Merge pull request #12071 from l-bat/l-bat:onnx_parser
* Add Squeezenet support in ONNX * Add AlexNet support in ONNX * Add Googlenet support in ONNX * Add CaffeNet and RCNN support in ONNX * Add VGG16 and VGG16 with batch normalization support in ONNX * Add RCNN, ZFNet, ResNet18v1 and ResNet50v1 support in ONNX * Add ResNet101_DUC_HDC * Add Tiny Yolov2 * Add CNN_MNIST, MobileNetv2 and LResNet100 support in ONNX * Add ONNX models for emotion recognition * Add DenseNet121 support in ONNX * Add Inception v1 support in ONNX * Refactoring * Fix tests * Fix tests * Skip unstable test * Modify Reshape operationpull/12488/head
Lubov Batanina
7 years ago
committed by
Alexander Alekhin
parent
c331a214d0
commit
0c8590027f
10 changed files with 14229 additions and 4 deletions
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@ -0,0 +1,585 @@ |
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2018, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "../precomp.hpp" |
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#ifdef HAVE_PROTOBUF |
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#include <iostream> |
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#include <fstream> |
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#include <string> |
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#include <limits> |
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#include <algorithm> |
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#if defined(__GNUC__) && __GNUC__ >= 5 |
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#pragma GCC diagnostic push |
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#pragma GCC diagnostic ignored "-Wsuggest-override" |
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#endif |
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#include "opencv-onnx.pb.h" |
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#if defined(__GNUC__) && __GNUC__ >= 5 |
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#pragma GCC diagnostic pop |
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#endif |
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namespace cv { |
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namespace dnn { |
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CV__DNN_EXPERIMENTAL_NS_BEGIN |
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class ONNXImporter |
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{ |
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opencv_onnx::ModelProto model_proto; |
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struct LayerInfo { |
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int layerId; |
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int outputId; |
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LayerInfo(int _layerId, int _outputId) : layerId(_layerId), outputId(_outputId) {} |
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}; |
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std::map<std::string, Mat> getGraphTensors( |
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const opencv_onnx::GraphProto& graph_proto); |
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Mat getBlob(const opencv_onnx::NodeProto& node_proto, const std::map<std::string, Mat>& constBlobs, int index); |
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LayerParams getLayerParams(const opencv_onnx::NodeProto& node_proto); |
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bool isCeilMode(const LayerParams& layerParams); |
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public: |
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ONNXImporter(const char *onnxFile) |
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{ |
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std::fstream input(onnxFile, std::ios::in | std::ios::binary); |
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if (!model_proto.ParseFromIstream(&input)) |
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CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model"); |
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} |
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void populateNet(Net dstNet); |
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}; |
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inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, const String& newKey) |
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{ |
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if (layerParams.has(oldKey)) { |
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layerParams.set(newKey, layerParams.get(oldKey)); |
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layerParams.erase(oldKey); |
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} |
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} |
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void releaseONNXTensor(opencv_onnx::TensorProto& tensor_proto) |
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{ |
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if (!tensor_proto.raw_data().empty()) { |
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delete tensor_proto.release_raw_data(); |
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} |
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} |
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template<typename T1, typename T2> |
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void convertInt64ToInt32(const T1& src, T2& dst, int size) |
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{ |
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for (int i = 0; i < size; i++) { |
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if (src[i] < std::numeric_limits<int32_t>::min() || src[i] > std::numeric_limits<int32_t>::max()) { |
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CV_Error(Error::StsOutOfRange, "Input is out of OpenCV 32S range"); |
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} |
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dst[i] = saturate_cast<int32_t>(src[i]); |
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} |
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} |
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Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto) |
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{ |
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CV_Assert(!tensor_proto.raw_data().empty() || !tensor_proto.float_data().empty() |
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|| !tensor_proto.double_data().empty() || !tensor_proto.int64_data().empty()); |
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opencv_onnx::TensorProto_DataType datatype = tensor_proto.data_type(); |
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Mat blob; |
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std::vector<int> sizes; |
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for (int i = 0; i < tensor_proto.dims_size(); i++) { |
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sizes.push_back(tensor_proto.dims(i)); |
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} |
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if (datatype == opencv_onnx::TensorProto_DataType_FLOAT) { |
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if (!tensor_proto.float_data().empty()) { |
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const ::google::protobuf::RepeatedField<float> field = tensor_proto.float_data(); |
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Mat(sizes, CV_32FC1, (void*)field.data()).copyTo(blob); |
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} |
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else { |
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char* val = const_cast<char*>(tensor_proto.raw_data().c_str()); |
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Mat(sizes, CV_32FC1, val).copyTo(blob); |
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} |
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} |
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else if (datatype == opencv_onnx::TensorProto_DataType_DOUBLE) |
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{ |
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const ::google::protobuf::RepeatedField<double> field = tensor_proto.double_data(); |
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CV_Assert(!field.empty()); |
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Mat(sizes, CV_64FC1, (void*)field.data()).convertTo(blob, CV_32FC1); |
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} |
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else if (datatype == opencv_onnx::TensorProto_DataType_INT64) |
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{ |
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blob.create(sizes, CV_32SC1); |
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int32_t* dst = reinterpret_cast<int32_t*>(blob.data); |
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if (!tensor_proto.int64_data().empty()) { |
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::google::protobuf::RepeatedField< ::google::protobuf::int64> src = tensor_proto.int64_data(); |
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convertInt64ToInt32(src, dst, blob.total()); |
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} |
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else |
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{ |
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char* val = const_cast<char*>(tensor_proto.raw_data().c_str()); |
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int64_t* src = reinterpret_cast<int64_t*>(val); |
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convertInt64ToInt32(src, dst, blob.total()); |
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} |
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} |
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else |
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CV_Error(Error::StsUnsupportedFormat, "Unsupported data type: " + |
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opencv_onnx::TensorProto_DataType_Name(datatype)); |
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return blob; |
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} |
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std::map<std::string, Mat> ONNXImporter::getGraphTensors( |
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const opencv_onnx::GraphProto& graph_proto) |
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{ |
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opencv_onnx::TensorProto tensor_proto; |
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std::map<std::string, Mat> layers_weights; |
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for (int i = 0; i < graph_proto.initializer_size(); i++) |
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{ |
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tensor_proto = graph_proto.initializer(i); |
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Mat mat = getMatFromTensor(tensor_proto); |
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releaseONNXTensor(tensor_proto); |
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layers_weights.insert(std::make_pair(tensor_proto.name(), mat)); |
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} |
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return layers_weights; |
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} |
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LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_proto) |
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{ |
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LayerParams lp; |
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for(int i = 0; i < node_proto.attribute_size(); i++) |
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{ |
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opencv_onnx::AttributeProto attribute_proto = node_proto.attribute(i); |
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std::string attribute_name = attribute_proto.name(); |
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if(attribute_name == "kernel_shape") |
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{ |
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CV_Assert(attribute_proto.ints_size() == 2); |
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lp.set("kernel_h", saturate_cast<int32_t>(attribute_proto.ints(0))); |
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lp.set("kernel_w", saturate_cast<int32_t>(attribute_proto.ints(1))); |
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} |
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else if(attribute_name == "strides") |
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{ |
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CV_Assert(attribute_proto.ints_size() == 2); |
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lp.set("stride_h", saturate_cast<int32_t>(attribute_proto.ints(0))); |
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lp.set("stride_w", saturate_cast<int32_t>(attribute_proto.ints(1))); |
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} |
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else if(attribute_name == "pads") |
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{ |
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CV_Assert(attribute_proto.ints_size() == 4); |
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lp.set("pad_h", saturate_cast<int32_t>(attribute_proto.ints(0))); |
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lp.set("pad_w", saturate_cast<int32_t>(attribute_proto.ints(1))); |
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// push pad_b and pad_r for compute ceil_mode
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lp.set("pad_b", saturate_cast<int32_t>(attribute_proto.ints(2))); |
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lp.set("pad_r", saturate_cast<int32_t>(attribute_proto.ints(3))); |
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} |
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else if(attribute_name == "auto_pad") |
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{ |
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if (attribute_proto.s() == "SAME_UPPER" || attribute_proto.s() == "SAME_LOWER") { |
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lp.set("pad_mode", "SAME"); |
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} |
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else if (attribute_proto.s() == "VALID") { |
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lp.set("pad_mode", "VALID"); |
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} |
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} |
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else if(attribute_name == "dilations") |
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{ |
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CV_Assert(attribute_proto.ints_size() == 2); |
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lp.set("dilation_h", saturate_cast<int32_t>(attribute_proto.ints(0))); |
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lp.set("dilation_w", saturate_cast<int32_t>(attribute_proto.ints(1))); |
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} |
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else if (attribute_proto.has_i()) |
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{ |
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::google::protobuf::int64 src = attribute_proto.i(); |
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if (src < std::numeric_limits<int32_t>::min() || src > std::numeric_limits<int32_t>::max()) |
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CV_Error(Error::StsOutOfRange, "Input is out of OpenCV 32S range"); |
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else |
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lp.set(attribute_name, saturate_cast<int32_t>(src)); |
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} |
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else if (attribute_proto.has_f()) |
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{ |
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lp.set(attribute_name, attribute_proto.f()); |
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} |
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else if (attribute_proto.has_s()) |
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{ |
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lp.set(attribute_name, attribute_proto.s()); |
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} |
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else if (attribute_proto.floats_size() > 0) |
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{ |
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lp.set(attribute_name, DictValue::arrayReal( |
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(float*)attribute_proto.mutable_floats(), attribute_proto.floats_size())); |
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} |
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else if (attribute_proto.ints_size() > 0) |
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{ |
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const ::google::protobuf::RepeatedField< ::google::protobuf::int64> src = attribute_proto.ints(); |
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std::vector<int32_t> dst(attribute_proto.ints_size()); |
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convertInt64ToInt32(src, dst, attribute_proto.ints_size()); |
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lp.set(attribute_proto.name(), DictValue::arrayInt(&dst[0], attribute_proto.ints_size())); |
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} |
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else if (attribute_proto.has_t()) |
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{ |
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opencv_onnx::TensorProto tensor = attribute_proto.t(); |
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Mat blob = getMatFromTensor(tensor); |
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lp.blobs.push_back(blob); |
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} |
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else if (attribute_proto.has_g() || attribute_proto.strings_size() > 0 || |
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attribute_proto.tensors_size() > 0 || attribute_proto.graphs_size() > 0) |
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{ |
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CV_Error(Error::StsNotImplemented, "Unexpected attribute type"); |
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} |
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else |
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CV_Error(Error::StsNotImplemented, "Unsupported attribute type"); |
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} |
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return lp; |
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} |
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Mat ONNXImporter::getBlob(const opencv_onnx::NodeProto& node_proto, |
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const std::map<std::string, Mat>& constBlobs, int index) |
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{ |
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CV_Assert(index < node_proto.input_size()); |
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std::map<std::string, Mat>::const_iterator constBlob; |
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constBlob = constBlobs.find(node_proto.input(index)); |
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if (constBlob == constBlobs.end()) { |
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CV_Error(Error::StsObjectNotFound, |
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"Blob " + node_proto.input(index) + " not found in const blobs"); |
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} |
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return constBlob->second; |
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} |
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bool ONNXImporter::isCeilMode(const LayerParams& layerParams) { |
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if (!layerParams.has("pad_mode")) { |
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if (layerParams.has("pad_h")) { |
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return layerParams.get<int>("pad_h") != layerParams.get<int>("pad_b") || |
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layerParams.get<int>("pad_w") != layerParams.get<int>("pad_r"); |
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} |
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else |
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return false; // all pads == 0
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} |
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return true; |
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} |
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void ONNXImporter::populateNet(Net dstNet) |
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{ |
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CV_Assert(model_proto.has_graph()); |
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opencv_onnx::GraphProto graph_proto = model_proto.graph(); |
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std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto); |
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std::string framework_name; |
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if (model_proto.has_producer_name()) { |
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framework_name = model_proto.producer_name(); |
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} |
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// create map with network inputs (without const blobs)
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std::map<std::string, LayerInfo> layer_id; |
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std::map<std::string, LayerInfo>::iterator layerId; |
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// fill map: push layer name, layer id and output id
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std::vector<String> netInputs; |
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for (int j = 0; j < graph_proto.input_size(); j++) |
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{ |
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const std::string& name = graph_proto.input(j).name(); |
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if (constBlobs.find(name) == constBlobs.end()) { |
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netInputs.push_back(name); |
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layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1))); |
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} |
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} |
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dstNet.setInputsNames(netInputs); |
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int layersSize = graph_proto.node_size(); |
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LayerParams layerParams; |
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opencv_onnx::NodeProto node_proto; |
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for(int i = 0; i < layersSize; i++) |
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{ |
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node_proto = graph_proto.node(i); |
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layerParams = getLayerParams(node_proto); |
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CV_Assert(node_proto.output_size() >= 1); |
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layerParams.name = node_proto.output(0); |
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std::string layer_type = node_proto.op_type(); |
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layerParams.type = layer_type; |
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if (layer_type == "MaxPool") |
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{ |
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layerParams.type = "Pooling"; |
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layerParams.set("pool", "MAX"); |
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layerParams.set("ceil_mode", isCeilMode(layerParams)); |
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} |
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else if (layer_type == "AveragePool") |
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{ |
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layerParams.type = "Pooling"; |
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layerParams.set("pool", "AVE"); |
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layerParams.set("ceil_mode", isCeilMode(layerParams)); |
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layerParams.set("ave_pool_padded_area", framework_name == "pytorch"); |
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} |
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else if (layer_type == "GlobalAveragePool") |
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{ |
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layerParams.type = "Pooling"; |
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layerParams.set("pool", "AVE"); |
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layerParams.set("global_pooling", true); |
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} |
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else if (layer_type == "Add" || layer_type == "Sum") |
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{ |
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if (layer_id.find(node_proto.input(1)) == layer_id.end()) |
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{ |
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Mat blob = getBlob(node_proto, constBlobs, 1); |
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blob = blob.reshape(1, 1); |
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if (blob.total() == 1) { |
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layerParams.type = "Power"; |
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layerParams.set("shift", blob.at<float>(0)); |
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} |
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else { |
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layerParams.type = "Shift"; |
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layerParams.blobs.push_back(blob); |
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} |
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} |
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else { |
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layerParams.type = "Eltwise"; |
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} |
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} |
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else if (layer_type == "Sub") |
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{ |
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Mat blob = (-1.0f) * getBlob(node_proto, constBlobs, 1); |
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blob = blob.reshape(1, 1); |
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if (blob.total() == 1) { |
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layerParams.type = "Power"; |
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layerParams.set("shift", blob.at<float>(0)); |
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} |
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else { |
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layerParams.type = "Shift"; |
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layerParams.blobs.push_back(blob); |
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} |
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} |
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else if (layer_type == "Constant") |
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{ |
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CV_Assert(node_proto.input_size() == 0); |
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CV_Assert(layerParams.blobs.size() == 1); |
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constBlobs.insert(std::make_pair(layerParams.name, layerParams.blobs[0])); |
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continue; |
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} |
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else if (layer_type == "ImageScaler") |
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{ |
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const float scale = layerParams.has("scale") ? layerParams.get<float>("scale") : 1.0f; |
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layerParams.erase("scale"); |
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if (layerParams.has("bias")) |
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{ |
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layerParams.type = "Scale"; |
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layerParams.blobs.push_back( |
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Mat(Size(1, layerParams.get("bias").size()), CV_32FC1, scale)); |
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layerParams.set("bias_term", true); |
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Mat bias(1, layerParams.get("bias").size(), CV_32FC1); |
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for (int j = 0; j < bias.total(); j++) { |
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bias.at<float>(0, j) = layerParams.get("bias").getRealValue(j); |
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} |
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layerParams.blobs.push_back(bias); |
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layerParams.erase("bias"); |
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} |
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else { |
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layerParams.set("scale", scale); |
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layerParams.type = "Power"; |
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} |
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} |
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else if (layer_type == "LeakyRelu") |
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{ |
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layerParams.type = "ReLU"; |
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replaceLayerParam(layerParams, "alpha", "negative_slope"); |
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} |
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else if (layer_type == "LRN") |
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{ |
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replaceLayerParam(layerParams, "size", "local_size"); |
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} |
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else if (layer_type == "BatchNormalization") |
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{ |
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if (node_proto.input_size() != 5) |
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CV_Error(Error::StsNotImplemented, |
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"Expected input, scale, bias, mean and var"); |
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layerParams.type = "BatchNorm"; |
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replaceLayerParam(layerParams, "epsilon", "eps"); |
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replaceLayerParam(layerParams, "spatial", "use_global_stats"); |
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Mat meanData = getBlob(node_proto, constBlobs, 3); |
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Mat stdData = getBlob(node_proto, constBlobs, 4); |
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layerParams.blobs.push_back(meanData); |
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layerParams.blobs.push_back(stdData); |
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if (!node_proto.input(1).empty()) { |
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layerParams.set("has_weight", true); |
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layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 1)); // weightData
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} else { |
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layerParams.set("has_weight", false); |
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} |
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if (!node_proto.input(2).empty()) { |
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layerParams.set("has_bias", true); |
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layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 2)); // biasData
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} else { |
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layerParams.set("has_bias", false); |
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} |
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} |
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else if (layer_type == "Gemm") |
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{ |
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CV_Assert(node_proto.input_size() >= 2); |
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layerParams.type = "InnerProduct"; |
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Mat weights = getBlob(node_proto, constBlobs, 1); |
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int ind_num_out = 0; |
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if (layerParams.has("transB") && !layerParams.get<int>("transB")) { |
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transpose(weights, weights); |
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ind_num_out = 1; |
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} |
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layerParams.blobs.push_back(weights); |
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if (node_proto.input_size() == 3) { |
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Mat bias = getBlob(node_proto, constBlobs, 2); |
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layerParams.blobs.push_back(bias); |
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} |
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layerParams.set("num_output", layerParams.blobs[0].size[ind_num_out]); |
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layerParams.set("bias_term", node_proto.input_size() == 3); |
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} |
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else if (layer_type == "MatMul") |
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{ |
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CV_Assert(node_proto.input_size() == 2); |
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layerParams.type = "InnerProduct"; |
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Mat blob = getBlob(node_proto, constBlobs, 1); |
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layerParams.blobs.push_back(blob.t()); |
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layerParams.set("bias_term", false); |
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layerParams.set("num_output", layerParams.blobs[0].size[0]); |
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} |
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else if (layer_type == "Mul") |
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{ |
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CV_Assert(node_proto.input_size() == 2); |
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if (layer_id.find(node_proto.input(1)) == layer_id.end()) { |
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Mat blob = getBlob(node_proto, constBlobs, 1); |
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blob = blob.reshape(1, 1); |
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if (blob.total() == 1) { |
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layerParams.set("scale", blob.at<float>(0)); |
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layerParams.type = "Power"; |
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} |
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else { |
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layerParams.blobs.push_back(blob); |
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layerParams.type = "Scale"; |
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} |
||||
} |
||||
else { |
||||
layerParams.type = "Eltwise"; |
||||
layerParams.set("operation", "prod"); |
||||
} |
||||
} |
||||
else if (layer_type == "Conv") |
||||
{ |
||||
CV_Assert(node_proto.input_size() >= 2); |
||||
layerParams.type = "Convolution"; |
||||
for (int j = 1; j < node_proto.input_size(); j++) { |
||||
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j)); |
||||
} |
||||
layerParams.set("num_output", layerParams.blobs[0].size[0]); |
||||
layerParams.set("bias_term", node_proto.input_size() == 3); |
||||
} |
||||
else if (layer_type == "Unsqueeze") |
||||
{ |
||||
CV_Assert(node_proto.input_size() == 1); |
||||
Mat input = getBlob(node_proto, constBlobs, 0); |
||||
|
||||
DictValue axes = layerParams.get("axes"); |
||||
std::vector<int> dims; |
||||
for (int j = 0; j < input.dims; j++) { |
||||
dims.push_back(input.size[j]); |
||||
} |
||||
CV_Assert(axes.getIntValue(axes.size()-1) <= dims.size()); |
||||
for (int j = 0; j < axes.size(); j++) { |
||||
dims.insert(dims.begin() + axes.getIntValue(j), 1); |
||||
} |
||||
|
||||
Mat out = input.reshape(0, dims); |
||||
constBlobs.insert(std::make_pair(layerParams.name, out)); |
||||
continue; |
||||
} |
||||
else if (layer_type == "Reshape") |
||||
{ |
||||
CV_Assert(node_proto.input_size() == 2 || layerParams.has("shape")); |
||||
|
||||
if (node_proto.input_size() == 2) { |
||||
Mat blob = getBlob(node_proto, constBlobs, 1); |
||||
CV_Assert(blob.type() == CV_32SC1); |
||||
|
||||
if (layer_id.find(node_proto.input(0)) == layer_id.end()) { |
||||
Mat input = getBlob(node_proto, constBlobs, 0); |
||||
Mat out = input.reshape(0, static_cast<std::vector<int> >(blob)); |
||||
constBlobs.insert(std::make_pair(layerParams.name, out)); |
||||
continue; |
||||
} |
||||
layerParams.set("dim", DictValue::arrayInt<int*>( |
||||
blob.ptr<int>(), blob.total() )); |
||||
} |
||||
else { |
||||
DictValue shape = layerParams.get("shape"); |
||||
std::vector<int> dim; |
||||
for (int j = 0; j < shape.size(); j++) { |
||||
dim.push_back(shape.getIntValue(j)); |
||||
} |
||||
|
||||
if (layer_id.find(node_proto.input(0)) == layer_id.end()) { |
||||
Mat input = getBlob(node_proto, constBlobs, 0); |
||||
Mat out = input.reshape(0, dim); |
||||
constBlobs.insert(std::make_pair(layerParams.name, out)); |
||||
continue; |
||||
} |
||||
replaceLayerParam(layerParams, "shape", "dim"); |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
for (int j = 0; j < node_proto.input_size(); j++) { |
||||
if (layer_id.find(node_proto.input(j)) == layer_id.end()) |
||||
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j)); |
||||
} |
||||
} |
||||
|
||||
int id = dstNet.addLayer(layerParams.name, layerParams.type, layerParams); |
||||
layer_id.insert(std::make_pair(layerParams.name, LayerInfo(id, 0))); |
||||
|
||||
for (int j = 0; j < node_proto.input_size(); j++) { |
||||
layerId = layer_id.find(node_proto.input(j)); |
||||
|
||||
if (layerId != layer_id.end()) { |
||||
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, j); |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
Net readNetFromONNX(const String& onnxFile) |
||||
{ |
||||
ONNXImporter onnxImporter(onnxFile.c_str()); |
||||
Net net; |
||||
onnxImporter.populateNet(net); |
||||
return net; |
||||
} |
||||
|
||||
Mat readTensorFromONNX(const String& path) |
||||
{ |
||||
opencv_onnx::TensorProto tensor_proto = opencv_onnx::TensorProto(); |
||||
std::fstream input(path.c_str(), std::ios::in | std::ios::binary); |
||||
if (!tensor_proto.ParseFromIstream(&input)) { |
||||
CV_Error(Error::StsUnsupportedFormat, "Failed to parse data"); |
||||
} |
||||
Mat mat = getMatFromTensor(tensor_proto); |
||||
releaseONNXTensor(tensor_proto); |
||||
return mat; |
||||
} |
||||
|
||||
CV__DNN_EXPERIMENTAL_NS_END |
||||
}} // namespace
|
||||
|
||||
#endif |
||||
@ -0,0 +1,446 @@ |
||||
// |
||||
// WARNING: This file is automatically generated! Please edit onnx.in.proto. |
||||
// |
||||
|
||||
|
||||
// Copyright (c) Facebook Inc. and Microsoft Corporation. |
||||
// Licensed under the MIT license. |
||||
|
||||
syntax = "proto2"; |
||||
|
||||
package opencv_onnx; |
||||
|
||||
// Overview |
||||
// |
||||
// ONNX is an open specification that is comprised of the following components: |
||||
// |
||||
// 1) A definition of an extensible computation graph model. |
||||
// 2) Definitions of standard data types. |
||||
// 3) Definitions of built-in operators. |
||||
// |
||||
// This document describes the syntax of models and their computation graphs, |
||||
// as well as the standard data types. Together, they are referred to as the ONNX |
||||
// Intermediate Representation, or 'IR' for short. |
||||
// |
||||
// The normative semantic specification of the ONNX IR is found in docs/IR.md. |
||||
// Definitions of the built-in neural network operators may be found in docs/Operators.md. |
||||
|
||||
// Notes |
||||
// |
||||
// Release |
||||
// |
||||
// We are still in the very early stage of defining ONNX. The current |
||||
// version of ONNX is a starting point. While we are actively working |
||||
// towards a complete spec, we would like to get the community involved |
||||
// by sharing our working version of ONNX. |
||||
// |
||||
// Protobuf compatibility |
||||
// |
||||
// To simplify framework compatibility, ONNX is defined using the subset of protobuf |
||||
// that is compatible with both protobuf v2 and v3. This means that we do not use any |
||||
// protobuf features that are only available in one of the two versions. |
||||
// |
||||
// Here are the most notable contortions we have to carry out to work around |
||||
// these limitations: |
||||
// |
||||
// - No 'map' (added protobuf 3.0). We instead represent mappings as lists |
||||
// of key-value pairs, where order does not matter and duplicates |
||||
// are not allowed. |
||||
|
||||
|
||||
// Versioning |
||||
// |
||||
// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md |
||||
// |
||||
// To be compatible with both proto2 and proto3, we will use a version number |
||||
// that is not defined by the default value but an explicit enum number. |
||||
enum Version { |
||||
// proto3 requires the first enum value to be zero. |
||||
// We add this just to appease the compiler. |
||||
_START_VERSION = 0; |
||||
// The version field is always serialized and we will use it to store the |
||||
// version that the graph is generated from. This helps us set up version |
||||
// control. |
||||
// For the IR, we are using simple numbers starting with with 0x00000001, |
||||
// which was the version we published on Oct 10, 2017. |
||||
IR_VERSION_2017_10_10 = 0x0000000000000001; |
||||
|
||||
// IR_VERSION 2 published on Oct 30, 2017 |
||||
// - Added type discriminator to AttributeProto to support proto3 users |
||||
IR_VERSION_2017_10_30 = 0x0000000000000002; |
||||
|
||||
// IR VERSION 3 published on Nov 3, 2017 |
||||
// - For operator versioning: |
||||
// - Added new message OperatorSetIdProto |
||||
// - Added opset_import in ModelProto |
||||
// - For vendor extensions, added domain in NodeProto |
||||
IR_VERSION = 0x0000000000000003; |
||||
} |
||||
|
||||
// Attributes |
||||
// |
||||
// A named attribute containing either singular float, integer, string, graph, |
||||
// and tensor values, or repeated float, integer, string, graph, and tensor values. |
||||
// An AttributeProto MUST contain the name field, and *only one* of the |
||||
// following content fields, effectively enforcing a C/C++ union equivalent. |
||||
message AttributeProto { |
||||
|
||||
// Note: this enum is structurally identical to the OpSchema::AttrType |
||||
// enum defined in schema.h. If you rev one, you likely need to rev the other. |
||||
enum AttributeType { |
||||
UNDEFINED = 0; |
||||
FLOAT = 1; |
||||
INT = 2; |
||||
STRING = 3; |
||||
TENSOR = 4; |
||||
GRAPH = 5; |
||||
|
||||
FLOATS = 6; |
||||
INTS = 7; |
||||
STRINGS = 8; |
||||
TENSORS = 9; |
||||
GRAPHS = 10; |
||||
} |
||||
|
||||
// The name field MUST be present for this version of the IR. |
||||
optional string name = 1; // namespace Attribute |
||||
|
||||
// if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function. |
||||
// In this case, this AttributeProto does not contain data, and it's a reference of attribute |
||||
// in parent scope. |
||||
// NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph. |
||||
optional string ref_attr_name = 21; |
||||
|
||||
// A human-readable documentation for this attribute. Markdown is allowed. |
||||
optional string doc_string = 13; |
||||
|
||||
// The type field MUST be present for this version of the IR. |
||||
// For 0.0.1 versions of the IR, this field was not defined, and |
||||
// implementations needed to use has_field hueristics to determine |
||||
// which value field was in use. For IR_VERSION 0.0.2 or later, this |
||||
// field MUST be set and match the f|i|s|t|... field in use. This |
||||
// change was made to accomodate proto3 implementations. |
||||
optional AttributeType type = 20; // discriminator that indicates which field below is in use |
||||
|
||||
// Exactly ONE of the following fields must be present for this version of the IR |
||||
optional float f = 2; // float |
||||
optional int64 i = 3; // int |
||||
optional bytes s = 4; // UTF-8 string |
||||
optional TensorProto t = 5; // tensor value |
||||
optional GraphProto g = 6; // graph |
||||
// Do not use field below, it's deprecated. |
||||
// optional ValueProto v = 12; // value - subsumes everything but graph |
||||
|
||||
repeated float floats = 7; // list of floats |
||||
repeated int64 ints = 8; // list of ints |
||||
repeated bytes strings = 9; // list of UTF-8 strings |
||||
repeated TensorProto tensors = 10; // list of tensors |
||||
repeated GraphProto graphs = 11; // list of graph |
||||
} |
||||
|
||||
// Defines information on value, including the name, the type, and |
||||
// the shape of the value. |
||||
message ValueInfoProto { |
||||
// This field MUST be present in this version of the IR. |
||||
optional string name = 1; // namespace Value |
||||
// This field MUST be present in this version of the IR. |
||||
optional TypeProto type = 2; |
||||
// A human-readable documentation for this value. Markdown is allowed. |
||||
optional string doc_string = 3; |
||||
} |
||||
|
||||
// Nodes |
||||
// |
||||
// Computation graphs are made up of a DAG of nodes, which represent what is |
||||
// commonly called a "layer" or "pipeline stage" in machine learning frameworks. |
||||
// |
||||
// For example, it can be a node of type "Conv" that takes in an image, a filter |
||||
// tensor and a bias tensor, and produces the convolved output. |
||||
message NodeProto { |
||||
repeated string input = 1; // namespace Value |
||||
repeated string output = 2; // namespace Value |
||||
|
||||
// An optional identifier for this node in a graph. |
||||
// This field MAY be absent in ths version of the IR. |
||||
optional string name = 3; // namespace Node |
||||
|
||||
// The symbolic identifier of the Operator to execute. |
||||
optional string op_type = 4; // namespace Operator |
||||
// The domain of the OperatorSet that specifies the operator named by op_type. |
||||
optional string domain = 7; // namespace Domain |
||||
|
||||
// Additional named attributes. |
||||
repeated AttributeProto attribute = 5; |
||||
|
||||
// A human-readable documentation for this node. Markdown is allowed. |
||||
optional string doc_string = 6; |
||||
} |
||||
|
||||
// Models |
||||
// |
||||
// ModelProto is a top-level file/container format for bundling a ML model and |
||||
// associating its computation graph with metadata. |
||||
// |
||||
// The semantics of the model are described by the associated GraphProto. |
||||
message ModelProto { |
||||
// The version of the IR this model targets. See Version enum above. |
||||
// This field MUST be present. |
||||
optional int64 ir_version = 1; |
||||
|
||||
// The OperatorSets this model relies on. |
||||
// All ModelProtos MUST have at least one entry that |
||||
// specifies which version of the ONNX OperatorSet is |
||||
// being imported. |
||||
// |
||||
// All nodes in the ModelProto's graph will bind against the operator |
||||
// with the same-domain/same-op_type operator with the HIGHEST version |
||||
// in the referenced operator sets. |
||||
repeated OperatorSetIdProto opset_import = 8; |
||||
|
||||
// The name of the framework or tool used to generate this model. |
||||
// This field SHOULD be present to indicate which implementation/tool/framework |
||||
// emitted the model. |
||||
optional string producer_name = 2; |
||||
|
||||
// The version of the framework or tool used to generate this model. |
||||
// This field SHOULD be present to indicate which implementation/tool/framework |
||||
// emitted the model. |
||||
optional string producer_version = 3; |
||||
|
||||
// Domain name of the model. |
||||
// We use reverse domain names as name space indicators. For example: |
||||
// `com.facebook.fair` or `com.microsoft.cognitiveservices` |
||||
// |
||||
// Together with `model_version` and GraphProto.name, this forms the unique identity of |
||||
// the graph. |
||||
optional string domain = 4; |
||||
|
||||
// The version of the graph encoded. See Version enum below. |
||||
optional int64 model_version = 5; |
||||
|
||||
// A human-readable documentation for this model. Markdown is allowed. |
||||
optional string doc_string = 6; |
||||
|
||||
// The parameterized graph that is evaluated to execute the model. |
||||
optional GraphProto graph = 7; |
||||
|
||||
// Named metadata values; keys should be distinct. |
||||
repeated StringStringEntryProto metadata_props = 14; |
||||
}; |
||||
|
||||
// StringStringEntryProto follows the pattern for cross-proto-version maps. |
||||
// See https://developers.google.com/protocol-buffers/docs/proto3#maps |
||||
message StringStringEntryProto { |
||||
optional string key = 1; |
||||
optional string value= 2; |
||||
}; |
||||
|
||||
// Graphs |
||||
// |
||||
// A graph defines the computational logic of a model and is comprised of a parameterized |
||||
// list of nodes that form a directed acyclic graph based on their inputs and outputs. |
||||
// This is the equivalent of the "network" or "graph" in many deep learning |
||||
// frameworks. |
||||
message GraphProto { |
||||
// The nodes in the graph, sorted topologically. |
||||
repeated NodeProto node = 1; |
||||
|
||||
// The name of the graph. |
||||
optional string name = 2; // namespace Graph |
||||
|
||||
// A list of named tensor values, used to specify constant inputs of the graph. |
||||
// Each TensorProto entry must have a distinct name (within the list) that |
||||
// also appears in the input list. |
||||
repeated TensorProto initializer = 5; |
||||
|
||||
// A human-readable documentation for this graph. Markdown is allowed. |
||||
optional string doc_string = 10; |
||||
|
||||
// The inputs and outputs of the graph. |
||||
repeated ValueInfoProto input = 11; |
||||
repeated ValueInfoProto output = 12; |
||||
|
||||
// Information for the values in the graph. The ValueInfoProto.name's |
||||
// must be distinct. It is optional for a value to appear in value_info list. |
||||
repeated ValueInfoProto value_info = 13; |
||||
|
||||
// DO NOT USE the following fields, they were deprecated from earlier versions. |
||||
// repeated string input = 3; |
||||
// repeated string output = 4; |
||||
// optional int64 ir_version = 6; |
||||
// optional int64 producer_version = 7; |
||||
// optional string producer_tag = 8; |
||||
// optional string domain = 9; |
||||
} |
||||
|
||||
// Tensors |
||||
// |
||||
// A serialized tensor value. |
||||
message TensorProto { |
||||
enum DataType { |
||||
UNDEFINED = 0; |
||||
// Basic types. |
||||
FLOAT = 1; // float |
||||
UINT8 = 2; // uint8_t |
||||
INT8 = 3; // int8_t |
||||
UINT16 = 4; // uint16_t |
||||
INT16 = 5; // int16_t |
||||
INT32 = 6; // int32_t |
||||
INT64 = 7; // int64_t |
||||
STRING = 8; // string |
||||
BOOL = 9; // bool |
||||
|
||||
// Advanced types |
||||
FLOAT16 = 10; |
||||
DOUBLE = 11; |
||||
UINT32 = 12; |
||||
UINT64 = 13; |
||||
COMPLEX64 = 14; // complex with float32 real and imaginary components |
||||
COMPLEX128 = 15; // complex with float64 real and imaginary components |
||||
// Future extensions go here. |
||||
} |
||||
|
||||
// The shape of the tensor. |
||||
repeated int64 dims = 1; |
||||
|
||||
// The data type of the tensor. |
||||
optional DataType data_type = 2; |
||||
|
||||
// For very large tensors, we may want to store them in chunks, in which |
||||
// case the following fields will specify the segment that is stored in |
||||
// the current TensorProto. |
||||
message Segment { |
||||
optional int64 begin = 1; |
||||
optional int64 end = 2; |
||||
} |
||||
optional Segment segment = 3; |
||||
|
||||
// Tensor content must be organized in row-major order. |
||||
// |
||||
// Depending on the data_type field, exactly one of the fields below with |
||||
// name ending in _data is used to store the elements of the tensor. |
||||
|
||||
// For float and complex64 values |
||||
// Complex64 tensors are encoded as a single array of floats, |
||||
// with the real components appearing in odd numbered positions, |
||||
// and the corresponding imaginary component apparing in the |
||||
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] |
||||
// is encoded as [1.0, 2.0 ,3.0 ,4.0] |
||||
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64. |
||||
repeated float float_data = 4 [packed = true]; |
||||
|
||||
// For int32, uint8, int8, uint16, int16, bool, and float16 values |
||||
// float16 values must be bit-wise converted to an uint16_t prior |
||||
// to writing to the buffer. |
||||
// When this field is present, the data_type field MUST be |
||||
// INT32, INT16, INT8, UINT16, INT8, BOOL, or FLOAT16 |
||||
repeated int32 int32_data = 5 [packed = true]; |
||||
|
||||
// For strings. |
||||
// Each element of string_data is a UTF-8 encoded Unicode |
||||
// string. No trailing null, no leading BOM. The protobuf "string" |
||||
// scalar type is not used to match ML community conventions. |
||||
// When this field is present, the data_type field MUST be STRING |
||||
repeated bytes string_data = 6; |
||||
|
||||
// For int64. |
||||
// When this field is present, the data_type field MUST be INT64 |
||||
repeated int64 int64_data = 7 [packed = true]; |
||||
|
||||
// Optionally, a name for the tensor. |
||||
optional string name = 8; // namespace Value |
||||
|
||||
// A human-readable documentation for this tensor. Markdown is allowed. |
||||
optional string doc_string = 12; |
||||
|
||||
// Serializations can either use one of the fields above, or use this |
||||
// raw bytes field. The only exception is the string case, where one is |
||||
// required to store the content in the repeated bytes string_data field. |
||||
// |
||||
// When this raw_data field is used to store tensor value, elements MUST |
||||
// be stored in as fixed-width, little-endian order. |
||||
// Floating-point data types MUST be stored in IEEE 754 format. |
||||
// Complex64 elements must be written as two consecutive FLOAT values, real component first. |
||||
// Complex128 elements must be written as two consecutive DOUBLE values, real component first. |
||||
// Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). |
||||
// |
||||
// Note: the advantage of specific field rather than the raw_data field is |
||||
// that in some cases (e.g. int data), protobuf does a better packing via |
||||
// variable length storage, and may lead to smaller binary footprint. |
||||
// When this field is present, the data_type field MUST NOT be STRING or UNDEFINED |
||||
optional bytes raw_data = 9; |
||||
|
||||
// For double |
||||
// Complex64 tensors are encoded as a single array of doubles, |
||||
// with the real components appearing in odd numbered positions, |
||||
// and the corresponding imaginary component apparing in the |
||||
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] |
||||
// is encoded as [1.0, 2.0 ,3.0 ,4.0] |
||||
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128 |
||||
repeated double double_data = 10 [packed = true]; |
||||
|
||||
// For uint64 and uint32 values |
||||
// When this field is present, the data_type field MUST be |
||||
// UINT32 or UINT64 |
||||
repeated uint64 uint64_data = 11 [packed = true]; |
||||
} |
||||
|
||||
// Defines a tensor shape. A dimension can be either an integer value |
||||
// or a symbolic variable. A symbolic variable represents an unknown |
||||
// dimension. |
||||
message TensorShapeProto { |
||||
message Dimension { |
||||
oneof value { |
||||
int64 dim_value = 1; |
||||
string dim_param = 2; // namespace Shape |
||||
}; |
||||
// Standard denotation can optionally be used to denote tensor |
||||
// dimensions with standard semantic descriptions to ensure |
||||
// that operations are applied to the correct axis of a tensor. |
||||
// Refer to https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition |
||||
// for pre-defined dimension denotations. |
||||
optional string denotation = 3; |
||||
}; |
||||
repeated Dimension dim = 1; |
||||
} |
||||
|
||||
// Types |
||||
// |
||||
// The standard ONNX data types. |
||||
message TypeProto { |
||||
|
||||
message Tensor { |
||||
// This field MUST NOT have the value of UNDEFINED |
||||
// This field MUST be present for this version of the IR. |
||||
optional TensorProto.DataType elem_type = 1; |
||||
optional TensorShapeProto shape = 2; |
||||
} |
||||
|
||||
|
||||
oneof value { |
||||
// The type of a tensor. |
||||
Tensor tensor_type = 1; |
||||
|
||||
} |
||||
|
||||
// An optional denotation can be used to denote the whole |
||||
// type with a standard semantic description as to what is |
||||
// stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition |
||||
// for pre-defined type denotations. |
||||
optional string denotation = 6; |
||||
} |
||||
|
||||
// Operator Sets |
||||
// |
||||
// OperatorSets are uniquely identified by a (domain, opset_version) pair. |
||||
message OperatorSetIdProto { |
||||
// The domain of the operator set being identified. |
||||
// The empty string ("") or absence of this field implies the operator |
||||
// set that is defined as part of the ONNX specification. |
||||
// This field MUST be present in this version of the IR when referring to any other operator set. |
||||
optional string domain = 1; |
||||
|
||||
// The version of the operator set being identified. |
||||
// This field MUST be present in this version of the IR. |
||||
optional int64 version = 2; |
||||
} |
||||
@ -0,0 +1,344 @@ |
||||
// This file is part of OpenCV project.
|
||||
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
||||
// of this distribution and at http://opencv.org/license.html.
|
||||
|
||||
// Copyright (C) 2018, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
|
||||
|
||||
#include "test_precomp.hpp" |
||||
#include "npy_blob.hpp" |
||||
#include <opencv2/dnn/shape_utils.hpp> |
||||
|
||||
namespace opencv_test { namespace { |
||||
|
||||
template<typename TString> |
||||
static std::string _tf(TString filename) |
||||
{ |
||||
String rootFolder = "dnn/onnx/"; |
||||
return findDataFile(rootFolder + filename, false); |
||||
} |
||||
|
||||
class Test_ONNX_layers : public DNNTestLayer |
||||
{ |
||||
public: |
||||
enum Extension |
||||
{ |
||||
npy, |
||||
pb |
||||
}; |
||||
|
||||
void testONNXModels(const String& basename, const Extension ext = npy, const double l1 = 0, const float lInf = 0) |
||||
{ |
||||
String onnxmodel = _tf("models/" + basename + ".onnx"); |
||||
Mat inp, ref; |
||||
if (ext == npy) { |
||||
inp = blobFromNPY(_tf("data/input_" + basename + ".npy")); |
||||
ref = blobFromNPY(_tf("data/output_" + basename + ".npy")); |
||||
} |
||||
else if (ext == pb) { |
||||
inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb")); |
||||
ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb")); |
||||
} |
||||
else |
||||
CV_Error(Error::StsUnsupportedFormat, "Unsupported extension"); |
||||
|
||||
checkBackend(&inp, &ref); |
||||
Net net = readNetFromONNX(onnxmodel); |
||||
ASSERT_FALSE(net.empty()); |
||||
|
||||
net.setPreferableBackend(backend); |
||||
net.setPreferableTarget(target); |
||||
|
||||
net.setInput(inp); |
||||
Mat out = net.forward(); |
||||
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf); |
||||
} |
||||
}; |
||||
|
||||
TEST_P(Test_ONNX_layers, MaxPooling) |
||||
{ |
||||
testONNXModels("maxpooling"); |
||||
testONNXModels("two_maxpooling"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, Convolution) |
||||
{ |
||||
testONNXModels("convolution"); |
||||
testONNXModels("two_convolution"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, Dropout) |
||||
{ |
||||
testONNXModels("dropout"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, Linear) |
||||
{ |
||||
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
||||
throw SkipTestException(""); |
||||
testONNXModels("linear"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, ReLU) |
||||
{ |
||||
testONNXModels("ReLU"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid) |
||||
{ |
||||
testONNXModels("maxpooling_sigmoid"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, Concatenation) |
||||
{ |
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
||||
(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD)) |
||||
throw SkipTestException(""); |
||||
testONNXModels("concatenation"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, AveragePooling) |
||||
{ |
||||
testONNXModels("average_pooling"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, BatchNormalization) |
||||
{ |
||||
testONNXModels("batch_norm"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, Multiplication) |
||||
{ |
||||
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16 || |
||||
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) |
||||
throw SkipTestException(""); |
||||
testONNXModels("mul"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, Constant) |
||||
{ |
||||
testONNXModels("constant"); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_layers, MultyInputs) |
||||
{ |
||||
const String model = _tf("models/multy_inputs.onnx"); |
||||
|
||||
Net net = readNetFromONNX(model); |
||||
ASSERT_FALSE(net.empty()); |
||||
|
||||
net.setPreferableBackend(backend); |
||||
net.setPreferableTarget(target); |
||||
|
||||
Mat inp1 = blobFromNPY(_tf("data/input_multy_inputs_0.npy")); |
||||
Mat inp2 = blobFromNPY(_tf("data/input_multy_inputs_1.npy")); |
||||
Mat ref = blobFromNPY(_tf("data/output_multy_inputs.npy")); |
||||
checkBackend(&inp1, &ref); |
||||
|
||||
net.setInput(inp1, "0"); |
||||
net.setInput(inp2, "1"); |
||||
Mat out = net.forward(); |
||||
|
||||
normAssert(ref, out, "", default_l1, default_lInf); |
||||
} |
||||
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets()); |
||||
|
||||
class Test_ONNX_nets : public Test_ONNX_layers {}; |
||||
TEST_P(Test_ONNX_nets, Alexnet) |
||||
{ |
||||
const String model = _tf("models/alexnet.onnx"); |
||||
|
||||
Net net = readNetFromONNX(model); |
||||
ASSERT_FALSE(net.empty()); |
||||
|
||||
net.setPreferableBackend(backend); |
||||
net.setPreferableTarget(target); |
||||
|
||||
Mat inp = imread(_tf("../grace_hopper_227.png")); |
||||
Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy")); |
||||
checkBackend(&inp, &ref); |
||||
|
||||
net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false)); |
||||
ASSERT_FALSE(net.empty()); |
||||
Mat out = net.forward(); |
||||
|
||||
normAssert(out, ref, "", default_l1, default_lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, Squeezenet) |
||||
{ |
||||
testONNXModels("squeezenet", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, Googlenet) |
||||
{ |
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE) |
||||
throw SkipTestException(""); |
||||
|
||||
const String model = _tf("models/googlenet.onnx"); |
||||
|
||||
Net net = readNetFromONNX(model); |
||||
ASSERT_FALSE(net.empty()); |
||||
|
||||
net.setPreferableBackend(backend); |
||||
net.setPreferableTarget(target); |
||||
|
||||
std::vector<Mat> images; |
||||
images.push_back( imread(_tf("../googlenet_0.png")) ); |
||||
images.push_back( imread(_tf("../googlenet_1.png")) ); |
||||
Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false); |
||||
Mat ref = blobFromNPY(_tf("../googlenet_prob.npy")); |
||||
checkBackend(&inp, &ref); |
||||
|
||||
net.setInput(inp); |
||||
ASSERT_FALSE(net.empty()); |
||||
Mat out = net.forward(); |
||||
|
||||
normAssert(ref, out, "", default_l1, default_lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, CaffeNet) |
||||
{ |
||||
testONNXModels("caffenet", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, RCNN_ILSVRC13) |
||||
{ |
||||
testONNXModels("rcnn_ilsvrc13", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, VGG16) |
||||
{ |
||||
double l1 = default_l1; |
||||
double lInf = default_lInf; |
||||
// output range: [-69; 72]
|
||||
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { |
||||
l1 = 0.087; |
||||
lInf = 0.585; |
||||
} |
||||
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) { |
||||
lInf = 1.2e-4; |
||||
} |
||||
testONNXModels("vgg16", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, VGG16_bn) |
||||
{ |
||||
double l1 = default_l1; |
||||
double lInf = default_lInf; |
||||
// output range: [-16; 27]
|
||||
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) { |
||||
l1 = 0.0086; |
||||
lInf = 0.037; |
||||
} |
||||
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
||||
(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)) { |
||||
l1 = 0.031; |
||||
lInf = 0.2; |
||||
} |
||||
testONNXModels("vgg16-bn", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, ZFNet) |
||||
{ |
||||
testONNXModels("zfnet512", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, ResNet18v1) |
||||
{ |
||||
// output range: [-16; 22]
|
||||
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.022 : default_l1; |
||||
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.12 : default_lInf; |
||||
testONNXModels("resnet18v1", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, ResNet50v1) |
||||
{ |
||||
// output range: [-67; 75]
|
||||
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.6 : 1.25e-5; |
||||
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.51 : 1.2e-4; |
||||
testONNXModels("resnet50v1", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC) |
||||
{ |
||||
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL |
||||
|| target == DNN_TARGET_MYRIAD) { |
||||
throw SkipTestException(""); |
||||
} |
||||
testONNXModels("resnet101_duc_hdc", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, TinyYolov2) |
||||
{ |
||||
if (cvtest::skipUnstableTests || |
||||
backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) { |
||||
throw SkipTestException(""); |
||||
} |
||||
// output range: [-11; 8]
|
||||
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.017 : default_l1; |
||||
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.14 : default_lInf; |
||||
testONNXModels("tiny_yolo2", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, CNN_MNIST) |
||||
{ |
||||
// output range: [-1952; 6574]
|
||||
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 3.82 : 4.3e-4; |
||||
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 13.5 : 1e-3; |
||||
|
||||
testONNXModels("cnn_mnist", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, MobileNet_v2) |
||||
{ |
||||
// output range: [-166; 317]
|
||||
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.38 : 7e-5; |
||||
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2.87 : 5e-4; |
||||
testONNXModels("mobilenetv2", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, LResNet100E_IR) |
||||
{ |
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE && |
||||
(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD)) |
||||
throw SkipTestException(""); |
||||
|
||||
double l1 = default_l1; |
||||
double lInf = default_lInf; |
||||
// output range: [-3; 3]
|
||||
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) { |
||||
l1 = 0.009; |
||||
lInf = 0.035; |
||||
} |
||||
testONNXModels("LResNet100E_IR", pb, l1, lInf); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, Emotion_ferplus) |
||||
{ |
||||
testONNXModels("emotion_ferplus", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, Inception_v2) |
||||
{ |
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE) |
||||
throw SkipTestException(""); |
||||
|
||||
testONNXModels("inception_v2", pb); |
||||
} |
||||
|
||||
TEST_P(Test_ONNX_nets, DenseNet121) |
||||
{ |
||||
// output range: [-87; 138]
|
||||
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.12 : 1.88e-5; |
||||
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.74 : 1.23e-4; |
||||
testONNXModels("densenet121", pb, l1, lInf); |
||||
} |
||||
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets()); |
||||
|
||||
}} // namespace
|
||||
Loading…
Reference in new issue