Merge pull request #10955 from pengli:dnn

modules/dnn/src/layers/convolution_layer.cpp

@@ -824,9 +824,6 @@ public:
         for (int i = 0; i < inputs.size(); ++i)
             CV_Assert(inputs[i].u != outputs[0].u);
 
-        if (padMode == "SAME")
-            return false;
-
         if (convolutionOp.empty())
         {
             OCL4DNNConvConfig config;

modules/dnn/src/ocl4dnn/include/ocl4dnn.hpp

@@ -285,6 +285,8 @@ class OCL4DNNConvSpatial
         int32_t width_;
         int32_t pad_h_;
         int32_t pad_w_;
+        int32_t pad_bottom_;
+        int32_t pad_right_;
         int32_t stride_h_;
         int32_t stride_w_;
         int32_t dilation_h_;

modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp

@@ -103,6 +103,12 @@ OCL4DNNConvSpatial<Dtype>::OCL4DNNConvSpatial(OCL4DNNConvConfig config)
     output_w_ = config.out_shape[dims - spatial_dims + 1];
     bottom_dim_ = channels_ * width_ * height_;
     top_dim_ = num_output_ * output_w_ * output_h_;
+    int Ph = (output_h_ - 1) * stride_h_ + (dilation_h_ * (kernel_h_ - 1) + 1) - height_;
+    int Pw = (output_w_ - 1) * stride_w_ + (dilation_w_ * (kernel_w_ - 1) + 1) - width_;
+    Ph = (Ph > 0) ? Ph : 0;
+    Pw = (Pw > 0) ? Pw : 0;
+    pad_right_  = (Pw + 1) / 2;
+    pad_bottom_ = (Ph + 1) / 2;
 
     cache_path_ = utils::getConfigurationParameterString("OPENCV_OCL4DNN_CONFIG_PATH", "");
     dwconv_ = (num_output_ == channels_ && channels_ == group_);
@@ -379,6 +385,8 @@ void OCL4DNNConvSpatial<Dtype>::setupKernel()
     {
         addDef("INPUT_PAD_W", pad_w_);
         addDef("INPUT_PAD_H", pad_h_);
+        addDef("INPUT_PAD_RIGHT", pad_right_);
+        addDef("INPUT_PAD_BOTTOM", pad_bottom_);
     }
 
     setupKernelDetails(kernelType_, blockM_, blockK_, blockN_);

modules/dnn/test/test_tf_importer.cpp

@@ -321,7 +321,7 @@ OCL_TEST(Test_TensorFlow, MobileNet_SSD)
     std::vector<Mat> output;
     net.forward(output, outNames);
 
-    normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1));
+    normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
     normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
     normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
 }