y)));\n }\n ";}}(),PadProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(","),i=t.map(function(t,n){return t[0]+e[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n "+o+" start = "+o+"("+a+");\n "+o+" end = "+o+"("+i+");\n\n void main() {\n "+o+" outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(float("+n+"));\n } else {\n "+o+" coords = outC - start;\n setOutput(getX("+s+"));\n }\n }\n ":"\n int start = "+a+";\n int end = "+i+";\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(float("+n+"));\n } else {\n setOutput(getX(outC - start));\n }\n }\n ";}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");var r=e.filterHeight,o=e.filterWidth,a=e.strideHeight,i=e.strideWidth,s=e.padInfo.top,u=e.padInfo.left;this.outputShape=e.outShape;var c="avg"===t,l="0.0";if(c||(l="-1.0 / 0.0"),n)this.userCode="\n const ivec2 strides = ivec2("+a+", "+i+");\n const ivec2 pads = ivec2("+s+", "+u+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < "+r+"; wR++) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+o+"; wC++) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= "+e.inWidth+") {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = wR * "+o+" + wC;\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n ";else{var p=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(p="avgValue / count");var d=4*Math.floor(o/4),h=o%4,f="\n if ("+c+") {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n ";this.userCode="\n const ivec2 strides = ivec2("+a+", "+i+");\n const ivec2 pads = ivec2("+s+", "+u+");\n const float initializationValue = "+l+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= "+e.inWidth+") {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4("+l+");\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < "+r+"; wR++) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+e.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+d+"; wC += 4) {\n int xC = xCCorner + wC;\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + 1, d),\n getValue(batch, xR, xC + 2, d),\n getValue(batch, xR, xC + 3, d)\n );\n\n "+f+"\n }\n\n int xC = xCCorner + "+d+";\n if ("+(1===h)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+f+"\n } else if ("+(2===h)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + 1, d),\n initializationValue,\n initializationValue\n );\n\n "+f+"\n } else if ("+(3===h)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + 1, d),\n getValue(batch, xR, xC + 2, d),\n initializationValue\n );\n\n "+f+"\n }\n }\n setOutput("+p+");\n }\n ";}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"min"===t?(i="1.0 / 0.0",s="min"):"max"===t&&(i="-1.0 / 0.0",s="max");var u=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===t?u="sumValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");var c=4*Math.floor(n/4),l=n%4,p="\n if ("+("sum"===t)+") {\n sumValue += dot(values, ones);\n } else {\n minMaxValue = "+s+"(values, minMaxValue);\n }\n ",d="vec4";"all"===t?(i="1.0",p="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",d="bvec4"):"any"===t&&(i="0.0",p="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",d="bvec4");var h="";o%n>0&&(h="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n "),this.userCode="\n const float initializationValue = "+i+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n "+h+"\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * "+n+";\n\n vec4 minMaxValue = vec4("+i+");\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < "+c+"; i += 4) {\n int inIdx = inOffset + i;\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n "+p+"\n }\n\n int inIdx = inOffset + "+c+";\n if ("+(1===l)+") {\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+p+"\n } else if ("+(2===l)+") {\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n "+p+"\n } else if ("+(3===l)+") {\n "+d+" values = "+d+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n "+p+"\n }\n setOutput("+u+");\n }\n ";}}(),ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=c[0]/l[0],d=c[1]/l[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+p+");\n const float widthScale = float("+d+");\n\n const float invHeightScale = float("+h+");\n const float invWidthScale = float("+f+");\n\n const int winHeight = int("+m+");\n const int winWidth = int("+g+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n ";}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n ";}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=c[0]/l[0],d=c[1]/l[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+p+");\n const float widthScale = float("+d+");\n\n const float invHeightScale = float("+h+");\n const float invWidthScale = float("+f+");\n\n const int winHeight = int("+m+");\n const int winWidth = int("+g+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float sourceFracRow =\n float("+c[0]+") *\n (float(dyR) / float("+l[0]+"));\n\n float sourceFracCol =\n float("+c[1]+") *\n (float(dyC) / float("+l[1]+"));\n\n int sourceNearestRow = int(min(\n float(int("+o+") - 1),\n "+n+" ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int("+a+") - 1),\n "+n+" ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n ";}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],l=r?"0.5":"0.0";this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+l+")));\n\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n ";}}(),ReverseProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return -1!==t.indexOf(n)&&1!==e[n]?e[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),o=getCoordsDataType(n);this.userCode="\n void main() {\n "+o+" coords = getOutputCoords();\n setOutput(getX("+r+"));\n }\n ";}else this.userCode="\n void main() {\n int coord = getOutputCoords();\n setOutput(getX("+e[0]+" - coord - 1));\n }\n ";}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=["x","segmentIds"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,c="\n sumValue += dot(values, filter);\n ",l="";o%n>0&&(l="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n ");var p="";o%n>0&&(p="\n if (inIdx < 0 || inIdx >= "+o+") {\n return -1.0;\n }\n "),this.userCode="\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n "+l+"\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n "+p+"\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n "+a+")) * float("+n+"));\n int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < "+s+"; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 filter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n "+c+"\n }\n\n int inIdx = inOffset + "+s+";\n if ("+(1===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 filter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(2===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 filter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(3===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 filter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n "+c+"\n }\n setOutput(sumValue);\n }\n ";}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u= 1.0) {\n setOutput(getA("+o+"));\n } else {\n setOutput(getB("+o+"));\n }\n }\n ";}}(),SliceProgram=function(){function e(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getCoords$1(this.rank);this.userCode="\n uniform "+t+" start;\n\n void main() {\n "+t+" sourceLoc = start + getOutputCoords();\n setOutput(getSource("+n+"));\n }\n ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+e.length+")");return function(n,r){if(null!=t.startLoc||(t.startLoc=n.getUniformLocationNoThrow(r,"start"),null!=t.startLoc))if(1===t.rank)n.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)n.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)n.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error("Slicing for rank "+t.rank+" is not yet supported");n.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3]);}}},e}();function getCoords$1(e){if(1===e)return "sourceLoc";if(2===e)return "sourceLoc.x, sourceLoc.y";if(3===e)return "sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===e)return "sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+e+" is not yet supported")}var StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=["x"];var o=n.filter(function(e,t){return -1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u="";if(1===a)u="coords * strides + begin";else{var c=0;u=n.map(function(e,t){return -1===r.indexOf(t)?(c++,1===o.length?"coords * strides["+t+"] + begin["+t+"]":"coords["+(c-1)+"] * strides["+t+"] + begin["+t+"]"):"begin["+t+"]"}).join(",");}this.userCode="\n "+i+" begin = "+i+"("+e+");\n "+i+" strides = "+i+"("+t+");\n\n void main() {\n "+s+" coords = getOutputCoords();\n setOutput(getX("+u+"));\n }\n ";}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={};}return e.prototype.acquireTexture=function(e,t){var n,r=getPhysicalFromLogicalTextureType(t),o=getKeyFromTextureShape(e,r);if(o in this.freeTextures||(this.freeTextures[o]=[]),o in this.usedTextures||(this.usedTextures[o]=[]),this.freeTextures[o].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var a=this.freeTextures[o].shift();return this.usedTextures[o].push(a),a}return this.numUsedTextures++,this.log(),r===PhysicalTextureType.FLOAT32?n=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===PhysicalTextureType.FLOAT16?n=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===PhysicalTextureType.UNSIGNED_BYTE&&(n=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[o].push(n),n},e.prototype.releaseTexture=function(e,t,n){var r=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n));r in this.freeTextures||(this.freeTextures[r]=[]),this.freeTextures[r].push(e),this.numFreeTextures++,this.numUsedTextures--;var o=this.usedTextures[r],a=o.indexOf(e);if(a<0)throw new Error("Cannot release a texture 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GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;athis.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h);}return p&&(s=this.endTimer(s),this.activeTimers.push(this.getQueryTime(s))),n},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixelsCanvas&&this.fromPixelsCanvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0;}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.throwIfNoData=function(e){if(!this.texData.has(e))throw new Error("WebGL backend: No data found for this tensor. Did you change your backend in the middle of the program? New backends can't use Tensors created with previous backends")},e.prototype.uploadToGPU=function(e){this.throwIfNoData(e);var t=this.texData.get(e),n=t.shape,r=t.values,o=t.texture,a=t.dtype,i=t.usage;if(null==o){var s,u=null!=this.activeTimers;u&&(s=performance.now());var c=getTextureShapeFromLogicalShape(this.gpgpu.gl,n);t.texShape=c;var l=this.acquireTexture(e,c,i);t.texture=l,null!=r&&(this.gpgpu.uploadMatrixToTexture(l,c[0],c[1],typedArrayToFloat32(r,a)),t.values=null,u&&(this.uploadWaitMs+=performance.now()-s));}else{this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e));}},e.prototype.cacheOnCPU=function(e,t){var n=this.delayedStorage,r=this.texData.get(e),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage;n&&null!=o&&(this.releaseTexture(e,o,a,s),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,null!=t&&(r.values=float32ToTypedArray(t,i));},e.prototype.releaseTexture=function(e,t,n,r){var o=this.texData.get(e),a=o.shape,i=o.dtype,s=this.lruDataGPU.indexOf(e);s>=0&&this.lruDataGPU.splice(s,1),this.numBytesInGPU-=this.computeBytes(a,i),this.textureManager.releaseTexture(t,n,r);},e.prototype.acquireTexture=function(e,t,n){var r=this.texData.get(e),o=r.shape,a=r.dtype;return this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(o,a),this.textureManager.acquireTexture(t,n)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if("float32"===t)return e;if("int32"===t||"bool"===t){for(var n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r=0?e:(assert(-1===e,"Bad value in size"),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n0&&(t=t.sum(r)),t.reshape(n.shape)},$b:function(){var t=e,n=getReductionAxes(r.shape,o);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return "The param passed to tf.addN() must be a list of tensors"}),assert(e.length>=1,function(){return "Must pass at least one tensor to tf.addN(), but got "+e.length});var t=e.map(function(e,t){return convertToTensor(e,"tensors"+t,"addN")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()};}),n})}function addStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in addStrict: "),e.add(t)}function sub_(e,t){var n=convertToTensor(e,"a","sub"),r=convertToTensor(t,"b","sub");assertTypesMatch(n,r);var o=assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.subtract(n,r)},{$a:n,$b:r},function(e){return {$a:function(){var t=e,r=getReductionAxes(n.shape,o);return r.length>0&&(t=t.sum(r)),t.reshape(n.shape)},$b:function(){var t=e,n=getReductionAxes(r.shape,o);return n.length>0&&(t=t.sum(n)),t.neg().reshape(r.shape)}}})}function subStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in subStrict: "),e.sub(t)}function pow_(e,t){var n=convertToTensor(e,"base","pow"),r=convertToTensor(t,"exp","pow"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(n,r))},{$base:n,$exp:r},function(e,t){var a=t[0];return {$base:function(){var t=e.mul(r.toFloat().mul(a.div(n))),i=getReductionAxes(n.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(n.shape)},$exp:function(){var t=e.mul(a.mul(n.log()).toFloat()),i=getReductionAxes(r.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in powStrict: "),e.pow(t)}function mul_(e,t){var n=convertToTensor(e,"a","mul"),r=convertToTensor(t,"b","mul");assertTypesMatch(n,r);var o=assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.multiply(n,r)},{$a:n,$b:r},function(e){return {$a:function(){var t=e.mul(r.toFloat()),a=getReductionAxes(n.shape,o);return a.length>0?t.sum(a).reshape(n.shape):t},$b:function(){var t=e.mul(n.toFloat()),a=getReductionAxes(r.shape,o);return a.length>0?t.sum(a).reshape(r.shape):t}}})}function mulStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in multiplyStrict: "),e.mul(t)}function div_(e,t){var n,r=convertToTensor(e,"a","div"),o=convertToTensor(t,"b","div");if(assertTypesMatch(r,o),"int32"===r.dtype&&"int32"===o.dtype)return floorDiv(r,o);n=function(e){return e.realDivide(r,o)};var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(n,{$a:r,$b:o},function(e){return {$a:function(){var t=e.div(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);n.length>0&&(t=t.sum(n).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function floorDiv_(e,t){var n=convertToTensor(e,"a","floorDiv"),r=convertToTensor(t,"b","floorDiv");assertTypesMatch(n,r);var o=assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(n,r)},{$a:n,$b:r},function(e){return {$a:function(){var t=e.div(r.toFloat()),a=getReductionAxes(n.shape,o);return a.length>0?t.sum(a).reshape(n.shape):t},$b:function(){var t=e.mul(n.toFloat()),a=getReductionAxes(r.shape,o);a.length>0&&(t=t.sum(a).reshape(r.shape));var i=r.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in divideStrict: "),e.div(t)}function mod_(e,t){var n=convertToTensor(e,"a","mod"),r=convertToTensor(t,"b","mod");assertTypesMatch(n,r);var o=assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.mod(n,r)},{$a:n,$b:r},function(e){return {$a:function(){var t=getReductionAxes(n.shape,o);return t.length>0?e.sum(t).reshape(n.shape):e},$b:function(){var t=e.mul(n.div(r).floor().neg()),a=getReductionAxes(r.shape,o);return a.length>0?t.sum(a).reshape(r.shape):t}}})}function modStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in modStrict: "),e.mod(t)}function minimum_(e,t){var n=convertToTensor(e,"a","minimum"),r=convertToTensor(t,"b","minimum");assertTypesMatch(n,r),"bool"===n.dtype&&(n=n.toInt()),"bool"===r.dtype&&(r=r.toInt()),assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.minimum(n,r)},{$a:n,$b:r},function(e){return {$a:function(){return e.mul(n.lessEqual(r).toFloat())},$b:function(){return e.mul(n.greater(r).toFloat())}}})}function minimumStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in minimumStrict: "),e.minimum(t)}function maximum_(e,t){var n=convertToTensor(e,"a","maximum"),r=convertToTensor(t,"b","maximum");assertTypesMatch(n,r),"bool"===n.dtype&&(n=n.toInt()),"bool"===r.dtype&&(r=r.toInt()),assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.maximum(n,r)},{$a:n,$b:r},function(e){return {$a:function(){return e.mul(n.greaterEqual(r).toFloat())},$b:function(){return e.mul(n.less(r).toFloat())}}})}function maximumStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in minimumStrict: "),e.maximum(t)}function squaredDifference_(e,t){var n=convertToTensor(e,"a","squaredDifference"),r=convertToTensor(t,"b","squaredDifference");assertTypesMatch(n,r),assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(n,r)},{$a:n,$b:r},function(e){var t=scalar(2);return {$a:function(){return e.mul(n.sub(r).mul(t))},$b:function(){return e.mul(r.sub(n).mul(t))}}})}function squaredDifferenceStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in squaredDifferenceStrict: "),e.squaredDifference(t)}function atan2_(e,t){var n=convertToTensor(e,"a","atan2"),r=convertToTensor(t,"b","atan2");assertTypesMatch(n,r);var o=assertAndGetBroadcastShape(n.shape,r.shape);return ENV.engine.runKernel(function(e){return e.atan2(n,r)},{$a:n,$b:r},function(e){return {$a:function(){var t=add(n.square(),r.square()),a=e.mul(r.div(t)),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$b:function(){var t=add(n.square(),r.square()),a=neg(e.mul(n.div(t))),i=getReductionAxes(r.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(r.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,"x","logicalNot","bool");return assert("bool"===t.dtype,"Error Array must be of type bool."),ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,"a","logicalAnd","bool"),r=convertToTensor(t,"b","logicalAnd","bool");return assert("bool"===n.dtype&&"bool"===r.dtype,"Error Array must be of type bool."),assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,"a","logicalOr","bool"),r=convertToTensor(t,"b","logicalOr","bool");return assert("bool"===n.dtype&&"bool"===r.dtype,"Error Array must be of type bool."),assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,"a","logicalXor","bool"),r=convertToTensor(t,"b","logicalXor","bool");return assert("bool"===n.dtype&&"bool"===r.dtype,"Error Array must be of type bool."),assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,"a","where"),o=convertToTensor(n,"b","where"),a=convertToTensor(e,"condition","where","bool");assert("bool"===a.dtype,"Error Condition must be of type bool."),assertShapesMatch(r.shape,o.shape,"Error in where: "),1===a.rank?assert(a.shape[0]===r.shape[0],"The first dimension of `a` must match the size of `condition`."):assertShapesMatch(a.shape,o.shape,"Error in where: ");return ENV.engine.runKernel(function(e){return e.select(a,r,o)},{$condition:a,$a:r,$b:o},function(e){return {$condition:function(){return zerosLike(a)},$a:function(){return e.mul(a.cast(r.dtype))},$b:function(){return e.mul(a.logicalNot().cast(o.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,n,r;return __generator(this,function(o){switch(o.label){case 0:return assert("bool"===(t=convertToTensor(e,"condition","where","bool")).dtype,"Condition must be of type bool."),[4,t.data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,"x","relu");if("bool"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var n=t.step();return {$x:function(){return e.mulStrict(n.toFloat())}}})}function elu_(e){var t=convertToTensor(e,"x","elu");return ENV.engine.runKernel(function(e,n){return n(e.elu(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,"x","selu");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return {$x:function(){var n=t.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(t.toFloat().exp());return where(n,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,"x","leakyRelu");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,"x","prelu"),r=convertToTensor(t,"alpha","prelu"),o=scalar(0);return maximum(o,n).add(r.mul(minimum(o,n)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,"x","transpose");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of perm "+t+"."),t.forEach(function(e){assert(e>=0&&eo)throw new Error("'k' passed to topk() must be <= the last dimension ("+o+") but got "+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return {values:a[0],indices:a[1]}}var Reduction,topk=op({topk_:topk_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,"losses","computeWeightedLoss"),o=null;null!=t&&(o=convertToTensor(t,"weights","computeWeightedLoss"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(r.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error("Unknown reduction: "+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","absoluteDifference"),a=convertToTensor(t,"predictions","absoluteDifference"),i=null;null!=n&&(i=convertToTensor(n,"weights","absoluteDifference")),assertShapesMatch(o.shape,a.shape,"Error in absoluteDifference: ");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","meanSquaredError"),a=convertToTensor(t,"predictions","meanSquaredError"),i=null;null!=n&&(i=convertToTensor(n,"weights","meanSquaredError")),assertShapesMatch(o.shape,a.shape,"Error in meanSquaredError: ");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","cosineDistance"),i=convertToTensor(t,"predictions","cosineDistance"),s=null;null!=r&&(s=convertToTensor(r,"weights","cosineDistance")),assertShapesMatch(a.shape,i.shape,"Error in cosineDistance: ");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","hingeLoss"),a=convertToTensor(t,"predictions","hingeLoss"),i=null;null!=n&&(i=convertToTensor(n,"weights","hingeLoss")),assertShapesMatch(o.shape,a.shape,"Error in hingeLoss: ");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","logLoss"),i=convertToTensor(t,"predictions","logLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","logLoss")),assertShapesMatch(a.shape,i.shape,"Error in logLoss: ");var u=scalar(1),c=scalar(r),l=a.mul(i.add(c).log()).neg().sub(u.sub(a).mul(u.sub(i).add(c).log()));return computeWeightedLoss(l,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,"labels","sigmoidCrossEntropyWithLogits"),r=convertToTensor(t,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"multiClassLabels","sigmoidCrossEntropy"),i=convertToTensor(t,"logits","sigmoidCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","sigmoidCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){var u=scalar(r),c=scalar(1),l=scalar(.5);a=a.mul(c.sub(u)).add(l.mul(u));}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","huberLoss"),i=convertToTensor(t,"predictions","huberLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","huberLoss")),assertShapesMatch(a.shape,i.shape,"Error in huberLoss: ");var u=scalar(r),c=i.sub(a).abs(),l=minimum(c,u),p=c.sub(l),d=scalar(.5).mul(l.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error("Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank "+t.rank+" and dim was "+n);return customGrad(function(e,t){var r=t.logSumExp([n],!0),o=t.toFloat().sub(r);return {value:o.mul(e).neg().sum([n]),gradFunc:function(t){var r=expandShapeToKeepDim(t.shape,[n]);return [t.reshape(r).mul(e.toFloat().sub(o.exp())),t.reshape(r).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"onehotLabels","softmaxCrossEntropy"),i=convertToTensor(t,"logits","softmaxCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","softmaxCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){var u=scalar(r),c=scalar(1),l=scalar(a.shape[1]);a=a.mul(c.sub(u)).add(u.div(l));}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,"Gram-Schmidt process: input must not be null, undefined, or empty");for(var n=e[0].shape[0],r=1;r0)for(var n=0;n= 2, but got rank "+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i);}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error("qr2d() requires a 2D Tensor, but got a "+e.shape.length+"D Tensor.");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,c=function(e){var t,u=a,c=s,l=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),c=a.slice([e,e],[1,1]),l=c.sign().neg(),p=c.sub(l.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=l.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,c,l]);},l=0;lr&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeBilinear");assert(3===r.rank||4===r.rank,"Error in resizeBilinear: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeBilinear: new shape must 2D, but got shape "+t+".");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeNearestNeighbor");assert(3===r.rank||4===r.rank,"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeNearestNeighbor: new shape must 2D, but got shape "+t+"."),assert("float32"===r.dtype||"int32"===r.dtype,"`images` must have `int32` or `float32` as dtype");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,"boxes","nonMaxSuppression"),i=convertToTensor(t,"scores","nonMaxSuppression"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,c,l;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,"boxes","nonMaxSuppressionAsync"),i=convertToTensor(t,"scores","nonMaxSuppressionAsync"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return c=p.sent(),l=nonMaxSuppressionImpl(u,c,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,l]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,"iouThreshold must be in [0, 1], but was '"+r+"'"),assert(2===e.rank,"boxes must be a 2D tensor, but was of rank '"+e.rank+"'"),assert(4===e.shape[1],"boxes must have 4 columns, but 2nd dimension was "+e.shape[1]),assert(1===t.rank,"scores must be a 1D tensor"),assert(t.shape[0]===a,"scores has incompatible shape with boxes. Expected "+a+", but was "+t.shape[0]),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,conv1d:conv1d,conv2d:conv2d,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,cast:cast,clone:clone,cumsum:cumsum,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,split:split,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,batchToSpaceND:batchToSpaceND,spaceToBatchND:spaceToBatchND,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk}),MathBackendCPU=function(){function e(){this.blockSize=48,this.data=new WeakMap,this.firstUse=!0,ENV.get("IS_BROWSER")&&(this.canvas=document.createElement("canvas"));}return e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get("IS_NODE")&&warn("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================\n")),this.data.has(e))throw new Error("Data buffer is already registered");this.data.set(e,null);},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendCPU.write(): values can not be null");this.throwIfNoData(e),this.data.set(e,t);},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n,r;if(ENV.get("IS_NODE")&&null==e.getContext)throw new Error("When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package");if(null!=e.getContext)n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(null==this.canvas)throw new Error("Can't read pixels from HTMLImageElement outside the browser.");this.canvas.width=e.width,this.canvas.height=e.height,this.canvas.getContext("2d").drawImage(e,0,0,e.width,e.height),n=this.canvas.getContext("2d").getImageData(0,0,e.width,e.height).data;}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;ad&&(d=m,h=f);}u[l]=h;}return i},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(e.rank-1)+" but got axis="+t);for(var o=upcastType(e.dtype,"int32"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],c=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},l=0;lt?1:0})},e.prototype.greaterEqual=function(e,t){return this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>=t?1:0})},e.prototype.logicalNot=function(e){for(var t=e.dataSync(),n=new Int32Array(t.length),r=0;r1||1===t.rank?1:t.shape[1],l=0;l=0&&t>=0?n:(n+t)%t})},e.prototype.max=function(e,t){assertAxesAreInnerMostDims("max",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;cp&&(p=h);}s[c]=p;}return a},e.prototype.maximum=function(e,t){return this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.max(e,t)})},e.prototype.all=function(e,t){assertAxesAreInnerMostDims("all",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),c=0;c0?n[r]=1:n[r]=0;return Tensor.make(e.shape,{values:n})},e.prototype.round=function(e){for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r.5?n[r]=Math.ceil(t[r]):n[r]=o%2==0?o:o+1;}return Tensor.make(e.shape,{values:n})},e.prototype.exp=function(e){for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r=0?o:Math.exp(o)-1;}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a=1?o[a]:o[a]*(i+1);}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a=0?n*i:t*(Math.exp(i)-1);}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;an?n:i-t,i=r[o]0?1:t;}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=buffer(n.outShape,e.dtype),l=e.dataSync(),p=t.dataSync(),d=c.values,h=0;h=n.inHeight))for(var S=x*t.strides[0],b=f+T*e.strides[1],E=0;E=n.inWidth))for(var C=S+A*t.strides[1],N=b+R*n.inChannels,k=C,I=0;I=n.inHeight))for(var b=T*t.strides[0],E=m+S*e.strides[1],w=0;w=n.inWidth))for(var N=b+R*t.strides[1],k=E+C*n.inChannels,I=_,M=N,D=0;DS?S=A:"avg"===n&&(b+=A,E++);}if(isNaN(S))break}s.set("avg"===n?b/E:S,p,h,v,d);}return s.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,"max")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,"int32"),r=t.strideHeight,o=t.strideWidth,a=t.filterHeight,i=t.filterWidth,s=t.padInfo.top,u=t.padInfo.left,c=0;cx&&(x=_,T=b*i+w);}n.set(T,c,p,m,l);}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.filterHeight,u=r.filterWidth,c=u-1-r.padInfo.left,l=s-1-r.padInfo.top,p=buffer(t.shape,"float32"),d=0;d=r.outHeight||Math.floor(T)!==T))for(var S=0;S=r.outWidth||Math.floor(b)!==b)){var E=s*u-1-o.get(d,T,b,h)===x*u+S?1:0;if(0!==E)y+=e.get(d,T,b,h)*E;}}}p.set(y,d,f,m,h);}return p.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=i-1-n.padInfo.left,u=a-1-n.padInfo.top,c=buffer(t.shape,"float32"),l=1/(a*i),p=0;p=n.outHeight||Math.floor(x)!==x))for(var T=0;T=n.outWidth||Math.floor(S)!==S))v+=e.get(p,x,S,d);}}c.set(v*l,p,h,f,d);}return c.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.pool(e,t,"avg").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],c=buffer([a,t,n,u],e.dtype),l=[r&&t>1?i-1:i,r&&n>1?s-1:s],p=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=0;d1?a-1:a,n&&l>1?i-1:i],h=[n&&c>1?c-1:c,n&&l>1?l-1:l],f=d[0]/h[0],m=d[1]/h[1],g=0;g1?i-1:i,r&&n>1?s-1:s],p=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=0;d1?a-1:a,n&&l>1?i-1:i],h=[n&&c>1?c-1:c,n&&l>1?l-1:l],f=1/(d[0]/h[0]),m=1/(d[1]/h[1]),g=2*Math.ceil(f)+2,v=2*Math.ceil(m)+2,y=0;y=c))for(var N=0;N=l)){var I=d[0]*(C/h[0]),M=d[1]*(k/h[1]),D=Math.min(a-1,n?Math.round(I):Math.floor(I)),O=Math.min(i-1,n?Math.round(M):Math.floor(M));x===D&&T===O&&(A+=e.get(y,C,k,_));}}}p.set(A,y,x,T,_);}return p.toTensor()},e.prototype.batchNormalization=function(e,t,n,r,o,a){for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),c=o?o.dataSync():new Float32Array([1]),l=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=l.length,h=c.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),c=new Float32Array(u);function l(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var c=s[r];u+=c*c;}return u}for(var p=0;p=0&&e.get(a)0,"scheme must not be an empty string.");var r=e.getInstance();assert(null==r.managers[t],"A model store manager is already registered for scheme '"+t+"'."),r.managers[t]=n;},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error("Cannot find model manager for scheme '"+e+"'");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+ModelStoreManagerRegistry.getSchemes().join(","));return {scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter(this,void 0,void 0,function(){var r,o,a,i,s,u,c,l,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,"Old path and new path are the same: '"+e+"'"),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,"Copying failed because no load handler is found for source URL "+e+"."),assert(r.length<2,"Copying failed because more than one ("+r.length+") load handlers for source URL "+e+"."),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,"Copying failed because no save handler is found for destination URL "+t+"."),assert(a.length<2,"Copying failed because more than one ("+r.length+") save handlers for destination URL "+t+"."),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,c=s===parseURL(e).scheme,[4,o.load()];case 1:return l=d.sent(),n&&c?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return [4,i.save(l)];case 4:return p=d.sent(),!n||c?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return [2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n0,"URL path for browserHTTPRequest must not be null, undefined or empty."),Array.isArray(e)&&assert(2===e.length,"URL paths for browserHTTPRequest must have a length of 2, (actual length is "+e.length+")."),this.path=e,null!=t&&null!=t.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t||{};}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");return (t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},t.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=e.weightData&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:"application/octet-stream"}),"model.weights.bin"),[4,fetch(this.path,t)];case 1:if(200===(o=a.sent()).status)return [2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+o.status+".")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){switch(t.label){case 0:return t.trys.push([0,3,,4]),[4,fetch(this.path[0],this.requestInit)];case 1:return [4,t.sent().arrayBuffer()];case 2:return [2,t.sent()];case 3:throw e=t.sent(),new Error(this.path[0]+" not found. "+e);case 4:return [2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.loadBinaryTopology(),[4,fetch(this.path[1],this.requestInit)];case 1:return n=u.sent(),[4,Promise.all([t,n])];case 2:return r=u.sent(),o=r[0],[4,r[1].json()];case 3:return null==(a=u.sent())?[3,5]:[4,this.loadWeights(a)];case 4:e=u.sent(),i=e[0],s=e[1],u.label=5;case 5:return [2,{modelTopology:o,weightSpecs:i,weightData:s}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:return [4,fetch(this.path,this.requestInit)];case 1:return [4,s.sent().json()];case 2:if(t=s.sent(),n=t.modelTopology,r=t.weightsManifest,null==n&&null==r)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==r?[3,4]:(i=t.weightsManifest,[4,this.loadWeights(i)]);case 3:e=s.sent(),o=e[0],a=e[1],s.label=4;case 4:return [2,{modelTopology:n,weightSpecs:o,weightData:a}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,c;return __generator(this,function(l){switch(l.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=[],r=0,o=e;r0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={};}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig];},e}(),serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){if(null==n&&(n=ENV.get("TEST_EPSILON")),e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error("Arrays are of different type actual: "+e.dtype+" vs expected: "+t.dtype+".");if(!arraysEqual(e.shape,t.shape))throw new Error("Arrays are of different shape actual: "+e.shape+" vs expected: "+t.shape+".")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error("Arrays are of different type actual: "+r+" vs expected: "+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error("Arrays have different lengths actual: "+a.length+" vs expected: "+i.length+".\nActual: "+a+".\nExpected: "+i+".");for(var s=0;sn)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;on)throw new Error("Value out of range:"+r[o]+" low: "+t+", high: "+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t));}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version="0.12.14",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get("EPSILON")),o.epsilonScalar=keep(scalar(r)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var c=t.c.mul(r).add(o);o.assign(c);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}));},t.prototype.getConfig=function(){return {learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className="AdadeltaOptimizer",t}(Optimizer);SerializationMap.register(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get("EPSILON"))),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className="AdagradOptimizer",t}(Optimizer);SerializationMap.register(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable();}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],c=t.accumulatedSecondMoment[o],l=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(c).add(t.oneMinusBeta2.mul(s.square())),d=l.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(l),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f);}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className="AdamOptimizer",t}(Optimizer);SerializationMap.register(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable();}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],c=t.accumulatedWeightedInfNorm[o],l=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(c),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(l),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(l.div(t.epsScalar.add(h))).add(a);a.assign(f);}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className="AdamaxOptimizer",t}(Optimizer);SerializationMap.register(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e);});});},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e));},t.prototype.dispose=function(){this.c.dispose();},t.prototype.getConfig=function(){return {learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className="SGDOptimizer",t}(Optimizer);SerializationMap.register(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1);});}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose();},t.prototype.setMomentum=function(e){this.momentum=e;},t.prototype.getConfig=function(){return {learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className="MomentumOptimizer",t}(SGDOptimizer);SerializationMap.register(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get("EPSILON")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1);});}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),c=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(c);var l=o.sub(c);o.assign(l);}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));c=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(c);l=o.sub(c);o.assign(l);}});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className="RMSPropOptimizer",t}(Optimizer);SerializationMap.register(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);
-
- var tfCore_esm = /*#__PURE__*/Object.freeze({
- setBackend: setBackend,
- getBackend: getBackend,
- disposeVariables: disposeVariables,
- memory: memory,
- version_core: version,
- nextFrame: nextFrame,
- environment: environment,
- io: io,
- serialization: serialization,
- test_util: test_util,
- util: util,
- webgl: webgl,
- AdadeltaOptimizer: AdadeltaOptimizer,
- AdagradOptimizer: AdagradOptimizer,
- AdamOptimizer: AdamOptimizer,
- AdamaxOptimizer: AdamaxOptimizer,
- MomentumOptimizer: MomentumOptimizer,
- Optimizer: Optimizer,
- RMSPropOptimizer: RMSPropOptimizer,
- SGDOptimizer: SGDOptimizer,
- Tensor: Tensor,
- TensorBuffer: TensorBuffer,
- variable: variable,
- Variable: Variable,
- get Rank () { return Rank; },
- get Reduction () { return Reduction; },
- ENV: ENV,
- Environment: Environment,
- image: image_ops,
- linalg: linalg_ops,
- losses: loss_ops,
- op: op,
- batchNormalization2d: batchNormalization2d,
- batchNormalization3d: batchNormalization3d,
- batchNormalization4d: batchNormalization4d,
- batchNormalization: batchNormalization,
- concat: concat,
- concat1d: concat1d,
- concat2d: concat2d,
- concat3d: concat3d,
- concat4d: concat4d,
- conv1d: conv1d,
- conv2d: conv2d,
- depthwiseConv2d: depthwiseConv2d,
- separableConv2d: separableConv2d,
- conv2dTranspose: conv2dTranspose,
- matMul: matMul,
- dot: dot,
- outerProduct: outerProduct,
- reverse: reverse,
- reverse1d: reverse1d,
- reverse2d: reverse2d,
- reverse3d: reverse3d,
- reverse4d: reverse4d,
- maxPool: maxPool,
- avgPool: avgPool,
- slice: slice,
- slice1d: slice1d,
- slice2d: slice2d,
- slice3d: slice3d,
- slice4d: slice4d,
- abs: abs,
- acos: acos,
- acosh: acosh,
- asin: asin,
- asinh: asinh,
- atan: atan,
- atanh: atanh,
- ceil: ceil,
- clipByValue: clipByValue,
- cos: cos,
- cosh: cosh,
- erf: erf,
- exp: exp,
- expm1: expm1,
- floor: floor,
- log: log$1,
- log1p: log1p,
- logSigmoid: logSigmoid,
- neg: neg,
- reciprocal: reciprocal,
- round: round,
- rsqrt: rsqrt,
- sigmoid: sigmoid,
- sign: sign,
- sin: sin,
- sinh: sinh,
- softplus: softplus,
- sqrt: sqrt,
- square: square,
- step: step,
- tan: tan,
- tanh: tanh$1,
- all: all,
- any: any,
- argMax: argMax,
- argMin: argMin,
- logSumExp: logSumExp,
- max: max,
- mean: mean,
- min: min,
- moments: moments,
- sum: sum,
- equal: equal,
- equalStrict: equalStrict,
- greater: greater,
- greaterEqual: greaterEqual,
- greaterEqualStrict: greaterEqualStrict,
- greaterStrict: greaterStrict,
- less: less,
- lessEqual: lessEqual,
- lessEqualStrict: lessEqualStrict,
- lessStrict: lessStrict,
- notEqual: notEqual,
- notEqualStrict: notEqualStrict,
- add: add,
- addN: addN,
- addStrict: addStrict,
- atan2: atan2,
- div: div,
- divStrict: divStrict,
- floorDiv: floorDiv,
- maximum: maximum,
- maximumStrict: maximumStrict,
- minimum: minimum,
- minimumStrict: minimumStrict,
- mod: mod,
- modStrict: modStrict,
- mul: mul,
- mulStrict: mulStrict,
- pow: pow,
- powStrict: powStrict,
- squaredDifference: squaredDifference,
- squaredDifferenceStrict: squaredDifferenceStrict,
- sub: sub,
- subStrict: subStrict,
- elu: elu,
- leakyRelu: leakyRelu,
- prelu: prelu,
- relu: relu,
- selu: selu,
- logicalAnd: logicalAnd,
- logicalNot: logicalNot,
- logicalOr: logicalOr,
- logicalXor: logicalXor,
- where: where,
- whereAsync: whereAsync,
- buffer: buffer,
- toPixels: toPixels,
- print: print,
- cast: cast,
- clone: clone,
- cumsum: cumsum,
- expandDims: expandDims,
- eye: eye,
- fromPixels: fromPixels,
- multinomial: multinomial,
- oneHot: oneHot,
- pad: pad,
- pad1d: pad1d,
- pad2d: pad2d,
- pad3d: pad3d,
- pad4d: pad4d,
- rand: rand,
- randomNormal: randomNormal,
- randomUniform: randomUniform,
- reshape: reshape,
- split: split,
- squeeze: squeeze,
- stack: stack,
- tile: tile,
- truncatedNormal: truncatedNormal,
- unstack: unstack,
- batchToSpaceND: batchToSpaceND,
- spaceToBatchND: spaceToBatchND,
- fill: fill,
- linspace: linspace,
- ones: ones$1,
- range: range,
- scalar: scalar,
- tensor: tensor,
- tensor1d: tensor1d,
- tensor2d: tensor2d,
- tensor3d: tensor3d,
- tensor4d: tensor4d,
- tensor5d: tensor5d,
- tensor6d: tensor6d,
- zeros: zeros,
- onesLike: onesLike,
- zerosLike: zerosLike,
- transpose: transpose,
- softmax: softmax,
- localResponseNormalization: localResponseNormalization,
- norm: norm,
- gather: gather,
- unsortedSegmentSum: unsortedSegmentSum,
- basicLSTMCell: basicLSTMCell,
- multiRNNCell: multiRNNCell,
- movingAverage: movingAverage,
- stridedSlice: stridedSlice,
- topk: topk,
- train: train,
- tidy: tidy,
- keep: keep,
- dispose: dispose,
- time: time,
- customGrad: customGrad,
- grad: grad,
- grads: grads,
- valueAndGrad: valueAndGrad,
- valueAndGrads: valueAndGrads,
- variableGrads: variableGrads
- });
-
- var Point = /** @class */ (function () {
- function Point(x, y) {
- this.x = x;
- this.y = y;
- }
- Point.prototype.add = function (pt) {
- return new Point(this.x + pt.x, this.y + pt.y);
- };
- Point.prototype.sub = function (pt) {
- return new Point(this.x - pt.x, this.y - pt.y);
- };
- Point.prototype.mul = function (pt) {
- return new Point(this.x * pt.x, this.y * pt.y);
- };
- Point.prototype.div = function (pt) {
- return new Point(this.x / pt.x, this.y / pt.y);
- };
- Point.prototype.abs = function () {
- return new Point(Math.abs(this.x), Math.abs(this.y));
- };
- Point.prototype.magnitude = function () {
- return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
- };
- Point.prototype.floor = function () {
- return new Point(Math.floor(this.x), Math.floor(this.y));
- };
- return Point;
- }());
-
- function isTensor(tensor$$1, dim) {
- return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
- }
- function isTensor1D(tensor$$1) {
- return isTensor(tensor$$1, 1);
- }
- function isTensor2D(tensor$$1) {
- return isTensor(tensor$$1, 2);
- }
- function isTensor3D(tensor$$1) {
- return isTensor(tensor$$1, 3);
- }
- function isTensor4D(tensor$$1) {
- return isTensor(tensor$$1, 4);
- }
- function isFloat(num) {
- return num % 1 !== 0;
- }
- function isEven(num) {
- return num % 2 === 0;
- }
- function round$1(num, prec) {
- if (prec === void 0) { prec = 2; }
- var f = Math.pow(10, prec);
- return Math.floor(num * f) / f;
- }
- function isDimensions(obj) {
- return obj && obj.width && obj.height;
- }
- function computeReshapedDimensions(_a, inputSize) {
- var width = _a.width, height = _a.height;
- var scale = inputSize / Math.max(height, width);
- return {
- height: Math.round(height * scale),
- width: Math.round(width * scale)
- };
- }
- function getCenterPoint(pts) {
- return pts.reduce(function (sum$$1, pt) { return sum$$1.add(pt); }, new Point(0, 0))
- .div(new Point(pts.length, pts.length));
- }
- function range$1(num, start, step$$1) {
- return Array(num).fill(0).map(function (_, i) { return start + (i * step$$1); });
- }
- function isValidNumber(num) {
- return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
- }
- function isValidProbablitiy(num) {
- return isValidNumber(num) && 0 <= num && num <= 1.0;
- }
-
- var Box = /** @class */ (function () {
- // TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove
- // allowNegativeDimensions flag again
- function Box(_box, allowNegativeDimensions) {
- if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
- var box = _box || {};
- var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
- var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
- if (!isRect && !isBbox) {
- throw new Error("Box.constructor - expected box to be IBoundingBox | IRect, instead have " + JSON.stringify(box));
- }
- var _a = isRect
- ? [box.x, box.y, box.width, box.height]
- : [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
- Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);
- this._x = x;
- this._y = y;
- this._width = width;
- this._height = height;
- }
- Box.isRect = function (rect) {
- return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
- };
- Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {
- if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
- if (!Box.isRect(box)) {
- throw new Error(callee + " - invalid box: " + JSON.stringify(box) + ", expected object with properties x, y, width, height");
- }
- if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
- throw new Error(callee + " - width (" + box.width + ") and height (" + box.height + ") must be positive numbers");
- }
- };
- Object.defineProperty(Box.prototype, "x", {
- get: function () {
- return this._x;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "y", {
- get: function () {
- return this._y;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "width", {
- get: function () {
- return this._width;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "height", {
- get: function () {
- return this._height;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "left", {
- get: function () {
- return this.x;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "top", {
- get: function () {
- return this.y;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "right", {
- get: function () {
- return this.x + this.width;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "bottom", {
- get: function () {
- return this.y + this.height;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(Box.prototype, "area", {
- get: function () {
- return this.width * this.height;
- },
- enumerable: true,
- configurable: true
- });
- Box.prototype.round = function () {
- var _a = [this.x, this.y, this.width, this.height]
- .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
- return new Box({ x: x, y: y, width: width, height: height });
- };
- Box.prototype.floor = function () {
- var _a = [this.x, this.y, this.width, this.height]
- .map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
- return new Box({ x: x, y: y, width: width, height: height });
- };
- Box.prototype.toSquare = function () {
- var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- var diff = Math.abs(width - height);
- if (width < height) {
- x -= (diff / 2);
- width += diff;
- }
- if (height < width) {
- y -= (diff / 2);
- height += diff;
- }
- return new Box({ x: x, y: y, width: width, height: height });
- };
- Box.prototype.rescale = function (s) {
- var scaleX = isDimensions(s) ? s.width : s;
- var scaleY = isDimensions(s) ? s.height : s;
- return new Box({
- x: this.x * scaleX,
- y: this.y * scaleY,
- width: this.width * scaleX,
- height: this.height * scaleY
- });
- };
- Box.prototype.pad = function (padX, padY) {
- var _a = [
- this.x - (padX / 2),
- this.y - (padY / 2),
- this.width + padX,
- this.height + padY
- ], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
- return new Box({ x: x, y: y, width: width, height: height });
- };
- Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
- var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
- var clippedX = Math.max(x, 0);
- var clippedY = Math.max(y, 0);
- var newWidth = right - clippedX;
- var newHeight = bottom - clippedY;
- var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
- var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
- return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();
- };
- Box.prototype.padAtBorders = function (imageHeight, imageWidth) {
- var w = this.width + 1;
- var h = this.height + 1;
- var dx = 1;
- var dy = 1;
- var edx = w;
- var edy = h;
- var x = this.left;
- var y = this.top;
- var ex = this.right;
- var ey = this.bottom;
- if (ex > imageWidth) {
- edx = -ex + imageWidth + w;
- ex = imageWidth;
- }
- if (ey > imageHeight) {
- edy = -ey + imageHeight + h;
- ey = imageHeight;
- }
- if (x < 1) {
- edy = 2 - x;
- x = 1;
- }
- if (y < 1) {
- edy = 2 - y;
- y = 1;
- }
- return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
- };
- Box.prototype.calibrate = function (region) {
- return new Box({
- left: this.left + (region.left * this.width),
- top: this.top + (region.top * this.height),
- right: this.right + (region.right * this.width),
- bottom: this.bottom + (region.bottom * this.height)
- }).toSquare().round();
- };
- return Box;
- }());
-
- /*! *****************************************************************************
- Copyright (c) Microsoft Corporation. All rights reserved.
- Licensed under the Apache License, Version 2.0 (the "License"); you may not use
- this file except in compliance with the License. You may obtain a copy of the
- License at http://www.apache.org/licenses/LICENSE-2.0
-
- THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
- WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
- MERCHANTABLITY OR NON-INFRINGEMENT.
-
- See the Apache Version 2.0 License for specific language governing permissions
- and limitations under the License.
- ***************************************************************************** */
- /* global Reflect, Promise */
-
- var extendStatics$1 = function(d, b) {
- extendStatics$1 = Object.setPrototypeOf ||
- ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
- function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
- return extendStatics$1(d, b);
- };
-
- function __extends$1(d, b) {
- extendStatics$1(d, b);
- function __() { this.constructor = d; }
- d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
- }
-
- var __assign$1 = function() {
- __assign$1 = Object.assign || function __assign(t) {
- for (var s, i = 1, n = arguments.length; i < n; i++) {
- s = arguments[i];
- for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
- }
- return t;
- };
- return __assign$1.apply(this, arguments);
- };
-
- function __awaiter$1(thisArg, _arguments, P, generator) {
- return new (P || (P = Promise))(function (resolve, reject) {
- function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
- function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
- function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
- step((generator = generator.apply(thisArg, _arguments || [])).next());
- });
- }
-
- function __generator$1(thisArg, body) {
- var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
- return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
- function verb(n) { return function (v) { return step([n, v]); }; }
- function step(op) {
- if (f) throw new TypeError("Generator is already executing.");
- while (_) try {
- if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
- if (y = 0, t) op = [op[0] & 2, t.value];
- switch (op[0]) {
- case 0: case 1: t = op; break;
- case 4: _.label++; return { value: op[1], done: false };
- case 5: _.label++; y = op[1]; op = [0]; continue;
- case 7: op = _.ops.pop(); _.trys.pop(); continue;
- default:
- if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
- if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
- if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
- if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
- if (t[2]) _.ops.pop();
- _.trys.pop(); continue;
- }
- op = body.call(thisArg, _);
- } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
- if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
- }
- }
-
- var BoundingBox = /** @class */ (function (_super) {
- __extends$1(BoundingBox, _super);
- function BoundingBox(left, top, right, bottom) {
- return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;
- }
- return BoundingBox;
- }(Box));
-
- var LabeledBox = /** @class */ (function (_super) {
- __extends$1(LabeledBox, _super);
- function LabeledBox(box, label) {
- var _this = _super.call(this, box) || this;
- _this._label = label;
- return _this;
- }
- LabeledBox.assertIsValidLabeledBox = function (box, callee) {
- Box.assertIsValidBox(box, callee);
- if (!isValidNumber(box.label)) {
- throw new Error(callee + " - expected property label (" + box.label + ") to be a number");
- }
- };
- Object.defineProperty(LabeledBox.prototype, "label", {
- get: function () {
- return this._label;
- },
- enumerable: true,
- configurable: true
- });
- return LabeledBox;
- }(Box));
-
- var Rect = /** @class */ (function (_super) {
- __extends$1(Rect, _super);
- function Rect(x, y, width, height) {
- return _super.call(this, { x: x, y: y, width: width, height: height }) || this;
- }
- return Rect;
- }(Box));
-
- var ObjectDetection = /** @class */ (function () {
- function ObjectDetection(score, classScore, className, relativeBox, imageDims) {
- var width = imageDims.width, height = imageDims.height;
- this._imageWidth = width;
- this._imageHeight = height;
- this._score = score;
- this._classScore = classScore;
- this._className = className;
- this._box = new Rect(relativeBox.x * width, relativeBox.y * height, relativeBox.width * width, relativeBox.height * height);
- }
- Object.defineProperty(ObjectDetection.prototype, "score", {
- get: function () {
- return this._score;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(ObjectDetection.prototype, "classScore", {
- get: function () {
- return this._classScore;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(ObjectDetection.prototype, "className", {
- get: function () {
- return this._className;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(ObjectDetection.prototype, "box", {
- get: function () {
- return this._box;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(ObjectDetection.prototype, "imageWidth", {
- get: function () {
- return this._imageWidth;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(ObjectDetection.prototype, "imageHeight", {
- get: function () {
- return this._imageHeight;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(ObjectDetection.prototype, "relativeBox", {
- get: function () {
- return new Rect(this._box.x / this._imageWidth, this._box.y / this._imageHeight, this._box.width / this._imageWidth, this._box.height / this._imageHeight);
- },
- enumerable: true,
- configurable: true
- });
- ObjectDetection.prototype.getScore = function () {
- return this.score;
- };
- ObjectDetection.prototype.getBox = function () {
- return this.box;
- };
- ObjectDetection.prototype.getImageWidth = function () {
- return this.imageWidth;
- };
- ObjectDetection.prototype.getImageHeight = function () {
- return this.imageHeight;
- };
- ObjectDetection.prototype.getRelativeBox = function () {
- return this.relativeBox;
- };
- ObjectDetection.prototype.forSize = function (width, height) {
- return new ObjectDetection(this.score, this.classScore, this.className, this.getRelativeBox(), { width: width, height: height });
- };
- return ObjectDetection;
- }());
-
- var PredictedBox = /** @class */ (function (_super) {
- __extends$1(PredictedBox, _super);
- function PredictedBox(box, label, score, classScore) {
- var _this = _super.call(this, box, label) || this;
- _this._score = score;
- _this._classScore = classScore;
- return _this;
- }
- PredictedBox.assertIsValidPredictedBox = function (box, callee) {
- LabeledBox.assertIsValidLabeledBox(box, callee);
- if (!isValidProbablitiy(box.score)
- || !isValidProbablitiy(box.classScore)) {
- throw new Error(callee + " - expected properties score (" + box.score + ") and (" + box.classScore + ") to be a number between [0, 1]");
- }
- };
- Object.defineProperty(PredictedBox.prototype, "score", {
- get: function () {
- return this._score;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(PredictedBox.prototype, "classScore", {
- get: function () {
- return this._classScore;
- },
- enumerable: true,
- configurable: true
- });
- return PredictedBox;
- }(LabeledBox));
-
- function disposeUnusedWeightTensors(weightMap, paramMappings) {
- Object.keys(weightMap).forEach(function (path) {
- if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
- weightMap[path].dispose();
- }
- });
- }
-
- function extractWeightEntryFactory(weightMap, paramMappings) {
- return function (originalPath, paramRank, mappedPath) {
- var tensor = weightMap[originalPath];
- if (!isTensor(tensor, paramRank)) {
- throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
- }
- paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
- return tensor;
- };
- }
-
- function extractWeightsFactory(weights) {
- var remainingWeights = weights;
- function extractWeights(numWeights) {
- var ret = remainingWeights.slice(0, numWeights);
- remainingWeights = remainingWeights.slice(numWeights);
- return ret;
- }
- function getRemainingWeights() {
- return remainingWeights;
- }
- return {
- extractWeights: extractWeights,
- getRemainingWeights: getRemainingWeights
- };
- }
-
- function getModelUris(uri, defaultModelName) {
- var defaultManifestFilename = defaultModelName + "-weights_manifest.json";
- if (!uri) {
- return {
- modelBaseUri: '',
- manifestUri: defaultManifestFilename
- };
- }
- if (uri === '/') {
- return {
- modelBaseUri: '/',
- manifestUri: "/" + defaultManifestFilename
- };
- }
- var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';
- uri = uri.replace(protocol, '');
- var parts = uri.split('/').filter(function (s) { return s; });
- var manifestFile = uri.endsWith('.json')
- ? parts[parts.length - 1]
- : defaultManifestFilename;
- var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');
- modelBaseUri = uri.startsWith('/') ? "/" + modelBaseUri : modelBaseUri;
- return {
- modelBaseUri: modelBaseUri,
- manifestUri: modelBaseUri === '/' ? "/" + manifestFile : modelBaseUri + "/" + manifestFile
- };
- }
-
- function isMediaLoaded(media) {
- return (media instanceof HTMLImageElement && media.complete)
- || (media instanceof HTMLVideoElement && media.readyState >= 3);
- }
-
- function awaitMediaLoaded(media) {
- return new Promise(function (resolve, reject) {
- if (media instanceof HTMLCanvasElement || isMediaLoaded(media)) {
- return resolve();
- }
- function onLoad(e) {
- if (!e.currentTarget)
- return;
- e.currentTarget.removeEventListener('load', onLoad);
- e.currentTarget.removeEventListener('error', onError);
- resolve(e);
- }
- function onError(e) {
- if (!e.currentTarget)
- return;
- e.currentTarget.removeEventListener('load', onLoad);
- e.currentTarget.removeEventListener('error', onError);
- reject(e);
- }
- media.addEventListener('load', onLoad);
- media.addEventListener('error', onError);
- });
- }
-
- function bufferToImage(buf) {
- return new Promise(function (resolve, reject) {
- if (!(buf instanceof Blob)) {
- return reject('bufferToImage - expected buf to be of type: Blob');
- }
- var reader = new FileReader();
- reader.onload = function () {
- if (typeof reader.result !== 'string') {
- return reject('bufferToImage - expected reader.result to be a string, in onload');
- }
- var img = new Image();
- img.onload = function () { return resolve(img); };
- img.onerror = reject;
- img.src = reader.result;
- };
- reader.onerror = reject;
- reader.readAsDataURL(buf);
- });
- }
-
- function getContext2dOrThrow(canvas) {
- var ctx = canvas.getContext('2d');
- if (!ctx) {
- throw new Error('canvas 2d context is null');
- }
- return ctx;
- }
-
- function getMediaDimensions(input) {
- if (input instanceof HTMLImageElement) {
- return { width: input.naturalWidth, height: input.naturalHeight };
- }
- if (input instanceof HTMLVideoElement) {
- return { width: input.videoWidth, height: input.videoHeight };
- }
- return input;
- }
-
- function createCanvas(_a) {
- var width = _a.width, height = _a.height;
- var canvas = document.createElement('canvas');
- canvas.width = width;
- canvas.height = height;
- return canvas;
- }
- function createCanvasFromMedia(media, dims) {
- if (!isMediaLoaded(media)) {
- throw new Error('createCanvasFromMedia - media has not finished loading yet');
- }
- var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
- var canvas = createCanvas({ width: width, height: height });
- getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
- return canvas;
- }
-
- function getDefaultDrawOptions(options) {
- if (options === void 0) { options = {}; }
- return Object.assign({}, {
- boxColor: 'blue',
- textColor: 'red',
- lineWidth: 2,
- fontSize: 20,
- fontStyle: 'Georgia',
- withScore: true,
- withClassName: true
- }, options);
- }
-
- function drawBox(ctx, x, y, w, h, options) {
- var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
- ctx.strokeStyle = drawOptions.boxColor;
- ctx.lineWidth = drawOptions.lineWidth;
- ctx.strokeRect(x, y, w, h);
- }
-
- function drawText(ctx, x, y, text, options) {
- var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
- var padText = 2 + drawOptions.lineWidth;
- ctx.fillStyle = drawOptions.textColor;
- ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
- ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
- }
-
- function resolveInput(arg) {
- if (typeof arg === 'string') {
- return document.getElementById(arg);
- }
- return arg;
- }
-
- function drawDetection(canvasArg, detection, options) {
- var canvas = resolveInput(canvasArg);
- if (!(canvas instanceof HTMLCanvasElement)) {
- throw new Error('drawBox - expected canvas to be of type: HTMLCanvasElement');
- }
- var detectionArray = Array.isArray(detection)
- ? detection
- : [detection];
- detectionArray.forEach(function (det) {
- var _a = det.getBox(), x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- var drawOptions = getDefaultDrawOptions(options);
- var ctx = getContext2dOrThrow(canvas);
- drawBox(ctx, x, y, width, height, drawOptions);
- if (drawOptions.withScore) {
- drawText(ctx, x, y, det.className + " (" + round$1(det.score) + ")", drawOptions);
- }
- });
- }
-
- function imageTensorToCanvas(imgTensor, canvas) {
- return __awaiter$1(this, void 0, void 0, function () {
- var targetCanvas, _a, height, width, numChannels, imgTensor3D;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- targetCanvas = canvas || document.createElement('canvas');
- _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
- imgTensor3D = tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
- return [4 /*yield*/, toPixels(imgTensor3D, targetCanvas)];
- case 1:
- _b.sent();
- imgTensor3D.dispose();
- return [2 /*return*/, targetCanvas];
- }
- });
- });
- }
-
- function imageToSquare(input, inputSize, centerImage) {
- if (centerImage === void 0) { centerImage = false; }
- if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) {
- throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
- }
- var dims = getMediaDimensions(input);
- var scale = inputSize / Math.max(dims.height, dims.width);
- var width = scale * dims.width;
- var height = scale * dims.height;
- var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
- var inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
- var offset = Math.abs(width - height) / 2;
- var dx = centerImage && width < height ? offset : 0;
- var dy = centerImage && height < width ? offset : 0;
- getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
- return targetCanvas;
- }
-
- function isMediaElement(input) {
- return input instanceof HTMLImageElement
- || input instanceof HTMLVideoElement
- || input instanceof HTMLCanvasElement;
- }
-
- function loadWeightMap(uri, defaultModelName) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _a, manifestUri, modelBaseUri, manifest;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
- return [4 /*yield*/, fetch(manifestUri)];
- case 1: return [4 /*yield*/, (_b.sent()).json()];
- case 2:
- manifest = _b.sent();
- return [2 /*return*/, io.loadWeights(manifest, modelBaseUri)];
- }
- });
- });
- }
-
- /**
- * Pads the smaller dimension of an image tensor with zeros, such that width === height.
- *
- * @param imgTensor The image tensor.
- * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
- * both sides of the minor dimension oof the image.
- * @returns The padded tensor with width === height.
- */
- function padToSquare(imgTensor, isCenterImage) {
- if (isCenterImage === void 0) { isCenterImage = false; }
- return tidy(function () {
- var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
- if (height === width) {
- return imgTensor;
- }
- var dimDiff = Math.abs(height - width);
- var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
- var paddingAxis = height > width ? 2 : 1;
- var createPaddingTensor = function (paddingAmount) {
- var paddingTensorShape = imgTensor.shape.slice();
- paddingTensorShape[paddingAxis] = paddingAmount;
- return fill(paddingTensorShape, 0);
- };
- var paddingTensorAppend = createPaddingTensor(paddingAmount);
- var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
- var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
- ? createPaddingTensor(remainingPaddingAmount)
- : null;
- var tensorsToStack = [
- paddingTensorPrepend,
- imgTensor,
- paddingTensorAppend
- ]
- .filter(function (t) { return t !== null; });
- return concat(tensorsToStack, paddingAxis);
- });
- }
-
- var NetInput = /** @class */ (function () {
- function NetInput(inputs, treatAsBatchInput) {
- if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }
- var _this = this;
- this._imageTensors = [];
- this._canvases = [];
- this._treatAsBatchInput = false;
- this._inputDimensions = [];
- if (!Array.isArray(inputs)) {
- throw new Error("NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have " + inputs);
- }
- this._treatAsBatchInput = treatAsBatchInput;
- this._batchSize = inputs.length;
- inputs.forEach(function (input, idx) {
- if (isTensor3D(input)) {
- _this._imageTensors[idx] = input;
- _this._inputDimensions[idx] = input.shape;
- return;
- }
- if (isTensor4D(input)) {
- var batchSize = input.shape[0];
- if (batchSize !== 1) {
- throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
- }
- _this._imageTensors[idx] = input;
- _this._inputDimensions[idx] = input.shape.slice(1);
- return;
- }
- var canvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
- _this._canvases[idx] = canvas;
- _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
- });
- }
- Object.defineProperty(NetInput.prototype, "imageTensors", {
- get: function () {
- return this._imageTensors;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NetInput.prototype, "canvases", {
- get: function () {
- return this._canvases;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NetInput.prototype, "isBatchInput", {
- get: function () {
- return this.batchSize > 1 || this._treatAsBatchInput;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NetInput.prototype, "batchSize", {
- get: function () {
- return this._batchSize;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NetInput.prototype, "inputDimensions", {
- get: function () {
- return this._inputDimensions;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NetInput.prototype, "inputSize", {
- get: function () {
- return this._inputSize;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
- get: function () {
- var _this = this;
- return range$1(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
- },
- enumerable: true,
- configurable: true
- });
- NetInput.prototype.getInput = function (batchIdx) {
- return this.canvases[batchIdx] || this.imageTensors[batchIdx];
- };
- NetInput.prototype.getInputDimensions = function (batchIdx) {
- return this._inputDimensions[batchIdx];
- };
- NetInput.prototype.getInputHeight = function (batchIdx) {
- return this._inputDimensions[batchIdx][0];
- };
- NetInput.prototype.getInputWidth = function (batchIdx) {
- return this._inputDimensions[batchIdx][1];
- };
- NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
- if (typeof this.inputSize !== 'number') {
- throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
- }
- var width = this.getInputWidth(batchIdx);
- var height = this.getInputHeight(batchIdx);
- return computeReshapedDimensions({ width: width, height: height }, this.inputSize);
- };
- /**
- * Create a batch tensor from all input canvases and tensors
- * with size [batchSize, inputSize, inputSize, 3].
- *
- * @param inputSize Height and width of the tensor.
- * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
- * both sides of the minor dimension oof the image.
- * @returns The batch tensor.
- */
- NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
- var _this = this;
- if (isCenterInputs === void 0) { isCenterInputs = true; }
- this._inputSize = inputSize;
- return tidy(function () {
- var inputTensors = range$1(_this.batchSize, 0, 1).map(function (batchIdx) {
- var input = _this.getInput(batchIdx);
- if (input instanceof Tensor) {
- var imgTensor = isTensor4D(input) ? input : input.expandDims();
- imgTensor = padToSquare(imgTensor, isCenterInputs);
- if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
- imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]);
- }
- return imgTensor.as3D(inputSize, inputSize, 3);
- }
- if (input instanceof HTMLCanvasElement) {
- return fromPixels(imageToSquare(input, inputSize, isCenterInputs));
- }
- throw new Error("toBatchTensor - at batchIdx " + batchIdx + ", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have " + input);
- });
- var batchTensor = stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);
- return batchTensor;
- });
- };
- return NetInput;
- }());
-
- /**
- * Validates the input to make sure, they are valid net inputs and awaits all media elements
- * to be finished loading.
- *
- * @param input The input, which can be a media element or an array of different media elements.
- * @returns A NetInput instance, which can be passed into one of the neural networks.
- */
- function toNetInput(inputs) {
- return __awaiter$1(this, void 0, void 0, function () {
- var inputArgArray, getIdxHint, inputArray;
- return __generator$1(this, function (_a) {
- switch (_a.label) {
- case 0:
- if (inputs instanceof NetInput) {
- return [2 /*return*/, inputs];
- }
- inputArgArray = Array.isArray(inputs)
- ? inputs
- : [inputs];
- if (!inputArgArray.length) {
- throw new Error('toNetInput - empty array passed as input');
- }
- getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
- inputArray = inputArgArray.map(resolveInput);
- inputArray.forEach(function (input, i) {
- if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
- if (typeof inputArgArray[i] === 'string') {
- throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
- }
- throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
- }
- if (isTensor4D(input)) {
- // if tf.Tensor4D is passed in the input array, the batch size has to be 1
- var batchSize = input.shape[0];
- if (batchSize !== 1) {
- throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
- }
- }
- });
- // wait for all media elements being loaded
- return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];
- case 1:
- // wait for all media elements being loaded
- _a.sent();
- return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];
- }
- });
- });
- }
-
- function iou(box1, box2, isIOU) {
- if (isIOU === void 0) { isIOU = true; }
- var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
- var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
- var interSection = width * height;
- return isIOU
- ? interSection / (box1.area + box2.area - interSection)
- : interSection / Math.min(box1.area, box2.area);
- }
-
- function nonMaxSuppression$1(boxes, scores, iouThreshold, isIOU) {
- if (isIOU === void 0) { isIOU = true; }
- var indicesSortedByScore = scores
- .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
- .sort(function (c1, c2) { return c1.score - c2.score; })
- .map(function (c) { return c.boxIndex; });
- var pick = [];
- var _loop_1 = function () {
- var curr = indicesSortedByScore.pop();
- pick.push(curr);
- var indices = indicesSortedByScore;
- var outputs = [];
- for (var i = 0; i < indices.length; i++) {
- var idx = indices[i];
- var currBox = boxes[curr];
- var idxBox = boxes[idx];
- outputs.push(iou(currBox, idxBox, isIOU));
- }
- indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
- };
- while (indicesSortedByScore.length > 0) {
- _loop_1();
- }
- return pick;
- }
-
- function normalize(x, meanRgb) {
- return tidy(function () {
- var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];
- var avg_r = fill(x.shape.slice(0, 3).concat([1]), r);
- var avg_g = fill(x.shape.slice(0, 3).concat([1]), g);
- var avg_b = fill(x.shape.slice(0, 3).concat([1]), b);
- var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
- return sub(x, avg_rgb);
- });
- }
-
- function shuffleArray(inputArray) {
- var array = inputArray.slice();
- for (var i = array.length - 1; i > 0; i--) {
- var j = Math.floor(Math.random() * (i + 1));
- var x = array[i];
- array[i] = array[j];
- array[j] = x;
- }
- return array;
- }
-
- function sigmoid$1(x) {
- return 1 / (1 + Math.exp(-x));
- }
- function inverseSigmoid(x) {
- return Math.log(x / (1 - x));
- }
-
- var NeuralNetwork = /** @class */ (function () {
- function NeuralNetwork(_name) {
- this._name = _name;
- this._params = undefined;
- this._paramMappings = [];
- }
- Object.defineProperty(NeuralNetwork.prototype, "params", {
- get: function () {
- return this._params;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
- get: function () {
- return this._paramMappings;
- },
- enumerable: true,
- configurable: true
- });
- NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
- var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
- return obj[objProp];
- };
- NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
- var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
- obj[objProp].dispose();
- obj[objProp] = tensor$$1;
- };
- NeuralNetwork.prototype.getParamList = function () {
- var _this = this;
- return this._paramMappings.map(function (_a) {
- var paramPath = _a.paramPath;
- return ({
- path: paramPath,
- tensor: _this.getParamFromPath(paramPath)
- });
- });
- };
- NeuralNetwork.prototype.getTrainableParams = function () {
- return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
- };
- NeuralNetwork.prototype.getFrozenParams = function () {
- return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
- };
- NeuralNetwork.prototype.variable = function () {
- var _this = this;
- this.getFrozenParams().forEach(function (_a) {
- var path = _a.path, tensor$$1 = _a.tensor;
- _this.reassignParamFromPath(path, tensor$$1.variable());
- });
- };
- NeuralNetwork.prototype.freeze = function () {
- var _this = this;
- this.getTrainableParams().forEach(function (_a) {
- var path = _a.path, variable$$1 = _a.tensor;
- var tensor$$1 = tensor(variable$$1.dataSync());
- variable$$1.dispose();
- _this.reassignParamFromPath(path, tensor$$1);
- });
- };
- NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
- if (throwOnRedispose === void 0) { throwOnRedispose = true; }
- this.getParamList().forEach(function (param) {
- if (throwOnRedispose && param.tensor.isDisposed) {
- throw new Error("param tensor has already been disposed for path " + param.path);
- }
- param.tensor.dispose();
- });
- this._params = undefined;
- };
- NeuralNetwork.prototype.serializeParams = function () {
- return new Float32Array(this.getParamList()
- .map(function (_a) {
- var tensor$$1 = _a.tensor;
- return Array.from(tensor$$1.dataSync());
- })
- .reduce(function (flat, arr) { return flat.concat(arr); }));
- };
- NeuralNetwork.prototype.load = function (weightsOrUrl) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _a, paramMappings, params;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- if (weightsOrUrl instanceof Float32Array) {
- this.extractWeights(weightsOrUrl);
- return [2 /*return*/];
- }
- if (weightsOrUrl && typeof weightsOrUrl !== 'string') {
- throw new Error(this._name + ".load - expected model uri, or weights as Float32Array");
- }
- return [4 /*yield*/, this.loadQuantizedParams(weightsOrUrl)];
- case 1:
- _a = _b.sent(), paramMappings = _a.paramMappings, params = _a.params;
- this._paramMappings = paramMappings;
- this._params = params;
- return [2 /*return*/];
- }
- });
- });
- };
- NeuralNetwork.prototype.extractWeights = function (weights) {
- var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
- this._paramMappings = paramMappings;
- this._params = params;
- };
- NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
- if (!this.params) {
- throw new Error("traversePropertyPath - model has no loaded params");
- }
- var result = paramPath.split('/').reduce(function (res, objProp) {
- if (!res.nextObj.hasOwnProperty(objProp)) {
- throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
- }
- return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
- }, { nextObj: this.params });
- var obj = result.obj, objProp = result.objProp;
- if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
- throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
- }
- return { obj: obj, objProp: objProp };
- };
- NeuralNetwork.prototype.loadQuantizedParams = function (_) {
- throw new Error(this._name + ".loadQuantizedParams - not implemented");
- };
- NeuralNetwork.prototype.extractParams = function (_) {
- throw new Error(this._name + ".extractParams - not implemented");
- };
- return NeuralNetwork;
- }());
-
- var FaceDetection = /** @class */ (function (_super) {
- __extends$1(FaceDetection, _super);
- function FaceDetection(score, relativeBox, imageDims) {
- return _super.call(this, score, score, '', relativeBox, imageDims) || this;
- }
- return FaceDetection;
- }(ObjectDetection));
-
- // face alignment constants
- var relX = 0.5;
- var relY = 0.43;
- var relScale = 0.45;
- var FaceLandmarks = /** @class */ (function () {
- function FaceLandmarks(relativeFaceLandmarkPositions, imageDims, shift) {
- if (shift === void 0) { shift = new Point(0, 0); }
- var width = imageDims.width, height = imageDims.height;
- this._imageWidth = width;
- this._imageHeight = height;
- this._shift = shift;
- this._faceLandmarks = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
- }
- FaceLandmarks.prototype.getShift = function () {
- return new Point(this._shift.x, this._shift.y);
- };
- FaceLandmarks.prototype.getImageWidth = function () {
- return this._imageWidth;
- };
- FaceLandmarks.prototype.getImageHeight = function () {
- return this._imageHeight;
- };
- FaceLandmarks.prototype.getPositions = function () {
- return this._faceLandmarks;
- };
- FaceLandmarks.prototype.getRelativePositions = function () {
- var _this = this;
- return this._faceLandmarks.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this._imageWidth, _this._imageHeight)); });
- };
- FaceLandmarks.prototype.forSize = function (width, height) {
- return new this.constructor(this.getRelativePositions(), { width: width, height: height });
- };
- FaceLandmarks.prototype.shift = function (x, y) {
- return new this.constructor(this.getRelativePositions(), { width: this._imageWidth, height: this._imageHeight }, new Point(x, y));
- };
- FaceLandmarks.prototype.shiftByPoint = function (pt) {
- return this.shift(pt.x, pt.y);
- };
- /**
- * Aligns the face landmarks after face detection from the relative positions of the faces
- * bounding box, or it's current shift. This function should be used to align the face images
- * after face detection has been performed, before they are passed to the face recognition net.
- * This will make the computed face descriptor more accurate.
- *
- * @param detection (optional) The bounding box of the face or the face detection result. If
- * no argument was passed the position of the face landmarks are assumed to be relative to
- * it's current shift.
- * @returns The bounding box of the aligned face.
- */
- FaceLandmarks.prototype.align = function (detection) {
- if (detection) {
- var box = detection instanceof FaceDetection
- ? detection.getBox().floor()
- : detection;
- return this.shift(box.x, box.y).align();
- }
- var centers = this.getRefPointsForAlignment();
- var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
- var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
- var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
- var size = Math.floor(eyeToMouthDist / relScale);
- var refPoint = getCenterPoint(centers);
- // TODO: pad in case rectangle is out of image bounds
- var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
- var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
- return new Rect(x, y, Math.min(size, this._imageWidth + x), Math.min(size, this._imageHeight + y));
- };
- FaceLandmarks.prototype.getRefPointsForAlignment = function () {
- throw new Error('getRefPointsForAlignment not implemented by base class');
- };
- return FaceLandmarks;
- }());
-
- var FaceLandmarks5 = /** @class */ (function (_super) {
- __extends$1(FaceLandmarks5, _super);
- function FaceLandmarks5() {
- return _super !== null && _super.apply(this, arguments) || this;
- }
- FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
- var pts = this.getPositions();
- return [
- pts[0],
- pts[1],
- getCenterPoint([pts[3], pts[4]])
- ];
- };
- return FaceLandmarks5;
- }(FaceLandmarks));
-
- var FaceLandmarks68 = /** @class */ (function (_super) {
- __extends$1(FaceLandmarks68, _super);
- function FaceLandmarks68() {
- return _super !== null && _super.apply(this, arguments) || this;
- }
- FaceLandmarks68.prototype.getJawOutline = function () {
- return this._faceLandmarks.slice(0, 17);
- };
- FaceLandmarks68.prototype.getLeftEyeBrow = function () {
- return this._faceLandmarks.slice(17, 22);
- };
- FaceLandmarks68.prototype.getRightEyeBrow = function () {
- return this._faceLandmarks.slice(22, 27);
- };
- FaceLandmarks68.prototype.getNose = function () {
- return this._faceLandmarks.slice(27, 36);
- };
- FaceLandmarks68.prototype.getLeftEye = function () {
- return this._faceLandmarks.slice(36, 42);
- };
- FaceLandmarks68.prototype.getRightEye = function () {
- return this._faceLandmarks.slice(42, 48);
- };
- FaceLandmarks68.prototype.getMouth = function () {
- return this._faceLandmarks.slice(48, 68);
- };
- FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
- return [
- this.getLeftEye(),
- this.getRightEye(),
- this.getMouth()
- ].map(getCenterPoint);
- };
- return FaceLandmarks68;
- }(FaceLandmarks));
-
- var FullFaceDescription = /** @class */ (function () {
- function FullFaceDescription(_detection, _landmarks, _descriptor) {
- this._detection = _detection;
- this._landmarks = _landmarks;
- this._descriptor = _descriptor;
- }
- Object.defineProperty(FullFaceDescription.prototype, "detection", {
- get: function () {
- return this._detection;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(FullFaceDescription.prototype, "landmarks", {
- get: function () {
- return this._landmarks;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(FullFaceDescription.prototype, "descriptor", {
- get: function () {
- return this._descriptor;
- },
- enumerable: true,
- configurable: true
- });
- FullFaceDescription.prototype.forSize = function (width, height) {
- return new FullFaceDescription(this._detection.forSize(width, height), this._landmarks.forSize(width, height), this._descriptor);
- };
- return FullFaceDescription;
- }());
-
- function drawContour(ctx, points, isClosed) {
- if (isClosed === void 0) { isClosed = false; }
- ctx.beginPath();
- points.slice(1).forEach(function (_a, prevIdx) {
- var x = _a.x, y = _a.y;
- var from = points[prevIdx];
- ctx.moveTo(from.x, from.y);
- ctx.lineTo(x, y);
- });
- if (isClosed) {
- var from = points[points.length - 1];
- var to = points[0];
- if (!from || !to) {
- return;
- }
- ctx.moveTo(from.x, from.y);
- ctx.lineTo(to.x, to.y);
- }
- ctx.stroke();
- }
-
- function drawLandmarks(canvasArg, faceLandmarks, options) {
- var canvas = resolveInput(canvasArg);
- if (!(canvas instanceof HTMLCanvasElement)) {
- throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
- }
- var drawOptions = Object.assign(getDefaultDrawOptions(options), (options || {}));
- var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
- var ctx = getContext2dOrThrow(canvas);
- var lineWidth = drawOptions.lineWidth, _a = drawOptions.color, color = _a === void 0 ? 'blue' : _a;
- var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
- faceLandmarksArray.forEach(function (landmarks) {
- if (drawLines && landmarks instanceof FaceLandmarks68) {
- ctx.strokeStyle = color;
- ctx.lineWidth = lineWidth;
- drawContour(ctx, landmarks.getJawOutline());
- drawContour(ctx, landmarks.getLeftEyeBrow());
- drawContour(ctx, landmarks.getRightEyeBrow());
- drawContour(ctx, landmarks.getNose());
- drawContour(ctx, landmarks.getLeftEye(), true);
- drawContour(ctx, landmarks.getRightEye(), true);
- drawContour(ctx, landmarks.getMouth(), true);
- return;
- }
- // else draw points
- var ptOffset = lineWidth / 2;
- ctx.fillStyle = color;
- landmarks.getPositions().forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
- });
- }
-
- /**
- * Extracts the image regions containing the detected faces.
- *
- * @param input The image that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns The Canvases of the corresponding image region for each detected face.
- */
- function extractFaces(input, detections) {
- return __awaiter$1(this, void 0, void 0, function () {
- var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- canvas = input;
- if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
- return [4 /*yield*/, toNetInput(input)];
- case 1:
- netInput = _b.sent();
- if (netInput.batchSize > 1) {
- throw new Error('extractFaces - batchSize > 1 not supported');
- }
- tensorOrCanvas = netInput.getInput(0);
- if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
- _a = tensorOrCanvas;
- return [3 /*break*/, 4];
- case 2: return [4 /*yield*/, imageTensorToCanvas(tensorOrCanvas)];
- case 3:
- _a = _b.sent();
- _b.label = 4;
- case 4:
- canvas = _a;
- _b.label = 5;
- case 5:
- ctx = getContext2dOrThrow(canvas);
- boxes = detections.map(function (det) { return det instanceof FaceDetection
- ? det.forSize(canvas.width, canvas.height).getBox().floor()
- : det; })
- .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
- return [2 /*return*/, boxes.map(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- var faceImg = createCanvas({ width: width, height: height });
- getContext2dOrThrow(faceImg)
- .putImageData(ctx.getImageData(x, y, width, height), 0, 0);
- return faceImg;
- })];
- }
- });
- });
- }
-
- /**
- * Extracts the tensors of the image regions containing the detected faces.
- * Useful if you want to compute the face descriptors for the face images.
- * Using this method is faster then extracting a canvas for each face and
- * converting them to tensors individually.
- *
- * @param imageTensor The image tensor that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns Tensors of the corresponding image region for each detected face.
- */
- function extractFaceTensors(imageTensor, detections) {
- return __awaiter$1(this, void 0, void 0, function () {
- return __generator$1(this, function (_a) {
- if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
- throw new Error('extractFaceTensors - batchSize > 1 not supported');
- }
- return [2 /*return*/, tidy(function () {
- var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
- var boxes = detections.map(function (det) { return det instanceof FaceDetection
- ? det.forSize(imgWidth, imgHeight).getBox()
- : det; })
- .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
- var faceTensors = boxes.map(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- return slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
- });
- return faceTensors;
- })];
- });
- });
- }
-
- function euclideanDistance(arr1, arr2) {
- if (arr1.length !== arr2.length)
- throw new Error('euclideanDistance: arr1.length !== arr2.length');
- var desc1 = Array.from(arr1);
- var desc2 = Array.from(arr2);
- return Math.sqrt(desc1
- .map(function (val, i) { return val - desc2[i]; })
- .reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
- }
-
- function extractorsFactory(extractWeights, paramMappings) {
- function extractDepthwiseConvParams(numChannels, mappedPrefix) {
- var filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
- var batch_norm_scale = tensor1d(extractWeights(numChannels));
- var batch_norm_offset = tensor1d(extractWeights(numChannels));
- var batch_norm_mean = tensor1d(extractWeights(numChannels));
- var batch_norm_variance = tensor1d(extractWeights(numChannels));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
- return {
- filters: filters,
- batch_norm_scale: batch_norm_scale,
- batch_norm_offset: batch_norm_offset,
- batch_norm_mean: batch_norm_mean,
- batch_norm_variance: batch_norm_variance
- };
- }
- function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
- var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
- var bias = tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
- return { filters: filters, bias: bias };
- }
- function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
- var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
- return {
- filters: filters,
- batch_norm_offset: bias
- };
- }
- function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
- var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
- var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
- return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
- }
- function extractMobilenetV1Params() {
- var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
- var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
- var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
- var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
- var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
- var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
- var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
- var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
- var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
- var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
- var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
- var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
- var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
- var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
- return {
- conv_0: conv_0,
- conv_1: conv_1,
- conv_2: conv_2,
- conv_3: conv_3,
- conv_4: conv_4,
- conv_5: conv_5,
- conv_6: conv_6,
- conv_7: conv_7,
- conv_8: conv_8,
- conv_9: conv_9,
- conv_10: conv_10,
- conv_11: conv_11,
- conv_12: conv_12,
- conv_13: conv_13
- };
- }
- function extractPredictionLayerParams() {
- var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
- var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
- var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
- var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
- var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
- var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
- var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
- var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
- var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
- var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
- var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
- var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
- var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
- var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
- var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
- var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
- var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
- var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
- var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
- var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
- var box_predictor_0 = {
- box_encoding_predictor: box_encoding_0_predictor,
- class_predictor: class_predictor_0
- };
- var box_predictor_1 = {
- box_encoding_predictor: box_encoding_1_predictor,
- class_predictor: class_predictor_1
- };
- var box_predictor_2 = {
- box_encoding_predictor: box_encoding_2_predictor,
- class_predictor: class_predictor_2
- };
- var box_predictor_3 = {
- box_encoding_predictor: box_encoding_3_predictor,
- class_predictor: class_predictor_3
- };
- var box_predictor_4 = {
- box_encoding_predictor: box_encoding_4_predictor,
- class_predictor: class_predictor_4
- };
- var box_predictor_5 = {
- box_encoding_predictor: box_encoding_5_predictor,
- class_predictor: class_predictor_5
- };
- return {
- conv_0: conv_0,
- conv_1: conv_1,
- conv_2: conv_2,
- conv_3: conv_3,
- conv_4: conv_4,
- conv_5: conv_5,
- conv_6: conv_6,
- conv_7: conv_7,
- box_predictor_0: box_predictor_0,
- box_predictor_1: box_predictor_1,
- box_predictor_2: box_predictor_2,
- box_predictor_3: box_predictor_3,
- box_predictor_4: box_predictor_4,
- box_predictor_5: box_predictor_5
- };
- }
- return {
- extractMobilenetV1Params: extractMobilenetV1Params,
- extractPredictionLayerParams: extractPredictionLayerParams
- };
- }
- function extractParams(weights) {
- var paramMappings = [];
- var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
- var mobilenetv1 = extractMobilenetV1Params();
- var prediction_layer = extractPredictionLayerParams();
- var extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
- var output_layer = {
- extra_dim: extra_dim
- };
- paramMappings.push({ paramPath: 'output_layer/extra_dim' });
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return {
- params: {
- mobilenetv1: mobilenetv1,
- prediction_layer: prediction_layer,
- output_layer: output_layer
- },
- paramMappings: paramMappings
- };
- }
-
- var DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model';
- function extractorsFactory$1(weightMap, paramMappings) {
- var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
- function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
- var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
- var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
- return { filters: filters, batch_norm_offset: batch_norm_offset };
- }
- function extractConvPairParams(idx) {
- var mappedPrefix = "mobilenetv1/conv_" + idx;
- var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
- var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
- var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
- var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
- var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
- var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
- var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
- var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
- return {
- depthwise_conv: {
- filters: filters,
- batch_norm_scale: batch_norm_scale,
- batch_norm_offset: batch_norm_offset,
- batch_norm_mean: batch_norm_mean,
- batch_norm_variance: batch_norm_variance
- },
- pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
- };
- }
- function extractMobilenetV1Params() {
- return {
- conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
- conv_1: extractConvPairParams(1),
- conv_2: extractConvPairParams(2),
- conv_3: extractConvPairParams(3),
- conv_4: extractConvPairParams(4),
- conv_5: extractConvPairParams(5),
- conv_6: extractConvPairParams(6),
- conv_7: extractConvPairParams(7),
- conv_8: extractConvPairParams(8),
- conv_9: extractConvPairParams(9),
- conv_10: extractConvPairParams(10),
- conv_11: extractConvPairParams(11),
- conv_12: extractConvPairParams(12),
- conv_13: extractConvPairParams(13)
- };
- }
- function extractConvParams(prefix, mappedPrefix) {
- var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
- var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
- return { filters: filters, bias: bias };
- }
- function extractBoxPredictorParams(idx) {
- var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
- var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
- return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
- }
- function extractPredictionLayerParams() {
- return {
- conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
- conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
- conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
- conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
- conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
- conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
- conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
- conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
- box_predictor_0: extractBoxPredictorParams(0),
- box_predictor_1: extractBoxPredictorParams(1),
- box_predictor_2: extractBoxPredictorParams(2),
- box_predictor_3: extractBoxPredictorParams(3),
- box_predictor_4: extractBoxPredictorParams(4),
- box_predictor_5: extractBoxPredictorParams(5)
- };
- }
- return {
- extractMobilenetV1Params: extractMobilenetV1Params,
- extractPredictionLayerParams: extractPredictionLayerParams
- };
- }
- function loadQuantizedParams(uri) {
- return __awaiter$1(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractMobilenetV1Params, extractPredictionLayerParams, extra_dim, params;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory$1(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
- extra_dim = weightMap['Output/extra_dim'];
- paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
- if (!isTensor3D(extra_dim)) {
- throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
- }
- params = {
- mobilenetv1: extractMobilenetV1Params(),
- prediction_layer: extractPredictionLayerParams(),
- output_layer: {
- extra_dim: extra_dim
- }
- };
- disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
- }
-
- function pointwiseConvLayer(x, params, strides) {
- return tidy(function () {
- var out = conv2d(x, params.filters, strides, 'same');
- out = add(out, params.batch_norm_offset);
- return clipByValue(out, 0, 6);
- });
- }
-
- var epsilon = 0.0010000000474974513;
- function depthwiseConvLayer(x, params, strides) {
- return tidy(function () {
- var out = depthwiseConv2d(x, params.filters, strides, 'same');
- out = batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
- return clipByValue(out, 0, 6);
- });
- }
- function getStridesForLayerIdx(layerIdx) {
- return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
- }
- function mobileNetV1(x, params) {
- return tidy(function () {
- var conv11 = null;
- var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
- var convPairParams = [
- params.conv_1,
- params.conv_2,
- params.conv_3,
- params.conv_4,
- params.conv_5,
- params.conv_6,
- params.conv_7,
- params.conv_8,
- params.conv_9,
- params.conv_10,
- params.conv_11,
- params.conv_12,
- params.conv_13
- ];
- convPairParams.forEach(function (param, i) {
- var layerIdx = i + 1;
- var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
- out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
- out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
- if (layerIdx === 11) {
- conv11 = out;
- }
- });
- if (conv11 === null) {
- throw new Error('mobileNetV1 - output of conv layer 11 is null');
- }
- return {
- out: out,
- conv11: conv11
- };
- });
- }
-
- function nonMaxSuppression$2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
- var numBoxes = boxes.shape[0];
- var outputSize = Math.min(maxOutputSize, numBoxes);
- var candidates = scores
- .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
- .filter(function (c) { return c.score > scoreThreshold; })
- .sort(function (c1, c2) { return c2.score - c1.score; });
- var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
- var selected = [];
- candidates.forEach(function (c) {
- if (selected.length >= outputSize) {
- return;
- }
- var originalScore = c.score;
- for (var j = selected.length - 1; j >= 0; --j) {
- var iou = IOU(boxes, c.boxIndex, selected[j]);
- if (iou === 0.0) {
- continue;
- }
- c.score *= suppressFunc(iou);
- if (c.score <= scoreThreshold) {
- break;
- }
- }
- if (originalScore === c.score) {
- selected.push(c.boxIndex);
- }
- });
- return selected;
- }
- function IOU(boxes, i, j) {
- var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
- var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
- var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
- var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
- var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
- var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
- var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
- var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
- var areaI = (ymaxI - yminI) * (xmaxI - xminI);
- var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
- if (areaI <= 0 || areaJ <= 0) {
- return 0.0;
- }
- var intersectionYmin = Math.max(yminI, yminJ);
- var intersectionXmin = Math.max(xminI, xminJ);
- var intersectionYmax = Math.min(ymaxI, ymaxJ);
- var intersectionXmax = Math.min(xmaxI, xmaxJ);
- var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
- Math.max(intersectionXmax - intersectionXmin, 0.0);
- return intersectionArea / (areaI + areaJ - intersectionArea);
- }
-
- function getCenterCoordinatesAndSizesLayer(x) {
- var vec = unstack(transpose(x, [1, 0]));
- var sizes = [
- sub(vec[2], vec[0]),
- sub(vec[3], vec[1])
- ];
- var centers = [
- add(vec[0], div(sizes[0], scalar(2))),
- add(vec[1], div(sizes[1], scalar(2)))
- ];
- return {
- sizes: sizes,
- centers: centers
- };
- }
- function decodeBoxesLayer(x0, x1) {
- var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
- var vec = unstack(transpose(x1, [1, 0]));
- var div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
- var add0_out = add(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
- var div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
- var add1_out = add(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
- return transpose(stack([
- sub(add0_out, div0_out),
- sub(add1_out, div1_out),
- add(add0_out, div0_out),
- add(add1_out, div1_out)
- ]), [1, 0]);
- }
- function outputLayer(boxPredictions, classPredictions, params) {
- return tidy(function () {
- var batchSize = boxPredictions.shape[0];
- var boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
- boxes = reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
- var scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
- var scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
- scores = reshape(scores, [batchSize, scores.shape[1]]);
- var boxesByBatch = unstack(boxes);
- var scoresByBatch = unstack(scores);
- return {
- boxes: boxesByBatch,
- scores: scoresByBatch
- };
- });
- }
-
- function convLayer(x, params, padding, withRelu) {
- if (padding === void 0) { padding = 'same'; }
- if (withRelu === void 0) { withRelu = false; }
- return tidy(function () {
- var out = add(conv2d(x, params.filters, [1, 1], padding), params.bias);
- return withRelu ? relu(out) : out;
- });
- }
-
- function extractConvParamsFactory(extractWeights, paramMappings) {
- return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
- var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
- var bias = tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
- return { filters: filters, bias: bias };
- };
- }
-
- function extractFCParamsFactory(extractWeights, paramMappings) {
- return function (channelsIn, channelsOut, mappedPrefix) {
- var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
- var fc_bias = tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
- return {
- weights: fc_weights,
- bias: fc_bias
- };
- };
- }
-
- var SeparableConvParams = /** @class */ (function () {
- function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
- this.depthwise_filter = depthwise_filter;
- this.pointwise_filter = pointwise_filter;
- this.bias = bias;
- }
- return SeparableConvParams;
- }());
- var SizeType;
- (function (SizeType) {
- SizeType["XS"] = "xs";
- SizeType["SM"] = "sm";
- SizeType["MD"] = "md";
- SizeType["LG"] = "lg";
- })(SizeType || (SizeType = {}));
-
- function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
- return function (channelsIn, channelsOut, mappedPrefix) {
- var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
- var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
- var bias = tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
- return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
- };
- }
- function loadSeparableConvParamsFactory(extractWeightEntry) {
- return function (prefix) {
- var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
- var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
- };
- }
-
- var isNumber = function (arg) { return typeof arg === 'number'; };
- function validateConfig(config) {
- if (!config) {
- throw new Error("invalid config: " + config);
- }
- if (typeof config.withSeparableConvs !== 'boolean') {
- throw new Error("config.withSeparableConvs has to be a boolean, have: " + config.withSeparableConvs);
- }
- if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {
- throw new Error("config.iouThreshold has to be a number between [0, 1], have: " + config.iouThreshold);
- }
- if (!Array.isArray(config.classes)
- || !config.classes.length
- || !config.classes.every(function (c) { return typeof c === 'string'; })) {
- throw new Error("config.classes has to be an array class names: string[], have: " + JSON.stringify(config.classes));
- }
- if (!Array.isArray(config.anchors)
- || !config.anchors.length
- || !config.anchors.map(function (a) { return a || {}; }).every(function (a) { return isNumber(a.x) && isNumber(a.y); })) {
- throw new Error("config.anchors has to be an array of { x: number, y: number }, have: " + JSON.stringify(config.anchors));
- }
- if (config.meanRgb && (!Array.isArray(config.meanRgb)
- || config.meanRgb.length !== 3
- || !config.meanRgb.every(isNumber))) {
- throw new Error("config.meanRgb has to be an array of shape [number, number, number], have: " + JSON.stringify(config.meanRgb));
- }
- }
- function validateTrainConfig(config) {
- if (![config.noObjectScale, config.objectScale, config.coordScale, config.classScale].every(isNumber)) {
- throw new Error("for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file");
- }
- return config;
- }
-
- var INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
- var CELL_SIZE = 32;
- var DEFAULT_FILTER_SIZES = [
- 3, 16, 32, 64, 128, 256, 512, 1024, 1024
- ];
-
- function leaky(x) {
- return tidy(function () {
- var min$$1 = mul(x, scalar(0.10000000149011612));
- return add(relu(sub(x, min$$1)), min$$1);
- //return tf.maximum(x, min)
- });
- }
-
- function convWithBatchNorm(x, params) {
- return tidy(function () {
- var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
- if (params instanceof SeparableConvParams) {
- out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
- out = add(out, params.bias);
- }
- else {
- out = conv2d(out, params.conv.filters, [1, 1], 'valid');
- out = sub(out, params.bn.sub);
- out = mul(out, params.bn.truediv);
- out = add(out, params.conv.bias);
- }
- return leaky(out);
- });
- }
-
- function extractorsFactory$2(extractWeights, paramMappings) {
- var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
- function extractBatchNormParams(size, mappedPrefix) {
- var sub$$1 = tensor1d(extractWeights(size));
- var truediv = tensor1d(extractWeights(size));
- paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
- return { sub: sub$$1, truediv: truediv };
- }
- function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
- var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
- var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
- return { conv: conv, bn: bn };
- }
- var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
- return {
- extractConvParams: extractConvParams,
- extractConvWithBatchNormParams: extractConvWithBatchNormParams,
- extractSeparableConvParams: extractSeparableConvParams
- };
- }
- function extractParams$1(weights, withSeparableConvs, boxEncodingSize, filterSizes) {
- var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var paramMappings = [];
- var _b = extractorsFactory$2(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
- var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
- var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
- var conv0 = extractConvFn(s0, s1, 'conv0');
- var conv1 = extractConvFn(s1, s2, 'conv1');
- var conv2 = extractConvFn(s2, s3, 'conv2');
- var conv3 = extractConvFn(s3, s4, 'conv3');
- var conv4 = extractConvFn(s4, s5, 'conv4');
- var conv5 = extractConvFn(s5, s6, 'conv5');
- var conv6 = extractConvFn(s6, s7, 'conv6');
- var conv7 = extractConvFn(s7, s8, 'conv7');
- var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- var params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
- return { params: params, paramMappings: paramMappings };
- }
-
- function getDefaultForwardParams(params) {
- return Object.assign({}, {
- inputSize: SizeType.MD,
- scoreThreshold: 0.5
- }, params);
- }
- function getDefaultBackwardOptions(options) {
- return Object.assign({}, {
- minBoxSize: CELL_SIZE
- }, options);
- }
-
- function extractorsFactory$3(weightMap, paramMappings) {
- var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
- function extractBatchNormParams(prefix) {
- var sub = extractWeightEntry(prefix + "/sub", 1);
- var truediv = extractWeightEntry(prefix + "/truediv", 1);
- return { sub: sub, truediv: truediv };
- }
- function extractConvParams(prefix) {
- var filters = extractWeightEntry(prefix + "/filters", 4);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { filters: filters, bias: bias };
- }
- function extractConvWithBatchNormParams(prefix) {
- var conv = extractConvParams(prefix + "/conv");
- var bn = extractBatchNormParams(prefix + "/bn");
- return { conv: conv, bn: bn };
- }
- var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
- return {
- extractConvParams: extractConvParams,
- extractConvWithBatchNormParams: extractConvWithBatchNormParams,
- extractSeparableConvParams: extractSeparableConvParams
- };
- }
- function loadQuantizedParams$1(uri, withSeparableConvs, defaultModelName) {
- if (defaultModelName === void 0) { defaultModelName = ''; }
- return __awaiter$1(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams, extractConvFn, params;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, loadWeightMap(uri, defaultModelName)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory$3(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
- extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
- params = {
- conv0: extractConvFn('conv0'),
- conv1: extractConvFn('conv1'),
- conv2: extractConvFn('conv2'),
- conv3: extractConvFn('conv3'),
- conv4: extractConvFn('conv4'),
- conv5: extractConvFn('conv5'),
- conv6: extractConvFn('conv6'),
- conv7: extractConvFn('conv7'),
- conv8: extractConvParams('conv8')
- };
- disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
- }
-
- var TinyYolov2 = /** @class */ (function (_super) {
- __extends$1(TinyYolov2, _super);
- function TinyYolov2(config) {
- var _this = _super.call(this, 'TinyYolov2') || this;
- validateConfig(config);
- _this._config = config;
- return _this;
- }
- Object.defineProperty(TinyYolov2.prototype, "config", {
- get: function () {
- return this._config;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2.prototype, "withClassScores", {
- get: function () {
- return this.config.withClassScores || this.config.classes.length > 1;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2.prototype, "boxEncodingSize", {
- get: function () {
- return 5 + (this.withClassScores ? this.config.classes.length : 0);
- },
- enumerable: true,
- configurable: true
- });
- TinyYolov2.prototype.forwardInput = function (input, inputSize) {
- var _this = this;
- var params = this.params;
- if (!params) {
- throw new Error('TinyYolov2 - load model before inference');
- }
- var out = tidy(function () {
- var batchTensor = input.toBatchTensor(inputSize, false).toFloat();
- batchTensor = _this.config.meanRgb
- ? normalize(batchTensor, _this.config.meanRgb)
- : batchTensor;
- batchTensor = batchTensor.div(scalar(256));
- var out = convWithBatchNorm(batchTensor, params.conv0);
- out = maxPool(out, [2, 2], [2, 2], 'same');
- out = convWithBatchNorm(out, params.conv1);
- out = maxPool(out, [2, 2], [2, 2], 'same');
- out = convWithBatchNorm(out, params.conv2);
- out = maxPool(out, [2, 2], [2, 2], 'same');
- out = convWithBatchNorm(out, params.conv3);
- out = maxPool(out, [2, 2], [2, 2], 'same');
- out = convWithBatchNorm(out, params.conv4);
- out = maxPool(out, [2, 2], [2, 2], 'same');
- out = convWithBatchNorm(out, params.conv5);
- out = maxPool(out, [2, 2], [1, 1], 'same');
- out = convWithBatchNorm(out, params.conv6);
- out = convWithBatchNorm(out, params.conv7);
- out = convLayer(out, params.conv8, 'valid', false);
- return out;
- });
- return out;
- };
- TinyYolov2.prototype.forward = function (input, inputSize) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, toNetInput(input)];
- case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
- case 2: return [2 /*return*/, _b.sent()];
- }
- });
- });
- };
- TinyYolov2.prototype.detect = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return __awaiter$1(this, void 0, void 0, function () {
- var _this = this;
- var _a, _inputSize, scoreThreshold, inputSize, netInput, out, out0, inputDimensions, results, boxes, scores, classScores, classNames, indices, detections;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = getDefaultForwardParams(forwardParams), _inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
- inputSize = typeof _inputSize === 'string'
- ? INPUT_SIZES[_inputSize]
- : _inputSize;
- if (typeof inputSize !== 'number') {
- throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
- }
- return [4 /*yield*/, toNetInput(input)];
- case 1:
- netInput = _b.sent();
- return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
- case 2:
- out = _b.sent();
- out0 = tidy(function () { return unstack(out)[0].expandDims(); });
- inputDimensions = {
- width: netInput.getInputWidth(0),
- height: netInput.getInputHeight(0)
- };
- results = this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
- out.dispose();
- out0.dispose();
- boxes = results.map(function (res) { return res.box; });
- scores = results.map(function (res) { return res.score; });
- classScores = results.map(function (res) { return res.classScore; });
- classNames = results.map(function (res) { return _this.config.classes[res.label]; });
- indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);
- detections = indices.map(function (idx) {
- return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);
- });
- return [2 /*return*/, detections];
- }
- });
- });
- };
- TinyYolov2.prototype.loadQuantizedParams = function (modelUri, defaultModelName) {
- if (defaultModelName === void 0) { defaultModelName = ''; }
- if (!modelUri) {
- throw new Error('loadQuantizedParams - please specify the modelUri');
- }
- return loadQuantizedParams$1(modelUri, this.config.withSeparableConvs, defaultModelName);
- };
- TinyYolov2.prototype.extractParams = function (weights) {
- var filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES;
- var numFilters = filterSizes ? filterSizes.length : undefined;
- if (numFilters !== 9) {
- throw new Error("TinyYolov2 - expected 9 convolutional filters, but found " + numFilters + " filterSizes in config");
- }
- return extractParams$1(weights, this.config.withSeparableConvs, this.boxEncodingSize, filterSizes);
- };
- TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {
- var _this = this;
- var width = inputBlobDimensions.width, height = inputBlobDimensions.height;
- var inputSize = Math.max(width, height);
- var correctionFactorX = inputSize / width;
- var correctionFactorY = inputSize / height;
- var numCells = outputTensor.shape[1];
- var numBoxes = this.config.anchors.length;
- var _a = tidy(function () {
- var reshaped = outputTensor.reshape([numCells, numCells, numBoxes, _this.boxEncodingSize]);
- var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
- var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
- var classScores = _this.withClassScores
- ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, _this.config.classes.length]), 3)
- : scalar(0);
- return [boxes, scores, classScores];
- }), boxesTensor = _a[0], scoresTensor = _a[1], classScoresTensor = _a[2];
- var results = [];
- for (var row = 0; row < numCells; row++) {
- for (var col = 0; col < numCells; col++) {
- for (var anchor = 0; anchor < numBoxes; anchor++) {
- var score = sigmoid$1(scoresTensor.get(row, col, anchor, 0));
- if (!scoreThreshold || score > scoreThreshold) {
- var ctX = ((col + sigmoid$1(boxesTensor.get(row, col, anchor, 0))) / numCells) * correctionFactorX;
- var ctY = ((row + sigmoid$1(boxesTensor.get(row, col, anchor, 1))) / numCells) * correctionFactorY;
- var width_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;
- var height_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;
- var x = (ctX - (width_1 / 2));
- var y = (ctY - (height_1 / 2));
- var pos = { row: row, col: col, anchor: anchor };
- var _b = this.withClassScores
- ? this.extractPredictedClass(classScoresTensor, pos)
- : { classScore: 1, label: 0 }, classScore = _b.classScore, label = _b.label;
- results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));
- }
- }
- }
- }
- boxesTensor.dispose();
- scoresTensor.dispose();
- classScoresTensor.dispose();
- return results;
- };
- TinyYolov2.prototype.extractPredictedClass = function (classesTensor, pos) {
- var row = pos.row, col = pos.col, anchor = pos.anchor;
- return Array(this.config.classes.length).fill(0)
- .map(function (_, i) { return classesTensor.get(row, col, anchor, i); })
- .map(function (classScore, label) { return ({
- classScore: classScore,
- label: label
- }); })
- .reduce(function (max$$1, curr) { return max$$1.classScore > curr.classScore ? max$$1 : curr; });
- };
- return TinyYolov2;
- }(NeuralNetwork));
-
- var TinyYolov2LossFunction = /** @class */ (function () {
- function TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config) {
- this._config = config;
- this._reshapedImgDims = reshapedImgDims;
- this._outputTensor = outputTensor;
- this._predictedBoxes = predictedBoxes;
- this.validateGroundTruthBoxes(groundTruth);
- this._groundTruth = this.assignGroundTruthToAnchors(groundTruth);
- var groundTruthMask = this.createGroundTruthMask();
- var _a = this.createCoordAndScoreMasks(), coordBoxOffsetMask = _a.coordBoxOffsetMask, coordBoxSizeMask = _a.coordBoxSizeMask, scoreMask = _a.scoreMask;
- this.noObjectLossMask = tidy(function () { return mul(scoreMask, sub(scalar(1), groundTruthMask)); });
- this.objectLossMask = tidy(function () { return mul(scoreMask, groundTruthMask); });
- this.coordBoxOffsetMask = tidy(function () { return mul(coordBoxOffsetMask, groundTruthMask); });
- this.coordBoxSizeMask = tidy(function () { return mul(coordBoxSizeMask, groundTruthMask); });
- var classScoresMask = tidy(function () { return sub(scalar(1), coordBoxOffsetMask.add(coordBoxSizeMask).add(scoreMask)); });
- this.groundTruthClassScoresMask = tidy(function () { return mul(classScoresMask, groundTruthMask); });
- }
- Object.defineProperty(TinyYolov2LossFunction.prototype, "config", {
- get: function () {
- return this._config;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "reshapedImgDims", {
- get: function () {
- return this._reshapedImgDims;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "outputTensor", {
- get: function () {
- return this._outputTensor;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "groundTruth", {
- get: function () {
- return this._groundTruth;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "predictedBoxes", {
- get: function () {
- return this._predictedBoxes;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "inputSize", {
- get: function () {
- return Math.max(this.reshapedImgDims.width, this.reshapedImgDims.height);
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "withClassScores", {
- get: function () {
- return this._config.withClassScores || this._config.classes.length > 1;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "boxEncodingSize", {
- get: function () {
- return 5 + (this.withClassScores ? this._config.classes.length : 0);
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "anchors", {
- get: function () {
- return this._config.anchors;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "numBoxes", {
- get: function () {
- return this.anchors.length;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "numCells", {
- get: function () {
- return this.inputSize / CELL_SIZE;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2LossFunction.prototype, "gridCellEncodingSize", {
- get: function () {
- return this.boxEncodingSize * this.numBoxes;
- },
- enumerable: true,
- configurable: true
- });
- TinyYolov2LossFunction.prototype.toOutputTensorShape = function (tensor$$1) {
- var _this = this;
- return tidy(function () { return tensor$$1.reshape([1, _this.numCells, _this.numCells, _this.gridCellEncodingSize]); });
- };
- TinyYolov2LossFunction.prototype.computeLoss = function () {
- var _this = this;
- return tidy(function () {
- var noObjectLoss = _this.computeNoObjectLoss();
- var objectLoss = _this.computeObjectLoss();
- var coordLoss = _this.computeCoordLoss();
- var classLoss = _this.withClassScores
- ? _this.computeClassLoss()
- : scalar(0);
- var totalLoss = tidy(function () { return noObjectLoss.add(objectLoss).add(coordLoss).add(classLoss); });
- return {
- noObjectLoss: noObjectLoss,
- objectLoss: objectLoss,
- coordLoss: coordLoss,
- classLoss: classLoss,
- totalLoss: totalLoss
- };
- });
- };
- TinyYolov2LossFunction.prototype.computeNoObjectLoss = function () {
- var _this = this;
- return tidy(function () {
- return _this.computeLossTerm(_this.config.noObjectScale, _this.toOutputTensorShape(_this.noObjectLossMask), sigmoid(_this.outputTensor));
- });
- };
- TinyYolov2LossFunction.prototype.computeObjectLoss = function () {
- var _this = this;
- return tidy(function () {
- return _this.computeLossTerm(_this.config.objectScale, _this.toOutputTensorShape(_this.objectLossMask), sub(_this.toOutputTensorShape(_this.computeIous()), sigmoid(_this.outputTensor)));
- });
- };
- TinyYolov2LossFunction.prototype.computeClassLoss = function () {
- var _this = this;
- return tidy(function () {
- var classLossTensor = tidy(function () {
- var predClassScores = mul(softmax(_this.outputTensor.reshape([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]), 3), _this.groundTruthClassScoresMask);
- var gtClassScores = _this.createOneHotClassScoreMask();
- return sub(gtClassScores, predClassScores);
- });
- return _this.computeLossTerm(_this.config.classScale, scalar(1), classLossTensor);
- });
- };
- TinyYolov2LossFunction.prototype.computeCoordLoss = function () {
- var _this = this;
- return tidy(function () {
- return _this.computeLossTerm(_this.config.coordScale, scalar(1), add(_this.computeCoordBoxOffsetError(), _this.computeCoordBoxSizeError()));
- });
- };
- TinyYolov2LossFunction.prototype.computeCoordBoxOffsetError = function () {
- var _this = this;
- return tidy(function () {
- var mask = _this.toOutputTensorShape(_this.coordBoxOffsetMask);
- var gtBoxOffsets = mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxOffsets()));
- var predBoxOffsets = mul(mask, sigmoid(_this.outputTensor));
- return sub(gtBoxOffsets, predBoxOffsets);
- });
- };
- TinyYolov2LossFunction.prototype.computeCoordBoxSizeError = function () {
- var _this = this;
- return tidy(function () {
- var mask = _this.toOutputTensorShape(_this.coordBoxSizeMask);
- var gtBoxSizes = mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxSizes()));
- var predBoxSizes = mul(mask, _this.outputTensor);
- return sub(gtBoxSizes, predBoxSizes);
- });
- };
- TinyYolov2LossFunction.prototype.computeLossTerm = function (scale, mask, lossTensor) {
- var _this = this;
- return tidy(function () { return mul(scalar(scale), _this.squaredSumOverMask(mask, lossTensor)); });
- };
- TinyYolov2LossFunction.prototype.squaredSumOverMask = function (mask, lossTensor) {
- return tidy(function () { return sum(square(mul(mask, lossTensor))); });
- };
- TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) {
- var _this = this;
- groundTruth.forEach(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
- if (typeof label !== 'number' || label < 0 || label > (_this.config.classes.length - 1)) {
- throw new Error("invalid ground truth data, expected label to be a number in [0, " + (_this.config.classes.length - 1) + "]");
- }
- if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {
- throw new Error("invalid ground truth data, box is out of image boundaries " + JSON.stringify({ x: x, y: y, width: width, height: height }));
- }
- });
- };
- TinyYolov2LossFunction.prototype.assignGroundTruthToAnchors = function (groundTruth) {
- var _this = this;
- var groundTruthBoxes = groundTruth
- .map(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
- return ({
- box: new Rect(x, y, width, height),
- label: label
- });
- });
- return groundTruthBoxes.map(function (_a) {
- var box = _a.box, label = _a.label;
- var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, bottom = _b.bottom, right = _b.right, x = _b.x, y = _b.y, width = _b.width, height = _b.height;
- var ctX = left + (width / 2);
- var ctY = top + (height / 2);
- var col = Math.floor((ctX / _this.inputSize) * _this.numCells);
- var row = Math.floor((ctY / _this.inputSize) * _this.numCells);
- var anchorsByIou = _this.anchors.map(function (anchor, idx) { return ({
- idx: idx,
- iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height))
- }); }).sort(function (a1, a2) { return a2.iou - a1.iou; });
- var anchor = anchorsByIou[0].idx;
- return { row: row, col: col, anchor: anchor, box: box, label: label };
- });
- };
- TinyYolov2LossFunction.prototype.createGroundTruthMask = function () {
- var _this = this;
- var mask = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
- var buf = mask.buffer();
- this.groundTruth.forEach(function (_a) {
- var row = _a.row, col = _a.col, anchor = _a.anchor;
- for (var i = 0; i < _this.boxEncodingSize; i++) {
- buf.set(1, row, col, anchor, i);
- }
- });
- return mask;
- };
- TinyYolov2LossFunction.prototype.createCoordAndScoreMasks = function () {
- var _this = this;
- return tidy(function () {
- var coordBoxOffsetMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
- var coordBoxSizeMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
- var scoreMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
- var coordBoxOffsetBuf = coordBoxOffsetMask.buffer();
- var coordBoxSizeBuf = coordBoxSizeMask.buffer();
- var scoreBuf = scoreMask.buffer();
- for (var row = 0; row < _this.numCells; row++) {
- for (var col = 0; col < _this.numCells; col++) {
- for (var anchor = 0; anchor < _this.numBoxes; anchor++) {
- coordBoxOffsetBuf.set(1, row, col, anchor, 0);
- coordBoxOffsetBuf.set(1, row, col, anchor, 1);
- coordBoxSizeBuf.set(1, row, col, anchor, 2);
- coordBoxSizeBuf.set(1, row, col, anchor, 3);
- scoreBuf.set(1, row, col, anchor, 4);
- }
- }
- }
- return { coordBoxOffsetMask: coordBoxOffsetMask, coordBoxSizeMask: coordBoxSizeMask, scoreMask: scoreMask };
- });
- };
- TinyYolov2LossFunction.prototype.createOneHotClassScoreMask = function () {
- var mask = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
- var buf = mask.buffer();
- var classValuesOffset = 5;
- this.groundTruth.forEach(function (_a) {
- var row = _a.row, col = _a.col, anchor = _a.anchor, label = _a.label;
- buf.set(1, row, col, anchor, classValuesOffset + label);
- });
- return mask;
- };
- TinyYolov2LossFunction.prototype.computeIous = function () {
- var _this = this;
- var isSameAnchor = function (p1) { return function (p2) {
- return p1.row === p2.row
- && p1.col === p2.col
- && p1.anchor === p2.anchor;
- }; };
- var ious = zeros([this.numCells, this.numCells, this.gridCellEncodingSize]);
- var buf = ious.buffer();
- this.groundTruth.forEach(function (_a) {
- var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
- var predBox = _this.predictedBoxes.find(isSameAnchor({ row: row, col: col, anchor: anchor }));
- if (!predBox) {
- throw new Error("no output box found for: row " + row + ", col " + col + ", anchor " + anchor);
- }
- var boxIou = iou(box.rescale(_this.reshapedImgDims), predBox.box.rescale(_this.reshapedImgDims));
- var anchorOffset = _this.boxEncodingSize * anchor;
- var scoreValueOffset = 4;
- buf.set(boxIou, row, col, anchorOffset + scoreValueOffset);
- });
- return ious;
- };
- TinyYolov2LossFunction.prototype.computeCoordBoxOffsets = function () {
- var _this = this;
- var offsets = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
- var buf = offsets.buffer();
- this.groundTruth.forEach(function (_a) {
- var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
- var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, right = _b.right, bottom = _b.bottom;
- var centerX = (left + right) / 2;
- var centerY = (top + bottom) / 2;
- var dCenterX = centerX - (col * CELL_SIZE);
- var dCenterY = centerY - (row * CELL_SIZE);
- // inverseSigmoid(0) === -Infinity, inverseSigmoid(1) === Infinity
- //const dx = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterX / CELL_SIZE)))
- //const dy = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterY / CELL_SIZE)))
- var dx = dCenterX / CELL_SIZE;
- var dy = dCenterY / CELL_SIZE;
- buf.set(dx, row, col, anchor, 0);
- buf.set(dy, row, col, anchor, 1);
- });
- return offsets;
- };
- TinyYolov2LossFunction.prototype.computeCoordBoxSizes = function () {
- var _this = this;
- var sizes = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
- var buf = sizes.buffer();
- this.groundTruth.forEach(function (_a) {
- var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
- var _b = box.rescale(_this.reshapedImgDims), width = _b.width, height = _b.height;
- var dw = Math.log(width / (_this.anchors[anchor].x * CELL_SIZE));
- var dh = Math.log(height / (_this.anchors[anchor].y * CELL_SIZE));
- buf.set(dw, row, col, anchor, 2);
- buf.set(dh, row, col, anchor, 3);
- });
- return sizes;
- };
- return TinyYolov2LossFunction;
- }());
-
- var TinyYolov2Trainable = /** @class */ (function (_super) {
- __extends$1(TinyYolov2Trainable, _super);
- function TinyYolov2Trainable(trainableConfig, optimizer) {
- var _this = _super.call(this, trainableConfig) || this;
- _this._trainableConfig = validateTrainConfig(trainableConfig);
- _this._optimizer = optimizer;
- return _this;
- }
- Object.defineProperty(TinyYolov2Trainable.prototype, "trainableConfig", {
- get: function () {
- return this._trainableConfig;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2Trainable.prototype, "optimizer", {
- get: function () {
- return this._optimizer;
- },
- enumerable: true,
- configurable: true
- });
- TinyYolov2Trainable.prototype.backward = function (img, groundTruth, inputSize, options) {
- if (options === void 0) { options = {}; }
- return __awaiter$1(this, void 0, void 0, function () {
- var _this = this;
- var _a, minBoxSize, reportLosses, reshapedImgDims, filteredGroundTruthBoxes, netInput, loss;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = getDefaultBackwardOptions(options), minBoxSize = _a.minBoxSize, reportLosses = _a.reportLosses;
- reshapedImgDims = computeReshapedDimensions(getMediaDimensions(img), inputSize);
- filteredGroundTruthBoxes = this.filterGroundTruthBoxes(groundTruth, reshapedImgDims, minBoxSize);
- if (!filteredGroundTruthBoxes.length) {
- return [2 /*return*/, null];
- }
- return [4 /*yield*/, toNetInput(imageToSquare(img, inputSize))];
- case 1:
- netInput = _b.sent();
- loss = this.optimizer.minimize(function () {
- var _a = _this.computeLoss(_this.forwardInput(netInput, inputSize), filteredGroundTruthBoxes, reshapedImgDims), noObjectLoss = _a.noObjectLoss, objectLoss = _a.objectLoss, coordLoss = _a.coordLoss, classLoss = _a.classLoss, totalLoss = _a.totalLoss;
- if (reportLosses) {
- var losses = {
- totalLoss: totalLoss.dataSync()[0],
- noObjectLoss: noObjectLoss.dataSync()[0],
- objectLoss: objectLoss.dataSync()[0],
- coordLoss: coordLoss.dataSync()[0],
- classLoss: classLoss.dataSync()[0]
- };
- var report = {
- losses: losses,
- numBoxes: filteredGroundTruthBoxes.length,
- inputSize: inputSize
- };
- reportLosses(report);
- }
- return totalLoss;
- }, true);
- return [2 /*return*/, loss];
- }
- });
- });
- };
- TinyYolov2Trainable.prototype.computeLoss = function (outputTensor, groundTruth, reshapedImgDims) {
- var config = validateTrainConfig(this.config);
- var inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height);
- if (!inputSize) {
- throw new Error("computeLoss - invalid inputSize: " + inputSize);
- }
- var predictedBoxes = this.extractBoxes(outputTensor, reshapedImgDims);
- return tidy(function () {
- var lossFunction = new TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config);
- return lossFunction.computeLoss();
- });
- };
- TinyYolov2Trainable.prototype.filterGroundTruthBoxes = function (groundTruth, imgDims, minBoxSize) {
- var imgHeight = imgDims.height, imgWidth = imgDims.width;
- return groundTruth.filter(function (_a) {
- var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
- var box = (new Rect(x, y, width, height))
- .rescale({ height: imgHeight, width: imgWidth });
- var isTooTiny = box.width < minBoxSize || box.height < minBoxSize;
- return !isTooTiny;
- });
- };
- TinyYolov2Trainable.prototype.load = function (weightsOrUrl) {
- return __awaiter$1(this, void 0, void 0, function () {
- return __generator$1(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, _super.prototype.load.call(this, weightsOrUrl)];
- case 1:
- _a.sent();
- this.variable();
- return [2 /*return*/];
- }
- });
- });
- };
- return TinyYolov2Trainable;
- }(TinyYolov2));
-
- var TinyYolov2Types;
- (function (TinyYolov2Types) {
- TinyYolov2Types.SizeType = SizeType;
- })(TinyYolov2Types || (TinyYolov2Types = {}));
-
- function boxPredictionLayer(x, params) {
- return tidy(function () {
- var batchSize = x.shape[0];
- var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
- var classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
- return {
- boxPredictionEncoding: boxPredictionEncoding,
- classPrediction: classPrediction
- };
- });
- }
-
- function predictionLayer(x, conv11, params) {
- return tidy(function () {
- var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
- var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
- var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
- var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
- var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
- var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
- var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
- var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
- var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
- var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
- var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
- var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
- var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
- var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
- var boxPredictions = concat([
- boxPrediction0.boxPredictionEncoding,
- boxPrediction1.boxPredictionEncoding,
- boxPrediction2.boxPredictionEncoding,
- boxPrediction3.boxPredictionEncoding,
- boxPrediction4.boxPredictionEncoding,
- boxPrediction5.boxPredictionEncoding
- ], 1);
- var classPredictions = concat([
- boxPrediction0.classPrediction,
- boxPrediction1.classPrediction,
- boxPrediction2.classPrediction,
- boxPrediction3.classPrediction,
- boxPrediction4.classPrediction,
- boxPrediction5.classPrediction
- ], 1);
- return {
- boxPredictions: boxPredictions,
- classPredictions: classPredictions
- };
- });
- }
-
- var FaceDetectionNet = /** @class */ (function (_super) {
- __extends$1(FaceDetectionNet, _super);
- function FaceDetectionNet() {
- return _super.call(this, 'FaceDetectionNet') || this;
- }
- FaceDetectionNet.prototype.forwardInput = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceDetectionNet - load model before inference');
- }
- return tidy(function () {
- var batchTensor = input.toBatchTensor(512, false).toFloat();
- var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
- var features = mobileNetV1(x, params.mobilenetv1);
- var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
- return outputLayer(boxPredictions, classPredictions, params.output_layer);
- });
- };
- FaceDetectionNet.prototype.forward = function (input) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceDetectionNet.prototype.locateFaces = function (input, minConfidence, maxResults) {
- if (minConfidence === void 0) { minConfidence = 0.8; }
- if (maxResults === void 0) { maxResults = 100; }
- return __awaiter$1(this, void 0, void 0, function () {
- var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
- return __generator$1(this, function (_d) {
- switch (_d.label) {
- case 0: return [4 /*yield*/, toNetInput(input)];
- case 1:
- netInput = _d.sent();
- _a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
- boxes = _boxes[0];
- scores = _scores[0];
- for (i = 1; i < _boxes.length; i++) {
- _boxes[i].dispose();
- _scores[i].dispose();
- }
- _c = (_b = Array).from;
- return [4 /*yield*/, scores.data()];
- case 2:
- scoresData = _c.apply(_b, [_d.sent()]);
- iouThreshold = 0.5;
- indices = nonMaxSuppression$2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
- reshapedDims = netInput.getReshapedInputDimensions(0);
- inputSize = netInput.inputSize;
- padX = inputSize / reshapedDims.width;
- padY = inputSize / reshapedDims.height;
- results = indices
- .map(function (idx) {
- var _a = [
- Math.max(0, boxes.get(idx, 0)),
- Math.min(1.0, boxes.get(idx, 2))
- ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
- var _b = [
- Math.max(0, boxes.get(idx, 1)),
- Math.min(1.0, boxes.get(idx, 3))
- ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
- return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
- height: netInput.getInputHeight(0),
- width: netInput.getInputWidth(0)
- });
- });
- boxes.dispose();
- scores.dispose();
- return [2 /*return*/, results];
- }
- });
- });
- };
- FaceDetectionNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams(uri);
- };
- FaceDetectionNet.prototype.extractParams = function (weights) {
- return extractParams(weights);
- };
- return FaceDetectionNet;
- }(NeuralNetwork));
-
- function createFaceDetectionNet(weights) {
- var net = new FaceDetectionNet();
- net.extractWeights(weights);
- return net;
- }
- function faceDetectionNet(weights) {
- console.warn('faceDetectionNet(weights: Float32Array) will be deprecated in future, use createFaceDetectionNet instead');
- return createFaceDetectionNet(weights);
- }
-
- function depthwiseSeparableConv(x, params, stride) {
- return tidy(function () {
- var out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
- out = add(out, params.bias);
- return out;
- });
- }
-
- function extractorsFactory$4(extractWeights, paramMappings) {
- function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
- var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
- var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
- var bias = tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
- return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
- }
- function extractFCParams(channelsIn, channelsOut, mappedPrefix) {
- var weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
- var bias = tensor1d(extractWeights(channelsOut));
- paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
- return {
- weights: weights,
- bias: bias
- };
- }
- var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
- function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var conv0 = isFirstLayer
- ? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv0")
- : extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/conv0");
- var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv1");
- var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv2");
- return { conv0: conv0, conv1: conv1, conv2: conv2 };
- }
- function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;
- var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv3");
- return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
- }
- return {
- extractDenseBlock3Params: extractDenseBlock3Params,
- extractDenseBlock4Params: extractDenseBlock4Params,
- extractFCParams: extractFCParams
- };
- }
-
- function extractParams$2(weights) {
- var paramMappings = [];
- var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var _b = extractorsFactory$4(extractWeights, paramMappings), extractDenseBlock4Params = _b.extractDenseBlock4Params, extractFCParams = _b.extractFCParams;
- var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
- var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
- var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
- var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
- var fc = extractFCParams(256, 136, 'fc');
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return {
- paramMappings: paramMappings,
- params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3, fc: fc }
- };
- }
-
- var FaceLandmark68NetBase = /** @class */ (function (_super) {
- __extends$1(FaceLandmark68NetBase, _super);
- function FaceLandmark68NetBase(_name) {
- var _this = _super.call(this, _name) || this;
- _this.__name = _name;
- return _this;
- }
- FaceLandmark68NetBase.prototype.runNet = function (_) {
- throw new Error(this.__name + " - runNet not implemented");
- };
- FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
- var inputDimensions = originalDimensions.map(function (_a) {
- var width = _a.width, height = _a.height;
- var scale = inputSize / Math.max(height, width);
- return {
- width: width * scale,
- height: height * scale
- };
- });
- var batchSize = inputDimensions.length;
- return tidy(function () {
- var createInterleavedTensor = function (fillX, fillY) {
- return stack([
- fill([68], fillX),
- fill([68], fillY)
- ], 1).as2D(1, 136).as1D();
- };
- var getPadding = function (batchIdx, cond) {
- var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
- return cond(width, height) ? Math.abs(width - height) / 2 : 0;
- };
- var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
- var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
- var landmarkTensors = output
- .mul(fill([batchSize, 136], inputSize))
- .sub(stack(Array.from(Array(batchSize), function (_, batchIdx) {
- return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
- })))
- .div(stack(Array.from(Array(batchSize), function (_, batchIdx) {
- return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
- })));
- return landmarkTensors;
- });
- };
- FaceLandmark68NetBase.prototype.forwardInput = function (input) {
- var _this = this;
- return tidy(function () {
- var out = _this.runNet(input);
- return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
- var height = _a[0], width = _a[1];
- return ({ height: height, width: width });
- }));
- });
- };
- FaceLandmark68NetBase.prototype.forward = function (input) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _this = this;
- var netInput, landmarkTensors, landmarksForBatch;
- return __generator$1(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, toNetInput(input)];
- case 1:
- netInput = _a.sent();
- landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
- return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
- var landmarksArray, _a, _b, xCoords, yCoords;
- return __generator$1(this, function (_c) {
- switch (_c.label) {
- case 0:
- _b = (_a = Array).from;
- return [4 /*yield*/, landmarkTensor.data()];
- case 1:
- landmarksArray = _b.apply(_a, [_c.sent()]);
- xCoords = landmarksArray.filter(function (_, i) { return isEven(i); });
- yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
- return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
- height: netInput.getInputHeight(batchIdx),
- width: netInput.getInputWidth(batchIdx),
- })];
- }
- });
- }); }))];
- case 2:
- landmarksForBatch = _a.sent();
- landmarkTensors.forEach(function (t) { return t.dispose(); });
- return [2 /*return*/, netInput.isBatchInput
- ? landmarksForBatch
- : landmarksForBatch[0]];
- }
- });
- });
- };
- return FaceLandmark68NetBase;
- }(NeuralNetwork));
-
- function fullyConnectedLayer(x, params) {
- return tidy(function () {
- return add(matMul(x, params.weights), params.bias);
- });
- }
-
- function loadParamsFactory(weightMap, paramMappings) {
- var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
- function extractConvParams(prefix) {
- var filters = extractWeightEntry(prefix + "/filters", 4);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { filters: filters, bias: bias };
- }
- function extractSeparableConvParams(prefix) {
- var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
- var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
- }
- function extractDenseBlock3Params(prefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var conv0 = isFirstLayer
- ? extractConvParams(prefix + "/conv0")
- : extractSeparableConvParams(prefix + "/conv0");
- var conv1 = extractSeparableConvParams(prefix + "/conv1");
- var conv2 = extractSeparableConvParams(prefix + "/conv2");
- return { conv0: conv0, conv1: conv1, conv2: conv2 };
- }
- function extractDenseBlock4Params(prefix, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- var conv0 = isFirstLayer
- ? extractConvParams(prefix + "/conv0")
- : extractSeparableConvParams(prefix + "/conv0");
- var conv1 = extractSeparableConvParams(prefix + "/conv1");
- var conv2 = extractSeparableConvParams(prefix + "/conv2");
- var conv3 = extractSeparableConvParams(prefix + "/conv3");
- return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
- }
- function extractFcParams(prefix) {
- var weights = extractWeightEntry(prefix + "/weights", 2);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { weights: weights, bias: bias };
- }
- return {
- extractDenseBlock3Params: extractDenseBlock3Params,
- extractDenseBlock4Params: extractDenseBlock4Params,
- extractFcParams: extractFcParams
- };
- }
-
- var DEFAULT_MODEL_NAME$1 = 'face_landmark_68_model';
- function loadQuantizedParams$2(uri) {
- return __awaiter$1(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractDenseBlock4Params, extractFcParams, params;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$1)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock4Params = _a.extractDenseBlock4Params, extractFcParams = _a.extractFcParams;
- params = {
- dense0: extractDenseBlock4Params('dense0', true),
- dense1: extractDenseBlock4Params('dense1'),
- dense2: extractDenseBlock4Params('dense2'),
- dense3: extractDenseBlock4Params('dense3'),
- fc: extractFcParams('fc')
- };
- disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
- }
-
- function denseBlock(x, denseBlockParams, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- return tidy(function () {
- var out1 = relu(isFirstLayer
- ? add(conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
- : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
- var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
- var in3 = relu(add(out1, out2));
- var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
- var in4 = relu(add(out1, add(out2, out3)));
- var out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
- return relu(add(out1, add(out2, add(out3, out4))));
- });
- }
- var FaceLandmark68Net = /** @class */ (function (_super) {
- __extends$1(FaceLandmark68Net, _super);
- function FaceLandmark68Net() {
- return _super.call(this, 'FaceLandmark68LargeNet') || this;
- }
- FaceLandmark68Net.prototype.runNet = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceLandmark68LargeNet - load model before inference');
- }
- return tidy(function () {
- var batchTensor = input.toBatchTensor(112, true);
- var meanRgb = [122.782, 117.001, 104.298];
- var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
- var out = denseBlock(normalized, params.dense0, true);
- out = denseBlock(out, params.dense1);
- out = denseBlock(out, params.dense2);
- out = denseBlock(out, params.dense3);
- out = avgPool(out, [7, 7], [2, 2], 'valid');
- return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
- });
- };
- FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams$2(uri);
- };
- FaceLandmark68Net.prototype.extractParams = function (weights) {
- return extractParams$2(weights);
- };
- return FaceLandmark68Net;
- }(FaceLandmark68NetBase));
-
- function extractParamsTiny(weights) {
- var paramMappings = [];
- var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var _b = extractorsFactory$4(extractWeights, paramMappings), extractDenseBlock3Params = _b.extractDenseBlock3Params, extractFCParams = _b.extractFCParams;
- var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);
- var dense1 = extractDenseBlock3Params(32, 64, 'dense1');
- var dense2 = extractDenseBlock3Params(64, 128, 'dense2');
- var fc = extractFCParams(128, 136, 'fc');
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return {
- paramMappings: paramMappings,
- params: { dense0: dense0, dense1: dense1, dense2: dense2, fc: fc }
- };
- }
-
- var DEFAULT_MODEL_NAME$2 = 'face_landmark_68_tiny_model';
- function loadQuantizedParamsTiny(uri) {
- return __awaiter$1(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractDenseBlock3Params, extractFcParams, params;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$2)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = loadParamsFactory(weightMap, paramMappings), extractDenseBlock3Params = _a.extractDenseBlock3Params, extractFcParams = _a.extractFcParams;
- params = {
- dense0: extractDenseBlock3Params('dense0', true),
- dense1: extractDenseBlock3Params('dense1'),
- dense2: extractDenseBlock3Params('dense2'),
- fc: extractFcParams('fc')
- };
- disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
- }
-
- function denseBlock$1(x, denseBlockParams, isFirstLayer) {
- if (isFirstLayer === void 0) { isFirstLayer = false; }
- return tidy(function () {
- var out1 = relu(isFirstLayer
- ? add(conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
- : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
- var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
- var in3 = relu(add(out1, out2));
- var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
- return relu(add(out1, add(out2, out3)));
- });
- }
- var FaceLandmark68TinyNet = /** @class */ (function (_super) {
- __extends$1(FaceLandmark68TinyNet, _super);
- function FaceLandmark68TinyNet() {
- return _super.call(this, 'FaceLandmark68TinyNet') || this;
- }
- FaceLandmark68TinyNet.prototype.runNet = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceLandmark68TinyNet - load model before inference');
- }
- return tidy(function () {
- var batchTensor = input.toBatchTensor(112, true);
- var meanRgb = [122.782, 117.001, 104.298];
- var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
- var out = denseBlock$1(normalized, params.dense0, true);
- out = denseBlock$1(out, params.dense1);
- out = denseBlock$1(out, params.dense2);
- out = avgPool(out, [14, 14], [2, 2], 'valid');
- return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc);
- });
- };
- FaceLandmark68TinyNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParamsTiny(uri);
- };
- FaceLandmark68TinyNet.prototype.extractParams = function (weights) {
- return extractParamsTiny(weights);
- };
- return FaceLandmark68TinyNet;
- }(FaceLandmark68NetBase));
-
- var FaceLandmarkNet = /** @class */ (function (_super) {
- __extends$1(FaceLandmarkNet, _super);
- function FaceLandmarkNet() {
- return _super !== null && _super.apply(this, arguments) || this;
- }
- return FaceLandmarkNet;
- }(FaceLandmark68Net));
- function createFaceLandmarkNet(weights) {
- var net = new FaceLandmarkNet();
- net.extractWeights(weights);
- return net;
- }
- function faceLandmarkNet(weights) {
- console.warn('faceLandmarkNet(weights: Float32Array) will be deprecated in future, use createFaceLandmarkNet instead');
- return createFaceLandmarkNet(weights);
- }
-
- function scale(x, params) {
- return add(mul(x, params.weights), params.biases);
- }
-
- function convLayer$1(x, params, strides, withRelu, padding) {
- if (padding === void 0) { padding = 'same'; }
- var _a = params.conv, filters = _a.filters, bias = _a.bias;
- var out = conv2d(x, filters, strides, padding);
- out = add(out, bias);
- out = scale(out, params.scale);
- return withRelu ? relu(out) : out;
- }
- function conv(x, params) {
- return convLayer$1(x, params, [1, 1], true);
- }
- function convNoRelu(x, params) {
- return convLayer$1(x, params, [1, 1], false);
- }
- function convDown(x, params) {
- return convLayer$1(x, params, [2, 2], true, 'valid');
- }
-
- function extractorsFactory$5(extractWeights, paramMappings) {
- function extractFilterValues(numFilterValues, numFilters, filterSize) {
- var weights = extractWeights(numFilterValues);
- var depth = weights.length / (numFilters * filterSize * filterSize);
- if (isFloat(depth)) {
- throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
- }
- return tidy(function () { return transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
- }
- function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
- var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
- var bias = tensor1d(extractWeights(numFilters));
- paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
- return { filters: filters, bias: bias };
- }
- function extractScaleLayerParams(numWeights, mappedPrefix) {
- var weights = tensor1d(extractWeights(numWeights));
- var biases = tensor1d(extractWeights(numWeights));
- paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
- return {
- weights: weights,
- biases: biases
- };
- }
- function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
- var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
- var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
- return { conv: conv, scale: scale };
- }
- function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
- if (isDown === void 0) { isDown = false; }
- var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
- var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
- return { conv1: conv1, conv2: conv2 };
- }
- return {
- extractConvLayerParams: extractConvLayerParams,
- extractResidualLayerParams: extractResidualLayerParams
- };
- }
- function extractParams$3(weights) {
- var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var paramMappings = [];
- var _b = extractorsFactory$5(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
- var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
- var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
- var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
- var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
- var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
- var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
- var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
- var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
- var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
- var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
- var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
- var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
- var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
- var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
- var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
- var fc = tidy(function () { return transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });
- paramMappings.push({ paramPath: "fc" });
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- var params = {
- conv32_down: conv32_down,
- conv32_1: conv32_1,
- conv32_2: conv32_2,
- conv32_3: conv32_3,
- conv64_down: conv64_down,
- conv64_1: conv64_1,
- conv64_2: conv64_2,
- conv64_3: conv64_3,
- conv128_down: conv128_down,
- conv128_1: conv128_1,
- conv128_2: conv128_2,
- conv256_down: conv256_down,
- conv256_1: conv256_1,
- conv256_2: conv256_2,
- conv256_down_out: conv256_down_out,
- fc: fc
- };
- return { params: params, paramMappings: paramMappings };
- }
-
- var DEFAULT_MODEL_NAME$3 = 'face_recognition_model';
- function extractorsFactory$6(weightMap, paramMappings) {
- var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
- function extractScaleLayerParams(prefix) {
- var weights = extractWeightEntry(prefix + "/scale/weights", 1);
- var biases = extractWeightEntry(prefix + "/scale/biases", 1);
- return { weights: weights, biases: biases };
- }
- function extractConvLayerParams(prefix) {
- var filters = extractWeightEntry(prefix + "/conv/filters", 4);
- var bias = extractWeightEntry(prefix + "/conv/bias", 1);
- var scale = extractScaleLayerParams(prefix);
- return { conv: { filters: filters, bias: bias }, scale: scale };
- }
- function extractResidualLayerParams(prefix) {
- return {
- conv1: extractConvLayerParams(prefix + "/conv1"),
- conv2: extractConvLayerParams(prefix + "/conv2")
- };
- }
- return {
- extractConvLayerParams: extractConvLayerParams,
- extractResidualLayerParams: extractResidualLayerParams
- };
- }
- function loadQuantizedParams$3(uri) {
- return __awaiter$1(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractConvLayerParams, extractResidualLayerParams, conv32_down, conv32_1, conv32_2, conv32_3, conv64_down, conv64_1, conv64_2, conv64_3, conv128_down, conv128_1, conv128_2, conv256_down, conv256_1, conv256_2, conv256_down_out, fc, params;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$3)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory$6(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
- conv32_down = extractConvLayerParams('conv32_down');
- conv32_1 = extractResidualLayerParams('conv32_1');
- conv32_2 = extractResidualLayerParams('conv32_2');
- conv32_3 = extractResidualLayerParams('conv32_3');
- conv64_down = extractResidualLayerParams('conv64_down');
- conv64_1 = extractResidualLayerParams('conv64_1');
- conv64_2 = extractResidualLayerParams('conv64_2');
- conv64_3 = extractResidualLayerParams('conv64_3');
- conv128_down = extractResidualLayerParams('conv128_down');
- conv128_1 = extractResidualLayerParams('conv128_1');
- conv128_2 = extractResidualLayerParams('conv128_2');
- conv256_down = extractResidualLayerParams('conv256_down');
- conv256_1 = extractResidualLayerParams('conv256_1');
- conv256_2 = extractResidualLayerParams('conv256_2');
- conv256_down_out = extractResidualLayerParams('conv256_down_out');
- fc = weightMap['fc'];
- paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
- if (!isTensor2D(fc)) {
- throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
- }
- params = {
- conv32_down: conv32_down,
- conv32_1: conv32_1,
- conv32_2: conv32_2,
- conv32_3: conv32_3,
- conv64_down: conv64_down,
- conv64_1: conv64_1,
- conv64_2: conv64_2,
- conv64_3: conv64_3,
- conv128_down: conv128_down,
- conv128_1: conv128_1,
- conv128_2: conv128_2,
- conv256_down: conv256_down,
- conv256_1: conv256_1,
- conv256_2: conv256_2,
- conv256_down_out: conv256_down_out,
- fc: fc
- };
- disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: params, paramMappings: paramMappings }];
- }
- });
- });
- }
-
- function residual(x, params) {
- var out = conv(x, params.conv1);
- out = convNoRelu(out, params.conv2);
- out = add(out, x);
- out = relu(out);
- return out;
- }
- function residualDown(x, params) {
- var out = convDown(x, params.conv1);
- out = convNoRelu(out, params.conv2);
- var pooled = avgPool(x, 2, 2, 'valid');
- var zeros$$1 = zeros(pooled.shape);
- var isPad = pooled.shape[3] !== out.shape[3];
- var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
- if (isAdjustShape) {
- var padShapeX = out.shape.slice();
- padShapeX[1] = 1;
- var zerosW = zeros(padShapeX);
- out = concat([out, zerosW], 1);
- var padShapeY = out.shape.slice();
- padShapeY[2] = 1;
- var zerosH = zeros(padShapeY);
- out = concat([out, zerosH], 2);
- }
- pooled = isPad ? concat([pooled, zeros$$1], 3) : pooled;
- out = add(pooled, out);
- out = relu(out);
- return out;
- }
-
- var FaceRecognitionNet = /** @class */ (function (_super) {
- __extends$1(FaceRecognitionNet, _super);
- function FaceRecognitionNet() {
- return _super.call(this, 'FaceRecognitionNet') || this;
- }
- FaceRecognitionNet.prototype.forwardInput = function (input) {
- var params = this.params;
- if (!params) {
- throw new Error('FaceRecognitionNet - load model before inference');
- }
- return tidy(function () {
- var batchTensor = input.toBatchTensor(150, true).toFloat();
- var meanRgb = [122.782, 117.001, 104.298];
- var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
- var out = convDown(normalized, params.conv32_down);
- out = maxPool(out, 3, 2, 'valid');
- out = residual(out, params.conv32_1);
- out = residual(out, params.conv32_2);
- out = residual(out, params.conv32_3);
- out = residualDown(out, params.conv64_down);
- out = residual(out, params.conv64_1);
- out = residual(out, params.conv64_2);
- out = residual(out, params.conv64_3);
- out = residualDown(out, params.conv128_down);
- out = residual(out, params.conv128_1);
- out = residual(out, params.conv128_2);
- out = residualDown(out, params.conv256_down);
- out = residual(out, params.conv256_1);
- out = residual(out, params.conv256_2);
- out = residualDown(out, params.conv256_down_out);
- var globalAvg = out.mean([1, 2]);
- var fullyConnected = matMul(globalAvg, params.fc);
- return fullyConnected;
- });
- };
- FaceRecognitionNet.prototype.forward = function (input) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
- }
- });
- });
- };
- FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
- return __awaiter$1(this, void 0, void 0, function () {
- var _this = this;
- var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
- return __generator$1(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, toNetInput(input)];
- case 1:
- netInput = _a.sent();
- faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
- return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
- case 2:
- faceDescriptorsForBatch = _a.sent();
- faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
- return [2 /*return*/, netInput.isBatchInput
- ? faceDescriptorsForBatch
- : faceDescriptorsForBatch[0]];
- }
- });
- });
- };
- FaceRecognitionNet.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams$3(uri);
- };
- FaceRecognitionNet.prototype.extractParams = function (weights) {
- return extractParams$3(weights);
- };
- return FaceRecognitionNet;
- }(NeuralNetwork));
-
- function createFaceRecognitionNet(weights) {
- var net = new FaceRecognitionNet();
- net.extractWeights(weights);
- return net;
- }
- function faceRecognitionNet(weights) {
- console.warn('faceRecognitionNet(weights: Float32Array) will be deprecated in future, use createFaceRecognitionNet instead');
- return createFaceRecognitionNet(weights);
- }
-
- function computeDescriptorsFactory(recognitionNet) {
- return function (input, alignedFaceBoxes, useBatchProcessing) {
- return __awaiter$1(this, void 0, void 0, function () {
- var alignedFaceCanvases, descriptors, _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, extractFaces(input, alignedFaceBoxes)];
- case 1:
- alignedFaceCanvases = _b.sent();
- if (!useBatchProcessing) return [3 /*break*/, 3];
- return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
- case 2:
- _a = _b.sent();
- return [3 /*break*/, 5];
- case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
- case 4:
- _a = _b.sent();
- _b.label = 5;
- case 5:
- descriptors = _a;
- return [2 /*return*/, descriptors];
- }
- });
- });
- };
- }
- function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
- var computeDescriptors = computeDescriptorsFactory(recognitionNet);
- return function (input, useBatchProcessing) {
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return __awaiter$1(this, void 0, void 0, function () {
- var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, detectFaces(input)];
- case 1:
- detections = _b.sent();
- return [4 /*yield*/, extractFaces(input, detections)];
- case 2:
- faceCanvases = _b.sent();
- if (!useBatchProcessing) return [3 /*break*/, 4];
- return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
- case 3:
- _a = _b.sent();
- return [3 /*break*/, 6];
- case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
- case 5:
- _a = _b.sent();
- _b.label = 6;
- case 6:
- faceLandmarksByFace = _a;
- alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
- return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
- case 7:
- descriptors = _b.sent();
- return [2 /*return*/, detections.map(function (detection, i) {
- return new FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(new Point(detection.box.x, detection.box.y)), descriptors[i]);
- })];
- }
- });
- });
- };
- }
- function allFacesSsdMobilenetv1Factory(ssdMobilenetv1, landmarkNet, recognitionNet) {
- return function (input, minConfidence, useBatchProcessing) {
- if (minConfidence === void 0) { minConfidence = 0.8; }
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return __awaiter$1(this, void 0, void 0, function () {
- var detectFaces, allFaces;
- return __generator$1(this, function (_a) {
- detectFaces = function (input) { return ssdMobilenetv1.locateFaces(input, minConfidence); };
- allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
- return [2 /*return*/, allFaces(input, useBatchProcessing)];
- });
- });
- };
- }
- function allFacesTinyYolov2Factory(tinyYolov2, landmarkNet, recognitionNet) {
- return function (input, forwardParams, useBatchProcessing) {
- if (forwardParams === void 0) { forwardParams = {}; }
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return __awaiter$1(this, void 0, void 0, function () {
- var detectFaces, allFaces;
- return __generator$1(this, function (_a) {
- detectFaces = function (input) { return tinyYolov2.locateFaces(input, forwardParams); };
- allFaces = allFacesFactory(detectFaces, landmarkNet, recognitionNet);
- return [2 /*return*/, allFaces(input, useBatchProcessing)];
- });
- });
- };
- }
- function allFacesMtcnnFactory(mtcnn, recognitionNet) {
- var computeDescriptors = computeDescriptorsFactory(recognitionNet);
- return function (input, mtcnnForwardParams, useBatchProcessing) {
- if (mtcnnForwardParams === void 0) { mtcnnForwardParams = {}; }
- if (useBatchProcessing === void 0) { useBatchProcessing = false; }
- return __awaiter$1(this, void 0, void 0, function () {
- var results, alignedFaceBoxes, descriptors;
- return __generator$1(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, mtcnn.forward(input, mtcnnForwardParams)];
- case 1:
- results = _a.sent();
- alignedFaceBoxes = results.map(function (_a) {
- var faceLandmarks = _a.faceLandmarks;
- return faceLandmarks.align();
- });
- return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
- case 2:
- descriptors = _a.sent();
- return [2 /*return*/, results.map(function (_a, i) {
- var faceDetection = _a.faceDetection, faceLandmarks = _a.faceLandmarks;
- return new FullFaceDescription(faceDetection, faceLandmarks, descriptors[i]);
- })];
- }
- });
- });
- };
- }
-
- function bgrToRgbTensor(tensor$$1) {
- return tidy(function () { return stack(unstack(tensor$$1, 3).reverse(), 3); });
- }
-
- var CELL_STRIDE = 2;
- var CELL_SIZE$1 = 12;
-
- function extractorsFactory$7(extractWeights, paramMappings) {
- var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
- var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
- function extractPReluParams(size, paramPath) {
- var alpha = tensor1d(extractWeights(size));
- paramMappings.push({ paramPath: paramPath });
- return alpha;
- }
- function extractSharedParams(numFilters, mappedPrefix, isRnet) {
- if (isRnet === void 0) { isRnet = false; }
- var conv1 = extractConvParams(numFilters[0], numFilters[1], 3, mappedPrefix + "/conv1");
- var prelu1_alpha = extractPReluParams(numFilters[1], mappedPrefix + "/prelu1_alpha");
- var conv2 = extractConvParams(numFilters[1], numFilters[2], 3, mappedPrefix + "/conv2");
- var prelu2_alpha = extractPReluParams(numFilters[2], mappedPrefix + "/prelu2_alpha");
- var conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, mappedPrefix + "/conv3");
- var prelu3_alpha = extractPReluParams(numFilters[3], mappedPrefix + "/prelu3_alpha");
- return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
- }
- function extractPNetParams() {
- var sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet');
- var conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1');
- var conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2');
- return __assign$1({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
- }
- function extractRNetParams() {
- var sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true);
- var fc1 = extractFCParams(576, 128, 'rnet/fc1');
- var prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha');
- var fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1');
- var fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2');
- return __assign$1({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
- }
- function extractONetParams() {
- var sharedParams = extractSharedParams([3, 32, 64, 64], 'onet');
- var conv4 = extractConvParams(64, 128, 2, 'onet/conv4');
- var prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha');
- var fc1 = extractFCParams(1152, 256, 'onet/fc1');
- var prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha');
- var fc2_1 = extractFCParams(256, 2, 'onet/fc2_1');
- var fc2_2 = extractFCParams(256, 4, 'onet/fc2_2');
- var fc2_3 = extractFCParams(256, 10, 'onet/fc2_3');
- return __assign$1({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
- }
- return {
- extractPNetParams: extractPNetParams,
- extractRNetParams: extractRNetParams,
- extractONetParams: extractONetParams
- };
- }
- function extractParams$4(weights) {
- var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
- var paramMappings = [];
- var _b = extractorsFactory$7(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
- var pnet = extractPNetParams();
- var rnet = extractRNetParams();
- var onet = extractONetParams();
- if (getRemainingWeights().length !== 0) {
- throw new Error("weights remaing after extract: " + getRemainingWeights().length);
- }
- return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
- }
-
- function getDefaultMtcnnForwardParams() {
- return {
- minFaceSize: 20,
- scaleFactor: 0.709,
- maxNumScales: 10,
- scoreThresholds: [0.6, 0.7, 0.7]
- };
- }
-
- function getSizesForScale(scale, _a) {
- var height = _a[0], width = _a[1];
- return {
- height: Math.floor(height * scale),
- width: Math.floor(width * scale)
- };
- }
-
- var DEFAULT_MODEL_NAME$4 = 'mtcnn_model';
- function extractorsFactory$8(weightMap, paramMappings) {
- var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
- function extractConvParams(prefix) {
- var filters = extractWeightEntry(prefix + "/weights", 4, prefix + "/filters");
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { filters: filters, bias: bias };
- }
- function extractFCParams(prefix) {
- var weights = extractWeightEntry(prefix + "/weights", 2);
- var bias = extractWeightEntry(prefix + "/bias", 1);
- return { weights: weights, bias: bias };
- }
- function extractPReluParams(paramPath) {
- return extractWeightEntry(paramPath, 1);
- }
- function extractSharedParams(prefix) {
- var conv1 = extractConvParams(prefix + "/conv1");
- var prelu1_alpha = extractPReluParams(prefix + "/prelu1_alpha");
- var conv2 = extractConvParams(prefix + "/conv2");
- var prelu2_alpha = extractPReluParams(prefix + "/prelu2_alpha");
- var conv3 = extractConvParams(prefix + "/conv3");
- var prelu3_alpha = extractPReluParams(prefix + "/prelu3_alpha");
- return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
- }
- function extractPNetParams() {
- var sharedParams = extractSharedParams('pnet');
- var conv4_1 = extractConvParams('pnet/conv4_1');
- var conv4_2 = extractConvParams('pnet/conv4_2');
- return __assign$1({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
- }
- function extractRNetParams() {
- var sharedParams = extractSharedParams('rnet');
- var fc1 = extractFCParams('rnet/fc1');
- var prelu4_alpha = extractPReluParams('rnet/prelu4_alpha');
- var fc2_1 = extractFCParams('rnet/fc2_1');
- var fc2_2 = extractFCParams('rnet/fc2_2');
- return __assign$1({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
- }
- function extractONetParams() {
- var sharedParams = extractSharedParams('onet');
- var conv4 = extractConvParams('onet/conv4');
- var prelu4_alpha = extractPReluParams('onet/prelu4_alpha');
- var fc1 = extractFCParams('onet/fc1');
- var prelu5_alpha = extractPReluParams('onet/prelu5_alpha');
- var fc2_1 = extractFCParams('onet/fc2_1');
- var fc2_2 = extractFCParams('onet/fc2_2');
- var fc2_3 = extractFCParams('onet/fc2_3');
- return __assign$1({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
- }
- return {
- extractPNetParams: extractPNetParams,
- extractRNetParams: extractRNetParams,
- extractONetParams: extractONetParams
- };
- }
- function loadQuantizedParams$4(uri) {
- return __awaiter$1(this, void 0, void 0, function () {
- var weightMap, paramMappings, _a, extractPNetParams, extractRNetParams, extractONetParams, pnet, rnet, onet;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0: return [4 /*yield*/, loadWeightMap(uri, DEFAULT_MODEL_NAME$4)];
- case 1:
- weightMap = _b.sent();
- paramMappings = [];
- _a = extractorsFactory$8(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;
- pnet = extractPNetParams();
- rnet = extractRNetParams();
- onet = extractONetParams();
- disposeUnusedWeightTensors(weightMap, paramMappings);
- return [2 /*return*/, { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings }];
- }
- });
- });
- }
-
- function pyramidDown(minFaceSize, scaleFactor, dims) {
- var height = dims[0], width = dims[1];
- var m = CELL_SIZE$1 / minFaceSize;
- var scales = [];
- var minLayer = Math.min(height, width) * m;
- var exp = 0;
- while (minLayer >= 12) {
- scales.push(m * Math.pow(scaleFactor, exp));
- minLayer = minLayer * scaleFactor;
- exp += 1;
- }
- return scales;
- }
-
- var MtcnnBox = /** @class */ (function (_super) {
- __extends$1(MtcnnBox, _super);
- function MtcnnBox(left, top, right, bottom) {
- return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
- }
- return MtcnnBox;
- }(Box));
-
- function normalize$1(x) {
- return tidy(function () { return mul(sub(x, scalar(127.5)), scalar(0.0078125)); });
- }
-
- function prelu$1(x, alpha) {
- return tidy(function () {
- return add(relu(x), mul(alpha, neg(relu(neg(x)))));
- });
- }
-
- function sharedLayer(x, params, isPnet) {
- if (isPnet === void 0) { isPnet = false; }
- return tidy(function () {
- var out = convLayer(x, params.conv1, 'valid');
- out = prelu$1(out, params.prelu1_alpha);
- out = maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');
- out = convLayer(out, params.conv2, 'valid');
- out = prelu$1(out, params.prelu2_alpha);
- out = isPnet ? out : maxPool(out, [3, 3], [2, 2], 'valid');
- out = convLayer(out, params.conv3, 'valid');
- out = prelu$1(out, params.prelu3_alpha);
- return out;
- });
- }
-
- function PNet(x, params) {
- return tidy(function () {
- var out = sharedLayer(x, params, true);
- var conv = convLayer(out, params.conv4_1, 'valid');
- var max$$1 = expandDims(max(conv, 3), 3);
- var prob = softmax(sub(conv, max$$1), 3);
- var regions = convLayer(out, params.conv4_2, 'valid');
- return { prob: prob, regions: regions };
- });
- }
-
- function rescaleAndNormalize(x, scale) {
- return tidy(function () {
- var _a = getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
- var resized = image_ops.resizeBilinear(x, [height, width]);
- var normalized = normalize$1(resized);
- return transpose(normalized, [0, 2, 1, 3]);
- });
- }
- function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold) {
- // TODO: fix this!, maybe better to use tf.gather here
- var indices = [];
- for (var y = 0; y < scoresTensor.shape[0]; y++) {
- for (var x = 0; x < scoresTensor.shape[1]; x++) {
- if (scoresTensor.get(y, x) >= scoreThreshold) {
- indices.push(new Point(x, y));
- }
- }
- }
- var boundingBoxes = indices.map(function (idx) {
- var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE$1) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE$1) / scale));
- var score = scoresTensor.get(idx.y, idx.x);
- var region = new MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
- return {
- cell: cell,
- score: score,
- region: region
- };
- });
- return boundingBoxes;
- }
- function stage1(imgTensor, scales, scoreThreshold, params, stats) {
- stats.stage1 = [];
- var pnetOutputs = scales.map(function (scale) { return tidy(function () {
- var statsForScale = { scale: scale };
- var resized = rescaleAndNormalize(imgTensor, scale);
- var ts = Date.now();
- var _a = PNet(resized, params), prob = _a.prob, regions = _a.regions;
- statsForScale.pnet = Date.now() - ts;
- var scoresTensor = unstack(unstack(prob, 3)[1])[0];
- var regionsTensor = unstack(regions)[0];
- return {
- scoresTensor: scoresTensor,
- regionsTensor: regionsTensor,
- scale: scale,
- statsForScale: statsForScale
- };
- }); });
- var boxesForScale = pnetOutputs.map(function (_a) {
- var scoresTensor = _a.scoresTensor, regionsTensor = _a.regionsTensor, scale = _a.scale, statsForScale = _a.statsForScale;
- var boundingBoxes = extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold);
- scoresTensor.dispose();
- regionsTensor.dispose();
- if (!boundingBoxes.length) {
- stats.stage1.push(statsForScale);
- return [];
- }
- var ts = Date.now();
- var indices = nonMaxSuppression$1(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
- statsForScale.nms = Date.now() - ts;
- statsForScale.numBoxes = indices.length;
- stats.stage1.push(statsForScale);
- return indices.map(function (boxIdx) { return boundingBoxes[boxIdx]; });
- });
- var allBoxes = boxesForScale.reduce(function (all$$1, boxes) { return all$$1.concat(boxes); }, []);
- var finalBoxes = [];
- var finalScores = [];
- if (allBoxes.length > 0) {
- var ts = Date.now();
- var indices = nonMaxSuppression$1(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
- stats.stage1_nms = Date.now() - ts;
- finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
- finalBoxes = indices
- .map(function (idx) { return allBoxes[idx]; })
- .map(function (_a) {
- var cell = _a.cell, region = _a.region;
- return new BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
- });
- }
- return {
- boxes: finalBoxes,
- scores: finalScores
- };
- }
-
- function extractImagePatches(img, boxes, _a) {
- var width = _a.width, height = _a.height;
- return __awaiter$1(this, void 0, void 0, function () {
- var _this = this;
- var imgCtx, bitmaps, imagePatchesDatas;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- imgCtx = getContext2dOrThrow(img);
- return [4 /*yield*/, Promise.all(boxes.map(function (box) { return __awaiter$1(_this, void 0, void 0, function () {
- var _a, y, ey, x, ex, fromX, fromY, imgData;
- return __generator$1(this, function (_b) {
- _a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;
- fromX = x - 1;
- fromY = y - 1;
- imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
- return [2 /*return*/, createImageBitmap(imgData)];
- });
- }); }))];
- case 1:
- bitmaps = _b.sent();
- imagePatchesDatas = [];
- bitmaps.forEach(function (bmp) {
- var patch = createCanvas({ width: width, height: height });
- var patchCtx = getContext2dOrThrow(patch);
- patchCtx.drawImage(bmp, 0, 0, width, height);
- var data = patchCtx.getImageData(0, 0, width, height).data;
- var currData = [];
- // RGBA -> BGR
- for (var i = 0; i < data.length; i += 4) {
- currData.push(data[i + 2]);
- currData.push(data[i + 1]);
- currData.push(data[i]);
- }
- imagePatchesDatas.push(currData);
- });
- return [2 /*return*/, imagePatchesDatas.map(function (data) {
- var t = tidy(function () {
- var imagePatchTensor = transpose(tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
- return normalize$1(imagePatchTensor);
- });
- return t;
- })];
- }
- });
- });
- }
-
- function RNet(x, params) {
- return tidy(function () {
- var convOut = sharedLayer(x, params);
- var vectorized = reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
- var fc1 = fullyConnectedLayer(vectorized, params.fc1);
- var prelu4 = prelu$1(fc1, params.prelu4_alpha);
- var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1);
- var max$$1 = expandDims(max(fc2_1, 1), 1);
- var prob = softmax(sub(fc2_1, max$$1), 1);
- var regions = fullyConnectedLayer(prelu4, params.fc2_2);
- var scores = unstack(prob, 1)[1];
- return { scores: scores, regions: regions };
- });
- }
-
- function stage2(img, inputBoxes, scoreThreshold, params, stats) {
- return __awaiter$1(this, void 0, void 0, function () {
- var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;
- return __generator$1(this, function (_c) {
- switch (_c.label) {
- case 0:
- ts = Date.now();
- return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
- case 1:
- rnetInputs = _c.sent();
- stats.stage2_extractImagePatches = Date.now() - ts;
- ts = Date.now();
- rnetOuts = rnetInputs.map(function (rnetInput) {
- var out = RNet(rnetInput, params);
- rnetInput.dispose();
- return out;
- });
- stats.stage2_rnet = Date.now() - ts;
- scoresTensor = rnetOuts.length > 1
- ? concat(rnetOuts.map(function (out) { return out.scores; }))
- : rnetOuts[0].scores;
- _b = (_a = Array).from;
- return [4 /*yield*/, scoresTensor.data()];
- case 2:
- scores = _b.apply(_a, [_c.sent()]);
- scoresTensor.dispose();
- indices = scores
- .map(function (score, idx) { return ({ score: score, idx: idx }); })
- .filter(function (c) { return c.score > scoreThreshold; })
- .map(function (_a) {
- var idx = _a.idx;
- return idx;
- });
- filteredBoxes = indices.map(function (idx) { return inputBoxes[idx]; });
- filteredScores = indices.map(function (idx) { return scores[idx]; });
- finalBoxes = [];
- finalScores = [];
- if (filteredBoxes.length > 0) {
- ts = Date.now();
- indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7);
- stats.stage2_nms = Date.now() - ts;
- regions_1 = indicesNms.map(function (idx) {
- return new MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
- });
- finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
- finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
- }
- rnetOuts.forEach(function (t) {
- t.regions.dispose();
- t.scores.dispose();
- });
- return [2 /*return*/, {
- boxes: finalBoxes,
- scores: finalScores
- }];
- }
- });
- });
- }
-
- function ONet(x, params) {
- return tidy(function () {
- var out = sharedLayer(x, params);
- out = maxPool(out, [2, 2], [2, 2], 'same');
- out = convLayer(out, params.conv4, 'valid');
- out = prelu$1(out, params.prelu4_alpha);
- var vectorized = reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
- var fc1 = fullyConnectedLayer(vectorized, params.fc1);
- var prelu5 = prelu$1(fc1, params.prelu5_alpha);
- var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1);
- var max$$1 = expandDims(max(fc2_1, 1), 1);
- var prob = softmax(sub(fc2_1, max$$1), 1);
- var regions = fullyConnectedLayer(prelu5, params.fc2_2);
- var points = fullyConnectedLayer(prelu5, params.fc2_3);
- var scores = unstack(prob, 1)[1];
- return { scores: scores, regions: regions, points: points };
- });
- }
-
- function stage3(img, inputBoxes, scoreThreshold, params, stats) {
- return __awaiter$1(this, void 0, void 0, function () {
- var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
- return __generator$1(this, function (_c) {
- switch (_c.label) {
- case 0:
- ts = Date.now();
- return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
- case 1:
- onetInputs = _c.sent();
- stats.stage3_extractImagePatches = Date.now() - ts;
- ts = Date.now();
- onetOuts = onetInputs.map(function (onetInput) {
- var out = ONet(onetInput, params);
- onetInput.dispose();
- return out;
- });
- stats.stage3_onet = Date.now() - ts;
- scoresTensor = onetOuts.length > 1
- ? concat(onetOuts.map(function (out) { return out.scores; }))
- : onetOuts[0].scores;
- _b = (_a = Array).from;
- return [4 /*yield*/, scoresTensor.data()];
- case 2:
- scores = _b.apply(_a, [_c.sent()]);
- scoresTensor.dispose();
- indices = scores
- .map(function (score, idx) { return ({ score: score, idx: idx }); })
- .filter(function (c) { return c.score > scoreThreshold; })
- .map(function (_a) {
- var idx = _a.idx;
- return idx;
- });
- filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
- filteredBoxes = indices
- .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
- filteredScores = indices.map(function (idx) { return scores[idx]; });
- finalBoxes = [];
- finalScores = [];
- points = [];
- if (filteredBoxes.length > 0) {
- ts = Date.now();
- indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7, false);
- stats.stage3_nms = Date.now() - ts;
- finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
- finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
- points = indicesNms.map(function (idx, i) {
- return Array(5).fill(0).map(function (_, ptIdx) {
- return new Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
- });
- });
- }
- onetOuts.forEach(function (t) {
- t.regions.dispose();
- t.scores.dispose();
- t.points.dispose();
- });
- return [2 /*return*/, {
- boxes: finalBoxes,
- scores: finalScores,
- points: points
- }];
- }
- });
- });
- }
-
- var Mtcnn = /** @class */ (function (_super) {
- __extends$1(Mtcnn, _super);
- function Mtcnn() {
- return _super.call(this, 'Mtcnn') || this;
- }
- Mtcnn.prototype.forwardInput = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return __awaiter$1(this, void 0, void 0, function () {
- var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
- return __generator$1(this, function (_c) {
- switch (_c.label) {
- case 0:
- params = this.params;
- if (!params) {
- throw new Error('Mtcnn - load model before inference');
- }
- inputCanvas = input.canvases[0];
- if (!inputCanvas) {
- throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
- }
- stats = {};
- tsTotal = Date.now();
- imgTensor = tidy(function () {
- return bgrToRgbTensor(expandDims(fromPixels(inputCanvas)).toFloat());
- });
- onReturn = function (results) {
- // dispose tensors on return
- imgTensor.dispose();
- stats.total = Date.now() - tsTotal;
- return results;
- };
- _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
- _b = Object.assign({}, getDefaultMtcnnForwardParams(), forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
- scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
- .filter(function (scale) {
- var sizes = getSizesForScale(scale, [height, width]);
- return Math.min(sizes.width, sizes.height) > CELL_SIZE$1;
- })
- .slice(0, maxNumScales);
- stats.scales = scales;
- stats.pyramid = scales.map(function (scale) { return getSizesForScale(scale, [height, width]); });
- ts = Date.now();
- return [4 /*yield*/, stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
- case 1:
- out1 = _c.sent();
- stats.total_stage1 = Date.now() - ts;
- if (!out1.boxes.length) {
- return [2 /*return*/, onReturn({ results: [], stats: stats })];
- }
- stats.stage2_numInputBoxes = out1.boxes.length;
- // using the inputCanvas to extract and resize the image patches, since it is faster
- // than doing this on the gpu
- ts = Date.now();
- return [4 /*yield*/, stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
- case 2:
- out2 = _c.sent();
- stats.total_stage2 = Date.now() - ts;
- if (!out2.boxes.length) {
- return [2 /*return*/, onReturn({ results: [], stats: stats })];
- }
- stats.stage3_numInputBoxes = out2.boxes.length;
- ts = Date.now();
- return [4 /*yield*/, stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
- case 3:
- out3 = _c.sent();
- stats.total_stage3 = Date.now() - ts;
- results = out3.boxes.map(function (box, idx) { return ({
- faceDetection: new FaceDetection(out3.scores[idx], new Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
- height: height,
- width: width
- }),
- faceLandmarks: new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.div(new Point(width, height)); }), { width: width, height: height })
- }); });
- return [2 /*return*/, onReturn({ results: results, stats: stats })];
- }
- });
- });
- };
- Mtcnn.prototype.forward = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return __awaiter$1(this, void 0, void 0, function () {
- var _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, toNetInput(input)];
- case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
- forwardParams])];
- case 2: return [2 /*return*/, (_b.sent()).results];
- }
- });
- });
- };
- Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
- if (forwardParams === void 0) { forwardParams = {}; }
- return __awaiter$1(this, void 0, void 0, function () {
- var _a;
- return __generator$1(this, function (_b) {
- switch (_b.label) {
- case 0:
- _a = this.forwardInput;
- return [4 /*yield*/, toNetInput(input)];
- case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
- forwardParams])];
- }
- });
- });
- };
- // none of the param tensors are quantized yet
- Mtcnn.prototype.loadQuantizedParams = function (uri) {
- return loadQuantizedParams$4(uri);
- };
- Mtcnn.prototype.extractParams = function (weights) {
- return extractParams$4(weights);
- };
- return Mtcnn;
- }(NeuralNetwork));
-
- var IOU_THRESHOLD = 0.4;
- var BOX_ANCHORS = [
- new Point(0.738768, 0.874946),
- new Point(2.42204, 2.65704),
- new Point(4.30971, 7.04493),
- new Point(10.246, 4.59428),
- new Point(12.6868, 11.8741)
- ];
- var BOX_ANCHORS_SEPARABLE = [
- new Point(1.603231, 2.094468),
- new Point(6.041143, 7.080126),
- new Point(2.882459, 3.518061),
- new Point(4.266906, 5.178857),
- new Point(9.041765, 10.66308)
- ];
- var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
- var DEFAULT_MODEL_NAME$5 = 'tiny_yolov2_model';
- var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
-
- var TinyYolov2$1 = /** @class */ (function (_super) {
- __extends$1(TinyYolov2$$1, _super);
- function TinyYolov2$$1(withSeparableConvs) {
- if (withSeparableConvs === void 0) { withSeparableConvs = true; }
- var _this = this;
- var config = Object.assign({}, {
- withSeparableConvs: withSeparableConvs,
- iouThreshold: IOU_THRESHOLD,
- classes: ['face']
- }, withSeparableConvs
- ? {
- anchors: BOX_ANCHORS_SEPARABLE,
- meanRgb: MEAN_RGB_SEPARABLE
- }
- : {
- anchors: BOX_ANCHORS,
- withClassScores: true
- });
- _this = _super.call(this, config) || this;
- return _this;
- }
- Object.defineProperty(TinyYolov2$$1.prototype, "withSeparableConvs", {
- get: function () {
- return this.config.withSeparableConvs;
- },
- enumerable: true,
- configurable: true
- });
- Object.defineProperty(TinyYolov2$$1.prototype, "anchors", {
- get: function () {
- return this.config.anchors;
- },
- enumerable: true,
- configurable: true
- });
- TinyYolov2$$1.prototype.locateFaces = function (input, forwardParams) {
- return __awaiter$1(this, void 0, void 0, function () {
- var objectDetections;
- return __generator$1(this, function (_a) {
- switch (_a.label) {
- case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
- case 1:
- objectDetections = _a.sent();
- return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
- }
- });
- });
- };
- TinyYolov2$$1.prototype.loadQuantizedParams = function (modelUri) {
- var defaultModelName = this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME$5;
- return _super.prototype.loadQuantizedParams.call(this, modelUri, defaultModelName);
- };
- return TinyYolov2$$1;
- }(TinyYolov2));
-
- var detectionNet = new FaceDetectionNet();
- var landmarkNet = new FaceLandmark68Net();
- var recognitionNet = new FaceRecognitionNet();
- // nets need more specific names, to avoid ambiguity in future
- // when alternative net implementations are provided
- var nets = {
- ssdMobilenetv1: detectionNet,
- faceLandmark68Net: landmarkNet,
- faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
- faceRecognitionNet: recognitionNet,
- mtcnn: new Mtcnn(),
- tinyYolov2: new TinyYolov2$1()
- };
- function loadSsdMobilenetv1Model(url) {
- return nets.ssdMobilenetv1.load(url);
- }
- function loadFaceLandmarkModel(url) {
- return nets.faceLandmark68Net.load(url);
- }
- function loadFaceLandmarkTinyModel(url) {
- return nets.faceLandmark68TinyNet.load(url);
- }
- function loadFaceRecognitionModel(url) {
- return nets.faceRecognitionNet.load(url);
- }
- function loadMtcnnModel(url) {
- return nets.mtcnn.load(url);
- }
- function loadTinyYolov2Model(url) {
- return nets.tinyYolov2.load(url);
- }
- function loadFaceDetectionModel(url) {
- return loadSsdMobilenetv1Model(url);
- }
- function loadModels(url) {
- console.warn('loadModels will be deprecated in future');
- return Promise.all([
- loadSsdMobilenetv1Model(url),
- loadFaceLandmarkModel(url),
- loadFaceRecognitionModel(url),
- loadMtcnnModel(url),
- loadTinyYolov2Model(url)
- ]);
- }
- function locateFaces(input, minConfidence, maxResults) {
- return nets.ssdMobilenetv1.locateFaces(input, minConfidence, maxResults);
- }
- var ssdMobilenetv1 = locateFaces;
- function detectLandmarks(input) {
- return nets.faceLandmark68Net.detectLandmarks(input);
- }
- function detectLandmarksTiny(input) {
- return nets.faceLandmark68TinyNet.detectLandmarks(input);
- }
- function computeFaceDescriptor(input) {
- return nets.faceRecognitionNet.computeFaceDescriptor(input);
- }
- function mtcnn(input, forwardParams) {
- return nets.mtcnn.forward(input, forwardParams);
- }
- function tinyYolov2(input, forwardParams) {
- return nets.tinyYolov2.locateFaces(input, forwardParams);
- }
- var allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(nets.ssdMobilenetv1, nets.faceLandmark68Net, nets.faceRecognitionNet);
- var allFacesTinyYolov2 = allFacesTinyYolov2Factory(nets.tinyYolov2, nets.faceLandmark68Net, nets.faceRecognitionNet);
- var allFacesMtcnn = allFacesMtcnnFactory(nets.mtcnn, nets.faceRecognitionNet);
- var allFaces = allFacesSsdMobilenetv1;
-
- function createMtcnn(weights) {
- var net = new Mtcnn();
- net.extractWeights(weights);
- return net;
- }
-
- function createTinyYolov2(weights, withSeparableConvs) {
- if (withSeparableConvs === void 0) { withSeparableConvs = true; }
- var net = new TinyYolov2$1(withSeparableConvs);
- net.extractWeights(weights);
- return net;
- }
-
- exports.tf = tfCore_esm;
- exports.Box = Box;
- exports.BoundingBox = BoundingBox;
- exports.LabeledBox = LabeledBox;
- exports.ObjectDetection = ObjectDetection;
- exports.Point = Point;
- exports.PredictedBox = PredictedBox;
- exports.Rect = Rect;
- exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
- exports.extractWeightEntryFactory = extractWeightEntryFactory;
- exports.extractWeightsFactory = extractWeightsFactory;
- exports.getModelUris = getModelUris;
- exports.awaitMediaLoaded = awaitMediaLoaded;
- exports.bufferToImage = bufferToImage;
- exports.createCanvas = createCanvas;
- exports.createCanvasFromMedia = createCanvasFromMedia;
- exports.drawBox = drawBox;
- exports.drawDetection = drawDetection;
- exports.drawText = drawText;
- exports.getContext2dOrThrow = getContext2dOrThrow;
- exports.getDefaultDrawOptions = getDefaultDrawOptions;
- exports.getMediaDimensions = getMediaDimensions;
- exports.imageTensorToCanvas = imageTensorToCanvas;
- exports.imageToSquare = imageToSquare;
- exports.isMediaElement = isMediaElement;
- exports.isMediaLoaded = isMediaLoaded;
- exports.loadWeightMap = loadWeightMap;
- exports.NetInput = NetInput;
- exports.resolveInput = resolveInput;
- exports.toNetInput = toNetInput;
- exports.sigmoid = sigmoid$1;
- exports.inverseSigmoid = inverseSigmoid;
- exports.iou = iou;
- exports.nonMaxSuppression = nonMaxSuppression$1;
- exports.normalize = normalize;
- exports.padToSquare = padToSquare;
- exports.shuffleArray = shuffleArray;
- exports.isTensor = isTensor;
- exports.isTensor1D = isTensor1D;
- exports.isTensor2D = isTensor2D;
- exports.isTensor3D = isTensor3D;
- exports.isTensor4D = isTensor4D;
- exports.isFloat = isFloat;
- exports.isEven = isEven;
- exports.round = round$1;
- exports.isDimensions = isDimensions;
- exports.computeReshapedDimensions = computeReshapedDimensions;
- exports.getCenterPoint = getCenterPoint;
- exports.range = range$1;
- exports.isValidNumber = isValidNumber;
- exports.isValidProbablitiy = isValidProbablitiy;
- exports.NeuralNetwork = NeuralNetwork;
- exports.FaceDetection = FaceDetection;
- exports.FaceLandmarks = FaceLandmarks;
- exports.FaceLandmarks5 = FaceLandmarks5;
- exports.FaceLandmarks68 = FaceLandmarks68;
- exports.FullFaceDescription = FullFaceDescription;
- exports.drawContour = drawContour;
- exports.drawLandmarks = drawLandmarks;
- exports.extractFaces = extractFaces;
- exports.extractFaceTensors = extractFaceTensors;
- exports.euclideanDistance = euclideanDistance;
- exports.createFaceDetectionNet = createFaceDetectionNet;
- exports.faceDetectionNet = faceDetectionNet;
- exports.FaceDetectionNet = FaceDetectionNet;
- exports.FaceLandmarkNet = FaceLandmarkNet;
- exports.createFaceLandmarkNet = createFaceLandmarkNet;
- exports.faceLandmarkNet = faceLandmarkNet;
- exports.FaceLandmark68Net = FaceLandmark68Net;
- exports.FaceLandmark68TinyNet = FaceLandmark68TinyNet;
- exports.createFaceRecognitionNet = createFaceRecognitionNet;
- exports.faceRecognitionNet = faceRecognitionNet;
- exports.FaceRecognitionNet = FaceRecognitionNet;
- exports.detectionNet = detectionNet;
- exports.landmarkNet = landmarkNet;
- exports.recognitionNet = recognitionNet;
- exports.nets = nets;
- exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
- exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
- exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
- exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
- exports.loadMtcnnModel = loadMtcnnModel;
- exports.loadTinyYolov2Model = loadTinyYolov2Model;
- exports.loadFaceDetectionModel = loadFaceDetectionModel;
- exports.loadModels = loadModels;
- exports.locateFaces = locateFaces;
- exports.ssdMobilenetv1 = ssdMobilenetv1;
- exports.detectLandmarks = detectLandmarks;
- exports.detectLandmarksTiny = detectLandmarksTiny;
- exports.computeFaceDescriptor = computeFaceDescriptor;
- exports.mtcnn = mtcnn;
- exports.tinyYolov2 = tinyYolov2;
- exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
- exports.allFacesTinyYolov2 = allFacesTinyYolov2;
- exports.allFacesMtcnn = allFacesMtcnn;
- exports.allFaces = allFaces;
- exports.createMtcnn = createMtcnn;
- exports.Mtcnn = Mtcnn;
- exports.createTinyYolov2 = createTinyYolov2;
- exports.TinyYolov2 = TinyYolov2$1;
-
- Object.defineProperty(exports, '__esModule', { value: true });
+ /**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+ var t=function(e,n){return (t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e;}||function(t,e){for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);})(e,n)};function e(e,n){function r(){this.constructor=e;}t(e,n),e.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r);}function n(t,e,n,r){return new(n||(n=Promise))((function(o,a){function i(t){try{u(r.next(t));}catch(t){a(t);}}function s(t){try{u(r.throw(t));}catch(t){a(t);}}function u(t){t.done?o(t.value):new n((function(e){e(t.value);})).then(i,s);}u((r=r.apply(t,e||[])).next());}))}function r(t,e){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=(o=i.trys).length>0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]0;)r=Math.random()*e|0,n=t[--e],t[e]=t[r],t[r]=n;}function x(t,e,n){return Math.max(t,Math.min(e,n))}function b(t){return t%2==0?t:t+1}function w(t){for(var e=0,n=0;n=n?o():setTimeout(i,s);}};i();}))}function _(t,e){for(var n=1,r=-1,o=0;o=0)n*=t[o];else if(-1===t[o]){if(-1!==r)throw Error("Shapes can only have 1 implicit size. 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y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n "+va+"\n "+ma+"\n "+ga+"\n "}(l);return e.isPacked?(a=function(t,e){switch(t.length){case 0:return "\n int getOutputCoords() {\n return 0;\n }\n ";case 1:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(1===n[0])return "\n int getOutputCoords() {\n return 2 * int(resultUV.x * "+n[1]+".0);\n }\n ";if(1===n[1])return "\n int getOutputCoords() {\n return 2 * int(resultUV.y * "+n[0]+".0);\n }\n ";return "\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n return 2 * (resTexRC.x * "+n[1]+" + resTexRC.y);\n }\n "}(0,e);case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(S(t,e))return "\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n }\n ";var r=Math.ceil(t[1]/2);return "\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+n[0]+", "+n[1]+"));\n\n int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n int r = 2 * (index / "+r+");\n int c = imod(index, "+r+") * 2;\n\n return ivec2(r, c);\n }\n "}(t,e);case 3:return n=t,r=e,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=Math.ceil(n[2]/2),i=a*Math.ceil(n[1]/2),"\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+o[0]+", "+o[1]+"));\n int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n int b = index / "+i+";\n index -= b * "+i+";\n\n int r = 2 * (index / "+a+");\n int c = imod(index, "+a+") * 2;\n\n return ivec3(b, r, c);\n }\n ";default:return function(t,e){for(var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],r=Math.ceil(t[t.length-1]/2),o=r*Math.ceil(t[t.length-2]/2),a=o,i="",s="b, r, c",u=2;u2,(function(){return "Packed arg"+(n.charAt(0).toUpperCase()+n.slice(1))+" supports only inputs with rank above 2."}));var o=t[t.length-1],a=Math.ceil(o/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),r||this.variableNames.push("bestIndicesA");var i,s,u=this.outputShape,c=u.length,l=wa(c),h=sa("coords",c);if(1===a){var f=wa(s=c+1);i="\n "+f+" sourceLocR = "+f+"("+h.join()+", 0);\n ++"+h[c-1]+";\n "+f+" sourceLocG = "+f+"("+h.join()+", 0);\n ++"+h[c-2]+";\n "+f+" sourceLocA = "+f+"("+h.join()+", 0);\n --"+h[c-1]+";\n "+f+" sourceLocB = "+f+"("+h.join()+", 0);\n --"+h[c-2]+";";}else s=c,i="\n "+l+" sourceLocR = coords;\n ++"+h[c-1]+";\n "+l+" sourceLocG = coords;\n ++"+h[c-2]+";\n "+l+" sourceLocA = coords;\n --"+h[c-1]+";\n "+l+" sourceLocB = coords;\n --"+h[c-2]+";";var d=["x","y","z","w","u","v"].slice(0,s),p="."+d[s-1],v=d.map((function(t){return "int "+t})),m=sa("sourceLocR",s-1).concat("inIdx.r"),g=sa("sourceLocG",s-1).concat("inIdx.g"),y=sa("sourceLocB",s-1).concat("inIdx.b"),x=sa("sourceLocA",s-1).concat("inIdx.a"),b="max"===n?"greaterThan":"lessThan",w=r?"":"\n inIdx = round(vec4(getBestIndicesAChannel("+m.join()+"),\n getBestIndicesAChannel("+g.join()+"),\n getBestIndicesAChannel("+y.join()+"),\n getBestIndicesAChannel("+x.join()+")));",E="vec4(\n getAChannel("+m.join()+"),\n hasNextCol ? getAChannel("+g.join()+") : 0.,\n hasNextRow ? getAChannel("+y.join()+") : 0.,\n hasNextRow && hasNextCol ? getAChannel("+x.join()+") : 0.)",R=r?"":"\n float getBestIndicesAChannel("+v.join()+") {\n return getChannel(getBestIndicesA("+d.join()+"),\n vec2("+d.slice(-2).join()+"));\n }";this.userCode="\n float getAChannel("+v.join()+") {\n return getChannel(getA("+d.join()+"),\n vec2("+d.slice(-2).join()+"));\n }\n "+R+"\n void main() {\n "+l+" coords = getOutputCoords();\n bool hasNextCol = "+h[c-1]+" < "+(u[c-1]-1)+";\n bool hasNextRow = "+h[c-2]+" < "+(u[c-2]-1)+";\n "+i+"\n ivec4 srcIdx = ivec4(sourceLocR"+p+", sourceLocG"+p+",\n sourceLocB"+p+", sourceLocA"+p+") * "+e+";\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = "+E+";\n\n for (int i = 0; i < "+e+"; i++) {\n inIdx = srcIdx;\n "+w+"\n vec4 candidate = "+E+";\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4("+b+"(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n ";},Ia=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=s-1-t.padInfo.top,l=u-1-t.padInfo.left,h=1/(e*n);this.userCode="\n const ivec2 pads = ivec2("+c+", "+l+");\n const float avgMultiplier = float("+h+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+s+";\n wR += "+a+") {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+u+";\n wC+= "+i+") {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n ";},ka=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,h=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=l-1-t.padInfo.front,p=h-1-t.padInfo.top,v=f-1-t.padInfo.left,m=1/(e*n*r);this.userCode="\n const ivec3 pads = ivec3("+d+", "+p+", "+v+");\n const float avgMultiplier = float("+m+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < "+l+";\n wD += "+s+") {\n float dyD = float(dyDCorner + wD) / "+o+".0;\n\n if (dyD < 0.0 || dyD >= "+t.outDepth+".0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < "+h+";\n wR += "+u+") {\n float dyR = float(dyRCorner + wR) / "+a+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+f+";\n wC += "+c+") {\n float dyC = float(dyCCorner + wC) / "+i+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Sa=function(t,e,n,r,o,a){this.outputShape=[],this.variableNames=["x","mean","variance"],Ro(t,e),Ro(t,n);var i="0.0";null!=r&&(Ro(t,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="1.0";null!=o&&(Ro(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = "+i+";\n float scale = "+s+";\n float inv = scale * inversesqrt(variance + float("+a+"));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n ";},Aa=function(t,e,n,r,o,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Ro(t,e),Ro(t,n);var i="vec4(0.0)";null!=r&&(Ro(t,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(Ro(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n void main() {\n vec4 offset = "+i+";\n vec4 scale = "+s+";\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n setOutput((x - mean) * inv + offset);\n }\n ";},Da="return areal * breal - aimag * bimag;",Ta="return areal * bimag + aimag * breal;",Na=function(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=Ro(e,n),this.userCode="\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n "+t+"\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n ";},Fa="return a + b;",_a="return a - b;",Oa="return a * b;",Ma="return (a < 0.) ? b * a : a;",Ba=function(t,e,n){this.variableNames=["A","B"],this.outputShape=Ro(e,n),this.userCode="\n float binaryOperation(float a, float b) {\n "+t+"\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n ";},Pa="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",La=function(t,e,n,r){void 0===r&&(r=!1),this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Ro(e,n);var o=this.outputShape.length,a="";if(r)if(0===o||1===k(this.outputShape))a="\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n ";else if(a="\n "+wa(o)+" coords = getOutputCoords();\n ",1===o)a+="\n result.y = (coords + 1) >= "+this.outputShape[0]+" ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n ";else {var i=sa("coords",o);a+="\n bool nextRowOutOfBounds =\n ("+i[o-2]+" + 1) >= "+this.outputShape[o-2]+";\n bool nextColOutOfBounds =\n ("+i[o-1]+" + 1) >= "+this.outputShape[o-1]+";\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n ";}this.userCode="\n vec4 binaryOperation(vec4 a, vec4 b) {\n "+t+"\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n "+a+"\n\n setOutput(result);\n }\n ";},Wa=function(){function t(t){this.variableNames=["A"],this.outputShape=t,this.userCode="\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n ";}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,"minVal"),n.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e);}},t}(),Ua=function(){function t(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode="\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n ";}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,"minVal"),n.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e);}},t}(),Va=function(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n ";},za=function(t){this.outputShape=[],this.outputShape=Sn(t,1),this.variableNames=t.map((function(t,e){return "T"+e}));var e=new Array(t.length-1);e[0]=t[0][1];for(var n=1;n= "+s[u-1]+") {\n return getChannel(\n getT"+u+"("+Ha(i,c,d)+"),\n vec2("+Ha(l,c,d)+"));\n }";}var p=s.length,v=s[s.length-1];f+="\n return getChannel(\n getT"+p+"("+Ha(i,c,v)+"),\n vec2("+Ha(l,c,v)+"));",this.userCode="\n float getValue("+i.map((function(t){return "int "+t}))+") {\n "+f+"\n }\n\n void main() {\n "+o+" coords = getOutputCoords();\n vec4 result = vec4(getValue("+a+"), 0., 0., 0.);\n\n "+a[r-1]+" = "+a[r-1]+" + 1;\n if ("+a[r-1]+" < "+n[r-1]+") {\n result.g = getValue("+a+");\n }\n\n "+a[r-2]+" = "+a[r-2]+" + 1;\n if ("+a[r-2]+" < "+n[r-2]+") {\n result.a = getValue("+a+");\n }\n\n "+a[r-1]+" = "+a[r-1]+" - 1;\n if ("+a[r-2]+" < "+n[r-2]+" &&\n "+a[r-1]+" < "+n[r-1]+") {\n result.b = getValue("+a+");\n }\n setOutput(result);\n }\n ";};function Ha(t,e,n){var r=t.indexOf(e);return t.map((function(t,e){return e===r?t+" - "+n:t})).join()}var qa=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,a="channelsLast"===t.dataFormat;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < "+t.batchSize+"; b++) {\n for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n int xR = wR + yR * "+e+" - "+r+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n if ("+a+") {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Ka=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a="channelsLast"===t.dataFormat,i=e-1-t.padInfo.top,s=n-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode="\n const ivec2 pads = ivec2("+i+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords["+l+"];\n\n ivec2 dyCorner = ivec2(coords["+u+"], coords["+c+"]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+e+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+e+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n\n if ("+a+") {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n ";},ja=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode="\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < "+t.batchSize+"; b++) {\n for (int yF = 0; yF < "+t.outDepth+"; yF++) {\n int xF = wF + yF * "+e+" - "+o+";\n\n if (xF < 0 || xF >= "+t.inDepth+") {\n continue;\n }\n\n for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n int xR = wR + yR * "+n+" - "+a+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n int xC = wC + yC * "+r+" - "+i+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Xa=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,c=r-1-t.padInfo.left;this.userCode="\n const ivec3 pads = ivec3("+s+", "+u+", "+c+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < "+e+"; wF++) {\n float dyF = float(dyFCorner + wF) / "+o+".0;\n\n if (dyF < 0.0 || dyF >= "+t.outDepth+".0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = "+e+" - 1 - wF;\n\n for (int wR = 0; wR < "+n+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+a+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+n+" - 1 - wR;\n\n for (int wC = 0; wC < "+r+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+i+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+r+" - 1 - wC;\n\n for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Ya=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * "+a+" + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < "+t.batchSize+"; b++) {\n for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n int xR = wR + yR * "+e+" - "+r+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n int xC = wC + yC * "+n+" - "+o+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n ";},$a=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=e-1-t.padInfo.top,i=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode="\n const ivec2 pads = ivec2("+a+", "+i+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < "+e+"; wR++) {\n float dyR = float(dyRCorner + wR) / "+r+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = "+e+" - 1 - wR;\n\n for (int wC = 0; wC < "+n+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+o+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = "+n+" - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < "+s+"; dm++) {\n int d2 = d1 * "+s+" + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Qa=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var o=t.padInfo.top,a=t.padInfo.left,i=t.strideHeight,s=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterHeight,h=t.filterWidth,f=4*Math.floor(t.inChannels/4),d=t.inChannels%4,p="channelsLast"===t.dataFormat,v=p?1:2,m=p?2:3,g=p?3:1,y="",x="";n&&(y=r?"float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n "+n+"\n }":"\n float activation(float x) {\n "+n+"\n }\n ",x="result = activation(result);");var b=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode="\n "+y+"\n\n const ivec2 strides = ivec2("+i+", "+s+");\n const ivec2 pads = ivec2("+o+", "+a+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords["+g+"];\n\n ivec2 xRCCorner =\n ivec2(coords["+v+"], coords["+m+"]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+l+"; wR++) {\n int xR = xRCorner + wR * "+u+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+h+"; wC++) {\n int xC = xCCorner + wC * "+c+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+f+"; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if ("+p+") {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if ("+(1===d)+") {\n\n if ("+p+") {\n dotProd +=\n getX(batch, xR, xC, "+f+") *\n getW(wR, wC, "+f+", d2);\n } else {\n dotProd +=\n getX(batch, "+f+", xR, xC) *\n getW(wR, wC, "+f+", d2);\n }\n\n } else if ("+(2===d)+") {\n vec2 wValues = vec2(\n getW(wR, wC, "+f+", d2),\n getW(wR, wC, "+f+" + 1, d2)\n );\n\n if ("+p+") {\n vec2 xValues = vec2(\n getX(batch, xR, xC, "+f+"),\n getX(batch, xR, xC, "+f+" + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, "+f+", xR, xC),\n getX(batch, "+f+" + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if ("+(3===d)+") {\n vec3 wValues = vec3(\n getW(wR, wC, "+f+", d2),\n getW(wR, wC, "+f+" + 1, d2),\n getW(wR, wC, "+f+" + 2, d2)\n );\n\n if ("+p+") {\n vec3 xValues = vec3(\n getX(batch, xR, xC, "+f+"),\n getX(batch, xR, xC, "+f+" + 1),\n getX(batch, xR, xC, "+f+" + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, "+f+", xR, xC),\n getX(batch, "+f+" + 1, xR, xC),\n getX(batch, "+f+" + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n "+b+"\n "+x+"\n setOutput(result);\n }\n ";},Ja=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,h=t.filterHeight,f=t.filterWidth,d=4*Math.floor(t.inChannels/4),p=t.inChannels%4;this.userCode="\n const ivec3 strides = ivec3("+o+", "+a+", "+i+");\n const ivec3 pads = ivec3("+e+", "+n+", "+r+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < "+l+"; wF++) {\n int xF = xFCorner + wF * "+s+";\n\n if (xF < 0 || xF >= "+t.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+h+"; wR++) {\n int xR = xRCorner + wR * "+u+";\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+f+"; wC++) {\n int xC = xCCorner + wC * "+c+";\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n for (int d1 = 0; d1 < "+d+"; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if ("+(1===p)+") {\n dotProd +=\n getX(batch, xF, xR, xC, "+d+") *\n getW(wF, wR, wC, "+d+", d2);\n } else if ("+(2===p)+") {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, "+d+"),\n getX(batch, xF, xR, xC, "+d+" + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, "+d+", d2),\n getW(wF, wR, wC, "+d+" + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if ("+(3===p)+") {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, "+d+"),\n getX(batch, xF, xR, xC, "+d+" + 1),\n getX(batch, xF, xR, xC, "+d+" + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, "+d+", d2),\n getW(wF, wR, wC, "+d+" + 1, d2),\n getW(wF, wR, wC, "+d+" + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Za=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.outputShape=t.outShape;var o=t.inHeight,a=t.inWidth,i=t.padInfo.top,s=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,f=t.filterHeight,d=t.filterWidth,p=t.outChannels/t.inChannels,v="",m="";n&&(v=r?"float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n "+n+"\n }":"\n float activation(float x) {\n "+n+"\n }\n ",m="result = activation(result);");var g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),this.userCode="\n "+v+"\n\n const ivec2 strides = ivec2("+u+", "+c+");\n const ivec2 pads = ivec2("+i+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / "+p+";\n int q = d2 - d1 * "+p+";\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < "+f+"; wR++) {\n int xR = xRCorner + wR * "+l+";\n\n if (xR < 0 || xR >= "+o+") {\n continue;\n }\n\n for (int wC = 0; wC < "+d+"; wC++) {\n int xC = xCCorner + wC * "+h+";\n\n if (xC < 0 || xC >= "+a+") {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n "+g+"\n "+m+"\n setOutput(result);\n }\n ";},ti=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;for(var o=t.inHeight,a=t.inWidth,i=t.padInfo.top,s=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,f=t.filterHeight,d=t.filterWidth,p=d,v="int xR; int xC; int xCOffset;",m=0;m= 0 && xR < "+o+" && xCOffset >= 0 && xCOffset < "+a+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if(xCOffset + 1 >= "+a+") {\n xTexelR"+m+"C"+g+".zw = vec2(0.);\n }\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n xCOffset = xC + 1 - 2;\n if(xR >= 0 && xR < "+o+" && xCOffset >= 0 && xCOffset < "+a+") {\n vec4 previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if(xCOffset + 1 >= "+a+") {\n previous.zw = vec2(0.);\n }\n\n xR"+m+"C"+g+" = vec4(previous.zw, xTexelR"+m+"C"+g+".xy);\n } else {\n xR"+m+"C"+g+" = vec4(0, 0, xTexelR"+m+"C"+g+".xy);\n }\n ":"\n if(xR >= 0 && xR < "+o+" && xC >= 0 && xC < "+a+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xC, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n xR"+m+"C"+g+" = xTexelR"+m+"C"+g+";\n ",g+1= 0 && xR < "+o+" &&\n xCOffset >= 0 && xCOffset < "+a+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xCOffset, d1);\n }\n ",h>1&&(v+="\n xCOffset -= 2;\n if(xR >= 0 && xR < "+o+" &&\n xCOffset >= 0 && xCOffset < "+a+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n "),v+="\n xR"+m+"C"+(g+1)+" = vec4(\n xTexelR"+m+"C"+g+".zw, xTexelR"+m+"C"+(g+2)+".xy);\n "):v+="\n xCOffset = xC + "+x+";\n\n if(xR >= 0 && xR < "+o+" &&\n xCOffset >= 0 && xCOffset < "+a+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xCOffset, d1);\n }\n\n xR"+m+"C"+(g+1)+" = xTexelR"+m+"C"+(g+2)+";\n ";}}else g= 0 && xR < "+o+") {\n ",s%2==1?(v+="\n xCOffset = xC + 1 - "+c+";\n if(xCOffset >= 0 && xCOffset < "+a+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n if(xC + 1 >= 0 && xC + 1 < "+a+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xC + 1, d1);\n } else {\n xTexelR"+m+"C"+(g+2)+" = vec4(0.);\n }\n\n xR"+m+"C"+g+" = vec4(\n xTexelR"+m+"C"+g+".zw, xTexelR"+m+"C"+(g+2)+".zw);\n ",g+1= 0 && xCOffset < "+a+") {\n final = getX(batch, xR, xCOffset, d1);\n }\n xR"+m+"C"+(g+1)+" = vec4(xTexelR"+m+"C"+(g+2)+".xy, final.xy);\n ")):(v+="\n if(xC >= 0 && xC < "+a+") {\n xTexelR"+m+"C"+g+" = getX(batch, xR, xC, d1);\n } else {\n xTexelR"+m+"C"+g+" = vec4(0.);\n }\n\n xCOffset = xC + "+c+";\n if(xCOffset >= 0 && xCOffset < "+a+") {\n xTexelR"+m+"C"+(g+2)+" = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR"+m+"C"+(g+2)+" = vec4(0.);\n }\n\n xR"+m+"C"+g+" = vec4(\n xTexelR"+m+"C"+g+".xy, xTexelR"+m+"C"+(g+2)+".xy);\n ",g+11?[""+(i-1)/(l-1),"(y2-y1) * height_ratio","y1*"+p+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+p],g=m[0],y=m[1],x=m[2],b=h>1?[""+(s-1)/(h-1),"(x2-x1) * width_ratio","x1*"+v+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+v],w=b[0],C=b[1],E=b[2];this.userCode="\n const float height_ratio = float("+g+");\n const float width_ratio = float("+w+");\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= "+a+") {\n return;\n }\n\n float height_scale = "+y+";\n float width_scale = "+C+";\n\n float in_y = "+x+";\n if( in_y < 0.0 || in_y > "+p+" ) {\n setOutput(float("+o+"));\n return;\n }\n float in_x = "+E+";\n if( in_x < 0.0 || in_x > "+v+" ) {\n setOutput(float("+o+"));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if("+f+" == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n ";},ni=function(t,e,n){this.variableNames=["x"],this.outputShape=t;var r=t.length,o=t[t.length-1],a=n?"<":">";this.userCode="\n int getIndex(int i) {\n "+(n?"return "+o+" -i - 1;":"return i;")+"\n }\n\n void main() {\n "+wa(r)+" coords = getOutputCoords();\n int end = "+ri(r,"coords")+";\n float val = 0.0;\n for (int i = "+o+" - 1; i >= 0; i -= 1) {\n int idx = getIndex(i);\n if (idx "+a+" end) {\n continue;\n }\n if (idx == end && "+e+") {\n continue;\n }\n "+ri(r,"coords")+" = idx;\n val += getX("+function(t,e){if(1===t)return ""+e;if(2===t)return e+".x, "+e+".y";if(3===t)return e+".x, "+e+".y, "+e+".z";if(4===t)return e+".x, "+e+".y, "+e+".z, "+e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}(r,"coords")+");\n }\n setOutput(val);\n }\n ";};function ri(t,e){if(1===t)return ""+e;if(2===t)return e+".y";if(3===t)return e+".z";if(4===t)return e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}var oi=function(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Vt.DENSE;var e=Yt(t),n=ua();this.outputShape=t,this.userCode="\n ivec3 outCoordsFromFlatIndex(int index) {\n "+ca(["r","c","d"],t)+"\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+e[0]+", "+e[1]+"));\n int index = 4 * (resTexRC.x * "+e[1]+" + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n "+n.output+" = result;\n }\n ";},ai=function(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Vt.DENSE;var e=Yt(t),n=ua();this.outputShape=t,this.userCode="\n ivec3 outCoordsFromFlatIndex(int index) {\n "+ca(["r","c","d"],t)+"\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2("+e[0]+", "+e[1]+"));\n int index = 4 * (resTexRC.x * "+e[1]+" + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n "+n.output+" = result;\n }\n ";},ii=function(){function t(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = "+this.getHeightCoordString()+";\n int w = "+this.getWidthCoordString()+";\n int d = "+this.getDepthCoordString()+";\n\n int in_h = h / "+e+";\n int offset_h = imod(h, "+e+");\n int in_w = w / "+e+";\n int offset_w = imod(w, "+e+");\n int offset_d = (offset_h * "+e+" + offset_w) *\n "+this.getOutputDepthSize()+";\n int in_d = d + offset_d;\n\n float result = "+this.getInputSamplingString()+";\n setOutput(result);\n }\n ";}return t.prototype.getHeightCoordString=function(){return "NHWC"===this.dataFormat?"coords[1]":"coords[2]"},t.prototype.getWidthCoordString=function(){return "NHWC"===this.dataFormat?"coords[2]":"coords[3]"},t.prototype.getDepthCoordString=function(){return "NHWC"===this.dataFormat?"coords[3]":"coords[1]"},t.prototype.getOutputDepthSize=function(){return "NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return "NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},t}(),si=function(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n ";},ui=function(t){this.variableNames=["A"],this.outTexUsage=zt.DOWNLOAD;var e=ua();this.outputShape=t,this.userCode="\n "+ha+"\n\n void main() {\n float x = getAAtOutCoords();\n "+e.output+" = encode_float(x);\n }\n ";},ci=function(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=zt.DOWNLOAD;var e=ua();this.outputShape=t,this.userCode="\n "+ha+"\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n "+e.output+" = encode_float(x);\n }\n ";},li=function(t,e,n){void 0===n&&(n=!1),this.variableNames=["A"];var r=ua(),o=e[0],a=e[1];this.outputShape=t;var i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode="\n "+la(t)+"\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n int flatIndex = getFlatIndex(coords);\n int offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n \n int r = flatIndex / "+a+";\n int c = imod(flatIndex, "+a+");\n vec2 uv = (vec2(c, r) + halfCR) / vec2("+a+".0, "+o+".0);\n vec4 values = "+r.texture2D+"(A, uv);\n\n float result;\n\n if(offset == 0) {\n result = values[0];\n } else if(offset == 1) {\n result = values[1];\n } else if(offset == 2) {\n result = values[2];\n } else {\n result = values[3];\n }\n\n "+r.output+" = vec4("+i+", 0., 0., 0.);\n }\n ";},hi=function(t,e,n){void 0===n&&(n=!1),this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;var r=ua(),o=e[0],a=e[1];this.outputShape=t;var i="",s="result";n&&(s="floor(result * 255. + 0.5)");for(var u=0;u<=1;u++)for(var c=0;c<=1;c++){var l=2*u+c;i+="\n localCoords = coords;\n if(localCoords[2] + "+c+" < "+t[2]+") {\n localCoords[2] += "+c+";\n if(localCoords[1] + "+u+" < "+t[1]+") {\n localCoords[1] += "+u+";\n\n flatIndex = getFlatIndex(localCoords);\n offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n r = flatIndex / "+a+";\n c = imod(flatIndex, "+a+");\n uv = (vec2(c, r) + halfCR) / vec2("+a+".0, "+o+".0);\n values = 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this.throwIfDisposed(),Ri(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.uploadPixelDataToTexture=function(t,e){this.throwIfDisposed(),Di(this.gl,this.debug,t,e);},t.prototype.uploadDenseMatrixToTexture=function(t,e,n,r){this.throwIfDisposed(),Ai(this.gl,this.debug,t,e,n,r,this.textureConfig);},t.prototype.createFloat16PackedMatrixTexture=function(t,e){return this.throwIfDisposed(),ki(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.createPackedMatrixTexture=function(t,e){return this.throwIfDisposed(),Ii(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.deleteMatrixTexture=function(t){var e=this;this.throwIfDisposed(),this.outputTexture===t&&(Ee(this.gl,this.debug,this.framebuffer),this.outputTexture=null),Jt(this.gl,this.debug,(function(){return e.gl.deleteTexture(t)}));},t.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,(function(){return Fi(r.gl,r.debug,e,n,r.textureConfig)}))},t.prototype.downloadPackedMatrixFromBuffer=function(t,e,n,r,o,a){return _i(this.gl,t,0,0,0,o,a,this.textureConfig)},t.prototype.downloadFloat32MatrixFromBuffer=function(t,e){return Ni(this.gl,t,e)},t.prototype.createBufferFromTexture=function(t,e,n){this.bindTextureToFrameBuffer(t);var r=Ti(this.gl,this.debug,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},t.prototype.createAndWaitForFence=function(){var t=this.createFence(this.gl);return this.pollFence(t)},t.prototype.createFence=function(t){var e,n,r=this;if(i().getBool("WEBGL_FENCE_API_ENABLED")){var o=t,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=function(){var t=o.clientWaitSync(a,0,0);return t===o.ALREADY_SIGNALED||t===o.CONDITION_SATISFIED},e=a;}else i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(e,i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return !0};return {query:e,isFencePassed:n}},t.prototype.downloadMatrixFromPackedTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,(function(){return Oi(r.gl,r.debug,e,n)}))},t.prototype.createProgram=function(t){this.throwIfDisposed();var e=this.gl,n=ae(e,this.debug,t),r=yi(e,this.debug),o=ce(e,this.debug);return Jt(e,this.debug,(function(){return e.attachShader(o,r)})),Jt(e,this.debug,(function(){return e.attachShader(o,n)})),le(e,this.debug,o),this.debug&&he(e,this.debug,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=Si(e,this.debug,this.program,this.vertexBuffer)),o},t.prototype.deleteProgram=function(t){var e=this;this.throwIfDisposed(),t===this.program&&(this.program=null),null!=t&&Jt(this.gl,this.debug,(function(){return e.gl.deleteProgram(t)}));},t.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,null!=this.program&&this.debug&&he(this.gl,this.debug,this.program),Jt(this.gl,this.debug,(function(){return e.gl.useProgram(t)}));},t.prototype.getUniformLocation=function(t,e,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?xe(this.gl,this.debug,t,e):be(this.gl,t,e)},t.prototype.getAttributeLocation=function(t,e){var n=this;return this.throwIfDisposed(),Jt(this.gl,this.debug,(function(){return n.gl.getAttribLocation(t,e)}))},t.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},t.prototype.setInputMatrixTexture=function(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),we(this.gl,this.debug,this.program,t,e,n);},t.prototype.setOutputMatrixTexture=function(t,e,n){this.setOutputMatrixTextureDriver(t,n,e);},t.prototype.setOutputPackedMatrixTexture=function(t,e,n){this.throwIfDisposed();var r=$t(e,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(t,o,a);},t.prototype.setOutputMatrixWriteRegion=function(t,e,n,r){this.setOutputMatrixWriteRegionDriver(n,t,r,e);},t.prototype.setOutputPackedMatrixWriteRegion=function(t,e,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},t.prototype.debugValidate=function(){null!=this.program&&he(this.gl,this.debug,this.program),Re(this.gl);},t.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.debug&&this.debugValidate(),Jt(t,this.debug,(function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)}));},t.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),Jt(this.gl,this.debug,(function(){return t.gl.finish()}));},t.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=re(this.gl,this.debug,2===i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},t.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},t.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},t.prototype.beginQuery=function(){if(2===i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),n=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},t.prototype.endQuery=function(){if(2!==i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT);}else {var e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);}},t.prototype.waitForQueryAndGetTime=function(t){return n(this,void 0,void 0,(function(){var e=this;return r(this,(function(n){switch(n.label){case 0:return [4,F((function(){return e.disposed||e.isQueryAvailable(t,i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}))];case 1:return n.sent(),[2,this.getQueryTime(t,i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}}))}))},t.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},t.prototype.isQueryAvailable=function(t,e){if(0===e)return !0;if(2===e){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},t.prototype.pollFence=function(t){var e=this;return new Promise((function(n){e.addItemToPoll((function(){return t.isFencePassed()}),(function(){return n()}));}))},t.prototype.pollItems=function(){for(var t=function(t){for(var e=0;e1||F((function(){return n.pollItems(),0===n.itemsToPoll.length}));},t.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),Ce(this.gl,this.debug,t,this.framebuffer),this.debug&&Re(this.gl);},t.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(Ce(this.gl,this.debug,this.outputTexture,this.framebuffer),this.debug&&Re(this.gl)):Ee(this.gl,this.debug,this.framebuffer);},t.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},t.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;Ce(r,this.debug,t,this.framebuffer),this.debug&&Re(r),this.outputTexture=t,Jt(r,this.debug,(function(){return r.viewport(0,0,e,n)})),Jt(r,this.debug,(function(){return r.scissor(0,0,e,n)}));},t.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var o=this;this.throwIfDisposed(),Jt(this.gl,this.debug,(function(){return o.gl.scissor(t,e,n,r)}));},t.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},t.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},t}();function Pi(t,e){if(t.length!==e.length)throw Error("Binary was compiled with "+t.length+" inputs, but was executed with "+e.length+" inputs");t.forEach((function(t,n){var r=t.logicalShape,o=e[n],a=o.shape;if(!S(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!t.isUniform||!o.isUniform){var i=t.texShape,s=o.isUniform?null:o.texData.texShape;if(!S(i,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+s+" must match")}}));}var Li=function(t,e,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,c=n.dilationWidth,l=n.dilationHeight,h=n.dataFormat,f=s.left,d=s.top,p=o*r,v=ua(),m="channelsLast"===h,g=m?0:1,y=m?1:2,x="",b=0;b<=1;b++)for(var w=0;w<=1;w++)x+="\n blockIndex = rc.y + "+w+";\n pos = rc.x + "+b+";\n\n if(blockIndex < "+t[1]+" && pos < "+t[0]+") {\n offsetY = int(blockIndex / ("+u+")) * "+i+" - "+d+";\n d0 = offsetY + "+l+" * (pos / "+p+");\n\n if(d0 < "+e[g]+" && d0 >= 0) {\n\n offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+f+".);\n d1 = offsetX + "+c+" * (int(mod(float(pos), "+p+".) / "+o+".));\n\n if(d1 < "+e[y]+" && d1 >= 0) {\n\n ch = int(mod(float(pos), "+o+".));\n\n if ("+m+") {\n innerDims = vec2(d1, ch);\n result["+(2*b+w)+"] = getChannel(\n getA(d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result["+(2*b+w)+"] = getChannel(\n getA(ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n ";this.userCode="\n void main() {\n ivec2 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n "+x+"\n\n "+v.output+" = result;\n }\n ";},Wi=function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[];var a,i=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -"+i+"; j <= "+i+"; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= "+s+") {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * "+a+";\n setOutput(val);\n }\n ";},Ui=function(t,e,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < "+this.depth+"; ++d) {\n int depthBegin = int(max(0.0, float(d - "+e+")));\n int depthEnd = int(min(float("+this.depth+"),\n float(d + "+e+" + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = "+this.depth+";\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float("+r+") * norm + float("+n+");\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float("+r+")\n * float("+o+")\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * "+o+");\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n ";},Vi=function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;var a,i=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < "+this.outputShape[3]+";\n bool hasNextRow = c < "+this.outputShape[2]+";\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - "+i+";\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - "+i+"; j <= "+i+"; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2("+s+"));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * "+a+";\n setOutput(result);\n }\n ";},zi=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,o=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=o-1-t.padInfo.top,s=a-1-t.padInfo.left,u=o*a-1;this.userCode="\n const ivec2 pads = ivec2("+i+", "+s+");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < "+o+";\n wR += "+r+") {\n float dyR = float(dyRCorner + wR) / "+e+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+a+"; wC++) {\n float dyC = float(dyCCorner + wC) / "+n+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * "+a+" + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n ";},Gi=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.front,h=u-1-t.padInfo.top,f=c-1-t.padInfo.left,d=s*u*c-1;this.userCode="\n const ivec3 pads = ivec3("+l+", "+h+", "+f+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < "+s+";\n wD += "+o+") {\n float dyD = float(dyDCorner + wD) / "+e+".0;\n\n if (dyD < 0.0 || dyD >= "+t.outDepth+".0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < "+u+";\n wR += "+a+") {\n float dyR = float(dyRCorner + wR) / "+n+".0;\n\n if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < "+c+";\n wC += "+i+") {\n float dyC = float(dyCCorner + wC) / "+r+".0;\n\n if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = "+d+" -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * "+u+" * "+c+" +\n wR * "+c+" + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n ";},Hi=function(t,e,n,r,o,a,i){void 0===n&&(n=!1),void 0===r&&(r=!1),void 0===o&&(o=!1),void 0===a&&(a=null),void 0===i&&(i=!1),this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;var s=n?t[1]:t[2],u=Math.ceil(s/2),c=n?"i * 2, rc.y":"rc.y, i * 2",l=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],d="",p="";a&&(d=i?"vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n "+a+"\n }":"vec4 activation(vec4 x) {\n "+a+"\n }",p="result = activation(result);");var v=o?"result += getBiasAtOutCoords();":"";o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.userCode="\n "+d+"\n\n const float sharedDimension = "+u+".0;\n\n vec4 dot2x2ARowBCol(ivec3 rc) {\n vec4 result = vec4(0);\n for (int i = 0; i < "+u+"; i++) {\n vec4 a = getMatrixA(rc.x, "+c+");\n vec4 b = getMatrixB(rc.x, "+l+");\n\n // These swizzled products need to be separately added.\n // See: https://github.com/tensorflow/tfjs/issues/1735\n result += ("+h[0]+" * "+f[0]+");\n result += ("+h[1]+" * "+f[1]+");\n }\n return result;\n }\n\n void main() {\n ivec3 rc = getOutputCoords();\n vec4 result = dot2x2ARowBCol(rc);\n\n "+v+"\n\n "+p+"\n\n setOutput(result);\n }\n ";},qi=function(){function t(t,e,n){this.variableNames=["probs"],this.outputShape=[t,n],this.userCode="\n uniform float seed;\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < "+(e-1)+"; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float("+(e-1)+"));\n }\n ";}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.seedLoc&&(e.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(e.seedLoc,t);}},t}(),Ki=function(t,e,n,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float("+r+"), float("+n+"),\n float(index == coords.y)));\n }\n ";},ji=function(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;var e=t.length;if(0===e)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else {var n=sa("rc",e),r=wa(e),o=function(t,e,n){if(1===t)return "rc > "+e[0];for(var r="",o=t-2;o= "+e[o],o= "+e+";\n bool rEdge = rp1 >= "+n+";\n "}(e,t[t.length-1],t[t.length-2],n),i=function(t,e){var n=t.length,r=function(t,e){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),i=2;i= "+t[0]+" ? 0. : getA(rc + 1),\n 0, 0":"getA("+r[0]+"),\n cEdge ? 0. : getA("+r[1]+"),\n rEdge ? 0. : getA("+r[2]+"),\n rEdge || cEdge ? 0. : getA("+r[3]+")"}(t,n);this.userCode="\n void main() {\n "+r+" rc = getOutputCoords();\n\n if("+o+") {\n setOutput(vec4(0));\n } else {\n "+a+"\n\n setOutput(vec4("+i+"));\n }\n }\n ";}};var Xi=function(t,e,n){this.variableNames=["x"],this.outputShape=e.map((function(e,n){return e[0]+t[n]+e[1]}));var r=t.length,o=wa(r),a=e.map((function(t){return t[0]})).join(","),i=e.map((function(e,n){return e[0]+t[n]})).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n "+o+" start = "+o+"("+a+");\n "+o+" end = "+o+"("+i+");\n\n void main() {\n "+o+" outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(float("+n+"));\n } else {\n "+o+" coords = outC - start;\n setOutput(getX("+s+"));\n }\n }\n ":"\n int start = "+a+";\n int end = "+i+";\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(float("+n+"));\n } else {\n setOutput(getX(outC - start));\n }\n }\n ";},Yi=function(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((function(e,n){return e[0]+t[n]+e[1]}));for(var r=t.length,o=wa(r),a=e.map((function(t){return t[0]})).join(","),i=e.map((function(e,n){return e[0]+t[n]})).join(","),s=sa("rc",r),u=sa("source",r),c=s[r-1]+" < "+this.outputShape[r-1],l=1===r?"source":"vec2("+u.slice(-2).join()+")",h=[o+" rc = outputLoc;",s[r-1]+" += 1;\n if("+c+") {\n ",1===r?"":"}\n rc = outputLoc;\n "+s[r-2]+" += 1;\n if("+s[r-2]+" < "+this.outputShape[r-2]+") {",1===r?"":" "+s[r-1]+" += 1;\n if("+c+") {"],f=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="",p=0,v=1===r?2:4;p= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+c+";\n wC += "+s+") {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = wR * "+c+" + wC;\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n ";else {var p=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(p="avgValue / count");var v=4*Math.floor(r/4),m=r%4,g="\n if ("+f+") {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n ";this.userCode="\n const ivec2 strides = ivec2("+o+", "+a+");\n const ivec2 pads = ivec2("+l+", "+h+");\n const float initializationValue = "+d+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= "+t.inWidth+") {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4("+d+");\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < "+u+";\n wR += "+i+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+v+"; wC += 4) {\n int xC = xCCorner + wC * "+s+";\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n getValue(batch, xR, xC + 2 * "+s+", d),\n getValue(batch, xR, xC + 3 * "+s+", d)\n );\n\n "+g+"\n }\n\n int xC = xCCorner + "+v+";\n if ("+(1===m)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+g+"\n } else if ("+(2===m)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n initializationValue,\n initializationValue\n );\n\n "+g+"\n } else if ("+(3===m)+") {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + "+s+", d),\n getValue(batch, xR, xC + 2 * "+s+", d),\n initializationValue\n );\n\n "+g+"\n }\n }\n setOutput("+p+");\n }\n ";}},Qi=function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,h=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,v=t.padInfo.left;this.outputShape=t.outShape;var m="avg"===e,g="0.0";if(m||(g="-1.0 / 1e-20"),n)this.userCode="\n const ivec3 strides =\n ivec3("+o+", "+a+", "+i+");\n const ivec3 pads = ivec3("+d+", "+p+", "+v+");\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < "+l+";\n wD += "+s+") {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= "+t.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+h+";\n wR += "+u+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+f+";\n wC += "+c+") {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= "+t.inWidth+") {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition =\n wD * "+h+" * "+f+" +\n wR * "+f+" + wC;;\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n ";else {var y=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(y="avgValue / count");var x=4*Math.floor(r/4),b=r%4,w="\n if ("+m+") {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n ";this.userCode="\n const ivec3 strides =\n ivec3("+o+", "+a+", "+i+");\n const ivec3 pads = ivec3("+d+", "+p+", "+v+");\n const float initializationValue = "+g+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= "+t.inWidth+") {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4("+g+");\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < "+l+";\n wD += "+s+") {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= "+t.inDepth+") {\n continue;\n }\n\n for (int wR = 0; wR < "+h+";\n wR += "+u+") {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= "+t.inHeight+") {\n continue;\n }\n\n for (int wC = 0; wC < "+x+"; wC += 4) {\n int xC = xCCorner + wC * "+c+";\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + "+c+", ch),\n getValue(batch, xD, xR, xC + 2 * "+c+", ch),\n getValue(batch, xD, xR, xC + 3 * "+c+", ch)\n );\n\n "+w+"\n }\n\n int xC = xCCorner + "+x+";\n if ("+(1===b)+") {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+w+"\n } else if ("+(2===b)+") {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + "+c+", ch),\n initializationValue,\n initializationValue\n );\n\n "+w+"\n } else if ("+(3===b)+") {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + "+c+", ch),\n getValue(batch, xD, xR, xC + 2 * "+c+", ch),\n initializationValue\n );\n\n "+w+"\n }\n }\n setOutput("+y+");\n }\n }\n ";}},Ji=function(t,e){this.variableNames=["x"];var n=t.windowSize,r=t.batchSize,o=t.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"prod"===e?i="1.0":"min"===e?(i="1.0 / 1e-20",s="min"):"max"===e&&(i="-1.0 / 1e-20",s="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===e?u="sumValue":"prod"===e?u="prodValue":"all"===e?u="allValue":"any"===e&&(u="anyValue");var c=4*Math.floor(n/4),l=n%4,h="\n if ("+("sum"===e)+") {\n sumValue += dot(values, ones);\n } else if ("+("prod"===e)+") {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = "+s+"(values, minMaxValue);\n }\n ",f="vec4";"all"===e?(i="1.0",h="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",f="bvec4"):"any"===e&&(i="0.0",h="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",f="bvec4");var d="";o%n>0&&(d="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n "),this.userCode="\n const float initializationValue = "+i+";\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n "+d+"\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * "+n+";\n\n vec4 minMaxValue = vec4("+i+");\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < "+c+"; i += 4) {\n int inIdx = inOffset + i;\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n "+h+"\n }\n\n int inIdx = inOffset + "+c+";\n if ("+(1===l)+") {\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n "+h+"\n } else if ("+(2===l)+") {\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n "+h+"\n } else if ("+(3===l)+") {\n "+f+" values = "+f+"(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n "+h+"\n }\n setOutput("+u+");\n }\n ";},Zi=function(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n "+o+"\n "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result["+r+"] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n "+(r>0?"}":"")+"\n ";}this.userCode="\n \n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n "+ca(["r","c","d"],e)+"\n return ivec3(r, c, d);\n }\n \n "+la(t)+"\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = "+t[1]+";\n int cols = "+t[2]+";\n\n "+n+"\n\n setOutput(result);\n }\n ";};var ts=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],a=r[2],i=t.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=c[0]/l[0],f=c[1]/l[1],d=1/h,p=1/f,v=2*Math.ceil(d)+2,m=2*Math.ceil(p)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+h+");\n const float widthScale = float("+f+");\n\n const float invHeightScale = float("+d+");\n const float invWidthScale = float("+p+");\n\n const int winHeight = int("+v+");\n const int winWidth = int("+m+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n ";},es=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],c=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n ";},ns=function(t,e,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],c=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+",\n "+u[1]/c[1]+");\n const vec3 inputShapeRC = vec3("+a+".0, "+i+".0,\n "+i+".0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < "+(s-1)+";\n bool hasNextRow = coords.z < "+(n-1)+";\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n ";},rs=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],a=r[2],i=t.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=c[0]/l[0],f=c[1]/l[1],d=1/h,p=1/f,v=2*Math.ceil(d)+2,m=2*Math.ceil(p)+2;this.userCode="\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float("+h+");\n const float widthScale = float("+f+");\n\n const float invHeightScale = float("+d+");\n const float invWidthScale = float("+p+");\n\n const int winHeight = int("+v+");\n const int winWidth = int("+m+");\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= "+s+") {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= "+u+") {\n continue;\n }\n\n float sourceFracRow =\n float("+c[0]+") *\n (float(dyR) / float("+l[0]+"));\n\n float sourceFracCol =\n float("+c[1]+") *\n (float(dyC) / float("+l[1]+"));\n\n int sourceNearestRow = int(min(\n float(int("+o+") - 1),\n "+n+" ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int("+a+") - 1),\n "+n+" ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n ";},os=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],c=[r&&e>1?e-1:e,r&&n>1?n-1:n],l=r?"0.5":"0.0";this.userCode="\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n "+u[0]/c[0]+",\n "+u[1]/c[1]+");\n const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+l+")));\n\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n ";},as=function(t,e){this.variableNames=["x"];var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=t,1!==n){var r=t.map((function(n,r){return function(n){return -1!==e.indexOf(n)&&1!==t[n]?t[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)})).join(","),o=wa(n);this.userCode="\n void main() {\n "+o+" coords = getOutputCoords();\n setOutput(getX("+r+"));\n }\n ";}else this.userCode="\n void main() {\n int coord = getOutputCoords();\n setOutput(getX("+t[0]+" - coord - 1));\n }\n ";},is=function(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");this.outputShape=t;var r=sa("rc",n),o=r[n-1]+" + 1 < "+this.outputShape[n-1],a=r[n-2]+" + 1 < "+this.outputShape[n-2],i=wa(n);function s(n){var r=t.map((function(r,o){return function(n,r){return -1!==e.indexOf(n)&&1!==t[n]?t[n]+" - "+r[n]+" - 1":""+r[n]}(o,n)}));return "getChannel(getX("+r.join(",")+"), vec2("+r.slice(-2).join(",")+"))"}this.userCode=1===n?"\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX("+t[0]+" - rc - 1),\n "+t[0]+" - rc - 1);\n if("+o+"){\n result.g = getChannel(getX("+t[0]+" - (rc + 1) - 1),\n "+t[0]+" - (rc + 1) - 1);\n }\n setOutput(result);\n }\n ":"\n void main() {\n "+i+" rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = "+function(t){return s(t)}(r.slice())+";\n if("+o+"){\n result.g = "+function(t){return t[n-1]="("+t[n-1]+" + 1)",s(t)}(r.slice())+";\n }\n if("+a+") {\n result.b = "+function(t){return t[n-2]="("+t[n-2]+" + 1)",s(t)}(r.slice())+";\n if("+o+") {\n result.a = "+function(t){return t[n-1]="("+t[n-1]+" + 1)",t[n-2]="("+t[n-2]+" + 1)",s(t)}(r.slice())+";\n }\n }\n setOutput(result);\n }\n ";},ss=function(t,e,n,r,o,a,i){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=wa(o.length),u=wa(a.length),c="";1===n?c="i":2===n&&(c="i, j");var l="getIndices("+c+")",h="";1===r?h="i":2===r&&(h="i, coords[1]");var f="getUpdates("+h+")",d=e>1?"strides[j]":"strides";this.userCode="\n "+s+" strides = "+s+"("+o+");\n\n void main() {\n "+u+" coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < "+t+"; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < "+e+"; j++) {\n int index = round("+l+");\n flattenedIndex += index * "+d+";\n }\n if (flattenedIndex == coords[0]) {\n sum += "+f+";\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n ";},us=function(t,e){this.variableNames=["x","segmentIds"];var n=t.windowSize,r=t.batchSize,o=t.inSize,a=t.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,c="\n sumValue += dot(values, segFilter);\n ",l="";o%n>0&&(l="\n if (inIdx < 0 || inIdx >= "+o+") {\n return initializationValue;\n }\n ");var h="";o%n>0&&(h="\n if (inIdx < 0 || inIdx >= "+o+") {\n return -1.0;\n }\n "),this.userCode="\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n "+l+"\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n "+h+"\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n "+a+")) * float("+n+"));\n int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < "+s+"; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n "+c+"\n }\n\n int inIdx = inOffset + "+s+";\n if ("+(1===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(2===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n "+c+"\n } else if ("+(3===u)+") {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n "+c+"\n }\n setOutput(sumValue);\n }\n ";},cs=function(t,e,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=e,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else {for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u= 1.0) {\n setOutput(getA("+o+"));\n } else {\n setOutput(getB("+o+"));\n }\n }\n ";},ls=function(){function t(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e,n=wa(this.rank),r="uniform int start["+this.rank+"];",o=function(t){if(1===t)return "sourceLoc";if(t<=6)return hs.slice(0,t).map((function(t){return "sourceLoc."+t})).join(",");throw Error("Slicing for rank "+t+" is not yet supported")}(this.rank);e="\n "+n+" sourceLoc;\n "+n+" coords = getOutputCoords();\n "+t.map((function(t,e){return "sourceLoc."+hs[e]+" = start["+e+"] + coords."+hs[e]+";"})).join("\n")+"\n ",this.userCode="\n "+r+"\n void main() {\n "+e+"\n setOutput(getSource("+o+"));\n }\n ";}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t);}},t}(),hs=["x","y","z","w","u","v"];var fs=function(){function t(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;var e=wa(this.rank),n=sa("coords",this.rank),r=sa("sourceLoc",this.rank),o=1===this.rank?"sourceLoc":"vec2("+r.slice(-2).join()+")",a="getChannel(getSource("+r.join()+"), "+o+")",i="\n result.x = "+a+";\n if (++"+n[this.rank-1]+" < "+t[this.rank-1]+") {\n ++"+r[this.rank-1]+";\n result.y = "+a+";\n --"+r[this.rank-1]+";\n }\n ",s=1===this.rank?"":"\n --"+n[this.rank-1]+";\n if (++"+n[this.rank-2]+" < "+t[this.rank-2]+") {\n ++"+r[this.rank-2]+";\n result.z = "+a+";\n if (++"+n[this.rank-1]+" < "+t[this.rank-1]+") {\n ++"+r[this.rank-1]+";\n result.w = "+a+";\n }\n }\n ",u=this.rank<=4?"sourceLoc = coords +\n "+e+"("+t.map((function(t,e){return "start["+e+"]"})).join()+");":t.map((function(t,e){return r[e]+" = "+n[e]+" + start["+e+"];"})).join("\n");this.userCode="\n uniform int start["+this.rank+"];\n void main() {\n "+e+" coords = getOutputCoords();\n "+e+" sourceLoc;\n "+u+"\n vec4 result = vec4(0.);\n "+i+"\n "+s+"\n setOutput(result);\n }\n ";}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t);}},t}(),ds=function(t,e,n){this.variableNames=["x"],this.outputShape=n;var r=n.length,o=wa(n.length),a=wa(n.length),i="";if(1===r)i="coords * strides + begin";else {var s=0;i=n.map((function(t,e){return s++,1===n.length?"coords * strides["+e+"] + begin["+e+"]":"coords["+(s-1)+"] * strides["+e+"] + begin["+e+"]"})).join(",");}this.userCode="\n "+o+" begin = "+o+"("+t+");\n "+o+" strides = "+o+"("+e+");\n\n void main() {\n "+a+" coords = getOutputCoords();\n setOutput(getX("+i+"));\n }\n ";},ps=function(){function t(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={};}return t.prototype.acquireTexture=function(t,e,n){var r,o=vs(e,n),a=ms(t,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===Gt.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Gt.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Gt.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Gt.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Gt.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[a].push(r),r},t.prototype.releaseTexture=function(t,e,n,r){if(null!=this.freeTextures){var o=ms(e,vs(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(t),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(t);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");a.splice(i,1),this.log();}},t.prototype.log=function(){if(this.logEnabled){var t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+t+")");}},t.prototype.getNumUsedTextures=function(){return this.numUsedTextures},t.prototype.getNumFreeTextures=function(){return this.numFreeTextures},t.prototype.dispose=function(){var t=this;if(null!=this.freeTextures){for(var e in this.freeTextures)this.freeTextures[e].forEach((function(e){t.gpgpu.deleteMatrixTexture(e);}));for(var e in this.usedTextures)this.usedTextures[e].forEach((function(e){t.gpgpu.deleteMatrixTexture(e);}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0;}},t}();function vs(t,e){if(t===zt.UPLOAD)return Gt.PACKED_2X2_FLOAT32;if(t===zt.RENDER||null==t)return function(t){return i().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?t?Gt.PACKED_2X2_FLOAT32:Gt.UNPACKED_FLOAT32:t?Gt.PACKED_2X2_FLOAT16:Gt.UNPACKED_FLOAT16}(e);if(t===zt.DOWNLOAD||t===zt.PIXELS)return Gt.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+t)}function ms(t,e,n){return t[0]+"_"+t[1]+"_"+e+"_"+n}var gs=function(t,e){this.variableNames=["A"];for(var n=new Array(t.length),r=0;r5)throw Error("Tile for rank "+e+" is not yet supported");if(1===e)return "imod(resRC, "+t[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],o=0;o6)throw Error("Transpose for rank "+e+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(e),o=0;o6)throw Error("Packed transpose for rank "+this.rank+" is not yet supported.");var o=wa(this.rank),a=ia("rc",this.rank),i=new Array(this.rank);for(r=0;r= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";var Ts="return -x;",Ns="return ceil(x);",Fs="return floor(x);",_s="return exp(x);",Os="return exp(x) - 1.0;",Ms=Es+"\n return sin(x);\n",Bs=Es+"\n return cos(x);\n",Ps=Es+"\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n",Ls=Es+"\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n",Ws=Es+"\n return atan(x);\n",Us=Es+"return log(x + sqrt(x * x + 1.0));",Vs=Es+"\n if (x < 1.0) return NAN;\n return log(x + sqrt(x * x - 1.0));",zs=Es+"\n if ((x < -1.0) || (x > 1.0)) return NAN;\n return (log(1.0 + x) - log(1.0 - x)) / 2.0;",Gs="return x;",Hs="return x;",qs="\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",Ks="\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",js="\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n",Xs=function(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode="\n vec4 unaryOperation(vec4 x) {\n "+e+"\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n ";},Ys=function(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;var e=t.length,n=sa("rc",e),r=wa(e),o=function(t,e){if(1===t)return "rc";for(var n="",r=0;r0?[4,Promise.all(a)]:[3,2];case 1:return c=r.sent(),u.kernelMs=w(c),u.getExtraProfileInfo=function(){return c.map((function(t,e){return {name:s[e],ms:t}})).map((function(t){return t.name+": "+t.ms})).join(", ")},[3,3];case 2:u.kernelMs={error:"WebGL query timers are not supported in this environment."},r.label=3;case 3:return this.uploadWaitMs=0,this.downloadWaitMs=0,[2,u]}}))}))},o.prototype.memory=function(){return {unreliable:!1,numBytesInGPU:this.numBytesInGPU}},o.prototype.startTimer=function(){return i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:et(),endMs:null}},o.prototype.endTimer=function(t){return i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=et(),t)},o.prototype.getQueryTime=function(t){return n(this,void 0,void 0,(function(){var e;return r(this,(function(n){return i().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?[2,this.gpgpu.waitForQueryAndGetTime(t)]:[2,(e=t).endMs-e.startMs]}))}))},o.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t)){if(this.pendingRead.has(t))return this.pendingDisposal.add(t),void this.pendingDeletes++;if(this.texData.has(t)){this.releaseGPUData(t);var e=this.texData.get(t).complexTensors;null!=e&&(e.real.dispose(),e.imag.dispose()),this.texData.delete(t);}}},o.prototype.releaseGPUData=function(t){var e=this.texData.get(t),n=e.texture,r=e.dtype,o=e.texShape,a=e.usage,i=e.isPacked,s=e.slice,u=s&&s.origDataId||t,c=this.dataRefCount.get(u);c>1?this.dataRefCount.set(u,c-1):(this.dataRefCount.delete(u),null!=n&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(n,o,a,i)));var l=this.texData.get(t);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null;},o.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},o.prototype.getDataInfo=function(t){return this.texData.get(t)},o.prototype.getCPUBackend=function(){return i().getBool("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=Lt.findBackend("cpu")),this.cpuBackend):null},o.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=128),null!=this.getCPUBackend()&&t.every((function(t){return null==n.texData.get(t.dataId).texture&&t.sizei().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){var o=Math.floor(t.length/2),a=this.concat(t.slice(0,o),e),s=this.concat(t.slice(o),e);return this.concat([a,s],e)}if(i().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&t[0].rank>1){var u=new Ga(t.map((function(t){return t.shape})),e);return this.compileAndRun(u,t)}var c=Sn(t.map((function(t){return t.shape})),e),l=t.map((function(t){return t.as2D(-1,k(t.shape.slice(e)))})),h=new za(l.map((function(t){return t.shape})));return this.compileAndRun(h,l).reshape(c)},o.prototype.neg=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.neg(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Ts,t.dtype);var e=new Cs(t.shape,Ts);return this.compileAndRun(e,[t])},o.prototype.batchMatMul=function(t,e,n,r){var o=n?t.shape[2]:t.shape[1],a=r?e.shape[1]:e.shape[2],i=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===o||1===a)&&i>1e3){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===a?t:t.as3D(s,i,1),c=1===a?2:1,l=1===a?e.as3D(s,1,i):e;return this.multiply(u,l).sum(c,!0)}var h=Dt(t.dtype,e.dtype),f=new Hi(t.shape,[s,o,a],n,r);return this.compileAndRun(f,[t,e],h)},o.prototype.fusedBatchMatMul=function(t){var e=t.a,n=t.b,r=t.transposeA,o=t.transposeB,a=t.bias,i=t.activation,s=t.preluActivationWeights,u=r?e.shape[2]:e.shape[1],c=o?n.shape[1]:n.shape[2],l=e.shape[0],h=Dt(e.dtype,n.dtype),f=null!=a,d=null!=s,p=i?Qs(i,!0):null,v=new Hi(e.shape,[l,u,c],r,o,f,p,d),m=[e,n];return a&&m.push(a),s&&m.push(s),this.compileAndRun(v,m,h)},o.prototype.multiply=function(t,e){if("complex64"===t.dtype){var n=this.texData.get(t.dataId),r=this.texData.get(e.dataId),o=new Na(Da,t.shape,e.shape),a=new Na(Ta,t.shape,e.shape),s=[this.makeComplexComponentTensorInfo(t,n.complexTensors.real),this.makeComplexComponentTensorInfo(t,n.complexTensors.imag),this.makeComplexComponentTensorInfo(e,r.complexTensors.real),this.makeComplexComponentTensorInfo(e,r.complexTensors.imag)],u=this.compileAndRun(o,s),c=this.compileAndRun(a,s),l=this.complex(u,c);return u.dispose(),c.dispose(),l}if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.multiply(t,e);if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,Oa,t.dtype);var h=new Ba(Oa,t.shape,e.shape);return this.compileAndRun(h,[t,e],t.dtype)},o.prototype.batchNormalization=function(t,e,n,r,o,a){var s=[t,e,n],u=null;null!=a&&(u=a.shape,s.push(a));var c=null;if(null!=o&&(c=o.shape,s.push(o)),i().getBool("WEBGL_PACK_NORMALIZATION")){var l=new Aa(t.shape,e.shape,n.shape,u,c,r);return this.compileAndRun(l,s)}var h=new Sa(t.shape,e.shape,n.shape,u,c,r);return this.compileAndRun(h,s)},o.prototype.localResponseNormalization4D=function(t,e,n,r,o){var a=i().getBool("WEBGL_PACK_NORMALIZATION")?new Vi(t.shape,e,n,r,o):new Wi(t.shape,e,n,r,o);return this.compileAndRun(a,[t])},o.prototype.LRNGrad=function(t,e,n,r,o,a,i){var s=new Ui(e.shape,r,o,a,i);return this.compileAndRun(s,[e,n,t])},o.prototype.tile=function(t,e){if("string"===t.dtype){var n=this.readSync(t.dataId).map((function(t){return ot(t)}));return ta(dr(t.shape,t.dtype,n),e)}var r=new gs(t.shape,e);return this.compileAndRun(r,[t])},o.prototype.pad=function(t,e,n){var r=i().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Yi(t.shape,e,n):new Xi(t.shape,e,n);return this.compileAndRun(r,[t])},o.prototype.transpose=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.transpose(t,e);var n=i().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xs(t.shape,e):new ys(t.shape,e);return this.compileAndRun(n,[t])},o.prototype.gather=function(t,e,n){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.gather(t,e,n);var r=new mi(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},o.prototype.batchToSpaceND=function(t,e,n){C(t.rank<=4,(function(){return "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"}));var r=e.reduce((function(t,e){return t*e})),o=zr(t.shape,e,r),a=Gr(o.length,e.length),i=Hr(t.shape,e,r),s=qr(n,e.length),u=Kr(i,n,e.length);return t.reshape(o).transpose(a).reshape(i).slice(s,u)},o.prototype.spaceToBatchND=function(t,e,n){C(t.rank<=4,(function(){return "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"}));var r=e.reduce((function(t,e){return t*e})),o=[[0,0]];o.push.apply(o,n);for(var a=1+e.length;ae||n===t?r=!0:n=Y(t,n+1);return n}(i,o),u=new us({windowSize:s,inSize:i,batchSize:a,numSegments:o},e),c=this.compileAndRun(u,[t,n],r);return c.shape[1]===o?c:(n=Kn(0,o).tile([i/s]),this.segOpCompute(c,e,n,r,o))},o.prototype.argMinMaxReduce=function(t,e,n){var r=[e];if(Cn("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.rank),!i().getBool("WEBGL_PACK_REDUCE")||t.rank<=2){var o=bn(t.shape,r),a=o[0],s=k(o[1]),u=t.as2D(-1,s);return this.argReduce(u,n).reshape(a)}return this.argReducePacked(t,n)},o.prototype.argMin=function(t,e){return this.argMinMaxReduce(t,e,"min")},o.prototype.argMax=function(t,e){return this.argMinMaxReduce(t,e,"max")},o.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(t.rank-1)+" but got axis="+e);var o=new ni(t.shape,n,r);return this.compileAndRun(o,[t])},o.prototype.equal=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(equal(a, b));\n","bool");var n=new Ba("return float(a == b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.notEqual=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(notEqual(a, b));\n","bool");var n=new Ba("return float(a != b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.less=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.less(t,e);if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(lessThan(a, b));\n","bool");var n=new Ba("return float(a < b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.lessEqual=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(lessThanEqual(a, b));\n","bool");var n=new Ba("return float(a <= b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.greater=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.greater(t,e);if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(greaterThan(a, b));\n","bool");var n=new Ba("return float(a > b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.greaterEqual=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(greaterThanEqual(a, b));\n","bool");var n=new Ba("return float(a >= b);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.logicalNot=function(t){var e=new Cs(t.shape,"return float(!(x >= 1.0));");return this.compileAndRun(e,[t])},o.prototype.logicalAnd=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n","bool");var n=new Ba("return float(a >= 1.0 && b >= 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.logicalOr=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n","bool");var n=new Ba("return float(a >= 1.0 || b >= 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e],"bool")},o.prototype.select=function(t,e,n){var r=new cs(t.rank,e.shape,e.rank);return this.compileAndRun(r,[t,e,n],Dt(e.dtype,n.dtype))},o.prototype.where=function(t){dn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var e=t.dataSync();return na(t.shape,e)},o.prototype.topk=function(t,e,n){return ea(t.dataSync(),t.shape,t.dtype,e)},o.prototype.min=function(t,e){Cn("min",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,"min",a.dtype).reshape(r)},o.prototype.minimum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.minimum(t,e);var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La("\n vec4 result = vec4(min(a, b));\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Ba("\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return min(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.mod=function(t,e){var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La("\n vec4 result = mod(a, b);\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Ba("if (b == 0.0) return NAN;\n return mod(a, b);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.max=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.max(t,e);Cn("max",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,"max",a.dtype).reshape(r)},o.prototype.maximum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.maximum(t,e);var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La("\n vec4 result = vec4(max(a, b));\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Ba("\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return max(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.all=function(t,e){Cn("all",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,"all",a.dtype).reshape(r)},o.prototype.any=function(t,e){Cn("any",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,"any",a.dtype).reshape(r)},o.prototype.realDivide=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS")){return this.packedBinaryOp(t,e,"\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n","float32",!0)}var n=new Ba("\nif (a == b) {\n return 1.0;\n};\nreturn a / b;",t.shape,e.shape);return this.compileAndRun(n,[t,e],"float32")},o.prototype.floorDiv=function(t,e){if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,"\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n","int32");var n=new Ba("\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n",t.shape,e.shape);return this.compileAndRun(n,[t,e],"int32")},o.prototype.add=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,Fa);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.add(t,e);var n=Dt(t.dtype,e.dtype);if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,Fa,n);var r=new Ba(Fa,t.shape,e.shape);return this.compileAndRun(r,[t,e],n)},o.prototype.packedUnaryOp=function(t,e,n){var r=new Xs(t.shape,e);return this.compileAndRun(r,[t],n)},o.prototype.packedBinaryOp=function(t,e,n,r,o){void 0===o&&(o=!1);var a=new La(n,t.shape,e.shape,o);return this.compileAndRun(a,[t,e],r)},o.prototype.complexSeparableBinaryOp=function(t,e,n){var r=this,o=this.texData.get(t.dataId),a=this.texData.get(e.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map((function(o){var a=o[0],i=o[1],s=r.makeComplexComponentTensorInfo(t,a),u=r.makeComplexComponentTensorInfo(e,i),c=new Ba(n,t.shape,e.shape);return r.compileAndRun(c,[s,u],Dt(a.dtype,i.dtype))})),s=i[0],u=i[1],c=this.complex(s,u);return s.dispose(),u.dispose(),c},o.prototype.makeComplexComponentTensorInfo=function(t,e){return {dataId:e.dataId,dtype:e.dtype,shape:t.shape}},o.prototype.addN=function(t){if(1===t.length)return t[0];if(t.length>i().get("WEBGL_MAX_TEXTURES_IN_SHADER")){var e=Math.floor(t.length/2),n=this.addN(t.slice(0,e)),r=this.addN(t.slice(e));return this.addN([n,r])}var o=t.map((function(t){return t.dtype})).reduce((function(t,e){return Dt(t,e)})),a=t.map((function(t){return t.shape})),s=i().getBool("WEBGL_PACK")?new oa(t[0].shape,a):new ra(t[0].shape,a);return this.compileAndRun(s,t,o)},o.prototype.subtract=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,_a);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.subtract(t,e);var n=Dt(t.dtype,e.dtype);if(i().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(t,e,_a,t.dtype);var r=new Ba(_a,t.shape,e.shape);return this.compileAndRun(r,[t,e],n)},o.prototype.pow=function(t,e){var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La("\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Ba("\nif(a < 0.0 && floor(b) < b){\n return NAN;\n}\nif (b == 0.0) {\n return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",t.shape,e.shape),r=Dt(t.dtype,e.dtype);return this.compileAndRun(n,[t,e],r)},o.prototype.ceil=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.ceil(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Ns,t.dtype);var e=new Cs(t.shape,Ns);return this.compileAndRun(e,[t])},o.prototype.floor=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.floor(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Fs,t.dtype);var e=new Cs(t.shape,Fs);return this.compileAndRun(e,[t])},o.prototype.sign=function(t){var e=new Cs(t.shape,"\n if (isnan(x)) { return 0.0; }\n return sign(x);\n");return this.compileAndRun(e,[t])},o.prototype.isNaN=function(t){var e=new Cs(t.shape,"return float(isnan(x));");return this.compileAndRun(e,[t],"bool")},o.prototype.isInf=function(t){var e=new Cs(t.shape,"return float(isinf(x));");return this.compileAndRun(e,[t],"bool")},o.prototype.isFinite=function(t){var e=new Cs(t.shape,"return float(!isnan(x) && !isinf(x));");return this.compileAndRun(e,[t],"bool")},o.prototype.round=function(t){var e=new Cs(t.shape,"\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n");return this.compileAndRun(e,[t])},o.prototype.exp=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.exp(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,_s,t.dtype);var e=new Cs(t.shape,_s);return this.compileAndRun(e,[t])},o.prototype.expm1=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.expm1(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Os,t.dtype);var e=new Cs(t.shape,Os);return this.compileAndRun(e,[t])},o.prototype.softmax=function(t,e){var n=O([e],t.shape),r=this.max(t,n),o=wn(r.shape,n),a=this.subtract(t,r.reshape(o)),i=this.exp(a),s=this.sum(i,n).reshape(o);return this.realDivide(i,s)},o.prototype.log=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.log(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,"\n vec4 result = log(x);\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n result.r = isNaN.r == 1.0 ? NAN : result.r;\n result.g = isNaN.g == 1.0 ? NAN : result.g;\n result.b = isNaN.b == 1.0 ? NAN : result.b;\n result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n return result;\n",t.dtype);var e=new Cs(t.shape,"if (x < 0.0) return NAN;\n return log(x);");return this.compileAndRun(e,[t])},o.prototype.log1p=function(t){var e=new Cs(t.shape,"return log(1.0 + x);");return this.compileAndRun(e,[t])},o.prototype.sqrt=function(t){var e=new Cs(t.shape,"return sqrt(x);");return this.compileAndRun(e,[t])},o.prototype.rsqrt=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.rsqrt(t);var e=new Cs(t.shape,"return inversesqrt(x);");return this.compileAndRun(e,[t])},o.prototype.reciprocal=function(t){var e=new Cs(t.shape,"return 1.0 / x;");return this.compileAndRun(e,[t])},o.prototype.relu=function(t){var e;return e=i().getBool("WEBGL_PACK")?new Xs(t.shape,qs):new Cs(t.shape,ks),this.compileAndRun(e,[t])},o.prototype.relu6=function(t){var e;return e=i().getBool("WEBGL_PACK")?new Xs(t.shape,Ks):new Cs(t.shape,Ss),this.compileAndRun(e,[t])},o.prototype.prelu=function(t,e){var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La(Pa,t.shape,e.shape):new Ba(Ma,t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.elu=function(t){if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,js,t.dtype);var e=new Cs(t.shape,As);return this.compileAndRun(e,[t])},o.prototype.eluDer=function(t,e){var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La("\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",t.shape,e.shape):new Ba("return (b >= 1.0) ? a : a * (b + 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.selu=function(t){var e=new Cs(t.shape,Ds);return this.compileAndRun(e,[t])},o.prototype.int=function(t){var e=new Cs(t.shape,"return float(int(x));");return this.compileAndRun(e,[t],"int32")},o.prototype.clip=function(t,e,n){var r,o=(r=i().getBool("WEBGL_PACK_CLIP")?new Ua(t.shape):new Wa(t.shape)).getCustomSetupFunc(e,n);return this.compileAndRun(r,[t],null,o)},o.prototype.abs=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.abs(t);if(i().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,Is,t.dtype);var e=new Cs(t.shape,Is);return this.compileAndRun(e,[t])},o.prototype.complexAbs=function(t){var e=this.texData.get(t.dataId),n=new Va(t.shape),r=[this.makeComplexComponentTensorInfo(t,e.complexTensors.real),this.makeComplexComponentTensorInfo(t,e.complexTensors.imag)];return this.compileAndRun(n,r)},o.prototype.sigmoid=function(t){var e=new Cs(t.shape,"return 1.0 / (1.0 + exp(-1.0 * x));");return this.compileAndRun(e,[t])},o.prototype.softplus=function(t){var e=new Cs(t.shape,"\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n");return this.compileAndRun(e,[t])},o.prototype.sin=function(t){var e=new Cs(t.shape,Ms);return this.compileAndRun(e,[t])},o.prototype.cos=function(t){var e=new Cs(t.shape,Bs);return this.compileAndRun(e,[t])},o.prototype.tan=function(t){var e=new Cs(t.shape,"return tan(x);");return this.compileAndRun(e,[t])},o.prototype.asin=function(t){var e=new Cs(t.shape,Ps);return this.compileAndRun(e,[t])},o.prototype.acos=function(t){var e=new Cs(t.shape,Ls);return this.compileAndRun(e,[t])},o.prototype.atan=function(t){var e=new Cs(t.shape,Ws);return this.compileAndRun(e,[t])},o.prototype.atan2=function(t,e){var n=i().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new La("\n vec4 result = atan(a, b);\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",t.shape,e.shape):new Ba("\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return atan(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.sinh=function(t){var e=new Cs(t.shape,"\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},o.prototype.cosh=function(t){var e=new Cs(t.shape,"\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},o.prototype.tanh=function(t){var e=new Cs(t.shape,"\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n");return this.compileAndRun(e,[t])},o.prototype.asinh=function(t){var e=new Cs(t.shape,Us);return this.compileAndRun(e,[t])},o.prototype.acosh=function(t){var e=new Cs(t.shape,Vs);return this.compileAndRun(e,[t])},o.prototype.atanh=function(t){var e=new Cs(t.shape,zs);return this.compileAndRun(e,[t])},o.prototype.erf=function(t){var e=new Cs(t.shape,'\n // Error function is calculated approximately with elementary function.\n // See "Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n float p = 0.3275911;\n float a1 = 0.254829592;\n float a2 = -0.284496736;\n float a3 = 1.421413741;\n float a4 = -1.453152027;\n float a5 = 1.061405429;\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n');return this.compileAndRun(e,[t])},o.prototype.step=function(t,e){var n=new Cs(t.shape,function(t){return void 0===t&&(t=0),Es+"\n return x > 0.0 ? 1.0 : float("+t+");\n "}(e));return this.compileAndRun(n,[t])},o.prototype.conv2dByMatMul=function(t,e,n,r,o,a){var s=t.shape,u=this.texData.get(t.dataId),c=n.inChannels,l=s[0]*s[1]*s[2],h=n.outChannels,f="channelsLast"===n.dataFormat,d=(1===l||1===h)&&c>1e3,p=s[2]%2!=0&&!!u.isPacked;if(d||!i().getBool("WEBGL_LAZILY_UNPACK")||!i().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!p){var v=f?s[0]*s[1]*s[2]:s[0]*s[2]*s[3],m=this.reshape(t,[1,v,n.inChannels]),g=this.reshape(e,[1,n.inChannels,n.outChannels]);return this.reshape(this.fusedBatchMatMul({a:m,b:g,transposeA:!1,transposeB:!1,bias:r,activation:o,preluActivationWeights:a}),n.outShape)}var y=f?s[0]*s[1]*(s[2]+1):s[0]*s[2]*(s[3]+1),x={dataId:t.dataId,shape:[1,y,n.inChannels],dtype:t.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,C(_e(u.shape,x.shape),(function(){return "packed reshape "+u.shape+" to "+x.shape+" isn't free"}));var w=this.reshape(e,[1,n.inChannels,n.outChannels]),E=this.fusedBatchMatMul({a:x,b:w,transposeA:!1,transposeB:!1,bias:r,activation:o,preluActivationWeights:a}),R=this.texData.get(E.dataId);return C(R.isPacked,(function(){return "batchMatMul result is expected to be packed"})),u.shape=b,R.shape=n.outShape,Lt.makeTensorFromDataId(E.dataId,n.outShape,E.dtype)},o.prototype.conv2dWithIm2Row=function(t,e,n,r,o,a){var i=n.filterWidth,s=n.filterHeight,u=n.inChannels,c=n.outWidth,l=n.outHeight,h="channelsLast"===n.dataFormat,f=i*s*u,d=l*c,p=[f,d],v=t.squeeze([0]),m=e.reshape([1,f,-1]),g=new Li(p,v.shape,n),y=this.compileAndRun(g,[v]).reshape([1,p[0],p[1]]),x=null!=r,b=null!=a,w=o?Qs(o,!0):null,C=new Hi(y.shape,[1,d,n.outChannels],!0,!1,x,w,b),E=[y,m];r&&E.push(r),b&&E.push(a);var R=this.compileAndRun(C,E);return h?R.reshape([1,l,c,n.outChannels]):R.reshape([1,n.outChannels,l,c])},o.prototype.fusedConv2d=function(t){var e=t.input,n=t.filter,r=t.convInfo,o=t.bias,a=t.activation,s=t.preluActivationWeights;if(1===r.filterHeight&&1===r.filterWidth&&1===r.dilationHeight&&1===r.dilationWidth&&1===r.strideHeight&&1===r.strideWidth&&("SAME"===r.padInfo.type||"VALID"===r.padInfo.type))return this.conv2dByMatMul(e,n,r,o,a,s);if(i().getBool("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,n,r,o,a,s);var u=null!=o,c=null!=s,l=a?Qs(a,!1):null,h=new Qa(r,u,l,c),f=[e,n];return o&&f.push(o),s&&f.push(s),this.compileAndRun(h,f)},o.prototype.conv2d=function(t,e,n){if(1===n.filterHeight&&1===n.filterWidth&&1===n.dilationHeight&&1===n.dilationWidth&&1===n.strideHeight&&1===n.strideWidth&&("SAME"===n.padInfo.type||"VALID"===n.padInfo.type))return this.conv2dByMatMul(t,e,n);if(i().getBool("WEBGL_CONV_IM2COL")&&1===t.shape[0])return this.conv2dWithIm2Row(t,e,n);var r=new Qa(n);return this.compileAndRun(r,[t,e])},o.prototype.conv2dDerInput=function(t,e,n){var r=new Ka(n);return this.compileAndRun(r,[t,e])},o.prototype.conv2dDerFilter=function(t,e,n){var r=new qa(n);return this.compileAndRun(r,[t,e])},o.prototype.fusedDepthwiseConv2D=function(t){var e,n=t.input,r=t.filter,o=t.convInfo,a=t.bias,s=t.activation,u=t.preluActivationWeights,c=i().getBool("WEBGL_PACK_DEPTHWISECONV")&&o.strideWidth<=2&&o.outChannels/o.inChannels==1,l=s?Qs(s,c):null,h=[n,r],f=null!=a,d=null!=u;return f&&h.push(a),d&&h.push(u),c?(e=new ti(o,f,l,d),this.compileAndRun(e,h)):(e=new Za(o,f,l,d),this.compileAndRun(e,h))},o.prototype.depthwiseConv2D=function(t,e,n){var r;return i().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new ti(n),this.compileAndRun(r,[t,e])):(r=new Za(n),this.compileAndRun(r,[t,e]))},o.prototype.depthwiseConv2DDerInput=function(t,e,n){var r=new $a(n);return this.compileAndRun(r,[t,e])},o.prototype.depthwiseConv2DDerFilter=function(t,e,n){var r=new Ya(n);return this.compileAndRun(r,[t,e])},o.prototype.conv3d=function(t,e,n){var r=new Ja(n);return this.compileAndRun(r,[t,e])},o.prototype.conv3dDerInput=function(t,e,n){var r=new Xa(n);return this.compileAndRun(r,[t,e])},o.prototype.conv3dDerFilter=function(t,e,n){var r=new ja(n);return this.compileAndRun(r,[t,e])},o.prototype.maxPool=function(t,e){var n=new $i(e,"max",!1);return this.compileAndRun(n,[t])},o.prototype.avgPool=function(t,e){var n=new $i(e,"avg",!1);return this.compileAndRun(n,[t],"float32")},o.prototype.maxPoolBackprop=function(t,e,n,r){var o=new $i(r,"max",!0),a=this.compileAndRun(o,[e]),i=new zi(r),s=this.compileAndRun(i,[t,a],e.dtype);return a.dispose(),s},o.prototype.avgPoolBackprop=function(t,e,n){var r=new Ia(n);return this.compileAndRun(r,[t],e.dtype)},o.prototype.cast=function(t,e){return Po(t,e,this)},o.prototype.unstack=function(t,e){for(var n=t.shape[e],r=new Array(t.rank-1),o=0,a=0;a1,(function(){return "blockSize should be > 1 for depthToSpace, but was: "+e}));var r=t.shape[0],o="NHWC"===n?t.shape[1]:t.shape[2],a="NHWC"===n?t.shape[2]:t.shape[3],i="NHWC"===n?t.shape[3]:t.shape[1],s=o*e,u=a*e,c=i/(e*e),l=new ii("NHWC"===n?[r,s,u,c]:[r,c,s,u],e,n);return this.compileAndRun(l,[t])},o.prototype.split=function(t,e,n){return Zo(t,e,n)},o.prototype.scatterND=function(t,e,n){var r=Zr(0,t,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,c=[u/i,i],l=t.reshape([a,o]),h=e.reshape([a,i]);if(0===u)return Lo(Fn([]),n);var f=On(0),d=new ss(a,o,l.rank,h.rank,s,c);return this.compileAndRun(d,[h,l,f]).reshape(n)},o.prototype.sparseToDense=function(t,e,n,r){var o=Zr(0,t,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,c=new ss(i,a,t.rank,e.rank,s,[u,1],!1);return this.compileAndRun(c,[e,t,r]).reshape(n)},o.prototype.fft=function(t){return this.fftImpl(t,!1)},o.prototype.ifft=function(t){return this.fftImpl(t,!0)},o.prototype.fftImpl=function(t,e){var n=this.texData.get(t.dataId),r=new pi(fi,t.shape,e),o=new pi(di,t.shape,e),a=[this.makeComplexComponentTensorInfo(t,n.complexTensors.real),this.makeComplexComponentTensorInfo(t,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(t.shape[0],t.shape[1]);return i.dispose(),s.dispose(),u},o.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=jr(t,e),a=o[0],i=o[1],s=o[2],u=o[3],c=e.reshape([i,r]),l=t.reshape([t.size/s,s]),h=new gi(r,u,[i,s]);return this.compileAndRun(h,[l,c]).reshape(a)},o.prototype.fill=function(t,e,n){if("string"===(n=n||j(e))){var r=P(n,k(t));return r.fill(e),Lt.makeTensor(r,t,n,this)}var o=new vi(t,e),a=o.getCustomSetupFunc(e);return this.compileAndRun(o,[],n,a)},o.prototype.onesLike=function(t){if("string"===t.dtype)throw new Error("onesLike is not supported under string dtype");return this.fill(t.shape,1,t.dtype)},o.prototype.zerosLike=function(t){return this.fill(t.shape,"string"===t.dtype?"":0,t.dtype)},o.prototype.linspace=function(t,e,n){return Wo(t,e,n)},o.prototype.makeTensorInfo=function(t,e){var n=this.write(null,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}},o.prototype.makeOutput=function(t,e){var n=this.makeTensorInfo(t,e).dataId;return Lt.makeTensorFromDataId(n,t,e,this)},o.prototype.unpackTensor=function(t){var e=new Ys(t.shape);return this.runWebGLProgram(e,[t],t.dtype)},o.prototype.packTensor=function(t){var e=new ji(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)},o.prototype.packedReshape=function(t,e){var n=[Ae(t.shape)].concat(De(t.shape)),r={dtype:t.dtype,shape:n,dataId:t.dataId},o=[Ae(e)].concat(De(e)),a=new Zi(o,n),i=this.runWebGLProgram(a,[r],t.dtype,null,!0);return {dataId:i.dataId,shape:e,dtype:i.dtype}},o.prototype.decode=function(t){var e,n=this.texData.get(t),r=n.isPacked,o=n.shape,a=n.dtype,i=Te(o);e=r?new ai(i):new oi(i);return {dtype:a,shape:o,dataId:this.runWebGLProgram(e,[{shape:i,dtype:a,dataId:t}],a,null,!0).dataId}},o.prototype.runWebGLProgram=function(t,e,n,r,o){var a=this;void 0===o&&(o=!1);var s=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(s.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Vt.DENSE){var c=Yt(t.outputShape);u.texShape=c.map((function(t){return 2*t}));}if(null!=t.outTexUsage&&(u.usage=t.outTexUsage),0===k(s.shape))return u.values=B(s.dtype,0),s;var l=[],h=e.map((function(e){if("complex64"===e.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var n=a.texData.get(e.dataId);if(null==n.texture){if(!t.packedInputs&&k(e.shape)<=i().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return {shape:e.shape,texData:null,isUniform:!0,uniformValues:n.values};t.packedInputs&&(n.isPacked=!0,n.shape=e.shape);}else if(!!n.isPacked!=!!t.packedInputs)e=n.isPacked?a.unpackTensor(e):a.packTensor(e),l.push(e),n=a.texData.get(e.dataId);else if(n.isPacked&&!_e(n.shape,e.shape)){var r=e,o=e.shape;e.shape=n.shape,e=a.packedReshape(e,o),l.push(e),n=a.texData.get(e.dataId),r.shape=o;}return a.uploadToGPU(e.dataId),{shape:e.shape,texData:n,isUniform:!1}}));this.uploadToGPU(s.dataId);var f,d={shape:s.shape,texData:u,isUniform:!1},p=function(t,e,n){var r="";e.concat(n).forEach((function(t){var e=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0,n=t.isUniform?"uniform":t.texData.texShape;r+=t.shape+"_"+n+"_"+e;}));var o=t.userCode,a=t.constructor.name;return a+="_"+r+"_"+o}(t,h,d),v=this.getAndSaveBinary(p,(function(){return function(t,e,n,r){var o=e.userCode,a=n.map((function(t,n){var r={logicalShape:t.shape,texShape:t.isUniform?null:t.texData.texShape,isUniform:t.isUniform,isPacked:!t.isUniform&&t.texData.isPacked,flatOffset:null};return null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0&&(r.flatOffset=t.texData.slice.flatOffset),{name:e.variableNames[n],shapeInfo:r}})),s=a.map((function(t){return t.shapeInfo})),u={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},c=fa(a,u,o,e.packedInputs),l=t.createProgram(c),h=null,f=t.getUniformLocation(l,"NAN",!1);1===i().getNumber("WEBGL_VERSION")&&(h=t.getUniformLocation(l,"INFINITY",!1));for(var d={},p=0;p0)return 32}return 16}))),this.floatPrecisionValue},o.prototype.epsilon=function(){return 32===this.floatPrecision()?1e-7:1e-4},o.prototype.uploadToGPU=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,a=n.values,i=n.texture,s=n.usage,u=n.isPacked;if(null==i){var c,l=null!=this.activeTimers;l&&(c=et());var h=n.texShape;if(null==h&&(h=Ne(r,u),n.texShape=h),null!=a){var f=Te(r),d=void 0,p=h[1],v=h[0],m=a instanceof Uint8Array;u?(p=(e=$t(h[0],h[1]))[0],v=e[1],d=new hi(f,[v,p],m)):d=new li(f,[v,p],m);var g=this.makeTensorInfo([v,p],o);this.texData.get(g.dataId).usage=m?zt.PIXELS:zt.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),p,v,a);var y=this.runWebGLProgram(d,[g],o,null,!0),x=this.texData.get(y.dataId);n.texture=x.texture,n.texShape=x.texShape,n.isPacked=x.isPacked,n.usage=x.usage,this.disposeData(g.dataId),this.texData.delete(y.dataId),n.values=null,l&&(this.uploadWaitMs+=et()-c);}else {var b=this.acquireTexture(h,s,o,u);n.texture=b;}}},o.prototype.convertAndCacheOnCPU=function(t,e){var n=this.texData.get(t),r=n.dtype;return this.releaseGPUData(t),null!=e&&(n.values=function(t,e){if("float32"===e||"complex64"===e)return t;if("int32"===e||"bool"===e){for(var n="int32"===e?new Int32Array(t.length):new Uint8Array(t.length),r=0;r1024*this.numMBBeforeWarning*1024){var o=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn("High memory usage in GPU: "+o+" MB, most likely due to a memory leak");}return this.textureManager.acquireTexture(t,e,r)},o.prototype.computeBytes=function(t,e){return t[0]*t[1]*z(e)},o}(bo);Wt()&&Lt.registerBackend("webgl",(function(){return new Zs}),2);var tu=An({square_:function(t){var e=mn(t,"x","square"),n=[e];return Lt.runKernelFunc((function(t,n){return n([e]),t.square(e)}),{x:e},null,"Square",{},n,[])}}),eu="SquaredDifference";var nu=An({squaredDifference_:function(t,e){var n,r=mn(t,"a","squaredDifference"),o=mn(e,"b","squaredDifference");n=Nt(r,o),r=n[0],o=n[1],Ro(r.shape,o.shape);var a={a:r,b:o},i=[r,o];return Lt.runKernelFunc((function(t,e){var n=t.squaredDifference(r,o);return e([r,o]),n}),a,(function(t,e){var n=e[0],r=e[1],o=On(2);return {a:function(){return t.mul(n.sub(r).mul(o))},b:function(){return t.mul(r.sub(n).mul(o))}}}),eu,{},i,[])}});var ru=An({abs_:function(t){var e=mn(t,"x","abs");return "complex64"===e.dtype?Lt.runKernelFunc((function(t){return t.complexAbs(e)}),{$x:e}):Lt.runKernelFunc((function(t,n){var r=t.abs(e);return n([e]),r}),{x:e},(function(t,e){var n=e[0];return {x:function(){return t.mul(n.toFloat().step(-1))}}}),"Abs")}}),ou=An({acos_:function(t){var e=mn(t,"x","acos");return Lt.runKernelFunc((function(t,n){var r=t.acos(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.divStrict(On(1).sub(n.toFloat().square()).sqrt()).neg()}}}))}}),au=An({acosh_:function(t){var e=mn(t,"x","acosh");return Lt.runKernelFunc((function(t,n){var r=t.acosh(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.divStrict(n.toFloat().square().sub(1).sqrt())}}}))}}),iu=An({asin_:function(t){var e=mn(t,"x","asin");return Lt.runKernelFunc((function(t,n){var r=t.asin(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.divStrict(On(1).sub(n.toFloat().square()).sqrt())}}}))}}),su=An({asinh_:function(t){var e=mn(t,"x","asinh");return Lt.runKernelFunc((function(t,n){var r=t.asinh(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.divStrict(On(1).add(n.toFloat().square()).sqrt())}}}))}}),uu=An({atan_:function(t){var e=mn(t,"x","atan");return Lt.runKernelFunc((function(t,n){var r=t.atan(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.div(n.toFloat().square().add(1))}}}))}}),cu=An({atanh_:function(t){var e=mn(t,"x","atanh");return Lt.runKernelFunc((function(t,n){var r=t.atanh(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.div(On(1).sub(n.toFloat().square()))}}}))}}),lu=An({ceil_:function(t){var e=mn(t,"x","ceil");return Lt.runKernelFunc((function(t){return t.ceil(e)}),{$x:e},(function(t){return {$x:function(){return Xn(t)}}}))}}),hu=An({clipByValue_:function(t,e,n){var r=mn(t,"x","clipByValue");C(e<=n,(function(){return "Error in clip: min ("+e+") must be less than or equal to max ("+n+")."}));var o=[r],a={min:e,max:n};return Lt.runKernelFunc((function(t,o){var a=t.clip(r,e,n);return o([r]),a}),{x:r},(function(t,r){var o=r[0];return {x:function(){return t.where(o.greaterEqual(e).logicalAnd(o.lessEqual(n)),Xn(t))}}}),"ClipByValue",a,o)}}),fu=An({cos_:function(t){var e=mn(t,"x","cos"),n=[e];return Lt.runKernelFunc((function(t,n){var r=t.cos(e);return n([e]),r}),{x:e},(function(t,e){var n=e[0];return {x:function(){return n.toFloat().sin().neg().mul(t)}}}),"Cos",{},n)}}),du=An({cosh_:function(t){var e=mn(t,"x","cosh");return Lt.runKernelFunc((function(t,n){var r=t.cosh(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return n.toFloat().sinh().mulStrict(t)}}}))}}),pu=An({erf_:function(t){var e=mn(t,"x","erf");return C("int32"===e.dtype||"float32"===e.dtype,(function(){return "Input dtype must be `int32` or `float32`."})),"int32"===e.dtype&&(e=e.toFloat()),Lt.runKernelFunc((function(t,n){var r=t.erf(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.mul(n.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}}))}}),vu=An({exp_:function(t){var e=mn(t,"x","exp");return Lt.runKernelFunc((function(t,n){var r=t.exp(e);return n([r]),r}),{x:e},(function(t,e){return {x:function(){return t.mulStrict(e[0])}}}),"Exp",{},[],[!0])}}),mu=An({expm1_:function(t){var e=mn(t,"x","expm1");return Lt.runKernelFunc((function(t,n){var r=t.expm1(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.mul(n.exp())}}}))}}),gu=An({floor_:function(t){var e=mn(t,"x","floor");return Lt.runKernelFunc((function(t){return t.floor(e)}),{$x:e},(function(t){return {$x:function(){return Xn(t)}}}))}}),yu=An({log_:function(t){var e=mn(t,"x","log"),n=[e];return Lt.runKernelFunc((function(t,n){var r=t.log(e);return n([e]),r}),{x:e},(function(t,e){var n=e[0];return {x:function(){return t.div(n.toFloat())}}}),"Log",{},n)}}),xu=An({log1p_:function(t){var e=mn(t,"x","log1p");return Lt.runKernelFunc((function(t,n){var r=t.log1p(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.div(n.add(1))}}}))}}),bu=An({logSigmoid_:function(t){var e=mn(t,"x","logSigmoid");return Lt.runKernelFunc((function(t,n){var r=t.softplus(e.neg()).neg();return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return {$x:function(){return t.mul(n.neg().sigmoid())}}}))}}),wu=An({neg_:function(t){var e=mn(t,"x","neg"),n=[e];return Lt.runKernelFunc((function(t){return t.neg(e)}),{x:e},(function(t){return {x:function(){return 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