Merge pull request #13989 from kohei-us:onnx-conv-transpose-output-shape

modules/dnn/src/onnx/onnx_importer.cpp

@@ -591,6 +591,37 @@ void ONNXImporter::populateNet(Net dstNet)
             }
             layerParams.set("num_output", layerParams.blobs[0].size[1] * layerParams.get<int>("group", 1));
             layerParams.set("bias_term", node_proto.input_size() == 3);
+
+            if (layerParams.has("output_shape"))
+            {
+                const DictValue& outShape = layerParams.get("output_shape");
+
+                if (outShape.size() != 4)
+                    CV_Error(Error::StsNotImplemented, "Output shape must have 4 elements.");
+
+                const int strideY = layerParams.get<int>("stride_h", 1);
+                const int strideX = layerParams.get<int>("stride_w", 1);
+                const int outH = outShape.getIntValue(2);
+                const int outW = outShape.getIntValue(3);
+
+                if (layerParams.get<String>("pad_mode") == "SAME")
+                {
+                    layerParams.set("adj_w", (outW - 1) % strideX);
+                    layerParams.set("adj_h", (outH - 1) % strideY);
+                }
+                else if (layerParams.get<String>("pad_mode") == "VALID")
+                {
+                    if (!layerParams.has("kernel_h") || !layerParams.has("kernel_w"))
+                        CV_Error(Error::StsNotImplemented,
+                                 "Required attributes 'kernel_h' and 'kernel_w' are not present.");
+
+                    int kernelH = layerParams.get<int>("kernel_h");
+                    int kernelW = layerParams.get<int>("kernel_w");
+
+                    layerParams.set("adj_w", (outW - kernelW) % strideX);
+                    layerParams.set("adj_h", (outH - kernelH) % strideY);
+                }
+            }
         }
         else if (layer_type == "Transpose")
         {

modules/dnn/test/test_onnx_importer.cpp

@@ -73,6 +73,7 @@ TEST_P(Test_ONNX_layers, Deconvolution)
     testONNXModels("deconvolution");
     testONNXModels("two_deconvolution");
     testONNXModels("deconvolution_group");
+    testONNXModels("deconvolution_output_shape");
 }
 
 TEST_P(Test_ONNX_layers, Dropout)