Merge pull request opencv#10240 from alalek:dnn_perf_ssd

alalek · alalek · commit 0b688cd23f36 · 2017-12-06T15:41:18.000Z
diff --git a/modules/dnn/perf/perf_net.cpp b/modules/dnn/perf/perf_net.cpp
@@ -32,7 +32,7 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa
     dnn::Net net;
 
     void processNet(std::string weights, std::string proto, std::string halide_scheduler,
-                        int inWidth, int inHeight, const std::string& outputLayer,
+                        const Mat& input, const std::string& outputLayer,
                         const std::string& framework)
     {
         backend = (dnn::Backend)(int)get<0>(GetParam());
@@ -48,15 +48,18 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa
             }
         }
 
-        Mat input(inHeight, inWidth, CV_32FC3);
         randu(input, 0.0f, 1.0f);
 
-
         weights = findDataFile(weights, false);
         if (!proto.empty())
             proto = findDataFile(proto, false);
-        if (!halide_scheduler.empty() && backend == DNN_BACKEND_HALIDE)
-            halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
+        if (backend == DNN_BACKEND_HALIDE)
+        {
+            if (halide_scheduler == "disabled")
+                throw ::SkipTestException("Halide test is disabled");
+            if (!halide_scheduler.empty())
+                halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);
+        }
         if (framework == "caffe")
         {
             net = cv::dnn::readNetFromCaffe(proto, weights);
@@ -80,7 +83,7 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa
             net.setHalideScheduler(halide_scheduler);
         }
 
-        MatShape netInputShape = shape(1, 3, inHeight, inWidth);
+        MatShape netInputShape = shape(1, 3, input.rows, input.cols);
         size_t weightsMemory = 0, blobsMemory = 0;
         net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
         int64 flops = net.getFLOPS(netInputShape);
@@ -104,40 +107,45 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa
 PERF_TEST_P_(DNNTestNetwork, AlexNet)
 {
     processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
-            "alexnet.yml", 227, 227, "prob", "caffe");
+            "alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3), "prob", "caffe");
 }
 
 PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
 {
     processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
-            "", 224, 224, "prob", "caffe");
+            "", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");
 }
 
 PERF_TEST_P_(DNNTestNetwork, ResNet50)
 {
     processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
-            "resnet_50.yml", 224, 224, "prob", "caffe");
+            "resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");
 }
 
 PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
 {
     processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
-            "squeezenet_v1_1.yml", 227, 227, "prob", "caffe");
+            "squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3), "prob", "caffe");
 }
 
 PERF_TEST_P_(DNNTestNetwork, Inception_5h)
 {
     processNet("dnn/tensorflow_inception_graph.pb", "",
             "inception_5h.yml",
-            224, 224, "softmax2", "tensorflow");
+            Mat(cv::Size(224, 224), CV_32FC3), "softmax2", "tensorflow");
 }
 
 PERF_TEST_P_(DNNTestNetwork, ENet)
 {
     processNet("dnn/Enet-model-best.net", "", "enet.yml",
-            512, 256, "l367_Deconvolution", "torch");
+            Mat(cv::Size(512, 256), CV_32FC3), "l367_Deconvolution", "torch");
 }
 
+PERF_TEST_P_(DNNTestNetwork, SSD)
+{
+    processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",
+            Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");
+}
 
 INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork,
     testing::Combine(

Original file line number	Diff line number	Diff line change
`@@ -32,7 +32,7 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa`
`32`	`32`	`dnn::Net net;`
`33`	`33`
`34`	`34`	`void processNet(std::string weights, std::string proto, std::string halide_scheduler,`
`35`		`- int inWidth, int inHeight, const std::string& outputLayer,`
	`35`	`+ const Mat& input, const std::string& outputLayer,`
`36`	`36`	`const std::string& framework)`
`37`	`37`	`{`
`38`	`38`	`backend = (dnn::Backend)(int)get<0>(GetParam());`
`@@ -48,15 +48,18 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa`
`48`	`48`	`}`
`49`	`49`	`}`
`50`	`50`
`51`		`- Mat input(inHeight, inWidth, CV_32FC3);`
`52`	`51`	`randu(input, 0.0f, 1.0f);`
`53`	`52`
`54`		`-`
`55`	`53`	`weights = findDataFile(weights, false);`
`56`	`54`	`if (!proto.empty())`
`57`	`55`	`proto = findDataFile(proto, false);`
`58`		`- if (!halide_scheduler.empty() && backend == DNN_BACKEND_HALIDE)`
`59`		`- halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);`
	`56`	`+ if (backend == DNN_BACKEND_HALIDE)`
	`57`	`+ {`
	`58`	`+ if (halide_scheduler == "disabled")`
	`59`	`+ throw ::SkipTestException("Halide test is disabled");`
	`60`	`+ if (!halide_scheduler.empty())`
	`61`	`+ halide_scheduler = findDataFile(std::string("dnn/halide_scheduler_") + (target == DNN_TARGET_OPENCL ? "opencl_" : "") + halide_scheduler, true);`
	`62`	`+ }`
`60`	`63`	`if (framework == "caffe")`
`61`	`64`	`{`
`62`	`65`	`net = cv::dnn::readNetFromCaffe(proto, weights);`
`@@ -80,7 +83,7 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa`
`80`	`83`	`net.setHalideScheduler(halide_scheduler);`
`81`	`84`	`}`
`82`	`85`
`83`		`- MatShape netInputShape = shape(1, 3, inHeight, inWidth);`
	`86`	`+ MatShape netInputShape = shape(1, 3, input.rows, input.cols);`
`84`	`87`	`size_t weightsMemory = 0, blobsMemory = 0;`
`85`	`88`	`net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);`
`86`	`89`	`int64 flops = net.getFLOPS(netInputShape);`
`@@ -104,40 +107,45 @@ class DNNTestNetwork : public ::perf::TestBaseWithParam< tuple<DNNBackend, DNNTa`
`104`	`107`	`PERF_TEST_P_(DNNTestNetwork, AlexNet)`
`105`	`108`	`{`
`106`	`109`	`processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",`
`107`		`- "alexnet.yml", 227, 227, "prob", "caffe");`
	`110`	`+ "alexnet.yml", Mat(cv::Size(227, 227), CV_32FC3), "prob", "caffe");`
`108`	`111`	`}`
`109`	`112`
`110`	`113`	`PERF_TEST_P_(DNNTestNetwork, GoogLeNet)`
`111`	`114`	`{`
`112`	`115`	`processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",`
`113`		`- "", 224, 224, "prob", "caffe");`
	`116`	`+ "", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");`
`114`	`117`	`}`
`115`	`118`
`116`	`119`	`PERF_TEST_P_(DNNTestNetwork, ResNet50)`
`117`	`120`	`{`
`118`	`121`	`processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",`
`119`		`- "resnet_50.yml", 224, 224, "prob", "caffe");`
	`122`	`+ "resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3), "prob", "caffe");`
`120`	`123`	`}`
`121`	`124`
`122`	`125`	`PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)`
`123`	`126`	`{`
`124`	`127`	`processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",`
`125`		`- "squeezenet_v1_1.yml", 227, 227, "prob", "caffe");`
	`128`	`+ "squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3), "prob", "caffe");`
`126`	`129`	`}`
`127`	`130`
`128`	`131`	`PERF_TEST_P_(DNNTestNetwork, Inception_5h)`
`129`	`132`	`{`
`130`	`133`	`processNet("dnn/tensorflow_inception_graph.pb", "",`
`131`	`134`	`"inception_5h.yml",`
`132`		`- 224, 224, "softmax2", "tensorflow");`
	`135`	`+ Mat(cv::Size(224, 224), CV_32FC3), "softmax2", "tensorflow");`
`133`	`136`	`}`
`134`	`137`
`135`	`138`	`PERF_TEST_P_(DNNTestNetwork, ENet)`
`136`	`139`	`{`
`137`	`140`	`processNet("dnn/Enet-model-best.net", "", "enet.yml",`
`138`		`- 512, 256, "l367_Deconvolution", "torch");`
	`141`	`+ Mat(cv::Size(512, 256), CV_32FC3), "l367_Deconvolution", "torch");`
`139`	`142`	`}`
`140`	`143`
	`144`	`+PERF_TEST_P_(DNNTestNetwork, SSD)`
	`145`	`+{`
	`146`	`+ processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", "dnn/ssd_vgg16.prototxt", "disabled",`
	`147`	`+ Mat(cv::Size(300, 300), CV_32FC3), "detection_out", "caffe");`
	`148`	`+}`
`141`	`149`
`142`	`150`	`INSTANTIATE_TEST_CASE_P(/nothing/, DNNTestNetwork,`
`143`	`151`	`testing::Combine(`