Merge pull request #18841 from JulienMaille:patch-2

Fixing dnn Resize layer for variable input size

* Fix onnx loading of resize/upsample layers for different opset

* group all DynamicResize tests

* cleaned up scales checks

* Simplify branching

modules/dnn/src/onnx/onnx_importer.cpp

@@ -1746,43 +1746,45 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
             for (int i = 1; i < node_proto.input_size(); i++)
                 CV_Assert(layer_id.find(node_proto.input(i)) == layer_id.end());
 
-            String interp_mode;
             if (layerParams.has("coordinate_transformation_mode"))
-                interp_mode = layerParams.get<String>("coordinate_transformation_mode");
-            else
-                interp_mode = layerParams.get<String>("mode");
-            CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn");
-
-            layerParams.set("align_corners", interp_mode == "align_corners");
-            Mat shapes = getBlob(node_proto, node_proto.input_size() - 1);
-            CV_CheckEQ(shapes.size[0], 4, "");
-            CV_CheckEQ(shapes.size[1], 1, "");
-            CV_CheckDepth(shapes.depth(), shapes.depth() == CV_32S || shapes.depth() == CV_32F, "");
-            if (shapes.depth() == CV_32F)
-                shapes.convertTo(shapes, CV_32S);
-            int height = shapes.at<int>(2);
-            int width  = shapes.at<int>(3);
-            if (hasDynamicShapes)
             {
-                layerParams.set("zoom_factor_x", width);
-                layerParams.set("zoom_factor_y", height);
+                String interp_mode = layerParams.get<String>("coordinate_transformation_mode");
+                CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn");
+
+                layerParams.set("align_corners", interp_mode == "align_corners");
+                if (layerParams.get<String>("mode") == "linear")
+                {
+                    layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
+                                            "opencv_linear" : "bilinear");
+                }
+            }
+            if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")
+                layerParams.set("mode", "opencv_linear");
+
+            // input = [X, scales], [X, roi, scales] or [x, roi, scales, sizes]
+            int foundScaleId = hasDynamicShapes ? node_proto.input_size() - 1
+                                                : node_proto.input_size() > 2 ? 2 : 1;
+
+            Mat scales = getBlob(node_proto, foundScaleId);
+            if (scales.total() == 4)
+            {
+                layerParams.set("zoom_factor_y", scales.at<float>(2));
+                layerParams.set("zoom_factor_x", scales.at<float>(3));
             }
             else
             {
-                if (node_proto.input_size() == 3) {
-                    IterShape_t shapeIt = outShapes.find(node_proto.input(0));
-                    CV_Assert(shapeIt != outShapes.end());
-                    MatShape scales = shapeIt->second;
-                    height *= scales[2];
-                    width *= scales[3];
+                const std::string& inputLast = node_proto.input(node_proto.input_size() - 1);
+                if (constBlobs.find(inputLast) != constBlobs.end())
+                {
+                    Mat shapes = getBlob(inputLast);
+                    CV_CheckEQ(shapes.size[0], 4, "");
+                    CV_CheckEQ(shapes.size[1], 1, "");
+                    CV_CheckDepth(shapes.depth(), shapes.depth() == CV_32S || shapes.depth() == CV_32F, "");
+                    if (shapes.depth() == CV_32F)
+                        shapes.convertTo(shapes, CV_32S);
+                    layerParams.set("width", shapes.at<int>(3));
+                    layerParams.set("height", shapes.at<int>(2));
                 }
-                layerParams.set("width", width);
-                layerParams.set("height", height);
-            }
-
-            if (layerParams.get<String>("mode") == "linear") {
-                layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
-                                        "opencv_linear" : "bilinear");
             }
             replaceLayerParam(layerParams, "mode", "interpolation");
         }
@@ -1822,10 +1824,14 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_)
             else
             {
                 // scales as input
-                Mat scales = getBlob(node_proto, 1);
-                CV_Assert(scales.total() == 4);
-                layerParams.set("zoom_factor_y", scales.at<float>(2));
-                layerParams.set("zoom_factor_x", scales.at<float>(3));
+                const std::string& input1 = node_proto.input(1);
+                if (constBlobs.find(input1) != constBlobs.end())
+                {
+                    Mat scales = getBlob(input1);
+                    CV_Assert(scales.total() == 4);
+                    layerParams.set("zoom_factor_y", scales.at<float>(2));
+                    layerParams.set("zoom_factor_x", scales.at<float>(3));
+                }
             }
             replaceLayerParam(layerParams, "mode", "interpolation");
         }

modules/dnn/test/test_onnx_importer.cpp

@@ -518,7 +518,12 @@ TEST_P(Test_ONNX_layers, Broadcast)
 
 TEST_P(Test_ONNX_layers, DynamicResize)
 {
-    testONNXModels("dynamic_resize", npy, 0, 0, false, true, 2);
+    testONNXModels("dynamic_resize_9", npy, 0, 0, false, true, 2);
+    testONNXModels("dynamic_resize_10", npy, 0, 0, false, true, 2);
+    testONNXModels("dynamic_resize_11", npy, 0, 0, false, true, 2);
+    testONNXModels("dynamic_resize_scale_9", npy, 0, 0, false, true, 2);
+    testONNXModels("dynamic_resize_scale_10", npy, 0, 0, false, true, 2);
+    testONNXModels("dynamic_resize_scale_11", npy, 0, 0, false, true, 2);
 }
 
 TEST_P(Test_ONNX_layers, Div)