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dnn(onnx): fix Resize inputs handling

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This commit is contained in:
Alexander Alekhin 2021-12-23 01:59:34 +00:00
parent cc02fcd889
commit 51e65db715
1 changed files with 20 additions and 11 deletions
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31
modules/dnn/src/onnx/onnx_importer.cpp
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@ -2348,6 +2348,7 @@ void ONNXImporter::parseConcat(LayerParams& layerParams, const opencv_onnx::Node
addLayer(layerParams, node_proto); addLayer(layerParams, node_proto);
} }
// https://github.com/onnx/onnx/blob/master/docs/Operators.md#Resize
void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
{ {
for (int i = 1; i < node_proto.input_size(); i++) for (int i = 1; i < node_proto.input_size(); i++)
@ -2368,30 +2369,38 @@ void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::Node
if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch") if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")
layerParams.set("mode", "opencv_linear"); layerParams.set("mode", "opencv_linear");
// input = [X, scales], [X, roi, scales] or [x, roi, scales, sizes] // opset-10: input = [X, scales]
int foundScaleId = hasDynamicShapes ? node_proto.input_size() - 1 // opset-11: input = [X, roi, scales] or [x, roi, scales, sizes]
: node_proto.input_size() > 2 ? 2 : 1; int scalesInputId = node_proto.input_size() == 2 ? 1 : 2;
Mat scales = getBlob(node_proto, foundScaleId); Mat scales = getBlob(node_proto, scalesInputId);
if (scales.total() == 4) if (!scales.empty())
{ {
CV_CheckEQ(scales.total(), (size_t)4, "HCHW layout is expected");
layerParams.set("zoom_factor_y", scales.at<float>(2)); layerParams.set("zoom_factor_y", scales.at<float>(2));
layerParams.set("zoom_factor_x", scales.at<float>(3)); layerParams.set("zoom_factor_x", scales.at<float>(3));
} }
else else if (node_proto.input_size() >= 4) // opset-11
{ {
const std::string& inputLast = node_proto.input(node_proto.input_size() - 1); const std::string& inputSizes = node_proto.input(3);
if (constBlobs.find(inputLast) != constBlobs.end()) if (constBlobs.find(inputSizes) != constBlobs.end())
{ {
Mat shapes = getBlob(inputLast); Mat shapes = getBlob(inputSizes);
CV_CheckEQ(shapes.size[0], 4, ""); CV_CheckEQ(shapes.total(), (size_t)4, "HCHW layout is expected");
CV_CheckEQ(shapes.size[1], 1, "");
CV_CheckDepth(shapes.depth(), shapes.depth() == CV_32S || shapes.depth() == CV_32F, ""); CV_CheckDepth(shapes.depth(), shapes.depth() == CV_32S || shapes.depth() == CV_32F, "");
if (shapes.depth() == CV_32F) if (shapes.depth() == CV_32F)
shapes.convertTo(shapes, CV_32S); shapes.convertTo(shapes, CV_32S);
layerParams.set("width", shapes.at<int>(3)); layerParams.set("width", shapes.at<int>(3));
layerParams.set("height", shapes.at<int>(2)); layerParams.set("height", shapes.at<int>(2));
} }
else
{
CV_Error(Error::StsNotImplemented, cv::format("ONNX/Resize: doesn't support dynamic non-constant 'sizes' input: %s", inputSizes.c_str()));
}
}
else
{
CV_Error(Error::StsNotImplemented, "ONNX/Resize: can't find neither 'scale' nor destination sizes parameters");
} }
replaceLayerParam(layerParams, "mode", "interpolation"); replaceLayerParam(layerParams, "mode", "interpolation");
addLayer(layerParams, node_proto); addLayer(layerParams, node_proto);