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https://github.com/opencv/opencv.git
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Merge pull request #11867 from dkurt:dnn_ie_layers
This commit is contained in:
Vadim Pisarevsky
@@ -78,141 +78,170 @@ static std::string path(const std::string& file)
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return findDataFile("dnn/tensorflow/" + file, false);
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}
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static void runTensorFlowNet(const std::string& prefix, int targetId = DNN_TARGET_CPU, bool hasText = false,
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double l1 = 1e-5, double lInf = 1e-4,
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bool memoryLoad = false)
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class Test_TensorFlow_layers : public DNNTestLayer
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{
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std::string netPath = path(prefix + "_net.pb");
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std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
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std::string inpPath = path(prefix + "_in.npy");
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std::string outPath = path(prefix + "_out.npy");
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Net net;
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if (memoryLoad)
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public:
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void runTensorFlowNet(const std::string& prefix, bool hasText = false,
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double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false)
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{
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// Load files into a memory buffers
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string dataModel;
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ASSERT_TRUE(readFileInMemory(netPath, dataModel));
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std::string netPath = path(prefix + "_net.pb");
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std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : "");
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std::string inpPath = path(prefix + "_in.npy");
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std::string outPath = path(prefix + "_out.npy");
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string dataConfig;
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if (hasText)
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ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
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cv::Mat input = blobFromNPY(inpPath);
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cv::Mat ref = blobFromNPY(outPath);
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checkBackend(&input, &ref);
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net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
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dataConfig.c_str(), dataConfig.size());
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Net net;
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if (memoryLoad)
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{
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// Load files into a memory buffers
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string dataModel;
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ASSERT_TRUE(readFileInMemory(netPath, dataModel));
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string dataConfig;
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if (hasText)
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ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
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net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
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dataConfig.c_str(), dataConfig.size());
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}
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else
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net = readNetFromTensorflow(netPath, netConfig);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(input);
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cv::Mat output = net.forward();
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normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
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}
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else
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net = readNetFromTensorflow(netPath, netConfig);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(targetId);
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cv::Mat input = blobFromNPY(inpPath);
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cv::Mat target = blobFromNPY(outPath);
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net.setInput(input);
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cv::Mat output = net.forward();
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normAssert(target, output, "", l1, lInf);
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}
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typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_layers;
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};
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TEST_P(Test_TensorFlow_layers, conv)
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{
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int targetId = GetParam();
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runTensorFlowNet("single_conv", targetId);
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runTensorFlowNet("atrous_conv2d_valid", targetId);
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runTensorFlowNet("atrous_conv2d_same", targetId);
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runTensorFlowNet("depthwise_conv2d", targetId);
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runTensorFlowNet("keras_atrous_conv2d_same", targetId);
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runTensorFlowNet("conv_pool_nchw", targetId);
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runTensorFlowNet("single_conv");
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runTensorFlowNet("atrous_conv2d_valid");
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runTensorFlowNet("atrous_conv2d_same");
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runTensorFlowNet("depthwise_conv2d");
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runTensorFlowNet("keras_atrous_conv2d_same");
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runTensorFlowNet("conv_pool_nchw");
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}
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TEST_P(Test_TensorFlow_layers, padding)
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{
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int targetId = GetParam();
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runTensorFlowNet("padding_same", targetId);
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runTensorFlowNet("padding_valid", targetId);
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runTensorFlowNet("spatial_padding", targetId);
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runTensorFlowNet("padding_same");
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runTensorFlowNet("padding_valid");
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runTensorFlowNet("spatial_padding");
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}
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TEST_P(Test_TensorFlow_layers, eltwise_add_mul)
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{
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runTensorFlowNet("eltwise_add_mul", GetParam());
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runTensorFlowNet("eltwise_add_mul");
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}
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TEST_P(Test_TensorFlow_layers, concat)
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TEST_P(Test_TensorFlow_layers, pad_and_concat)
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{
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runTensorFlowNet("pad_and_concat", GetParam());
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runTensorFlowNet("concat_axis_1", GetParam());
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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runTensorFlowNet("pad_and_concat");
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}
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TEST_P(Test_TensorFlow_layers, concat_axis_1)
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{
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runTensorFlowNet("concat_axis_1");
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}
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TEST_P(Test_TensorFlow_layers, batch_norm)
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{
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int targetId = GetParam();
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runTensorFlowNet("batch_norm", targetId);
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runTensorFlowNet("fused_batch_norm", targetId);
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runTensorFlowNet("batch_norm_text", targetId, true);
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runTensorFlowNet("mvn_batch_norm", targetId);
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runTensorFlowNet("mvn_batch_norm_1x1", targetId);
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runTensorFlowNet("unfused_batch_norm", targetId);
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runTensorFlowNet("fused_batch_norm_no_gamma", targetId);
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runTensorFlowNet("unfused_batch_norm_no_gamma", targetId);
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runTensorFlowNet("batch_norm");
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runTensorFlowNet("batch_norm", false, 0.0, 0.0, true);
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runTensorFlowNet("fused_batch_norm");
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runTensorFlowNet("fused_batch_norm", false, 0.0, 0.0, true);
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runTensorFlowNet("batch_norm_text", true);
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runTensorFlowNet("batch_norm_text", true, 0.0, 0.0, true);
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runTensorFlowNet("unfused_batch_norm");
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runTensorFlowNet("fused_batch_norm_no_gamma");
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runTensorFlowNet("unfused_batch_norm_no_gamma");
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}
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TEST_P(Test_TensorFlow_layers, mvn_batch_norm)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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runTensorFlowNet("mvn_batch_norm");
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runTensorFlowNet("mvn_batch_norm_1x1");
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}
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TEST_P(Test_TensorFlow_layers, pooling)
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{
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int targetId = GetParam();
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cv::ocl::Device d = cv::ocl::Device::getDefault();
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bool loosenFlag = targetId == DNN_TARGET_OPENCL && d.isIntel() && d.type() == cv::ocl::Device::TYPE_CPU;
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runTensorFlowNet("max_pool_even", targetId);
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runTensorFlowNet("max_pool_odd_valid", targetId);
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runTensorFlowNet("ave_pool_same", targetId);
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runTensorFlowNet("max_pool_odd_same", targetId, false, loosenFlag ? 3e-5 : 1e-5, loosenFlag ? 3e-4 : 1e-4);
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runTensorFlowNet("reduce_mean", targetId); // an average pooling over all spatial dimensions.
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runTensorFlowNet("max_pool_even");
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runTensorFlowNet("max_pool_odd_valid");
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runTensorFlowNet("max_pool_odd_same");
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runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions.
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}
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// TODO: fix tests and replace to pooling
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TEST_P(Test_TensorFlow_layers, ave_pool_same)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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runTensorFlowNet("ave_pool_same");
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}
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TEST_P(Test_TensorFlow_layers, deconvolution)
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{
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int targetId = GetParam();
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runTensorFlowNet("deconvolution", targetId);
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runTensorFlowNet("deconvolution_same", targetId);
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runTensorFlowNet("deconvolution_stride_2_same", targetId);
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runTensorFlowNet("deconvolution_adj_pad_valid", targetId);
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runTensorFlowNet("deconvolution_adj_pad_same", targetId);
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runTensorFlowNet("keras_deconv_valid", targetId);
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runTensorFlowNet("keras_deconv_same", targetId);
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runTensorFlowNet("deconvolution");
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runTensorFlowNet("deconvolution_same");
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runTensorFlowNet("deconvolution_stride_2_same");
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runTensorFlowNet("deconvolution_adj_pad_valid");
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runTensorFlowNet("deconvolution_adj_pad_same");
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runTensorFlowNet("keras_deconv_valid");
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runTensorFlowNet("keras_deconv_same");
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}
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TEST_P(Test_TensorFlow_layers, matmul)
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{
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int targetId = GetParam();
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runTensorFlowNet("matmul", targetId);
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runTensorFlowNet("nhwc_reshape_matmul", targetId);
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runTensorFlowNet("nhwc_transpose_reshape_matmul", targetId);
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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throw SkipTestException("");
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runTensorFlowNet("matmul");
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runTensorFlowNet("nhwc_reshape_matmul");
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runTensorFlowNet("nhwc_transpose_reshape_matmul");
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}
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TEST_P(Test_TensorFlow_layers, reshape)
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{
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int targetId = GetParam();
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runTensorFlowNet("shift_reshape_no_reorder", targetId);
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runTensorFlowNet("reshape_no_reorder", targetId);
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runTensorFlowNet("reshape_reduce", targetId);
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runTensorFlowNet("flatten", targetId, true);
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runTensorFlowNet("unfused_flatten", targetId);
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runTensorFlowNet("unfused_flatten_unknown_batch", targetId);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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runTensorFlowNet("shift_reshape_no_reorder");
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runTensorFlowNet("reshape_no_reorder");
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runTensorFlowNet("reshape_reduce");
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}
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TEST_P(Test_TensorFlow_layers, flatten)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
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throw SkipTestException("");
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runTensorFlowNet("flatten", true);
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runTensorFlowNet("unfused_flatten");
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runTensorFlowNet("unfused_flatten_unknown_batch");
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}
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TEST_P(Test_TensorFlow_layers, l2_normalize)
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{
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int targetId = GetParam();
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runTensorFlowNet("l2_normalize", targetId);
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runTensorFlowNet("l2_normalize_3d", targetId);
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runTensorFlowNet("l2_normalize");
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, availableDnnTargets());
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// TODO: fix it and add to l2_normalize
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TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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runTensorFlowNet("l2_normalize_3d");
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}
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typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_nets;
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@@ -359,91 +388,96 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
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INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, availableDnnTargets());
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typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_fp16;
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TEST_P(Test_TensorFlow_fp16, tests)
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TEST_P(Test_TensorFlow_layers, fp16_weights)
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{
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int targetId = GetParam();
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const float l1 = 7e-4;
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const float lInf = 1e-2;
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runTensorFlowNet("fp16_single_conv", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_deconvolution", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_same", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_padding_valid", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_eltwise_add_mul", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_valid", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_pad_and_concat", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_even", targetId, false, l1, lInf);
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runTensorFlowNet("fp16_padding_same", targetId, false, l1, lInf);
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const float l1 = 0.00071;
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const float lInf = 0.012;
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runTensorFlowNet("fp16_single_conv", false, l1, lInf);
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runTensorFlowNet("fp16_deconvolution", false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
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runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
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runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf);
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runTensorFlowNet("fp16_max_pool_even", false, l1, lInf);
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runTensorFlowNet("fp16_padding_same", false, l1, lInf);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_fp16,
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Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
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// TODO: fix pad_and_concat and add this test case to fp16_weights
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TEST_P(Test_TensorFlow_layers, fp16_pad_and_concat)
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{
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const float l1 = 0.00071;
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const float lInf = 0.012;
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
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}
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TEST(Test_TensorFlow, defun)
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TEST_P(Test_TensorFlow_layers, defun)
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{
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runTensorFlowNet("defun_dropout");
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}
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TEST(Test_TensorFlow, quantized)
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TEST_P(Test_TensorFlow_layers, quantized)
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{
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runTensorFlowNet("uint8_single_conv");
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}
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TEST(Test_TensorFlow, lstm)
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TEST_P(Test_TensorFlow_layers, lstm)
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{
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runTensorFlowNet("lstm", DNN_TARGET_CPU, true);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
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(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
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throw SkipTestException("");
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runTensorFlowNet("lstm", true);
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runTensorFlowNet("lstm", true, 0.0, 0.0, true);
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}
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TEST(Test_TensorFlow, split)
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TEST_P(Test_TensorFlow_layers, split)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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runTensorFlowNet("split_equals");
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}
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TEST(Test_TensorFlow, resize_nearest_neighbor)
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TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_MYRIAD)
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throw SkipTestException("");
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runTensorFlowNet("resize_nearest_neighbor");
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runTensorFlowNet("keras_upsampling2d");
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}
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TEST(Test_TensorFlow, slice)
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TEST_P(Test_TensorFlow_layers, slice)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
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throw SkipTestException("");
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runTensorFlowNet("slice_4d");
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}
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TEST(Test_TensorFlow, softmax)
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TEST_P(Test_TensorFlow_layers, softmax)
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{
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runTensorFlowNet("keras_softmax");
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}
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TEST(Test_TensorFlow, relu6)
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TEST_P(Test_TensorFlow_layers, relu6)
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{
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runTensorFlowNet("keras_relu6");
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runTensorFlowNet("keras_relu6", DNN_TARGET_CPU, /*hasText*/ true);
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runTensorFlowNet("keras_relu6", /*hasText*/ true);
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}
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TEST(Test_TensorFlow, keras_mobilenet_head)
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TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
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{
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runTensorFlowNet("keras_mobilenet_head");
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}
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TEST(Test_TensorFlow, memory_read)
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{
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double l1 = 1e-5;
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double lInf = 1e-4;
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runTensorFlowNet("lstm", DNN_TARGET_CPU, true, l1, lInf, true);
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runTensorFlowNet("batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
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runTensorFlowNet("fused_batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
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runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
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}
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TEST(Test_TensorFlow, resize_bilinear)
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TEST_P(Test_TensorFlow_layers, resize_bilinear)
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{
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runTensorFlowNet("resize_bilinear");
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runTensorFlowNet("resize_bilinear_factor");
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, dnnBackendsAndTargets());
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TEST(Test_TensorFlow, two_inputs)
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{
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Net net = readNet(path("two_inputs_net.pbtxt"));
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