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Added TF resize_bilinear behaviour alignment

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Fixes OpenCV issue #18721
This commit is contained in:
Anastasia Murzova 2020-12-03 01:48:18 +03:00
parent e8348e5f64
commit a82c50eac2
3 changed files with 53 additions and 5 deletions
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4
modules/dnn/src/layers/resize_layer.cpp
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@ -42,6 +42,7 @@ public:
CV_Check(interpolation, interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear", ""); CV_Check(interpolation, interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear", "");
alignCorners = params.get<bool>("align_corners", false); alignCorners = params.get<bool>("align_corners", false);
halfPixelCenters = params.get<bool>("half_pixel_centers", false);
} }
bool getMemoryShapes(const std::vector<MatShape> &inputs, bool getMemoryShapes(const std::vector<MatShape> &inputs,
@ -114,7 +115,7 @@ public:
Mat& inp = inputs[0]; Mat& inp = inputs[0];
Mat& out = outputs[0]; Mat& out = outputs[0];
if (interpolation == "nearest" || interpolation == "opencv_linear") if (interpolation == "nearest" || interpolation == "opencv_linear" || (interpolation == "bilinear" && halfPixelCenters))
{ {
InterpolationFlags mode = interpolation == "nearest" ? INTER_NEAREST : INTER_LINEAR; InterpolationFlags mode = interpolation == "nearest" ? INTER_NEAREST : INTER_LINEAR;
for (size_t n = 0; n < inputs[0].size[0]; ++n) for (size_t n = 0; n < inputs[0].size[0]; ++n)
@ -236,6 +237,7 @@ protected:
String interpolation; String interpolation;
float scaleWidth, scaleHeight; float scaleWidth, scaleHeight;
bool alignCorners; bool alignCorners;
bool halfPixelCenters;
}; };

3
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1962,6 +1962,9 @@ void TFImporter::populateNet(Net dstNet)
if (hasLayerAttr(layer, "align_corners")) if (hasLayerAttr(layer, "align_corners"))
layerParams.set("align_corners", getLayerAttr(layer, "align_corners").b()); layerParams.set("align_corners", getLayerAttr(layer, "align_corners").b());
if (hasLayerAttr(layer, "half_pixel_centers"))
layerParams.set("half_pixel_centers", getLayerAttr(layer, "half_pixel_centers").b());
int id = dstNet.addLayer(name, "Resize", layerParams); int id = dstNet.addLayer(name, "Resize", layerParams);
layer_id[name] = id; layer_id[name] = id;

51
modules/dnn/test/test_tf_importer.cpp
View File

@ -81,12 +81,12 @@ class Test_TensorFlow_layers : public DNNTestLayer
{ {
public: public:
void runTensorFlowNet(const std::string& prefix, bool hasText = false, void runTensorFlowNet(const std::string& prefix, bool hasText = false,
double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false) double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false, const std::string& groupPrefix = "")
{ {
std::string netPath = path(prefix + "_net.pb"); std::string netPath = path(prefix + groupPrefix + "_net.pb");
std::string netConfig = (hasText ? path(prefix + "_net.pbtxt") : ""); std::string netConfig = (hasText ? path(prefix + groupPrefix + "_net.pbtxt") : "");
std::string inpPath = path(prefix + "_in.npy"); std::string inpPath = path(prefix + "_in.npy");
std::string outPath = path(prefix + "_out.npy"); std::string outPath = path(prefix + groupPrefix + "_out.npy");
cv::Mat input = blobFromNPY(inpPath); cv::Mat input = blobFromNPY(inpPath);
cv::Mat ref = blobFromNPY(outPath); cv::Mat ref = blobFromNPY(outPath);
@ -975,10 +975,53 @@ TEST_P(Test_TensorFlow_layers, keras_mobilenet_head)
runTensorFlowNet("keras_learning_phase"); runTensorFlowNet("keras_learning_phase");
} }
// TF case: align_corners=False, half_pixel_centers=False
TEST_P(Test_TensorFlow_layers, resize_bilinear) TEST_P(Test_TensorFlow_layers, resize_bilinear)
{ {
runTensorFlowNet("resize_bilinear"); runTensorFlowNet("resize_bilinear");
}
// TF case: align_corners=True, half_pixel_centers=False
TEST_P(Test_TensorFlow_layers, resize_bilinear_align_corners)
{
runTensorFlowNet("resize_bilinear",
false, 0.0, 0.0, false, // default parameters
"_align_corners");
}
// TF case: align_corners=False, half_pixel_centers=True
TEST_P(Test_TensorFlow_layers, resize_bilinear_half_pixel)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("resize_bilinear", false, 0.0, 0.0, false, "_half_pixel");
}
// TF case: align_corners=False, half_pixel_centers=False
TEST_P(Test_TensorFlow_layers, resize_bilinear_factor)
{
runTensorFlowNet("resize_bilinear_factor"); runTensorFlowNet("resize_bilinear_factor");
}
// TF case: align_corners=False, half_pixel_centers=True
TEST_P(Test_TensorFlow_layers, resize_bilinear_factor_half_pixel)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTensorFlowNet("resize_bilinear_factor", false, 0.0, 0.0, false, "_half_pixel");
}
// TF case: align_corners=True, half_pixel_centers=False
TEST_P(Test_TensorFlow_layers, resize_bilinear_factor_align_corners)
{
runTensorFlowNet("resize_bilinear_factor", false, 0.0, 0.0, false, "_align_corners");
}
// TF case: align_corners=False, half_pixel_centers=False
TEST_P(Test_TensorFlow_layers, resize_bilinear_down)
{
runTensorFlowNet("resize_bilinear_down"); runTensorFlowNet("resize_bilinear_down");
} }