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@ -64,6 +64,7 @@ public: |
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ONNXImporter(Net& net, const char *onnxFile) |
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: dstNet(net) |
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{ |
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hasDynamicShapes = false; |
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CV_Assert(onnxFile); |
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CV_LOG_DEBUG(NULL, "DNN/ONNX: processing ONNX model from file: " << onnxFile); |
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@ -84,6 +85,7 @@ public: |
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ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer) |
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: dstNet(net) |
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{ |
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hasDynamicShapes = false; |
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CV_LOG_DEBUG(NULL, "DNN/ONNX: processing in-memory ONNX model (" << sizeBuffer << " bytes)"); |
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struct _Buf : public std::streambuf |
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@ -115,6 +117,7 @@ protected: |
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std::map<std::string, Mat> constBlobs; |
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std::map<std::string, MatShape> outShapes; // List of internal blobs shapes.
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bool hasDynamicShapes; // Whether the model has inputs with dynamic shapes
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typedef std::map<std::string, MatShape>::iterator IterShape_t; |
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std::map<std::string, LayerInfo> layer_id; |
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@ -413,8 +416,10 @@ void ONNXImporter::populateNet() |
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for (int j = 0; j < inpShape.size(); ++j) |
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{ |
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inpShape[j] = tensorShape.dim(j).dim_value(); |
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if (!tensorShape.dim(j).dim_param().empty()) |
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hasDynamicShapes = true; |
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} |
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if (!inpShape.empty()) |
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if (!inpShape.empty() && !hasDynamicShapes) |
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{ |
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inpShape[0] = std::max(inpShape[0], 1); // It's OK to have undetermined batch size
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} |
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@ -461,6 +466,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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layerParams.name = name; |
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layerParams.type = layer_type; |
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layerParams.set("has_dynamic_shapes", hasDynamicShapes); |
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if (layer_type == "MaxPool") |
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{ |
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@ -551,11 +557,36 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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CV_Assert(axes.size() <= inpShape.size() - 2); |
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std::vector<int> kernel_size(inpShape.size() - 2, 1); |
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for (int i = 0; i < axes.size(); i++) { |
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int axis = clamp(axes.get<int>(i), inpShape.size()); |
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CV_Assert_N(axis >= 2 + i, axis < inpShape.size()); |
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kernel_size[axis - 2] = inpShape[axis]; |
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if (axes.size() == 1 && (clamp(axes.get<int>(0), inpShape.size()) <= 1)) |
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{ |
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int axis = clamp(axes.get<int>(0), inpShape.size()); |
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MatShape newShape = inpShape; |
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newShape[axis + 1] = total(newShape, axis + 1); |
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newShape.resize(axis + 2); |
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newShape.insert(newShape.begin(), 2 - axis, 1); |
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LayerParams reshapeLp; |
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reshapeLp.type = "Reshape"; |
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reshapeLp.name = layerParams.name + "/reshape"; |
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CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end()); |
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reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], newShape.size())); |
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node_proto.set_output(0, reshapeLp.name); |
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addLayer(reshapeLp, node_proto); |
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kernel_size.resize(2); |
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kernel_size[0] = inpShape[axis]; |
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node_proto.set_input(0, node_proto.output(0)); |
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} |
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else |
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{ |
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for (int i = 0; i < axes.size(); i++) { |
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int axis = clamp(axes.get<int>(i), inpShape.size()); |
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CV_Assert_N(axis >= 2 + i, axis < inpShape.size()); |
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kernel_size[axis - 2] = inpShape[axis]; |
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} |
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} |
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LayerParams poolLp = layerParams; |
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poolLp.name = layerParams.name + "/avg"; |
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CV_Assert(layer_id.find(poolLp.name) == layer_id.end()); |
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@ -1276,6 +1307,20 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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{ |
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layerParams.type = "Reshape"; |
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layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size())); |
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if (hasDynamicShapes) |
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{ |
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std::vector<int> dynamicAxes; |
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std::vector<int> inputIndices; |
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for (int index = 0; index < inpShape.size(); ++index) |
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{ |
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if (!maskedAxes[index]) |
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inputIndices.push_back(index); |
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} |
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for (int index = 0; index < outShape.size(); ++index) |
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dynamicAxes.push_back(index); |
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layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size())); |
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layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size())); |
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} |
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} |
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else |
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layerParams.type = "Identity"; |
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@ -1338,6 +1383,19 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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outShape.insert(outShape.begin() + axis, 1); |
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layerParams.type = "Reshape"; |
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layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size())); |
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if (hasDynamicShapes) |
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{ |
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std::vector<int> dynamicAxes; |
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std::vector<int> inputIndices; |
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for (int index = 0; index < outShape.size(); ++index) { |
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if (index != axis) |
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dynamicAxes.push_back(index); |
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} |
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for (int index = 0; index < inpShape.size(); ++index) |
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inputIndices.push_back(index); |
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layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size())); |
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layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size())); |
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} |
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} |
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else if (layer_type == "Expand") |
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{ |
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@ -1625,6 +1683,7 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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cv::dnn::DictValue paramEnd = cv::dnn::DictValue::arrayInt(end.data(), end.size()); |
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sliceLp.set("begin", paramBegin); |
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sliceLp.set("end", paramEnd); |
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sliceLp.set("has_dynamic_shapes", hasDynamicShapes); |
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if (inpShape.size() > 1) |
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{ |
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@ -1637,6 +1696,17 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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layerParams.type = "Reshape"; |
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layerParams.set("axis", 0); |
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layerParams.set("dim", DictValue::arrayInt(&inpShape[0], inpShape.size())); |
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if (hasDynamicShapes) |
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{ |
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std::vector<int> dynamicAxes; |
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std::vector<int> inputIndices; |
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for (int index = 0; index < inpShape.size(); ++index) |
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dynamicAxes.push_back(index); |
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for (int index = 0; index < inpShape.size(); ++index) |
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inputIndices.push_back(index); |
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layerParams.set("dynamic_axes", DictValue::arrayInt(dynamicAxes.data(), dynamicAxes.size())); |
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layerParams.set("input_indices", DictValue::arrayInt(inputIndices.data(), inputIndices.size())); |
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} |
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node_proto.set_input(0, sliceLp.name); |
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} |
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else |
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@ -1676,7 +1746,11 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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for (int i = 1; i < node_proto.input_size(); i++) |
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CV_Assert(layer_id.find(node_proto.input(i)) == layer_id.end()); |
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String interp_mode = layerParams.get<String>("coordinate_transformation_mode"); |
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String interp_mode; |
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if (layerParams.has("coordinate_transformation_mode")) |
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interp_mode = layerParams.get<String>("coordinate_transformation_mode"); |
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else |
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interp_mode = layerParams.get<String>("mode"); |
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CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn"); |
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layerParams.set("align_corners", interp_mode == "align_corners"); |
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@ -1688,16 +1762,23 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) |
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shapes.convertTo(shapes, CV_32S); |
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int height = shapes.at<int>(2); |
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int width = shapes.at<int>(3); |
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if (node_proto.input_size() == 3) |
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if (hasDynamicShapes) |
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{ |
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IterShape_t shapeIt = outShapes.find(node_proto.input(0)); |
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CV_Assert(shapeIt != outShapes.end()); |
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MatShape scales = shapeIt->second; |
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height *= scales[2]; |
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width *= scales[3]; |
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layerParams.set("zoom_factor_x", width); |
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layerParams.set("zoom_factor_y", height); |
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} |
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else |
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{ |
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if (node_proto.input_size() == 3) { |
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IterShape_t shapeIt = outShapes.find(node_proto.input(0)); |
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CV_Assert(shapeIt != outShapes.end()); |
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MatShape scales = shapeIt->second; |
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height *= scales[2]; |
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width *= scales[3]; |
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} |
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layerParams.set("width", width); |
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layerParams.set("height", height); |
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} |
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layerParams.set("width", width); |
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layerParams.set("height", height); |
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if (layerParams.get<String>("mode") == "linear") { |
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layerParams.set("mode", interp_mode == "pytorch_half_pixel" ? |
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Alexander Alekhin
4 years ago