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https://github.com/opencv/opencv.git
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e8348e5f64
commit
a82c50eac2
@ -42,6 +42,7 @@ public:
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CV_Check(interpolation, interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear", "");
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alignCorners = params.get<bool>("align_corners", false);
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halfPixelCenters = params.get<bool>("half_pixel_centers", false);
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}
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bool getMemoryShapes(const std::vector<MatShape> &inputs,
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@ -114,7 +115,7 @@ public:
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Mat& inp = inputs[0];
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Mat& out = outputs[0];
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if (interpolation == "nearest" || interpolation == "opencv_linear")
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if (interpolation == "nearest" || interpolation == "opencv_linear" || (interpolation == "bilinear" && halfPixelCenters))
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{
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InterpolationFlags mode = interpolation == "nearest" ? INTER_NEAREST : INTER_LINEAR;
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for (size_t n = 0; n < inputs[0].size[0]; ++n)
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@ -236,6 +237,7 @@ protected:
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String interpolation;
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float scaleWidth, scaleHeight;
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bool alignCorners;
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bool halfPixelCenters;
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};
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@ -1962,6 +1962,9 @@ void TFImporter::populateNet(Net dstNet)
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if (hasLayerAttr(layer, "align_corners"))
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layerParams.set("align_corners", getLayerAttr(layer, "align_corners").b());
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if (hasLayerAttr(layer, "half_pixel_centers"))
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layerParams.set("half_pixel_centers", getLayerAttr(layer, "half_pixel_centers").b());
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int id = dstNet.addLayer(name, "Resize", layerParams);
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layer_id[name] = id;
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@ -81,12 +81,12 @@ class Test_TensorFlow_layers : public DNNTestLayer
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{
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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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double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false, const std::string& groupPrefix = "")
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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 netPath = path(prefix + groupPrefix + "_net.pb");
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std::string netConfig = (hasText ? path(prefix + groupPrefix + "_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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std::string outPath = path(prefix + groupPrefix + "_out.npy");
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cv::Mat input = blobFromNPY(inpPath);
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cv::Mat ref = blobFromNPY(outPath);
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@ -975,10 +975,53 @@ TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
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runTensorFlowNet("keras_learning_phase");
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}
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// TF case: align_corners=False, half_pixel_centers=False
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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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}
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// TF case: align_corners=True, half_pixel_centers=False
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TEST_P(Test_TensorFlow_layers, resize_bilinear_align_corners)
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{
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runTensorFlowNet("resize_bilinear",
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false, 0.0, 0.0, false, // default parameters
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"_align_corners");
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}
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// TF case: align_corners=False, half_pixel_centers=True
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TEST_P(Test_TensorFlow_layers, resize_bilinear_half_pixel)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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runTensorFlowNet("resize_bilinear", false, 0.0, 0.0, false, "_half_pixel");
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}
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// TF case: align_corners=False, half_pixel_centers=False
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TEST_P(Test_TensorFlow_layers, resize_bilinear_factor)
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{
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runTensorFlowNet("resize_bilinear_factor");
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}
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// TF case: align_corners=False, half_pixel_centers=True
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TEST_P(Test_TensorFlow_layers, resize_bilinear_factor_half_pixel)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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runTensorFlowNet("resize_bilinear_factor", false, 0.0, 0.0, false, "_half_pixel");
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}
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// TF case: align_corners=True, half_pixel_centers=False
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TEST_P(Test_TensorFlow_layers, resize_bilinear_factor_align_corners)
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{
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runTensorFlowNet("resize_bilinear_factor", false, 0.0, 0.0, false, "_align_corners");
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}
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// TF case: align_corners=False, half_pixel_centers=False
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TEST_P(Test_TensorFlow_layers, resize_bilinear_down)
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{
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runTensorFlowNet("resize_bilinear_down");
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}
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