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| 1 | +import * as tf from '@tensorflow/tfjs-core'; |
| 2 | + |
| 3 | +import { createCanvasFromMedia, NetInput, toNetInput } from '../../../src'; |
| 4 | +import { FaceExpressionPrediction } from '../../../src/faceExpressionNet/types'; |
| 5 | +import { loadImage } from '../../env'; |
| 6 | +import { describeWithNets, expectAllTensorsReleased } from '../../utils'; |
| 7 | + |
| 8 | +describe('faceExpressionNet', () => { |
| 9 | + |
| 10 | + let imgElAngry: HTMLImageElement |
| 11 | + let imgElSurprised: HTMLImageElement |
| 12 | + |
| 13 | + beforeAll(async () => { |
| 14 | + imgElAngry = await loadImage('test/images/angry_cropped.jpg') |
| 15 | + imgElSurprised = await loadImage('test/images/surprised_cropped.jpg') |
| 16 | + }) |
| 17 | + |
| 18 | + describeWithNets('quantized weights', { withFaceExpressionNet: { quantized: true } }, ({ faceExpressionNet }) => { |
| 19 | + |
| 20 | + it('recognizes facial expressions', async () => { |
| 21 | + const result = await faceExpressionNet.predictExpressions(imgElAngry) as FaceExpressionPrediction[] |
| 22 | + |
| 23 | + expect(Array.isArray(result)).toBe(true) |
| 24 | + expect(result.length).toEqual(7) |
| 25 | + |
| 26 | + const angry = result.find(res => res.expression === 'angry') as FaceExpressionPrediction |
| 27 | + |
| 28 | + expect(angry).not.toBeUndefined() |
| 29 | + expect(angry.probability).toBeGreaterThan(0.95) |
| 30 | + }) |
| 31 | + |
| 32 | + }) |
| 33 | + |
| 34 | + describeWithNets('batch inputs', { withFaceExpressionNet: { quantized: true } }, ({ faceExpressionNet }) => { |
| 35 | + |
| 36 | + it('recognizes facial expressions for batch of image elements', async () => { |
| 37 | + const inputs = [imgElAngry, imgElSurprised] |
| 38 | + |
| 39 | + const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][] |
| 40 | + expect(Array.isArray(results)).toBe(true) |
| 41 | + expect(results.length).toEqual(2) |
| 42 | + |
| 43 | + const [resultAngry, resultSurprised] = results |
| 44 | + |
| 45 | + expect(Array.isArray(resultAngry)).toBe(true) |
| 46 | + expect(resultAngry.length).toEqual(7) |
| 47 | + expect(Array.isArray(resultSurprised)).toBe(true) |
| 48 | + expect(resultSurprised.length).toEqual(7) |
| 49 | + |
| 50 | + const angry = resultAngry.find(res => res.expression === 'angry') as FaceExpressionPrediction |
| 51 | + const surprised = resultSurprised.find(res => res.expression === 'surprised') as FaceExpressionPrediction |
| 52 | + |
| 53 | + expect(angry).not.toBeUndefined() |
| 54 | + expect(angry.probability).toBeGreaterThan(0.95) |
| 55 | + expect(surprised).not.toBeUndefined() |
| 56 | + expect(surprised.probability).toBeGreaterThan(0.95) |
| 57 | + }) |
| 58 | + |
| 59 | + it('computes face landmarks for batch of tf.Tensor3D', async () => { |
| 60 | + const inputs = [imgElAngry, imgElSurprised].map(el => tf.fromPixels(createCanvasFromMedia(el))) |
| 61 | + |
| 62 | + const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][] |
| 63 | + expect(Array.isArray(results)).toBe(true) |
| 64 | + expect(results.length).toEqual(2) |
| 65 | + |
| 66 | + const [resultAngry, resultSurprised] = results |
| 67 | + |
| 68 | + expect(Array.isArray(resultAngry)).toBe(true) |
| 69 | + expect(resultAngry.length).toEqual(7) |
| 70 | + expect(Array.isArray(resultSurprised)).toBe(true) |
| 71 | + expect(resultSurprised.length).toEqual(7) |
| 72 | + |
| 73 | + const angry = resultAngry.find(res => res.expression === 'angry') as FaceExpressionPrediction |
| 74 | + const surprised = resultSurprised.find(res => res.expression === 'surprised') as FaceExpressionPrediction |
| 75 | + |
| 76 | + expect(angry).not.toBeUndefined() |
| 77 | + expect(angry.probability).toBeGreaterThan(0.95) |
| 78 | + expect(surprised).not.toBeUndefined() |
| 79 | + expect(surprised.probability).toBeGreaterThan(0.95) |
| 80 | + }) |
| 81 | + |
| 82 | + it('computes face landmarks for batch of mixed inputs', async () => { |
| 83 | + const inputs = [imgElAngry, tf.fromPixels(createCanvasFromMedia(imgElSurprised))] |
| 84 | + |
| 85 | + const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][] |
| 86 | + expect(Array.isArray(results)).toBe(true) |
| 87 | + expect(results.length).toEqual(2) |
| 88 | + |
| 89 | + const [resultAngry, resultSurprised] = results |
| 90 | + |
| 91 | + expect(Array.isArray(resultAngry)).toBe(true) |
| 92 | + expect(resultAngry.length).toEqual(7) |
| 93 | + expect(Array.isArray(resultSurprised)).toBe(true) |
| 94 | + expect(resultSurprised.length).toEqual(7) |
| 95 | + |
| 96 | + const angry = resultAngry.find(res => res.expression === 'angry') as FaceExpressionPrediction |
| 97 | + const surprised = resultSurprised.find(res => res.expression === 'surprised') as FaceExpressionPrediction |
| 98 | + |
| 99 | + expect(angry).not.toBeUndefined() |
| 100 | + expect(angry.probability).toBeGreaterThan(0.95) |
| 101 | + expect(surprised).not.toBeUndefined() |
| 102 | + expect(surprised.probability).toBeGreaterThan(0.95) |
| 103 | + }) |
| 104 | + |
| 105 | + }) |
| 106 | + |
| 107 | + describeWithNets('no memory leaks', { withFaceExpressionNet: { quantized: true } }, ({ faceExpressionNet }) => { |
| 108 | + |
| 109 | + describe('forwardInput', () => { |
| 110 | + |
| 111 | + it('single image element', async () => { |
| 112 | + await expectAllTensorsReleased(async () => { |
| 113 | + const netInput = new NetInput([imgElAngry]) |
| 114 | + const outTensor = await faceExpressionNet.forwardInput(netInput) |
| 115 | + outTensor.dispose() |
| 116 | + }) |
| 117 | + }) |
| 118 | + |
| 119 | + it('multiple image elements', async () => { |
| 120 | + await expectAllTensorsReleased(async () => { |
| 121 | + const netInput = new NetInput([imgElAngry, imgElAngry]) |
| 122 | + const outTensor = await faceExpressionNet.forwardInput(netInput) |
| 123 | + outTensor.dispose() |
| 124 | + }) |
| 125 | + }) |
| 126 | + |
| 127 | + it('single tf.Tensor3D', async () => { |
| 128 | + const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry)) |
| 129 | + |
| 130 | + await expectAllTensorsReleased(async () => { |
| 131 | + const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor)) |
| 132 | + outTensor.dispose() |
| 133 | + }) |
| 134 | + |
| 135 | + tensor.dispose() |
| 136 | + }) |
| 137 | + |
| 138 | + it('multiple tf.Tensor3Ds', async () => { |
| 139 | + const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el))) |
| 140 | + |
| 141 | + await expectAllTensorsReleased(async () => { |
| 142 | + const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors)) |
| 143 | + outTensor.dispose() |
| 144 | + }) |
| 145 | + |
| 146 | + tensors.forEach(t => t.dispose()) |
| 147 | + }) |
| 148 | + |
| 149 | + it('single batch size 1 tf.Tensor4Ds', async () => { |
| 150 | + const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D |
| 151 | + |
| 152 | + await expectAllTensorsReleased(async () => { |
| 153 | + const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor)) |
| 154 | + outTensor.dispose() |
| 155 | + }) |
| 156 | + |
| 157 | + tensor.dispose() |
| 158 | + }) |
| 159 | + |
| 160 | + it('multiple batch size 1 tf.Tensor4Ds', async () => { |
| 161 | + const tensors = [imgElAngry, imgElAngry, imgElAngry] |
| 162 | + .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] |
| 163 | + |
| 164 | + await expectAllTensorsReleased(async () => { |
| 165 | + const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors)) |
| 166 | + outTensor.dispose() |
| 167 | + }) |
| 168 | + |
| 169 | + tensors.forEach(t => t.dispose()) |
| 170 | + }) |
| 171 | + |
| 172 | + }) |
| 173 | + |
| 174 | + describe('predictExpressions', () => { |
| 175 | + |
| 176 | + it('single image element', async () => { |
| 177 | + await expectAllTensorsReleased(async () => { |
| 178 | + await faceExpressionNet.predictExpressions(imgElAngry) |
| 179 | + }) |
| 180 | + }) |
| 181 | + |
| 182 | + it('multiple image elements', async () => { |
| 183 | + await expectAllTensorsReleased(async () => { |
| 184 | + await faceExpressionNet.predictExpressions([imgElAngry, imgElAngry, imgElAngry]) |
| 185 | + }) |
| 186 | + }) |
| 187 | + |
| 188 | + it('single tf.Tensor3D', async () => { |
| 189 | + const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry)) |
| 190 | + |
| 191 | + await expectAllTensorsReleased(async () => { |
| 192 | + await faceExpressionNet.predictExpressions(tensor) |
| 193 | + }) |
| 194 | + |
| 195 | + tensor.dispose() |
| 196 | + }) |
| 197 | + |
| 198 | + it('multiple tf.Tensor3Ds', async () => { |
| 199 | + const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el))) |
| 200 | + |
| 201 | + |
| 202 | + await expectAllTensorsReleased(async () => { |
| 203 | + await faceExpressionNet.predictExpressions(tensors) |
| 204 | + }) |
| 205 | + |
| 206 | + tensors.forEach(t => t.dispose()) |
| 207 | + }) |
| 208 | + |
| 209 | + it('single batch size 1 tf.Tensor4Ds', async () => { |
| 210 | + const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D |
| 211 | + |
| 212 | + await expectAllTensorsReleased(async () => { |
| 213 | + await faceExpressionNet.predictExpressions(tensor) |
| 214 | + }) |
| 215 | + |
| 216 | + tensor.dispose() |
| 217 | + }) |
| 218 | + |
| 219 | + it('multiple batch size 1 tf.Tensor4Ds', async () => { |
| 220 | + const tensors = [imgElAngry, imgElAngry, imgElAngry] |
| 221 | + .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] |
| 222 | + |
| 223 | + await expectAllTensorsReleased(async () => { |
| 224 | + await faceExpressionNet.predictExpressions(tensors) |
| 225 | + }) |
| 226 | + |
| 227 | + tensors.forEach(t => t.dispose()) |
| 228 | + }) |
| 229 | + |
| 230 | + }) |
| 231 | + }) |
| 232 | + |
| 233 | +}) |
| 234 | + |
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