Added ResizeBilinear op for tf (#11050)

* Added ResizeBilinear op for tf

Combined ResizeNearestNeighbor and ResizeBilinear layers into Resize (with an interpolation param).

Minor changes to tf_importer and resize layer to save some code lines

Minor changes in init.cpp

Minor changes in tf_importer.cpp

* Replaced implementation of a custom ResizeBilinear layer to all layers

* Use Mat::ptr. Replace interpolation flags

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -565,14 +565,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
     };
 
     /**
-     * @brief Resize input 4-dimensional blob by nearest neighbor strategy.
+     * @brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
      *
-     * Layer is used to support TensorFlow's resize_nearest_neighbor op.
+     * Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops.
      */
-    class CV_EXPORTS ResizeNearestNeighborLayer : public Layer
+    class CV_EXPORTS ResizeLayer : public Layer
     {
     public:
-        static Ptr<ResizeNearestNeighborLayer> create(const LayerParams& params);
+        static Ptr<ResizeLayer> create(const LayerParams& params);
     };
 
     class CV_EXPORTS ProposalLayer : public Layer

modules/dnn/perf/perf_net.cpp

@@ -236,6 +236,14 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
     processNet("dnn/yolov3.cfg", "dnn/yolov3.weights", "", inp / 255);
 }
 
+PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
+{
+    if (backend == DNN_BACKEND_HALIDE ||
+        backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+    processNet("dnn/frozen_east_text_detection.pb", "", "", Mat(cv::Size(320, 320), CV_32FC3));
+}
+
 const tuple<DNNBackend, DNNTarget> testCases[] = {
 #ifdef HAVE_HALIDE
     tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),

modules/dnn/src/darknet/darknet_io.cpp

@@ -395,9 +395,10 @@ namespace cv {
                 {
                     cv::dnn::LayerParams param;
                     param.name = "Upsample-name";
-                    param.type = "ResizeNearestNeighbor";
+                    param.type = "Resize";
 
                     param.set<int>("zoom_factor", scaleFactor);
+                    param.set<String>("interpolation", "nearest");
 
                     darknet::LayerParameter lp;
                     std::string layer_name = cv::format("upsample_%d", layer_id);

modules/dnn/src/init.cpp

@@ -83,7 +83,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(Concat,         ConcatLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Reshape,        ReshapeLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Flatten,        FlattenLayer);
-    CV_DNN_REGISTER_LAYER_CLASS(ResizeNearestNeighbor, ResizeNearestNeighborLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Resize,         ResizeLayer);
     CV_DNN_REGISTER_LAYER_CLASS(CropAndResize,  CropAndResizeLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(Convolution,    ConvolutionLayer);

modules/dnn/src/layers/crop_and_resize_layer.cpp

@@ -68,7 +68,7 @@ public:
             {
                 float input_y = top * (inpHeight - 1) + y * heightScale;
                 int y0 = static_cast<int>(input_y);
-                const float* inpData_row0 = (float*)inp.data + y0 * inpWidth;
+                const float* inpData_row0 = inp.ptr<float>(0, 0, y0);
                 const float* inpData_row1 = (y0 + 1 < inpHeight) ? (inpData_row0 + inpWidth) : inpData_row0;
                 for (int x = 0; x < outWidth; ++x)
                 {

modules/dnn/src/layers/resize_nearest_neighbor_layer.cpp → modules/dnn/src/layers/resize_layer.cpp

@@ -11,20 +11,31 @@
 
 namespace cv { namespace dnn {
 
-class ResizeNearestNeighborLayerImpl CV_FINAL : public ResizeNearestNeighborLayer
+class ResizeLayerImpl CV_FINAL : public ResizeLayer
 {
 public:
-    ResizeNearestNeighborLayerImpl(const LayerParams& params)
+    ResizeLayerImpl(const LayerParams& params)
     {
         setParamsFrom(params);
-        CV_Assert(params.has("width") && params.has("height") || params.has("zoom_factor"));
-        CV_Assert(!params.has("width") && !params.has("height") || !params.has("zoom_factor"));
         outWidth = params.get<float>("width", 0);
         outHeight = params.get<float>("height", 0);
-        zoomFactor = params.get<int>("zoom_factor", 1);
+        if (params.has("zoom_factor"))
+        {
+            CV_Assert(!params.has("zoom_factor_x") && !params.has("zoom_factor_y"));
+            zoomFactorWidth = zoomFactorHeight = params.get<int>("zoom_factor");
+        }
+        else if (params.has("zoom_factor_x") || params.has("zoom_factor_y"))
+        {
+            CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y"));
+            zoomFactorWidth = params.get<int>("zoom_factor_x");
+            zoomFactorHeight = params.get<int>("zoom_factor_y");
+        }
+        interpolation = params.get<String>("interpolation");
+        CV_Assert(interpolation == "nearest" || interpolation == "bilinear");
+
         alignCorners = params.get<bool>("align_corners", false);
         if (alignCorners)
-            CV_Error(Error::StsNotImplemented, "Nearest neighborhood resize with align_corners=true is not implemented");
+            CV_Error(Error::StsNotImplemented, "Resize with align_corners=true is not implemented");
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -34,8 +45,8 @@ public:
     {
         CV_Assert(inputs.size() == 1, inputs[0].size() == 4);
         outputs.resize(1, inputs[0]);
-        outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactor);
-        outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactor);
+        outputs[0][2] = outHeight > 0 ? outHeight : (outputs[0][2] * zoomFactorHeight);
+        outputs[0][3] = outWidth > 0 ? outWidth : (outputs[0][3] * zoomFactorWidth);
         // We can work in-place (do nothing) if input shape == output shape.
         return (outputs[0][2] == inputs[0][2]) && (outputs[0][3] == inputs[0][3]);
     }
@@ -43,7 +54,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine();
+               backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && interpolation == "nearest";
     }
 
     virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
@@ -73,6 +84,8 @@ public:
 
         Mat& inp = *inputs[0];
         Mat& out = outputs[0];
+        if (interpolation == "nearest")
+        {
             for (size_t n = 0; n < inputs[0]->size[0]; ++n)
             {
                 for (size_t ch = 0; ch < inputs[0]->size[1]; ++ch)
@@ -82,6 +95,51 @@ public:
                 }
             }
         }
+        else if (interpolation == "bilinear")
+        {
+            const int inpHeight = inp.size[2];
+            const int inpWidth = inp.size[3];
+            const int inpSpatialSize = inpHeight * inpWidth;
+            const int outSpatialSize = outHeight * outWidth;
+            const float heightScale = static_cast<float>(inpHeight) / (outHeight);
+            const float widthScale = static_cast<float>(inpWidth) / (outWidth);
+            const int numPlanes = inp.size[0] * inp.size[1];
+            CV_Assert(inp.isContinuous(), out.isContinuous());
+
+            Mat inpPlanes = inp.reshape(1, numPlanes * inpHeight);
+            Mat outPlanes = out.reshape(1, numPlanes * outHeight);
+            for (int y = 0; y < outHeight; ++y)
+            {
+                float input_y = y * heightScale;
+                int y0 = static_cast<int>(input_y);
+                const float* inpData_row0 = inpPlanes.ptr<float>(y0);
+                const float* inpData_row1 = inpPlanes.ptr<float>(std::min(y0 + 1, inpHeight - 1));
+                for (int x = 0; x < outWidth; ++x)
+                {
+                    float input_x = x * widthScale;
+                    int x0 = static_cast<int>(input_x);
+                    int x1 = std::min(x0 + 1, inpWidth - 1);
+
+                    float* outData = outPlanes.ptr<float>(y, x);
+                    const float* inpData_row0_c = inpData_row0;
+                    const float* inpData_row1_c = inpData_row1;
+                    for (int c = 0; c < numPlanes; ++c)
+                    {
+                        *outData = inpData_row0_c[x0] +
+                            (input_y - y0) * (inpData_row1_c[x0] - inpData_row0_c[x0]) +
+                            (input_x - x0) * (inpData_row0_c[x1] - inpData_row0_c[x0] +
+                            (input_y - y0) * (inpData_row1_c[x1] - inpData_row0_c[x1] - inpData_row1_c[x0] + inpData_row0_c[x0]));
+
+                        inpData_row0_c += inpSpatialSize;
+                        inpData_row1_c += inpSpatialSize;
+                        outData += outSpatialSize;
+                    }
+                }
+            }
+        }
+        else
+            CV_Error(Error::StsNotImplemented, "Unknown interpolation: " + interpolation);
+    }
 
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
@@ -103,14 +161,15 @@ public:
     }
 
 private:
-    int outWidth, outHeight, zoomFactor;
+    int outWidth, outHeight, zoomFactorWidth, zoomFactorHeight;
+    String interpolation;
     bool alignCorners;
 };
 
 
-Ptr<ResizeNearestNeighborLayer> ResizeNearestNeighborLayer::create(const LayerParams& params)
+Ptr<ResizeLayer> ResizeLayer::create(const LayerParams& params)
 {
-    return Ptr<ResizeNearestNeighborLayer>(new ResizeNearestNeighborLayerImpl(params));
+    return Ptr<ResizeLayer>(new ResizeLayerImpl(params));
 }
 
 }  // namespace dnn

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -1450,18 +1450,36 @@ void TFImporter::populateNet(Net dstNet)
             connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
             data_layouts[name] = DATA_LAYOUT_UNKNOWN;
         }
-        else if (type == "ResizeNearestNeighbor")
+        else if (type == "ResizeNearestNeighbor" || type == "ResizeBilinear")
+        {
+            if (layer.input_size() == 2)
             {
                 Mat outSize = getTensorContent(getConstBlob(layer, value_id, 1));
                 CV_Assert(outSize.type() == CV_32SC1, outSize.total() == 2);
-
                 layerParams.set("height", outSize.at<int>(0, 0));
                 layerParams.set("width", outSize.at<int>(0, 1));
+            }
+            else if (layer.input_size() == 3)
+            {
+                Mat factorHeight = getTensorContent(getConstBlob(layer, value_id, 1));
+                Mat factorWidth = getTensorContent(getConstBlob(layer, value_id, 2));
+                CV_Assert(factorHeight.type() == CV_32SC1, factorHeight.total() == 1,
+                          factorWidth.type() == CV_32SC1, factorWidth.total() == 1);
+                layerParams.set("zoom_factor_x", factorWidth.at<int>(0));
+                layerParams.set("zoom_factor_y", factorHeight.at<int>(0));
+            }
+            else
+                CV_Assert(layer.input_size() == 2 || layer.input_size() == 3);
+
+            if (type == "ResizeNearestNeighbor")
+                layerParams.set("interpolation", "nearest");
+            else
+                layerParams.set("interpolation", "bilinear");
 
             if (hasLayerAttr(layer, "align_corners"))
                 layerParams.set("align_corners", getLayerAttr(layer, "align_corners").b());
 
-            int id = dstNet.addLayer(name, "ResizeNearestNeighbor", layerParams);
+            int id = dstNet.addLayer(name, "Resize", layerParams);
             layer_id[name] = id;
 
             connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);

modules/dnn/test/test_tf_importer.cpp

@@ -317,6 +317,43 @@ TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
     normAssertDetections(ref, out, "", 0.9, 3.4e-3, 1e-2);
 }
 
+// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
+// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
+// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
+//                  sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
+//                 feed_dict={'input_images:0': inp})
+// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
+// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
+// np.save('east_text_detection.scores.npy', scores)
+// np.save('east_text_detection.geometry.npy', geometry)
+TEST_P(Test_TensorFlow_nets, EAST_text_detection)
+{
+    std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
+    std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
+    std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
+    std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);
+
+    Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
+
+    net.setPreferableTarget(GetParam());
+
+    Mat img = imread(imgPath);
+    Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
+    net.setInput(inp);
+
+    std::vector<Mat> outs;
+    std::vector<String> outNames(2);
+    outNames[0] = "feature_fusion/Conv_7/Sigmoid";
+    outNames[1] = "feature_fusion/concat_3";
+    net.forward(outs, outNames);
+
+    Mat scores = outs[0];
+    Mat geometry = outs[1];
+
+    normAssert(scores, blobFromNPY(refScoresPath), "scores");
+    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 1e-4, 3e-3);
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, availableDnnTargets());
 
 typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_fp16;
@@ -396,159 +433,10 @@ TEST(Test_TensorFlow, memory_read)
     runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
 }
 
-// Test a custom layer.
-class ResizeBilinearLayer CV_FINAL : public Layer
-{
-public:
-    ResizeBilinearLayer(const LayerParams &params) : Layer(params),
-        outWidth(0), outHeight(0), factorWidth(1), factorHeight(1)
-    {
-        CV_Assert(!params.get<bool>("align_corners", false));
-        CV_Assert(!blobs.empty());
-
-        for (size_t i = 0; i < blobs.size(); ++i)
-            CV_Assert(blobs[i].type() == CV_32SC1);
-
-        if (blobs.size() == 1)
-        {
-            CV_Assert(blobs[0].total() == 2);
-            outHeight = blobs[0].at<int>(0, 0);
-            outWidth = blobs[0].at<int>(0, 1);
-        }
-        else
-        {
-            CV_Assert(blobs.size() == 2, blobs[0].total() == 1, blobs[1].total() == 1);
-            factorHeight = blobs[0].at<int>(0, 0);
-            factorWidth = blobs[1].at<int>(0, 0);
-            outHeight = outWidth = 0;
-        }
-    }
-
-    static Ptr<Layer> create(LayerParams& params)
-    {
-        return Ptr<Layer>(new ResizeBilinearLayer(params));
-    }
-
-    virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
-                                 const int requiredOutputs,
-                                 std::vector<std::vector<int> > &outputs,
-                                 std::vector<std::vector<int> > &internals) const CV_OVERRIDE
-    {
-        std::vector<int> outShape(4);
-        outShape[0] = inputs[0][0];  // batch size
-        outShape[1] = inputs[0][1];  // number of channels
-        outShape[2] = outHeight != 0 ? outHeight : (inputs[0][2] * factorHeight);
-        outShape[3] = outWidth != 0 ? outWidth : (inputs[0][3] * factorWidth);
-        outputs.assign(1, outShape);
-        return false;
-    }
-
-    virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
-    {
-        if (!outWidth && !outHeight)
-        {
-            outHeight = outputs[0].size[2];
-            outWidth = outputs[0].size[3];
-        }
-    }
-
-    // This implementation is based on a reference implementation from
-    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
-    virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
-    {
-        Mat& inp = *inputs[0];
-        Mat& out = outputs[0];
-        const float* inpData = (float*)inp.data;
-        float* outData = (float*)out.data;
-
-        const int batchSize = inp.size[0];
-        const int numChannels = inp.size[1];
-        const int inpHeight = inp.size[2];
-        const int inpWidth = inp.size[3];
-
-        float heightScale = static_cast<float>(inpHeight) / outHeight;
-        float widthScale = static_cast<float>(inpWidth) / outWidth;
-        for (int b = 0; b < batchSize; ++b)
-        {
-            for (int y = 0; y < outHeight; ++y)
-            {
-                float input_y = y * heightScale;
-                int y0 = static_cast<int>(std::floor(input_y));
-                int y1 = std::min(y0 + 1, inpHeight - 1);
-                for (int x = 0; x < outWidth; ++x)
-                {
-                    float input_x = x * widthScale;
-                    int x0 = static_cast<int>(std::floor(input_x));
-                    int x1 = std::min(x0 + 1, inpWidth - 1);
-                    for (int c = 0; c < numChannels; ++c)
-                    {
-                        float interpolation =
-                            inpData[offset(inp.size, c, x0, y0, b)] * (1 - (input_y - y0)) * (1 - (input_x - x0)) +
-                            inpData[offset(inp.size, c, x0, y1, b)] * (input_y - y0) * (1 - (input_x - x0)) +
-                            inpData[offset(inp.size, c, x1, y0, b)] * (1 - (input_y - y0)) * (input_x - x0) +
-                            inpData[offset(inp.size, c, x1, y1, b)] * (input_y - y0) * (input_x - x0);
-                        outData[offset(out.size, c, x, y, b)] = interpolation;
-                    }
-                }
-            }
-        }
-    }
-
-    virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
-
-private:
-    static inline int offset(const MatSize& size, int c, int x, int y, int b)
-    {
-        return x + size[3] * (y + size[2] * (c + size[1] * b));
-    }
-
-    int outWidth, outHeight, factorWidth, factorHeight;
-};
-
 TEST(Test_TensorFlow, resize_bilinear)
 {
-    CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
     runTensorFlowNet("resize_bilinear");
     runTensorFlowNet("resize_bilinear_factor");
-    LayerFactory::unregisterLayer("ResizeBilinear");
-}
-
-// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
-// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
-// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
-//                  sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
-//                 feed_dict={'input_images:0': inp})
-// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
-// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
-// np.save('east_text_detection.scores.npy', scores)
-// np.save('east_text_detection.geometry.npy', geometry)
-TEST(Test_TensorFlow, EAST_text_detection)
-{
-    CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
-    std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
-    std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
-    std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
-    std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);
-
-    Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
-    net.setPreferableBackend(DNN_BACKEND_OPENCV);
-
-    Mat img = imread(imgPath);
-    Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
-    net.setInput(inp);
-
-    std::vector<Mat> outs;
-    std::vector<String> outNames(2);
-    outNames[0] = "feature_fusion/Conv_7/Sigmoid";
-    outNames[1] = "feature_fusion/concat_3";
-    net.forward(outs, outNames);
-
-    Mat scores = outs[0];
-    Mat geometry = outs[1];
-
-    normAssert(scores, blobFromNPY(refScoresPath), "scores");
-    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 1e-4, 3e-3);
-    LayerFactory::unregisterLayer("ResizeBilinear");
 }
 
 }

samples/dnn/text_detection.cpp

@@ -2,8 +2,6 @@
 #include <opencv2/highgui.hpp>
 #include <opencv2/dnn.hpp>
 
-#include "custom_layers.hpp"
-
 using namespace cv;
 using namespace cv::dnn;
 
@@ -38,9 +36,6 @@ int main(int argc, char** argv)
     CV_Assert(parser.has("model"));
     String model = parser.get<String>("model");
 
-    // Register a custom layer.
-    CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
-
     // Load network.
     Net net = readNet(model);