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opencv/modules/dnn/src/onnx/onnx_importer.cpp

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// 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 "../precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_PROTOBUF
#include <iostream>
#include <fstream>
#include <string>
#include <limits>
#include <algorithm>
#if defined(__GNUC__) && __GNUC__ >= 5
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wsuggest-override"
#endif
#include "opencv-onnx.pb.h"
#if defined(__GNUC__) && __GNUC__ >= 5
#pragma GCC diagnostic pop
#endif
#include "onnx_graph_simplifier.hpp"
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
class ONNXImporter
{
opencv_onnx::ModelProto model_proto;
struct LayerInfo {
int layerId;
int outputId;
LayerInfo(int _layerId = 0, int _outputId = 0) : layerId(_layerId), outputId(_outputId) {}
};
std::map<std::string, Mat> getGraphTensors(
const opencv_onnx::GraphProto& graph_proto);
Mat getBlob(const opencv_onnx::NodeProto& node_proto, const std::map<std::string, Mat>& constBlobs, int index);
LayerParams getLayerParams(const opencv_onnx::NodeProto& node_proto);
bool isCeilMode(const LayerParams& layerParams);
void addLayer(Net& dstNet, LayerParams& layerParams,
const opencv_onnx::NodeProto& node_proto,
std::map<std::string, LayerInfo>& layer_id,
std::map<std::string, MatShape>& outShapes);
public:
ONNXImporter(const char *onnxFile)
{
std::fstream input(onnxFile, std::ios::in | std::ios::binary);
if (!model_proto.ParseFromIstream(&input))
CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model");
}
ONNXImporter(const char* buffer, size_t sizeBuffer)
{
struct _Buf : public std::streambuf
{
_Buf(const char* buffer, size_t sizeBuffer)
{
char* p = const_cast<char*>(buffer);
setg(p, p, p + sizeBuffer);
}
};
_Buf buf(buffer, sizeBuffer);
std::istream input(&buf);
if (!model_proto.ParseFromIstream(&input))
CV_Error(Error::StsUnsupportedFormat, "Failed to parse onnx model from in-memory byte array.");
}
void populateNet(Net dstNet);
};
inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, const String& newKey)
{
if (layerParams.has(oldKey)) {
layerParams.set(newKey, layerParams.get(oldKey));
layerParams.erase(oldKey);
}
}
void releaseONNXTensor(opencv_onnx::TensorProto& tensor_proto)
{
if (!tensor_proto.raw_data().empty()) {
delete tensor_proto.release_raw_data();
}
}
void runLayer(LayerParams& params, const std::vector<Mat>& inputs,
std::vector<Mat>& outputs)
{
Ptr<Layer> layer = LayerFactory::createLayerInstance(params.type, params);
CV_Assert((bool)layer);
std::vector<MatShape> inpShapes(inputs.size());
int ddepth = CV_32F;
for (size_t i = 0; i < inputs.size(); ++i)
{
inpShapes[i] = shape(inputs[i]);
if (i > 0 && ddepth != inputs[i].depth())
CV_Error(Error::StsNotImplemented, "Mixed input data types.");
ddepth = inputs[i].depth();
}
std::vector<MatShape> outShapes, internalShapes;
layer->getMemoryShapes(inpShapes, 0, outShapes, internalShapes);
std::vector<Mat> internals(internalShapes.size());
outputs.resize(outShapes.size());
for (size_t i = 0; i < outShapes.size(); ++i)
outputs[i].create(outShapes[i], ddepth);
for (size_t i = 0; i < internalShapes.size(); ++i)
internals[i].create(internalShapes[i], ddepth);
layer->finalize(inputs, outputs);
layer->forward(inputs, outputs, internals);
}
std::map<std::string, Mat> ONNXImporter::getGraphTensors(
const opencv_onnx::GraphProto& graph_proto)
{
opencv_onnx::TensorProto tensor_proto;
std::map<std::string, Mat> layers_weights;
for (int i = 0; i < graph_proto.initializer_size(); i++)
{
tensor_proto = graph_proto.initializer(i);
Mat mat = getMatFromTensor(tensor_proto);
releaseONNXTensor(tensor_proto);
layers_weights.insert(std::make_pair(tensor_proto.name(), mat));
}
return layers_weights;
}
static DictValue parse(const ::google::protobuf::RepeatedField< ::google::protobuf::int64>& src) {
std::vector<int32_t> dst(src.size());
convertInt64ToInt32(src, dst, src.size());
return DictValue::arrayInt(&dst[0], src.size());
}
LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_proto)
{
LayerParams lp;
for(int i = 0; i < node_proto.attribute_size(); i++)
{
opencv_onnx::AttributeProto attribute_proto = node_proto.attribute(i);
std::string attribute_name = attribute_proto.name();
if(attribute_name == "kernel_shape")
{
CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
lp.set("kernel_size", parse(attribute_proto.ints()));
}
else if(attribute_name == "strides")
{
CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
lp.set("stride", parse(attribute_proto.ints()));
}
else if(attribute_name == "pads")
{
if (node_proto.op_type() == "Pad")
{
// Padding layer.
// Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
// We need to shuffle it to begin0, end0, begin1, end1, ...
CV_Assert(attribute_proto.ints_size() % 2 == 0);
const int dims = attribute_proto.ints_size() / 2;
std::vector<int32_t> paddings;
paddings.reserve(attribute_proto.ints_size());
for (int i = 0; i < dims; ++i)
{
paddings.push_back(attribute_proto.ints(i));
paddings.push_back(attribute_proto.ints(dims + i));
}
lp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
}
else
{
// Convolution or pooling.
CV_Assert(attribute_proto.ints_size() == 4 || attribute_proto.ints_size() == 6);
lp.set("pad", parse(attribute_proto.ints()));
}
}
else if(attribute_name == "auto_pad")
{
if (attribute_proto.s() == "SAME_UPPER" || attribute_proto.s() == "SAME_LOWER") {
lp.set("pad_mode", "SAME");
}
else if (attribute_proto.s() == "VALID") {
lp.set("pad_mode", "VALID");
}
}
else if(attribute_name == "dilations")
{
CV_Assert(attribute_proto.ints_size() == 2 || attribute_proto.ints_size() == 3);
lp.set("dilation", parse(attribute_proto.ints()));
}
else if (attribute_proto.has_i())
{
::google::protobuf::int64 src = attribute_proto.i();
if (src < std::numeric_limits<int32_t>::min() || src > std::numeric_limits<int32_t>::max())
CV_Error(Error::StsOutOfRange, "Input is out of OpenCV 32S range");
else
lp.set(attribute_name, saturate_cast<int32_t>(src));
}
else if (attribute_proto.has_f())
{
lp.set(attribute_name, attribute_proto.f());
}
else if (attribute_proto.has_s())
{
lp.set(attribute_name, attribute_proto.s());
}
else if (attribute_proto.floats_size() > 0)
{
lp.set(attribute_name, DictValue::arrayReal(
attribute_proto.floats().data(), attribute_proto.floats_size()));
}
else if (attribute_proto.ints_size() > 0)
{
lp.set(attribute_proto.name(), parse(attribute_proto.ints()));
}
else if (attribute_proto.has_t())
{
opencv_onnx::TensorProto tensor = attribute_proto.t();
Mat blob = getMatFromTensor(tensor);
lp.blobs.push_back(blob);
}
else if (attribute_proto.has_g() || attribute_proto.strings_size() > 0 ||
attribute_proto.tensors_size() > 0 || attribute_proto.graphs_size() > 0)
{
CV_Error(Error::StsNotImplemented, "Unexpected attribute type");
}
else
CV_Error(Error::StsNotImplemented, "Unsupported attribute type");
}
return lp;
}
Mat ONNXImporter::getBlob(const opencv_onnx::NodeProto& node_proto,
const std::map<std::string, Mat>& constBlobs, int index)
{
CV_Assert(index < node_proto.input_size());
std::map<std::string, Mat>::const_iterator constBlob;
constBlob = constBlobs.find(node_proto.input(index));
if (constBlob == constBlobs.end()) {
CV_Error(Error::StsObjectNotFound,
"Blob " + node_proto.input(index) + " not found in const blobs");
}
return constBlob->second;
}
void ONNXImporter::addLayer(Net& dstNet, LayerParams& layerParams,
const opencv_onnx::NodeProto& node_proto,
std::map<std::string, LayerInfo>& layer_id,
std::map<std::string, MatShape>& outShapes)
{
std::map<std::string, LayerInfo>::iterator layerId;
std::map<std::string, MatShape>::iterator shapeIt;
int id = dstNet.addLayer(layerParams.name, layerParams.type, layerParams);
for (int i = 0; i < node_proto.output_size(); ++i)
{
layer_id.insert(std::make_pair(node_proto.output(i), LayerInfo(id, i)));
}
std::vector<MatShape> layerInpShapes, layerOutShapes, layerInternalShapes;
int inpNum = 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, inpNum);
++inpNum;
// Collect input shapes.
shapeIt = outShapes.find(node_proto.input(j));
CV_Assert(shapeIt != outShapes.end());
layerInpShapes.push_back(shapeIt->second);
}
}
// Compute shape of output blob for this layer.
Ptr<Layer> layer = dstNet.getLayer(id);
layer->getMemoryShapes(layerInpShapes, 0, layerOutShapes, layerInternalShapes);
for (int i = 0; i < node_proto.output_size() && i < (int)layerOutShapes.size(); ++i)
{
outShapes[node_proto.output(i)] = layerOutShapes[i];
}
}
static void addConstant(const std::string& name,
const Mat& blob,
std::map<std::string, Mat>& constBlobs,
std::map<std::string, MatShape>& outShapes)
{
constBlobs.insert(std::make_pair(name, blob));
outShapes.insert(std::make_pair(name, shape(blob)));
}
void ONNXImporter::populateNet(Net dstNet)
{
CV_Assert(model_proto.has_graph());
opencv_onnx::GraphProto graph_proto = model_proto.graph();
simplifySubgraphs(graph_proto);
std::map<std::string, Mat> constBlobs = getGraphTensors(graph_proto);
// List of internal blobs shapes.
std::map<std::string, MatShape> outShapes;
// Add all the inputs shapes. It includes as constant blobs as network's inputs shapes.
for (int i = 0; i < graph_proto.input_size(); ++i)
{
opencv_onnx::ValueInfoProto valueInfoProto = graph_proto.input(i);
CV_Assert(valueInfoProto.has_type());
opencv_onnx::TypeProto typeProto = valueInfoProto.type();
CV_Assert(typeProto.has_tensor_type());
opencv_onnx::TypeProto::Tensor tensor = typeProto.tensor_type();
CV_Assert(tensor.has_shape());
opencv_onnx::TensorShapeProto tensorShape = tensor.shape();
MatShape inpShape(tensorShape.dim_size());
for (int j = 0; j < inpShape.size(); ++j)
{
inpShape[j] = tensorShape.dim(j).dim_value();
}
outShapes[valueInfoProto.name()] = inpShape;
}
std::string framework_name;
if (model_proto.has_producer_name()) {
framework_name = model_proto.producer_name();
}
// create map with network inputs (without const blobs)
std::map<std::string, LayerInfo> layer_id;
std::map<std::string, LayerInfo>::iterator layerId;
std::map<std::string, MatShape>::iterator shapeIt;
// fill map: push layer name, layer id and output id
std::vector<String> netInputs;
for (int j = 0; j < graph_proto.input_size(); j++)
{
const std::string& name = graph_proto.input(j).name();
if (constBlobs.find(name) == constBlobs.end()) {
netInputs.push_back(name);
layer_id.insert(std::make_pair(name, LayerInfo(0, netInputs.size() - 1)));
}
}
dstNet.setInputsNames(netInputs);
int layersSize = graph_proto.node_size();
LayerParams layerParams;
opencv_onnx::NodeProto node_proto;
for(int li = 0; li < layersSize; li++)
{
node_proto = graph_proto.node(li);
layerParams = getLayerParams(node_proto);
CV_Assert(node_proto.output_size() >= 1);
layerParams.name = node_proto.output(0);
std::string layer_type = node_proto.op_type();
layerParams.type = layer_type;
if (layer_type == "MaxPool")
{
layerParams.type = "Pooling";
layerParams.set("pool", "MAX");
layerParams.set("ceil_mode", layerParams.has("pad_mode"));
}
else if (layer_type == "AveragePool")
{
layerParams.type = "Pooling";
layerParams.set("pool", "AVE");
layerParams.set("ceil_mode", layerParams.has("pad_mode"));
layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
}
else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool" || layer_type == "ReduceMean")
{
CV_Assert(node_proto.input_size() == 1);
layerParams.type = "Pooling";
layerParams.set("pool", layer_type == "GlobalMaxPool"? "MAX" : "AVE");
layerParams.set("global_pooling", layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool");
if (layer_type == "ReduceMean")
{
if (layerParams.get<int>("keepdims") == 0 || !layerParams.has("axes"))
CV_Error(Error::StsNotImplemented, "Unsupported mode of ReduceMean operation.");
MatShape inpShape = outShapes[node_proto.input(0)];
DictValue axes = layerParams.get("axes");
if (inpShape.size() == 3 && axes.size() <= 2)
{
int axis = axes.get<int>(0);
CV_CheckNE(axis, 0, "");
outShapes[layerParams.name] = inpShape;
outShapes[layerParams.name][axis] = 1;
LayerParams reshapeLp;
reshapeLp.name = layerParams.name + "/reshape";
reshapeLp.type = "Reshape";
CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
reshapeLp.set("axis", 0);
reshapeLp.set("num_axes", 1);
int newShape[] = {1, -1};
reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], 2));
opencv_onnx::NodeProto proto;
proto.add_input(node_proto.input(0));
proto.add_output(reshapeLp.name);
addLayer(dstNet, reshapeLp, proto, layer_id, outShapes);
LayerParams avgLp;
avgLp.name = layerParams.name + "/avg";
avgLp.type = "Pooling";
CV_Assert(layer_id.find(avgLp.name) == layer_id.end());
avgLp.set("pool", "ave");
if (axes.size() == 2)
{
CV_CheckEQ(axes.get<int>(0), 1, "Unsupported ReduceMean mode");
CV_CheckEQ(axes.get<int>(1), 2, "Unsupported ReduceMean mode");
avgLp.set("global_pooling", true);
outShapes[layerParams.name][axes.get<int>(1)] = 1;
}
else
{
avgLp.set(axis == 2 ? "global_pooling_w" : "global_pooling_h", true);
avgLp.set(axis == 2 ? "kernel_h" : "kernel_w", 1);
}
node_proto.set_input(0, reshapeLp.name);
node_proto.set_output(0, avgLp.name);
addLayer(dstNet, avgLp, node_proto, layer_id, outShapes);
layerParams.type = "Flatten";
layerParams.set("axis", 0);
layerParams.set("end_axis", 1);
node_proto.set_input(0, avgLp.name);
node_proto.set_output(0, layerParams.name);
}
else
{
if (inpShape.size() != 4 && inpShape.size() != 5)
CV_Error(Error::StsNotImplemented, "Unsupported input shape of reduce_mean operation.");
CV_Assert(axes.size() <= inpShape.size() - 2);
std::vector<int> kernel_size(inpShape.size() - 2, 1);
for (int i = 0; i < axes.size(); i++) {
int axis = axes.get<int>(i);
CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
kernel_size[axis - 2] = inpShape[axis];
}
layerParams.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size()));
}
}
}
else if (layer_type == "Slice")
{
int axis = 0;
std::vector<int> begin;
std::vector<int> end;
int inp_size = node_proto.input_size();
if (inp_size == 1)
{
if (layerParams.has("steps"))
{
DictValue steps = layerParams.get("steps");
for (int i = 0; i < steps.size(); ++i)
{
if (steps.get<int>(i) != 1)
CV_Error(Error::StsNotImplemented,
"Slice layer only supports steps = 1");
}
}
if (layerParams.has("axes")) {
DictValue axes = layerParams.get("axes");
for (int i = 1; i < axes.size(); ++i) {
CV_Assert(axes.get<int>(i - 1) == axes.get<int>(i) - 1);
}
axis = axes.get<int>(0);
}
DictValue starts = layerParams.get("starts");
DictValue ends = layerParams.get("ends");
CV_Assert(starts.size() == ends.size());
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
}
for (int i = 0; i < starts.size(); ++i)
{
begin.push_back(starts.get<int>(i));
int finish = ends.get<int>(i);
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
}
} else {
CV_Assert(inp_size >= 3);
for (int i = 1; i < inp_size; i++) {
CV_Assert(constBlobs.find(node_proto.input(i)) != constBlobs.end());
}
Mat start_blob = getBlob(node_proto, constBlobs, 1);
Mat end_blob = getBlob(node_proto, constBlobs, 2);
CV_Assert(start_blob.total() == end_blob.total());
if (inp_size > 3) {
Mat axes_blob = getBlob(node_proto, constBlobs, 3);
const int* axes = (int*)axes_blob.data;
for (int i = 1; i < axes_blob.total(); ++i) {
CV_Assert(axes[i - 1] == axes[i] - 1);
}
axis = axes[0];
}
const int* starts = start_blob.ptr<int>();
const int* ends = end_blob.ptr<int>();
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
}
std::copy(starts, starts + start_blob.total(), std::back_inserter(begin));
for (int i = 0; i < end_blob.total(); ++i)
{
int finish = ends[i];
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
}
if (inp_size == 5) {
CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end());
Mat step_blob = getBlob(node_proto, constBlobs, 4);
// Very strange application for Slice op with tensor reversing.
// We just workaround it for 2d constants.
if (constBlobs.find(node_proto.input(0)) != constBlobs.end() &&
axis == 0 &&
start_blob.at<int>(0) == -1 && step_blob.at<int>(0) == -1 &&
end_blob.at<int>(0) == std::numeric_limits<int32_t>::min())
{
Mat inp = getBlob(node_proto, constBlobs, 0);
if (inp.dims == 2)
{
Mat flipped;
flip(inp, flipped, 0);
addConstant(layerParams.name, flipped, constBlobs, outShapes);
continue;
}
}
CV_CheckEQ(countNonZero(step_blob != 1), 0, "Slice layer only supports steps = 1");
}
}
layerParams.set("begin", DictValue::arrayInt(&begin[0], begin.size()));
layerParams.set("end", DictValue::arrayInt(&end[0], end.size()));
layerParams.set("axis", axis);
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
Mat inp = getBlob(node_proto, constBlobs, 0);
std::vector<Mat> inputs, sliced;
inputs.push_back(inp);
runLayer(layerParams, inputs, sliced);
CV_Assert(sliced.size() == 1);
addConstant(layerParams.name, sliced[0], constBlobs, outShapes);
continue;
}
}
else if (layer_type == "Split")
{
if (layerParams.has("split"))
{
DictValue splits = layerParams.get("split");
const int numSplits = splits.size();
CV_Assert(numSplits > 1);
std::vector<int> slicePoints(numSplits - 1, splits.get<int>(0));
for (int i = 1; i < splits.size() - 1; ++i)
{
slicePoints[i] = slicePoints[i - 1] + splits.get<int>(i - 1);
}
layerParams.set("slice_point", DictValue::arrayInt(&slicePoints[0], slicePoints.size()));
}
else
{
layerParams.set("num_split", node_proto.output_size());
}
layerParams.type = "Slice";
}
else if (layer_type == "Add" || layer_type == "Sum" || layer_type == "Sub")
{
bool isSub = layer_type == "Sub";
CV_CheckEQ(node_proto.input_size(), 2, "");
bool is_const_0 = layer_id.find(node_proto.input(0)) == layer_id.end();
bool is_const_1 = layer_id.find(node_proto.input(1)) == layer_id.end();
if (is_const_0 && is_const_1)
{
Mat blob_0 = getBlob(node_proto, constBlobs, 0);
Mat blob_1 = getBlob(node_proto, constBlobs, 1);
CV_Assert(blob_0.size == blob_1.size);
Mat output = isSub ? (blob_0 - blob_1) : (blob_0 + blob_1);
addConstant(layerParams.name, output, constBlobs, outShapes);
continue;
}
else if (is_const_0 || is_const_1)
{
int const_blob_id = is_const_0 ? 0 : 1;
Mat blob = getBlob(node_proto, constBlobs, const_blob_id);
int blob_total = blob.total();
if (blob_total == 1) {
layerParams.type = "Power";
layerParams.set("shift", (isSub ? -1 : 1) * blob.at<float>(0));
}
else {
MatShape inpShape = outShapes[node_proto.input(1 - const_blob_id)];
if (shape(blob) == inpShape)
{
LayerParams constParams;
constParams.name = layerParams.name + "/const";
constParams.type = "Const";
constParams.blobs.push_back(blob);
int id = dstNet.addLayer(constParams.name, constParams.type, constParams);
layer_id.insert(std::make_pair(constParams.name, LayerInfo(id, 0)));
outShapes[constParams.name] = shape(blob);
layerParams.type = "Eltwise";
node_proto.set_input(const_blob_id, constParams.name);
}
else
{
layerParams.type = "Scale";
layerParams.set("bias_term", true);
blob = blob.reshape(1, 1);
layerParams.blobs.push_back((isSub ? -1 : 1) * blob);
}
}
}
else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)])
{
layerParams.type = "Eltwise";
if (isSub)
{
static float subCoeffs[] = {1.f, -1.f};
layerParams.set("coeff", DictValue::arrayReal<float*>(subCoeffs, 2));
}
}
else
{
if (isSub)
{
LayerParams powerParams;
powerParams.name = layerParams.name + "/neg";
powerParams.type = "Power";
powerParams.set("scale", -1);
//Create Power layer
int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
//Connect to input
layerId = layer_id.find(node_proto.input(1));
CV_Assert(layerId != layer_id.end());
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
//Add shape
layer_id.insert(std::make_pair(powerParams.name, LayerInfo(id, 0)));
outShapes[powerParams.name] = outShapes[node_proto.input(1)];
//Replace input to Power
node_proto.set_input(1, powerParams.name);
}
layerParams.type = "Scale";
layerParams.set("bias_term", true);
}
}
else if (layer_type == "Max")
{
layerParams.type = "Eltwise";
layerParams.set("operation", "max");
}
else if (layer_type == "Neg")
{
layerParams.type = "Power";
layerParams.set("scale", -1);
}
else if (layer_type == "Constant")
{
CV_Assert(node_proto.input_size() == 0);
CV_Assert(layerParams.blobs.size() == 1);
addConstant(layerParams.name, layerParams.blobs[0], constBlobs, outShapes);
continue;
}
else if (layer_type == "LSTM")
{
LayerParams lstmParams = layerParams;
lstmParams.name += "/lstm";
// https://pytorch.org/docs/stable/nn.html#lstm
CV_Assert(node_proto.input_size() == 7);
Mat Wx = getBlob(node_proto, constBlobs, 1);
Mat Wh = getBlob(node_proto, constBlobs, 2);
Mat b = getBlob(node_proto, constBlobs, 3);
CV_CheckEQ(countNonZero(getBlob(node_proto, constBlobs, 5)), 0, "Unsupported non zero initial_h");
CV_CheckEQ(countNonZero(getBlob(node_proto, constBlobs, 6)), 0, "Unsupported non zero initial_c");
b = b.reshape(1, b.size[0]);
const int numHidden = lstmParams.get<int>("hidden_size");
const int numDirs = Wx.size[0]; // Is 1 for forward only and 2 for bidirectional LSTM.
const int numFeatures = Wx.size[2];
Mat bx = b.colRange(0, b.cols / 2);
Mat bh = b.colRange(b.cols / 2, b.cols);
b = bx + bh;
// IFGO->IGFO
for (int k = 0; k < numDirs; ++k)
{
float* WxData = Wx.ptr<float>(k);
float* WhData = Wh.ptr<float>(k);
float* biasData = b.ptr<float>(k);
for (int j = 0; j < numHidden; ++j)
{
for (int i = 0; i < numFeatures; ++i)
{
std::swap(WxData[(numHidden + j) * numFeatures + i],
WxData[(numHidden * 2 + j) * numFeatures + i]);
}
for (int i = 0; i < numHidden; ++i)
{
std::swap(WhData[(numHidden + j) * numHidden + i],
WhData[(numHidden * 2 + j) * numHidden + i]);
}
std::swap(biasData[numHidden + j], biasData[numHidden * 2 + j]);
}
}
Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]);
Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]);
lstmParams.blobs.resize(3);
lstmParams.blobs[0] = Wh;
lstmParams.blobs[1] = Wx;
lstmParams.blobs[2] = b;
lstmParams.set("bidirectional", lstmParams.get<String>("direction", "") == "bidirectional");
node_proto.set_output(0, lstmParams.name); // set different name so output shapes will be registered on that name
addLayer(dstNet, lstmParams, node_proto, layer_id, outShapes);
MatShape lstmShape = outShapes[node_proto.output(0)];
// Add fake 1 as it is done in ONNX
lstmShape.insert(lstmShape.begin() + 1, 1);
layerParams.type = "Reshape";
layerParams.set("dim", DictValue::arrayInt(&lstmShape[0], lstmShape.size()));
node_proto.set_input(0, lstmParams.name); // redirect input to LSTM
node_proto.set_output(0, layerParams.name); // keep origin LSTM's name
}
else if (layer_type == "ImageScaler")
{
const float scale = layerParams.has("scale") ? layerParams.get<float>("scale") : 1.0f;
layerParams.erase("scale");
if (layerParams.has("bias"))
{
layerParams.type = "Scale";
layerParams.blobs.push_back(
Mat(Size(1, layerParams.get("bias").size()), CV_32FC1, scale));
layerParams.set("bias_term", true);
Mat bias(1, layerParams.get("bias").size(), CV_32FC1);
for (int j = 0; j < bias.total(); j++) {
bias.at<float>(0, j) = layerParams.get("bias").getRealValue(j);
}
layerParams.blobs.push_back(bias);
layerParams.erase("bias");
}
else {
layerParams.set("scale", scale);
layerParams.type = "Power";
}
}
else if (layer_type == "Clip")
{
layerParams.type = "ReLU6";
replaceLayerParam(layerParams, "min", "min_value");
replaceLayerParam(layerParams, "max", "max_value");
}
else if (layer_type == "LeakyRelu")
{
layerParams.type = "ReLU";
replaceLayerParam(layerParams, "alpha", "negative_slope");
}
else if (layer_type == "Relu")
{
layerParams.type = "ReLU";
}
else if (layer_type == "Elu")
{
layerParams.type = "ELU";
}
else if (layer_type == "PRelu")
{
layerParams.type = "PReLU";
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 1));
}
else if (layer_type == "LRN")
{
replaceLayerParam(layerParams, "size", "local_size");
}
else if (layer_type == "InstanceNormalization")
{
if (node_proto.input_size() != 3)
CV_Error(Error::StsNotImplemented,
"Expected input, scale, bias");
layerParams.blobs.resize(4);
layerParams.blobs[2] = getBlob(node_proto, constBlobs, 1); // weightData
layerParams.blobs[3] = getBlob(node_proto, constBlobs, 2); // biasData
layerParams.set("has_bias", true);
layerParams.set("has_weight", true);
// Get number of channels in input
int size = layerParams.blobs[2].total();
layerParams.blobs[0] = Mat::zeros(size, 1, CV_32F); // mean
layerParams.blobs[1] = Mat::ones(size, 1, CV_32F); // std
LayerParams mvnParams;
mvnParams.name = layerParams.name + "/MVN";
mvnParams.type = "MVN";
mvnParams.set("eps", layerParams.get<float>("epsilon"));
layerParams.erase("epsilon");
//Create MVN layer
int id = dstNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
//Connect to input
layerId = layer_id.find(node_proto.input(0));
CV_Assert(layerId != layer_id.end());
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
//Add shape
layer_id.insert(std::make_pair(mvnParams.name, LayerInfo(id, 0)));
outShapes[mvnParams.name] = outShapes[node_proto.input(0)];
//Replace Batch Norm's input to MVN
node_proto.set_input(0, mvnParams.name);
layerParams.type = "BatchNorm";
}
else if (layer_type == "BatchNormalization")
{
if (node_proto.input_size() != 5)
CV_Error(Error::StsNotImplemented,
"Expected input, scale, bias, mean and var");
layerParams.type = "BatchNorm";
replaceLayerParam(layerParams, "epsilon", "eps");
replaceLayerParam(layerParams, "spatial", "use_global_stats");
Mat meanData = getBlob(node_proto, constBlobs, 3);
Mat stdData = getBlob(node_proto, constBlobs, 4);
layerParams.blobs.push_back(meanData);
layerParams.blobs.push_back(stdData);
if (!node_proto.input(1).empty()) {
layerParams.set("has_weight", true);
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 1)); // weightData
} else {
layerParams.set("has_weight", false);
}
if (!node_proto.input(2).empty()) {
layerParams.set("has_bias", true);
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, 2)); // biasData
} else {
layerParams.set("has_bias", false);
}
}
else if (layer_type == "Gemm")
{
CV_Assert(node_proto.input_size() >= 2);
layerParams.type = "InnerProduct";
Mat weights = getBlob(node_proto, constBlobs, 1);
int ind_num_out = 0;
if (layerParams.has("transB") && !layerParams.get<int>("transB")) {
transpose(weights, weights);
ind_num_out = 1;
}
layerParams.blobs.push_back(weights);
if (node_proto.input_size() == 3) {
Mat bias = getBlob(node_proto, constBlobs, 2);
layerParams.blobs.push_back(bias);
}
layerParams.set("num_output", layerParams.blobs[0].size[ind_num_out]);
layerParams.set("bias_term", node_proto.input_size() == 3);
}
else if (layer_type == "MatMul")
{
CV_Assert(node_proto.input_size() == 2);
layerParams.type = "InnerProduct";
layerParams.set("bias_term", false);
if (constBlobs.find(node_proto.input(1)) != constBlobs.end())
{
Mat blob = getBlob(node_proto, constBlobs, 1);
layerParams.blobs.push_back(blob.t());
layerParams.set("num_output", layerParams.blobs[0].size[0]);
}
}
else if (layer_type == "Mul" || layer_type == "Div")
{
CV_Assert(node_proto.input_size() == 2);
bool isDiv = layer_type == "Div";
int constId = -1;
bool haveVariables = false;
for (int i = 0; i < 2; ++i)
{
if (constBlobs.find(node_proto.input(i)) != constBlobs.end())
constId = i;
else
haveVariables = true;
}
if (constId != -1 && haveVariables)
{
Mat blob = getBlob(node_proto, constBlobs, constId);
blob = blob.reshape(1, 1);
if (blob.total() == 1) {
float coeff = isDiv ? 1.0 / blob.at<float>(0) : blob.at<float>(0);
layerParams.set("scale", coeff);
layerParams.type = "Power";
}
else {
if (isDiv)
divide(1.0, blob, blob);
layerParams.blobs.push_back(blob);
layerParams.type = "Scale";
}
}
else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)])
{
layerParams.type = "Eltwise";
layerParams.set("operation", isDiv ? "div" : "prod");
}
else
{
if (isDiv)
{
LayerParams powerParams;
powerParams.name = layerParams.name + "/inv";
powerParams.type = "Power";
powerParams.set("power", -1);
//Create Power layer
int id = dstNet.addLayer(powerParams.name, powerParams.type, powerParams);
//Connect to input
layerId = layer_id.find(node_proto.input(1));
CV_Assert(layerId != layer_id.end());
dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
//Add shape
layer_id.insert(std::make_pair(powerParams.name, LayerInfo(id, 0)));
outShapes[powerParams.name] = outShapes[node_proto.input(1)];
//Replace input to Power
node_proto.set_input(1, powerParams.name);
}
layerParams.type = "Scale";
}
if (!haveVariables)
{
Mat inp0 = getBlob(node_proto, constBlobs, 0);
Mat inp1 = getBlob(node_proto, constBlobs, 1);
if (inp0.size != inp1.size)
CV_Error(Error::StsNotImplemented, "Constant multiply with different shapes");
Mat out;
if (isDiv)
divide(inp0, inp1, out);
else
multiply(inp0, inp1, out);
out = out.reshape(1, inp0.dims, inp0.size);
out.dims = inp0.dims; // to workaround dims == 1
addConstant(layerParams.name, out, constBlobs, outShapes);
continue;
}
}
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 == "ConvTranspose")
{
CV_Assert(node_proto.input_size() >= 2);
layerParams.type = "Deconvolution";
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[1] * layerParams.get<int>("group", 1));
layerParams.set("bias_term", node_proto.input_size() == 3);
if (!layerParams.has("kernel_size"))
CV_Error(Error::StsNotImplemented,
"Required attribute 'kernel_size' is not present.");
if (layerParams.has("output_shape"))
{
const DictValue& outShape = layerParams.get("output_shape");
DictValue strides = layerParams.get("stride");
DictValue kernel = layerParams.get("kernel_size");
String padMode;
std::vector<int> adjust_pads;
if (layerParams.has("pad_mode"))
{
padMode = toUpperCase(layerParams.get<String>("pad_mode"));
if (padMode != "SAME" && padMode != "VALID")
CV_Error(Error::StsError, "Unsupported padding mode " + padMode);
for (int i = 0; i < strides.size(); i++)
{
int sz = outShape.get<int>(2 + i);
int stride = strides.get<int>(i);
adjust_pads.push_back(padMode == "SAME"? (sz - 1) % stride :
(sz - kernel.get<int>(i)) % stride);
}
layerParams.set("adj", DictValue::arrayInt(&adjust_pads[0], adjust_pads.size()));
}
}
else if (layerParams.has("output_padding"))
{
replaceLayerParam(layerParams, "output_padding", "adj");
}
}
else if (layer_type == "Transpose")
{
layerParams.type = "Permute";
replaceLayerParam(layerParams, "perm", "order");
CV_Assert(node_proto.input_size() == 1);
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
std::vector<Mat> inputs(1, getBlob(node_proto, constBlobs, 0)), transposed;
runLayer(layerParams, inputs, transposed);
CV_Assert(transposed.size() == 1);
addConstant(layerParams.name, transposed[0], constBlobs, outShapes);
continue;
}
}
else if (layer_type == "Squeeze")
{
CV_Assert_N(node_proto.input_size() == 1, layerParams.has("axes"));
DictValue axes_dict = layerParams.get("axes");
MatShape inpShape = outShapes[node_proto.input(0)];
std::vector<bool> maskedAxes(inpShape.size(), false);
for (int i = 0; i < axes_dict.size(); ++i)
{
int axis = axes_dict.getIntValue(i);
CV_CheckLE(axis, static_cast<int>(inpShape.size()), "Squeeze axis");
maskedAxes[axis] = inpShape[axis] == 1;
}
MatShape outShape;
for (int i = 0; i < inpShape.size(); ++i)
{
if (!maskedAxes[i])
outShape.push_back(inpShape[i]);
}
if (outShape.size() != inpShape.size())
{
layerParams.type = "Reshape";
layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
}
else
layerParams.type = "Identity";
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
Mat inp = getBlob(node_proto, constBlobs, 0);
Mat out = inp.reshape(1, outShape);
out.dims = outShape.size(); // to workaround dims == 1
addConstant(layerParams.name, out, constBlobs, outShapes);
continue;
}
}
else if (layer_type == "Flatten")
{
CV_CheckEQ(node_proto.input_size(), 1, "");
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
Mat input = getBlob(node_proto, constBlobs, 0);
int axis = clamp(layerParams.get<int>("axis", 1), input.dims);
std::vector<int> out_size(&input.size[0], &input.size[0] + axis);
out_size.push_back(input.total(axis));
Mat output = input.reshape(1, out_size);
addConstant(layerParams.name, output, constBlobs, outShapes);
continue;
}
}
else if (layer_type == "Unsqueeze")
{
CV_Assert(node_proto.input_size() == 1);
DictValue axes = layerParams.get("axes");
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
// Constant input.
Mat input = getBlob(node_proto, constBlobs, 0);
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);
addConstant(layerParams.name, out, constBlobs, outShapes);
continue;
}
// Variable input.
if (axes.size() != 1)
CV_Error(Error::StsNotImplemented, "Multidimensional unsqueeze");
MatShape inpShape = outShapes[node_proto.input(0)];
int axis = axes.getIntValue(0);
CV_Assert(0 <= axis && axis <= inpShape.size());
std::vector<int> outShape = inpShape;
outShape.insert(outShape.begin() + axis, 1);
layerParams.type = "Reshape";
layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
}
else if (layer_type == "Expand")
{
CV_CheckEQ(node_proto.input_size(), 2, "");
CV_Assert(constBlobs.find(node_proto.input(1)) != constBlobs.end());
Mat newShapeMat = getBlob(node_proto, constBlobs, 1);
MatShape targetShape(newShapeMat.ptr<int>(), newShapeMat.ptr<int>() + newShapeMat.total());
shapeIt = outShapes.find(node_proto.input(0));
CV_Assert(shapeIt != outShapes.end());
MatShape inpShape = shapeIt->second;
CV_CheckEQ(inpShape.size(), targetShape.size(), "Unsupported Expand op with different dims");
std::vector<int> broadcast_axes;
for (int i = 0; i < targetShape.size(); i++)
{
if (targetShape[i] != inpShape[i])
{
if (inpShape[i] == 1)
broadcast_axes.push_back(i);
else
CV_Error(Error::StsError, format("Could not be broadcast by axis: %d", i));
}
}
if (broadcast_axes.size() == 2 &&
broadcast_axes[0] == broadcast_axes[1] - 1 && broadcast_axes[1] == inpShape.size() - 1)
{
LayerParams constParams;
constParams.name = layerParams.name + "/const";
CV_Assert(layer_id.find(constParams.name) == layer_id.end());
constParams.type = "Const";
Mat inp = Mat::ones(newShapeMat.total(), newShapeMat.ptr<int>(), CV_32F);
constParams.blobs.push_back(inp);
opencv_onnx::NodeProto proto;
proto.add_output(constParams.name);
addLayer(dstNet, constParams, proto, layer_id, outShapes);
layerParams.type = "Scale";
layerParams.set("bias_term", false);
node_proto.set_input(0, constParams.name);
node_proto.set_input(1, shapeIt->first);
}
else if (broadcast_axes.size() == 1 && broadcast_axes[0] <= 1)
{
String base_name = layerParams.name + "/copy_";
std::vector<std::string> input_names;
for (int j = 0; j < targetShape[broadcast_axes[0]]; j++)
{
std::ostringstream ss;
ss << j;
LayerParams copyLP;
copyLP.name = base_name + ss.str();
copyLP.type = "Identity";
CV_Assert(layer_id.find(copyLP.name) == layer_id.end());
input_names.push_back(copyLP.name);
node_proto.set_output(0, copyLP.name);
addLayer(dstNet, copyLP, node_proto, layer_id, outShapes);
}
node_proto.clear_input();
for (int i = 0; i < input_names.size(); i++)
{
node_proto.add_input(input_names[i]);
}
layerParams.set("axis", broadcast_axes[0]);
layerParams.type = "Concat";
}
else
CV_Error(Error::StsNotImplemented, "Unsupported Expand op");
}
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);
layerParams.set("dim", DictValue::arrayInt<int*>(
blob.ptr<int>(), blob.total() ));
if (layer_id.find(node_proto.input(0)) == layer_id.end()) {
std::vector<Mat> inputs(1, getBlob(node_proto, constBlobs, 0)), outputs;
runLayer(layerParams, inputs, outputs);
addConstant(layerParams.name, outputs[0], constBlobs, outShapes);
continue;
}
}
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);
addConstant(layerParams.name, out, constBlobs, outShapes);
continue;
}
replaceLayerParam(layerParams, "shape", "dim");
}
}
else if (layer_type == "Pad")
{
layerParams.type = "Padding";
replaceLayerParam(layerParams, "mode", "type");
if (node_proto.input_size() == 3 || node_proto.input_size() == 2)
{
// Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
// We need to shuffle it to begin0, end0, begin1, end1, ...
Mat paddings = getBlob(node_proto, constBlobs, 1).reshape(1, 2);
paddings = paddings.t();
layerParams.set("paddings", DictValue::arrayInt(paddings.ptr<int>(), paddings.total()));
if (node_proto.input_size() == 3)
{
Mat value = getBlob(node_proto, constBlobs, 2);
layerParams.set("value", value.at<float>(0));
}
}
}
else if (layer_type == "Shape")
{
CV_Assert(node_proto.input_size() == 1);
shapeIt = outShapes.find(node_proto.input(0));
CV_Assert(shapeIt != outShapes.end());
MatShape inpShape = shapeIt->second;
Mat shapeMat(inpShape.size(), 1, CV_32S);
for (int j = 0; j < inpShape.size(); ++j)
shapeMat.at<int>(j) = inpShape[j];
shapeMat.dims = 1;
addConstant(layerParams.name, shapeMat, constBlobs, outShapes);
continue;
}
else if (layer_type == "Cast")
{
if (constBlobs.find(node_proto.input(0)) != constBlobs.end())
{
Mat blob = getBlob(node_proto, constBlobs, 0);
int type;
switch (layerParams.get<int>("to"))
{
case opencv_onnx::TensorProto_DataType_FLOAT: type = CV_32F; break;
case opencv_onnx::TensorProto_DataType_UINT8: type = CV_8U; break;
case opencv_onnx::TensorProto_DataType_UINT16: type = CV_16U; break;
case opencv_onnx::TensorProto_DataType_FLOAT16: type = CV_16S; break;
case opencv_onnx::TensorProto_DataType_INT8:
case opencv_onnx::TensorProto_DataType_INT16:
case opencv_onnx::TensorProto_DataType_INT32:
case opencv_onnx::TensorProto_DataType_INT64: type = CV_32S; break;
default: type = blob.type();
}
blob.convertTo(blob, type);
addConstant(layerParams.name, blob, constBlobs, outShapes);
continue;
}
else
layerParams.type = "Identity";
}
else if (layer_type == "ConstantOfShape" || layer_type == "ConstantFill")
{
int depth = CV_32F;
float fill_value;
if (!layerParams.blobs.empty())
{
CV_Assert(!layerParams.has("value"));
depth = layerParams.blobs[0].depth();
Mat floats;
layerParams.blobs[0].convertTo(floats, CV_32F);
fill_value = floats.at<float>(0, 0);
}
else
fill_value = layerParams.get("value", 0);
MatShape inpShape = getBlob(node_proto, constBlobs, 0);
for (int i = 0; i < inpShape.size(); i++)
CV_CheckGT(inpShape[i], 0, "");
Mat tensor(inpShape.size(), &inpShape[0], depth, Scalar(fill_value));
addConstant(layerParams.name, tensor, constBlobs, outShapes);
continue;
}
else if (layer_type == "Gather")
{
CV_Assert(node_proto.input_size() == 2);
Mat input = getBlob(node_proto, constBlobs, 0);
Mat indexMat = getBlob(node_proto, constBlobs, 1);
CV_Assert_N(indexMat.type() == CV_32S, indexMat.total() == 1);
int index = indexMat.at<int>(0);
Mat out;
if (layerParams.has("axis"))
{
int axis = layerParams.get<int>("axis");
std::vector<cv::Range> ranges(input.dims, Range::all());
ranges[axis] = Range(index, index + 1);
out = input(ranges);
}
else
{
CV_Assert(index < input.total());
const int dims = input.dims;
input = input.reshape(1, 1);
input.dims = 2;
out = input.reshape(1, 1).colRange(index, index + 1);
out.dims = dims;
}
addConstant(layerParams.name, out, constBlobs, outShapes);
continue;
}
else if (layer_type == "Concat")
{
bool hasVariableInps = false;
for (int i = 0; i < node_proto.input_size(); ++i)
{
if (layer_id.find(node_proto.input(i)) != layer_id.end())
{
hasVariableInps = true;
break;
}
}
if (!hasVariableInps)
{
std::vector<Mat> inputs(node_proto.input_size()), concatenated;
for (size_t i = 0; i < inputs.size(); ++i)
{
inputs[i] = getBlob(node_proto, constBlobs, i);
}
runLayer(layerParams, inputs, concatenated);
CV_Assert(concatenated.size() == 1);
addConstant(layerParams.name, concatenated[0], constBlobs, outShapes);
continue;
}
}
else if (layer_type == "Resize")
{
for (int i = 1; i < node_proto.input_size(); i++)
CV_Assert(layer_id.find(node_proto.input(i)) == layer_id.end());
String interp_mode = layerParams.get<String>("coordinate_transformation_mode");
CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn");
layerParams.set("align_corners", interp_mode == "align_corners");
Mat shapes = getBlob(node_proto, constBlobs, node_proto.input_size() - 1);
CV_CheckEQ(shapes.size[0], 4, "");
CV_CheckEQ(shapes.size[1], 1, "");
CV_CheckTypeEQ(shapes.depth(), CV_32S, "");
int height = shapes.at<int>(2);
int width = shapes.at<int>(3);
if (node_proto.input_size() == 3)
{
shapeIt = outShapes.find(node_proto.input(0));
CV_Assert(shapeIt != outShapes.end());
MatShape scales = shapeIt->second;
height *= scales[2];
width *= scales[3];
}
layerParams.set("width", width);
layerParams.set("height", height);
if (layerParams.get<String>("mode") == "linear") {
layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
"opencv_linear" : "bilinear");
}
replaceLayerParam(layerParams, "mode", "interpolation");
}
else if (layer_type == "Upsample")
{
//fused from Resize Subgraph
if (layerParams.has("coordinate_transformation_mode"))
{
String interp_mode = layerParams.get<String>("coordinate_transformation_mode");
CV_Assert_N(interp_mode != "tf_crop_and_resize", interp_mode != "tf_half_pixel_for_nn");
layerParams.set("align_corners", interp_mode == "align_corners");
if (layerParams.get<String>("mode") == "linear")
{
layerParams.set("mode", interp_mode == "pytorch_half_pixel" ?
"opencv_linear" : "bilinear");
}
}
if (layerParams.get<String>("mode") == "linear" && framework_name == "pytorch")
layerParams.set("mode", "opencv_linear");
layerParams.type = "Resize";
if (layerParams.has("scales"))
{
// Pytorch layer
DictValue scales = layerParams.get("scales");
CV_Assert(scales.size() == 4);
layerParams.set("zoom_factor_y", scales.getIntValue(2));
layerParams.set("zoom_factor_x", scales.getIntValue(3));
}
else if (layerParams.has("height_scale") && layerParams.has("width_scale"))
{
// Caffe2 layer
replaceLayerParam(layerParams, "height_scale", "zoom_factor_y");
replaceLayerParam(layerParams, "width_scale", "zoom_factor_x");
}
else
{
// scales as input
Mat scales = getBlob(node_proto, constBlobs, 1);
CV_Assert(scales.total() == 4);
layerParams.set("zoom_factor_y", scales.at<float>(2));
layerParams.set("zoom_factor_x", scales.at<float>(3));
}
replaceLayerParam(layerParams, "mode", "interpolation");
}
else if (layer_type == "SoftMax" || layer_type == "LogSoftmax")
{
layerParams.type = "Softmax";
layerParams.set("log_softmax", layer_type == "LogSoftmax");
}
else if (layer_type == "DetectionOutput")
{
CV_CheckEQ(node_proto.input_size(), 3, "");
if (constBlobs.find(node_proto.input(2)) != constBlobs.end())
{
Mat priors = getBlob(node_proto, constBlobs, 2);
LayerParams constParams;
constParams.name = layerParams.name + "/priors";
constParams.type = "Const";
constParams.blobs.push_back(priors);
opencv_onnx::NodeProto priorsProto;
priorsProto.add_output(constParams.name);
addLayer(dstNet, constParams, priorsProto, layer_id, outShapes);
node_proto.set_input(2, constParams.name);
}
}
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));
}
}
addLayer(dstNet, layerParams, node_proto, layer_id, outShapes);
}
}
Net readNetFromONNX(const String& onnxFile)
{
ONNXImporter onnxImporter(onnxFile.c_str());
Net net;
onnxImporter.populateNet(net);
return net;
}
Net readNetFromONNX(const char* buffer, size_t sizeBuffer)
{
ONNXImporter onnxImporter(buffer, sizeBuffer);
Net net;
onnxImporter.populateNet(net);
return net;
}
Net readNetFromONNX(const std::vector<uchar>& buffer)
{
return readNetFromONNX(reinterpret_cast<const char*>(buffer.data()), buffer.size());
}
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_INLINE_NS_END
}} // namespace
#endif