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opencv/modules/dnn/src/tensorflow/tf_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) 2016, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
/*
Implementation of Tensorflow models parser
*/
#include "../precomp.hpp"
#ifdef HAVE_PROTOBUF
#include "graph.pb.h"
#include <iostream>
#include <fstream>
#include <algorithm>
#include <string>
#include <google/protobuf/message.h>
#include <google/protobuf/text_format.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include "tf_io.hpp"
#endif
namespace cv {
namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
#if HAVE_PROTOBUF
using ::google::protobuf::RepeatedField;
using ::google::protobuf::RepeatedPtrField;
using ::google::protobuf::Message;
using ::google::protobuf::Descriptor;
using ::google::protobuf::FieldDescriptor;
using ::google::protobuf::Reflection;
namespace
{
static int toNCHW[] = {0, 2, 3, 1};
// This values are used to indicate layer output's data layout where it's possible.
enum DataLayout
{
DATA_LAYOUT_NHWC,
DATA_LAYOUT_NCHW,
DATA_LAYOUT_UNKNOWN
};
typedef std::vector<std::pair<String, int> > StrIntVector;
struct Pin
{
Pin(const std::string &_name, int _blobIndex = 0) :
name(_name), blobIndex(_blobIndex) {}
Pin() :
name(""), blobIndex(-1) {}
std::string name;
int blobIndex;
};
void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape)
{
shape.clear();
if (tensor.has_tensor_shape())
{
const tensorflow::TensorShapeProto &_shape = tensor.tensor_shape();
int i, n = _shape.dim_size();
if (n)
{
shape.resize(n);
for (i = 0; i < n; i++)
shape[i] = (int)_shape.dim(i).size();
}
else
shape.resize(1, 1); // Scalar.
}
else
{
CV_Error(Error::StsError, "Unknown shape of input tensor");
}
}
static Mat getTensorContent(const tensorflow::TensorProto &tensor)
{
std::string content = tensor.tensor_content();
switch (tensor.dtype())
{
case tensorflow::DT_FLOAT:
{
if (!content.empty())
return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone();
else
{
const RepeatedField<float>& field = tensor.float_val();
CV_Assert(!field.empty());
return Mat(1, field.size(), CV_32FC1, (void*)field.data()).clone();
}
}
case tensorflow::DT_DOUBLE:
{
if (!content.empty())
return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone();
else
{
const RepeatedField<double>& field = tensor.double_val();
CV_Assert(!field.empty());
return Mat(1, field.size(), CV_64FC1, (void*)field.data()).clone();
}
}
case tensorflow::DT_INT32:
{
if (!content.empty())
return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone();
else
{
const RepeatedField<int32_t>& field = tensor.int_val();
CV_Assert(!field.empty());
return Mat(1, field.size(), CV_32SC1, (void*)field.data()).clone();
}
}
case tensorflow::DT_HALF:
{
Mat halfs;
if (!content.empty())
{
static const int kHalfSize = 2;
halfs = Mat(1, content.size() / kHalfSize, CV_16UC1, (void*)content.c_str());
}
else
{
const RepeatedField<int32_t>& field = tensor.half_val();
CV_Assert(!field.empty());
Mat ints(1, field.size(), CV_32SC1, (void*)field.data());
ints.convertTo(halfs, CV_16UC1);
}
// Reinterpret as a signed shorts just for a convertFp16 call.
Mat halfsSigned(halfs.size(), CV_16SC1, halfs.data);
Mat floats(halfs.size(), CV_32FC1);
convertFp16(halfsSigned, floats);
return floats;
}
case tensorflow::DT_QUINT8:
{
CV_Assert(!content.empty());
return Mat(1, content.size(), CV_8UC1, (void*)content.c_str()).clone();
}
default:
CV_Error(Error::StsError, "Tensor's data type is not supported");
break;
}
return Mat();
}
template <typename T>
void parseTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
{
MatShape shape;
blobShapeFromTensor(tensor, shape);
int dims = (int)shape.size();
if (dims == 4)
{
// REORDER blob NHWC to NCHW
swap(shape[2], shape[3]); // NHCW
swap(shape[1], shape[2]); // NCHW
}
dstBlob.create(shape, CV_32F);
Mat tensorContent = getTensorContent(tensor);
int size = tensorContent.total();
CV_Assert(size == (int)dstBlob.total());
float *dstData = dstBlob.ptr<float>();
const T *data = reinterpret_cast<const T*>(tensorContent.data);
if (dims == 4)
{
int num = shape[0], channels = shape[1], height = shape[2], width = shape[3];
int total = num*channels*height*width;
for(int i_n = 0; i_n < shape[0]; i_n++) {
for(int i_c = 0; i_c < shape[1]; i_c++) {
for(int i_h = 0; i_h < shape[2]; i_h++) {
for(int i_w = 0; i_w < shape[3]; i_w++) {
int dst_i = channels*height*width*i_n + height*width*i_c + width*i_h + i_w;
int src_i = channels*height*width*i_n + i_c + channels*width*i_h + channels*i_w;
CV_Assert(dst_i < total);
CV_Assert(src_i < total);
dstData[dst_i] = data[src_i];
}
}
}
}
} else {
for (int i = 0; i < size; i++)
dstData[i] = data[i];
}
}
void blobFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
{
switch (tensor.dtype()) {
case tensorflow::DT_FLOAT:
case tensorflow::DT_HALF:
parseTensor<float>(tensor, dstBlob);
break;
case tensorflow::DT_DOUBLE:
parseTensor<double>(tensor, dstBlob);
break;
default:
CV_Error(Error::StsError, "Tensor's data type is not supported");
break;
}
}
void printList(const tensorflow::AttrValue::ListValue &val)
{
std::cout << "(";
for (int i = 0; i < val.i_size(); i++)
std::cout << " " << val.i(i);
std::cout << " )";
}
void printTensorShape(const tensorflow::TensorShapeProto &shape)
{
std::cout << "[ ";
for (int d = 0; d < shape.dim_size(); d++)
std::cout << shape.dim(d).name() <<
":" << shape.dim(d).size() << " ";
std::cout << "]";
}
void printTensor(const tensorflow::TensorProto &tensor)
{
printTensorShape(tensor.tensor_shape());
if (tensor.tensor_content().empty())
return;
switch (tensor.dtype())
{
case tensorflow::DT_FLOAT:
{
const float *data = reinterpret_cast<const float*>(tensor.tensor_content().c_str());
int size = tensor.tensor_content().size() / sizeof(float);
for (int i = 0; i < std::min(10, size); i++)
std::cout << " " << data[i];
if (size > 10)
std::cout << " ... " << size - 10 << " more";
break;
}
case tensorflow::DT_INT32:
{
const int *data = reinterpret_cast<const int*>(tensor.tensor_content().c_str());
int size = tensor.tensor_content().size() / sizeof(int);
for (int i = 0; i < std::min(10, size); i++)
std::cout << " " << data[i];
if (size > 10)
std::cout << " ... " << size - 10 << " more";
break;
}
default:
CV_Error(Error::StsError, "Tensor type is not supported");
break;
}
}
void printLayerAttr(const tensorflow::NodeDef &layer)
{
std::cout << std::endl << layer.name() << ":" << layer.op();
for (int ii = 0; ii < layer.input_size(); ii++)
std::cout << "(" << layer.input(ii) << ")";
std::cout << std::endl;
google::protobuf::Map<std::string, tensorflow::AttrValue> attr
= layer.attr();
for (google::protobuf::Map<std::string, tensorflow::AttrValue>::const_iterator ai = attr.begin();
ai != attr.end(); ++ai)
{
std::cout << ai->first << ":";
if (ai->first == "dtype" || ai->first == "T")
std::cout << ai->second.i();
else if (ai->first == "padding")
std::cout << ai->second.s();
else if (ai->first == "transpose_a" || ai->first == "transpose_b")
std::cout << ai->second.b();
// else if (ai->first == "shape")
// printTensorShape(ai->second.shape());
else if (ai->first == "strides" || ai->first == "ksize")
printList(ai->second.list());
else
printTensor(ai->second.tensor());
std::cout << std::endl;
}
}
bool hasLayerAttr(const tensorflow::NodeDef &layer, const std::string &name)
{
google::protobuf::Map<std::string, tensorflow::AttrValue> attr = layer.attr();
return attr.find(name) != attr.end();
}
const tensorflow::AttrValue& getLayerAttr(const tensorflow::NodeDef &layer, const std::string &name)
{
return layer.attr().at(name);
}
void setStrides(LayerParams &layerParams, const tensorflow::NodeDef &layer)
{
if (hasLayerAttr(layer, "strides"))
{
const tensorflow::AttrValue& val = getLayerAttr(layer, "strides");
if (val.list().i_size() != 4 ||
val.list().i(0) != 1 || val.list().i(3) != 1)
CV_Error(Error::StsError, "Unsupported strides");
layerParams.set("stride_h", static_cast<int>(val.list().i(1)));
layerParams.set("stride_w", static_cast<int>(val.list().i(2)));
}
}
DictValue parseDims(const tensorflow::TensorProto &tensor) {
MatShape shape;
blobShapeFromTensor(tensor, shape);
int dims = (int)shape.size();
CV_Assert(tensor.dtype() == tensorflow::DT_INT32);
CV_Assert(dims == 1);
Mat values = getTensorContent(tensor);
CV_Assert(values.type() == CV_32SC1);
// TODO: add reordering shape if dims == 4
return DictValue::arrayInt((int*)values.data, values.total());
}
void setKSize(LayerParams &layerParams, const tensorflow::NodeDef &layer)
{
if (hasLayerAttr(layer, "ksize"))
{
const tensorflow::AttrValue& val = getLayerAttr(layer, "ksize");
if (val.list().i_size() != 4 ||
val.list().i(0) != 1 || val.list().i(3) != 1)
CV_Error(Error::StsError, "Unsupported ksize");
layerParams.set("kernel_h", static_cast<int>(val.list().i(1)));
layerParams.set("kernel_w", static_cast<int>(val.list().i(2)));
}
else
{
layerParams.set("kernel_h", 1);
layerParams.set("kernel_w", 1);
}
}
void setPadding(LayerParams &layerParams, const tensorflow::NodeDef &layer)
{
if (hasLayerAttr(layer, "padding"))
layerParams.set("pad_mode", getLayerAttr(layer, "padding").s());
}
void RemoveIdentityOps(tensorflow::GraphDef& net) {
typedef std::map<String, String> IdentityOpsMap;
IdentityOpsMap identity_ops;
std::vector<int> identity_ops_idx;
int layersCount = net.node_size();
for (int li = 0; li < layersCount; li++)
{
const tensorflow::NodeDef &layer = net.node(li);
String type = layer.op();
if (type == "Identity" || type == "Dropout") {
identity_ops_idx.push_back(li);
identity_ops[layer.name()] = layer.input(0);
}
}
for (int li = 0; li < layersCount; li++)
{
tensorflow::NodeDef* layer = net.mutable_node(li);
for (int input_id = 0; input_id < layer->input_size(); input_id++) {
String input_op_name = layer->input(input_id);
IdentityOpsMap::iterator it = identity_ops.find(input_op_name);
if (it != identity_ops.end()) {
layer->set_input(input_id, it->second);
}
}
}
std::sort(identity_ops_idx.begin(), identity_ops_idx.end());
int removed_nodes = 0;
for(size_t i = 0; i < identity_ops_idx.size(); i++) {
int start_id = identity_ops_idx[i] - removed_nodes;
net.mutable_node()->DeleteSubrange(start_id, 1);
removed_nodes++;
}
}
Pin parsePin(const std::string &name)
{
Pin pin(name);
size_t delimiter_pos = name.find_first_of(":");
if (delimiter_pos != std::string::npos)
{
pin.name = name.substr(0, delimiter_pos);
std::istringstream(name.substr(delimiter_pos + 1)) >> pin.blobIndex;
}
return pin;
}
StrIntVector getNextLayers(const tensorflow::GraphDef& net, const String& layer_name, const String& type = "")
{
StrIntVector layers;
for (int li = 0; li < net.node_size(); li++)
{
const tensorflow::NodeDef& layer = net.node(li);
for (int input_id = 0; input_id < layer.input_size(); input_id++) {
String input_op_name = parsePin(layer.input(input_id)).name;
bool type_ok = type.empty() ? true : type == layer.op();
if (input_op_name == layer_name && type_ok)
layers.push_back(std::make_pair(layer.name(), li));
}
}
return layers;
}
void ExcludeLayer(tensorflow::GraphDef& net, const int layer_index, const int input_blob_index, bool remove_from_net = true) {
String layer_name = net.node(layer_index).name();
StrIntVector layers = getNextLayers(net, layer_name);
String removed_layer_input = net.node(layer_index).input(input_blob_index);
for (size_t i = 0; i < layers.size(); i++)
{
tensorflow::NodeDef* layer = net.mutable_node(layers[i].second);
for (int input_id = 0; input_id < layer->input_size(); input_id++) {
String input_op_name = layer->input(input_id);
if (input_op_name == layer_name) {
layer->set_input(input_id, removed_layer_input);
}
}
}
if (remove_from_net)
net.mutable_node()->DeleteSubrange(layer_index, 1);
}
class TFImporter {
public:
TFImporter(const char *model, const char *config = NULL);
TFImporter(const char *dataModel, size_t lenModel,
const char *dataConfig = NULL, size_t lenConfig = 0);
void populateNet(Net dstNet);
private:
void kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob);
void connect(const std::map<String, int>& layers_name_id_map, Net& network, const Pin& outPin,
const int input_layer_id, const int input_blob_id);
void connectToAllBlobs(const std::map<String, int>& layer_id, Net& network, const Pin& outPin,
const int input_layer_id, const int input_blobs_count);
const tensorflow::TensorProto& getConstBlob(const tensorflow::NodeDef &layer, std::map<String, int> const_layers,
int input_blob_index = -1, int* actual_inp_blob_idx = 0);
// Binary serialized TensorFlow graph includes weights.
tensorflow::GraphDef netBin;
// Optional text definition of TensorFlow graph. More flexible than binary format
// and may be used to build the network using binary format only as a weights storage.
// This approach is similar to Caffe's `.prorotxt` and `.caffemodel`.
tensorflow::GraphDef netTxt;
};
TFImporter::TFImporter(const char *model, const char *config)
{
if (model && model[0])
ReadTFNetParamsFromBinaryFileOrDie(model, &netBin);
if (config && config[0])
ReadTFNetParamsFromTextFileOrDie(config, &netTxt);
}
TFImporter::TFImporter(const char *dataModel, size_t lenModel,
const char *dataConfig, size_t lenConfig)
{
if (dataModel != NULL && lenModel > 0)
ReadTFNetParamsFromBinaryBufferOrDie(dataModel, lenModel, &netBin);
if (dataConfig != NULL && lenConfig > 0)
ReadTFNetParamsFromTextBufferOrDie(dataConfig, lenConfig, &netTxt);
}
void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
{
MatShape shape;
blobShapeFromTensor(tensor, shape);
int dims = (int)shape.size();
// TODO: other blob types
CV_Assert(tensor.dtype() == tensorflow::DT_FLOAT ||
tensor.dtype() == tensorflow::DT_HALF);
CV_Assert(dims == 4);
// REORDER kernel HWIO to OIHW
swap(shape[0], shape[2]); // IWHO
swap(shape[1], shape[3]); // IOHW
swap(shape[0], shape[1]); // OIHW
dstBlob.create(shape, CV_32F);
Mat tensorContent = getTensorContent(tensor);
int size = tensorContent.total();
CV_Assert(size == (int)dstBlob.total());
float *dstData = dstBlob.ptr<float>();
const float *data = reinterpret_cast<const float*>(tensorContent.data);
int out_c = shape[0], input_c = shape[1], height = shape[2], width = shape[3];
int total = out_c*input_c*height*width;
for(int i_oc = 0; i_oc < out_c; i_oc++) {
for(int i_ic = 0; i_ic < input_c; i_ic++) {
for(int i_h = 0; i_h < height; i_h++) {
for(int i_w = 0; i_w < width; i_w++) {
int dst_i = input_c*height*width*i_oc + height*width*i_ic + width*i_h + i_w;
int src_i = out_c*input_c*width*i_h + out_c*input_c*i_w + out_c*i_ic + i_oc;
CV_Assert(dst_i < total);
CV_Assert(src_i < total);
dstData[dst_i] = data[src_i];
}
}
}
}
}
void TFImporter::connect(const std::map<String, int>& layers_name_id_map, Net& network, const Pin& outPin,
const int input_layer_id, const int input_blob_id)
{
std::map<String, int>::const_iterator it = layers_name_id_map.find(outPin.name);
if (it == layers_name_id_map.end())
CV_Error(Error::StsError, "Input layer not found: " + outPin.name);
network.connect(it->second, outPin.blobIndex, input_layer_id, input_blob_id);
}
void TFImporter::connectToAllBlobs(const std::map<String, int>& layer_id, Net& network, const Pin& outPin,
const int input_layer_id, const int input_blobs_count)
{
for (int input_blob_id = 0; input_blob_id < input_blobs_count; input_blob_id++)
connect(layer_id, network, outPin, input_layer_id, input_blob_id);
}
const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDef &layer, std::map<String, int> const_layers,
int input_blob_index, int* actual_inp_blob_idx) {
if (input_blob_index == -1) {
for(int i = 0; i < layer.input_size(); i++) {
Pin input = parsePin(layer.input(i));
if (const_layers.find(input.name) != const_layers.end()) {
if (input_blob_index != -1)
CV_Error(Error::StsError, "More than one input is Const op");
input_blob_index = i;
}
}
}
if (input_blob_index == -1)
CV_Error(Error::StsError, "Const input blob for weights not found");
Pin kernel_inp = parsePin(layer.input(input_blob_index));
if (const_layers.find(kernel_inp.name) == const_layers.end())
CV_Error(Error::StsError, "Const kernel input not found");
if (kernel_inp.blobIndex != 0)
CV_Error(Error::StsError, "Unsupported kernel input");
if(actual_inp_blob_idx) {
*actual_inp_blob_idx = input_blob_index;
}
int nodeIdx = const_layers.at(kernel_inp.name);
if (nodeIdx < netBin.node_size() && netBin.node(nodeIdx).name() == kernel_inp.name)
{
return netBin.node(nodeIdx).attr().at("value").tensor();
}
else
{
CV_Assert(nodeIdx < netTxt.node_size(),
netTxt.node(nodeIdx).name() == kernel_inp.name);
return netTxt.node(nodeIdx).attr().at("value").tensor();
}
}
static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& const_layers,
std::set<String>& layers_to_ignore)
{
for (int li = 0; li < net.node_size(); li++)
{
const tensorflow::NodeDef &layer = net.node(li);
String name = layer.name();
String type = layer.op();
if (type == "Dequantize")
{
// Example of Dequantize node:
// name: "conv2d_1/bias"
// op: "Dequantize"
// input: "conv2d_1/bias_quantized_const" (tensor of dtype DT_QUINT8)
// input: "conv2d_1/bias_quantized_min"
// input: "conv2d_1/bias_quantized_max"
// attr { key: "T" value { type: DT_QUINT8 } } (quantized type)
// attr { key: "mode" value { s: "MIN_FIRST" } } (quantization technique)
CV_Assert(layer.input_size() == 3);
for (int i = 0; i < 3; ++i)
CV_Assert(const_layers.find(layer.input(i)) != const_layers.end());
CV_Assert(hasLayerAttr(layer, "mode") &&
getLayerAttr(layer, "mode").s() == "MIN_FIRST");
int tensorId = const_layers[layer.input(0)];
int minId = const_layers[layer.input(1)];
int maxId = const_layers[layer.input(2)];
tensorflow::TensorProto* tensor = net.mutable_node(tensorId)
->mutable_attr()->at("value")
.mutable_tensor();
CV_Assert(tensor->dtype() == tensorflow::DT_QUINT8);
Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor());
Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor());
CV_Assert(qMin.total() == 1, qMin.type() == CV_32FC1,
qMax.total() == 1, qMax.type() == CV_32FC1);
Mat content = getTensorContent(*tensor);
float minVal = qMin.at<float>(0);
float rangeScale = (qMax.at<float>(0) - minVal) / 255;
CV_Assert(rangeScale >= 0);
content.convertTo(content, CV_32FC1, rangeScale,
rangeScale * cvRound(minVal / rangeScale));
tensor->set_dtype(tensorflow::DT_FLOAT);
tensor->set_tensor_content(content.data, content.total() * content.elemSize1());
net.mutable_node(tensorId)->set_name(name);
CV_Assert(const_layers.insert(std::make_pair(name, tensorId)).second);
layers_to_ignore.insert(name);
continue;
}
else if (type != "Const")
continue; // only Const parameters are supported
if (layer.attr().find("value") != layer.attr().end())
{
CV_Assert(const_layers.insert(std::make_pair(name, li)).second);
}
layers_to_ignore.insert(name);
}
}
// If all inputs of specific layer have the same data layout we can say that
// this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise.
static int predictOutputDataLayout(const tensorflow::NodeDef& layer, const std::map<String, int>& data_layouts)
{
int layout = DATA_LAYOUT_UNKNOWN;
std::map<String, int>::const_iterator it;
for (int i = 0, n = layer.input_size(); i < n; ++i)
{
it = data_layouts.find(layer.input(i));
if (it != data_layouts.end())
{
if (it->second == DATA_LAYOUT_UNKNOWN)
return DATA_LAYOUT_UNKNOWN;
else if (it->second != layout)
{
if (layout == DATA_LAYOUT_UNKNOWN)
layout = it->second;
else
return DATA_LAYOUT_UNKNOWN;
}
}
}
return layout;
}
void TFImporter::populateNet(Net dstNet)
{
RemoveIdentityOps(netBin);
RemoveIdentityOps(netTxt);
std::set<String> layers_to_ignore;
tensorflow::GraphDef& net = netTxt.ByteSize() != 0 ? netTxt : netBin;
int layersSize = net.node_size();
std::map<String, int> data_layouts;
// find all Const layers for params
std::map<String, int> value_id;
addConstNodes(netBin, value_id, layers_to_ignore);
addConstNodes(netTxt, value_id, layers_to_ignore);
std::map<String, int> layer_id;
for (int li = 0; li < layersSize; li++)
{
tensorflow::NodeDef layer = net.node(li);
String name = layer.name();
String type = layer.op();
LayerParams layerParams;
if(layers_to_ignore.find(name) != layers_to_ignore.end())
continue;
data_layouts[name] = predictOutputDataLayout(layer, data_layouts);
if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative")
{
// The first node of dilated convolution subgraph.
// Extract input node, dilation rate and paddings.
std::string input = layer.input(0);
if (type == "SpaceToBatchND")
{
// op: "SpaceToBatchND"
// input: "input"
// input: "SpaceToBatchND/block_shape"
// input: "SpaceToBatchND/paddings"
CV_Assert(layer.input_size() == 3);
DictValue dilation = parseDims(getConstBlob(layer, value_id, 1));
CV_Assert(dilation.size() == 2 && dilation.get<int>(0) == dilation.get<int>(1));
layerParams.set("dilation", dilation.get<int>(0));
Mat paddings;
parseTensor<int>(getConstBlob(layer, value_id, 2), paddings);
// paddings is a 2x2 matrix: [[top, bot], [left, right]]
layerParams.set("pad_h", paddings.at<float>(0));
layerParams.set("pad_w", paddings.at<float>(2));
StrIntVector next_layers = getNextLayers(net, name, "Conv2D");
CV_Assert(next_layers.size() == 1);
layer = net.node(next_layers[0].second);
layers_to_ignore.insert(next_layers[0].first);
name = layer.name();
type = layer.op();
}
layerParams.set("bias_term", false);
layerParams.blobs.resize(1);
StrIntVector next_layers = getNextLayers(net, name, "BiasAdd");
if (next_layers.size() == 1) {
layerParams.set("bias_term", true);
layerParams.blobs.resize(2);
int weights_layer_index = next_layers[0].second;
blobFromTensor(getConstBlob(net.node(weights_layer_index), value_id), layerParams.blobs[1]);
ExcludeLayer(net, weights_layer_index, 0, false);
layers_to_ignore.insert(next_layers[0].first);
}
kernelFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
int* kshape = layerParams.blobs[0].size.p;
if (type == "DepthwiseConv2dNative")
{
const int chMultiplier = kshape[0];
const int inCh = kshape[1];
const int height = kshape[2];
const int width = kshape[3];
Mat copy = layerParams.blobs[0].clone();
float* src = (float*)copy.data;
float* dst = (float*)layerParams.blobs[0].data;
for (int i = 0; i < chMultiplier; ++i)
for (int j = 0; j < inCh; ++j)
for (int s = 0; s < height * width; ++s)
{
int src_i = (i * inCh + j) * height * width + s;
int dst_i = (j * chMultiplier + i) * height* width + s;
dst[dst_i] = src[src_i];
}
// TODO Use reshape instead
kshape[0] = inCh * chMultiplier;
kshape[1] = 1;
size_t* kstep = layerParams.blobs[0].step.p;
kstep[0] = kstep[1]; // fix steps too
}
layerParams.set("kernel_h", kshape[2]);
layerParams.set("kernel_w", kshape[3]);
layerParams.set("num_output", kshape[0]);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
// The final node of dilated convolution subgraph.
next_layers = getNextLayers(net, name, "BatchToSpaceND");
if (!next_layers.empty())
{
layerParams.set("pad_mode", ""); // We use padding values.
CV_Assert(next_layers.size() == 1);
ExcludeLayer(net, next_layers[0].second, 0, false);
layers_to_ignore.insert(next_layers[0].first);
}
int id = dstNet.addLayer(name, "Convolution", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(input), id, 0);
if (hasLayerAttr(layer, "data_format"))
{
std::string format = getLayerAttr(layer, "data_format").s();
if (format == "NHWC")
data_layouts[name] = DATA_LAYOUT_NHWC;
else if (format == "NCHW")
data_layouts[name] = DATA_LAYOUT_NCHW;
else
CV_Error(Error::StsParseError, "Unknown data_format value: " + format);
}
else
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else if (type == "BiasAdd" || type == "Add")
{
bool haveConst = false;
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
{
Pin input = parsePin(layer.input(ii));
haveConst = value_id.find(input.name) != value_id.end();
}
CV_Assert(!haveConst || layer.input_size() == 2);
if (haveConst)
{
layerParams.blobs.resize(1);
blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
int id = dstNet.addLayer(name, "Shift", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else
{
layerParams.set("operation", "sum");
int id = dstNet.addLayer(name, "Eltwise", layerParams);
layer_id[name] = id;
for (int ii = 0; ii < layer.input_size(); ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
}
}
}
else if (type == "MatMul")
{
CV_Assert(layer.input_size() == 2);
layerParams.set("bias_term", false);
layerParams.blobs.resize(1);
StrIntVector next_layers = getNextLayers(net, name, "BiasAdd");
if (next_layers.empty())
{
next_layers = getNextLayers(net, name, "Add");
}
if (next_layers.size() == 1) {
layerParams.set("bias_term", true);
layerParams.blobs.resize(2);
int weights_layer_index = next_layers[0].second;
blobFromTensor(getConstBlob(net.node(weights_layer_index), value_id), layerParams.blobs[1]);
ExcludeLayer(net, weights_layer_index, 0, false);
layers_to_ignore.insert(next_layers[0].first);
}
int kernel_blob_index = -1;
blobFromTensor(getConstBlob(layer, value_id, -1, &kernel_blob_index), layerParams.blobs[0]);
if (kernel_blob_index == 1) { // In this case output is computed by x*W formula - W should be transposed
Mat data = layerParams.blobs[0].t();
layerParams.blobs[0] = data.clone();
}
layerParams.set("num_output", layerParams.blobs[0].size[0]);
int id = dstNet.addLayer(name, "InnerProduct", layerParams);
layer_id[name] = id;
// one input only
int input_blob_index = kernel_blob_index == 0 ? 1 : 0;
connect(layer_id, dstNet, parsePin(layer.input(input_blob_index)), id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Reshape")
{
Pin inpId = parsePin(layer.input(0));
DictValue newShape = parseDims(getConstBlob(layer, value_id, 1));
if (newShape.size() != 4 && data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
}
layerParams.set("dim", newShape);
int id = dstNet.addLayer(name, "Reshape", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, inpId, id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Flatten")
{
Pin inpId = parsePin(layer.input(0));
if (data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
}
int id = dstNet.addLayer(name, "Flatten", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, inpId, id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Transpose")
{
Mat perm = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(perm.type() == CV_32SC1);
int* permData = (int*)perm.data;
if (perm.total() == 4)
{
// Only NHWC <-> NCHW permutations are allowed. OpenCV is always
// keep NCHW layout this way.
if (data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
{
if (permData[0] == 0 && permData[1] == 3 && permData[2] == 1 && permData[3] == 2)
{
// in TensorFlow: NHWC->NCHW
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NCHW;
}
else if (permData[0] == 0 && permData[1] == 1 && permData[2] == 2 && permData[3] == 3)
{
// in TensorFlow: NHWC->NHWC
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else
CV_Error(Error::StsParseError, "Only NHWC <-> NCHW permutations are allowed.");
}
else if (data_layouts[layer.input(0)] == DATA_LAYOUT_NCHW)
{
if (permData[0] == 0 && permData[1] == 2 && permData[2] == 3 && permData[3] == 1)
{
// in TensorFlow: NCHW->NHWC
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else if (permData[0] == 0 && permData[1] == 1 && permData[2] == 2 && permData[3] == 3)
{
// in TensorFlow: NCHW->NCHW
// in OpenCV: NCHW->NCHW
data_layouts[name] = DATA_LAYOUT_NCHW;
}
else
CV_Error(Error::StsParseError, "Only NHWC <-> NCHW permutations are allowed.");
}
int id = dstNet.addLayer(name, "Identity", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else
{
layerParams.set("order", DictValue::arrayInt<int*>(permData, perm.total()));
int id = dstNet.addLayer(name, "Permute", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
}
else if (type == "Const")
{
}
else if (type == "LRN")
{
if(hasLayerAttr(layer, "alpha")) {
layerParams.set("alpha", getLayerAttr(layer, "alpha").f());
}
if(hasLayerAttr(layer, "beta")) {
layerParams.set("beta", getLayerAttr(layer, "beta").f());
}
if(hasLayerAttr(layer, "depth_radius")) {
int radius = (int)getLayerAttr(layer, "depth_radius").i();
layerParams.set("local_size", 2*radius + 1);
}
if(hasLayerAttr(layer, "bias")) {
layerParams.set("bias", getLayerAttr(layer, "bias").f());
}
layerParams.set("norm_by_size", false);
int id = dstNet.addLayer(name, "LRN", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "Concat" || type == "ConcatV2")
{
int axisId = (type == "Concat" ? 0 : layer.input_size() - 1);
int axis = getConstBlob(layer, value_id, axisId).int_val().Get(0);
layerParams.set("axis", 0 <= axis && axis < 4 ? toNCHW[axis] : axis);
int id = dstNet.addLayer(name, "Concat", layerParams);
layer_id[name] = id;
int from = (type == "Concat" ? 1 : 0);
int to = (type == "Concat" ? layer.input_size() : layer.input_size() - 1);
// input(0) or input(n-1) is concat_dim
for (int ii = from; ii < to; ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii - from);
}
}
else if (type == "MaxPool")
{
layerParams.set("pool", "max");
setKSize(layerParams, layer);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "AvgPool")
{
layerParams.set("pool", "ave");
layerParams.set("ave_pool_padded_area", false);
setKSize(layerParams, layer);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "Placeholder")
{
std::vector<String> netInputs(1);
netInputs[0] = name;
layer_id[name] = 0;
dstNet.setInputsNames(netInputs);
}
else if (type == "Split") {
// TODO: determining axis index remapping by input dimensions order of input blob
// TODO: slicing input may be Const op
// TODO: slicing kernels for convolutions - in current implementation it is impossible
// TODO: add parsing num of slices parameter
CV_Assert(layer.input_size() == 2);
// num_split
// 1st blob is dims tensor
int axis = getConstBlob(layer, value_id, 0).int_val().Get(0);
layerParams.set("axis", toNCHW[axis]);
int id = dstNet.addLayer(name, "Slice", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
}
else if (type == "Slice")
{
// op: "Slice"
// input: "input_node"
// input: "Slice/begin"
// input: "Slice/size"
CV_Assert(layer.input_size() == 3);
Mat begins = getTensorContent(getConstBlob(layer, value_id, 1));
Mat sizes = getTensorContent(getConstBlob(layer, value_id, 2));
CV_Assert(!begins.empty(), !sizes.empty(), begins.type() == CV_32SC1,
sizes.type() == CV_32SC1);
if (begins.total() == 4)
{
// Perhabs, we have an NHWC order. Swap it to NCHW.
std::swap(*begins.ptr<int32_t>(0, 2), *begins.ptr<int32_t>(0, 3));
std::swap(*begins.ptr<int32_t>(0, 1), *begins.ptr<int32_t>(0, 2));
std::swap(*sizes.ptr<int32_t>(0, 2), *sizes.ptr<int32_t>(0, 3));
std::swap(*sizes.ptr<int32_t>(0, 1), *sizes.ptr<int32_t>(0, 2));
}
layerParams.set("begin", DictValue::arrayInt((int*)begins.data, begins.total()));
layerParams.set("size", DictValue::arrayInt((int*)sizes.data, sizes.total()));
int id = dstNet.addLayer(name, "Slice", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "Mul")
{
bool haveConst = false;
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
{
Pin input = parsePin(layer.input(ii));
haveConst = value_id.find(input.name) != value_id.end();
}
CV_Assert(!haveConst || layer.input_size() == 2);
if (haveConst)
{
// Multiplication by constant.
CV_Assert(layer.input_size() == 2);
Mat scaleMat = getTensorContent(getConstBlob(layer, value_id));
CV_Assert(scaleMat.type() == CV_32FC1);
int id;
if (scaleMat.total() == 1) // is a scalar.
{
// Try to match with a LeakyRelu:
// node {
// name: "LeakyRelu/mul"
// op: "Mul"
// input: "LeakyRelu/alpha"
// input: "input"
// }
// node {
// name: "LeakyRelu/Maximum"
// op: "Maximum"
// input: "LeakyRelu/mul"
// input: "input"
// }
StrIntVector next_layers = getNextLayers(net, name, "Maximum");
if (!next_layers.empty())
{
int maximumLayerIdx = next_layers[0].second;
ExcludeLayer(net, maximumLayerIdx, 0, false);
layers_to_ignore.insert(next_layers[0].first);
layerParams.set("negative_slope", scaleMat.at<float>(0));
id = dstNet.addLayer(name, "ReLU", layerParams);
}
else
{
// Just a multiplication.
layerParams.set("scale", scaleMat.at<float>(0));
id = dstNet.addLayer(name, "Power", layerParams);
}
}
else // is a vector
{
layerParams.blobs.resize(1, scaleMat);
StrIntVector next_layers = getNextLayers(net, name, "Add");
if (!next_layers.empty())
{
layerParams.set("bias_term", true);
layerParams.blobs.resize(2);
int weights_layer_index = next_layers[0].second;
blobFromTensor(getConstBlob(net.node(weights_layer_index), value_id), layerParams.blobs.back());
ExcludeLayer(net, weights_layer_index, 0, false);
layers_to_ignore.insert(next_layers[0].first);
}
id = dstNet.addLayer(name, "Scale", layerParams);
}
layer_id[name] = id;
Pin inp0 = parsePin(layer.input(0));
if (layer_id.find(inp0.name) != layer_id.end())
// First operand is a constant.
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
else
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
}
else
{
layerParams.set("operation", "prod");
int id = dstNet.addLayer(name, "Eltwise", layerParams);
layer_id[name] = id;
for (int ii = 0; ii < layer.input_size(); ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
}
}
}
else if (type == "Pad")
{
Mat paddings = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(paddings.type() == CV_32SC1);
if (paddings.total() == 8)
{
// Perhabs, we have NHWC padding dimensions order.
// N H W C
// 0 1 2 3 4 5 6 7
std::swap(*paddings.ptr<int32_t>(0, 2), *paddings.ptr<int32_t>(0, 6));
std::swap(*paddings.ptr<int32_t>(0, 3), *paddings.ptr<int32_t>(0, 7));
// N C W H
// 0 1 2 3 4 5 6 7
std::swap(*paddings.ptr<int32_t>(0, 4), *paddings.ptr<int32_t>(0, 6));
std::swap(*paddings.ptr<int32_t>(0, 5), *paddings.ptr<int32_t>(0, 7));
// N C H W
// 0 1 2 3 4 5 6 7
}
layerParams.set("paddings", DictValue::arrayInt<int*>((int*)paddings.data, paddings.total()));
int id = dstNet.addLayer(name, "Padding", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "FusedBatchNorm")
{
// op: "FusedBatchNorm"
// input: "input"
// input: "BatchNorm/gamma"
// input: "BatchNorm/beta"
// input: "BatchNorm/moving_mean"
// input: "BatchNorm/moving_variance"
if (layer.input_size() != 5)
CV_Error(Error::StsNotImplemented,
"Expected gamma, beta, mean and std");
Pin inpId = parsePin(layer.input(0));
bool isTraining = hasLayerAttr(layer, "is_training") && getLayerAttr(layer, "is_training").b();
layerParams.blobs.resize(4);
Mat gamma, beta, mean, std;
blobFromTensor(getConstBlob(layer, value_id, 1), gamma);
blobFromTensor(getConstBlob(layer, value_id, 2), beta);
if (isTraining)
{
mean = Mat::zeros(1, beta.total(), CV_32F);
std = Mat::ones(1, beta.total(), CV_32F);
// Add an extra layer: Mean-Variance normalization
LayerParams mvnParams;
std::string mvnName = name + "/MVN";
CV_Assert(layer_id.find(mvnName) == layer_id.end());
int mvnId = dstNet.addLayer(mvnName, "MVN", mvnParams);
layer_id[mvnName] = mvnId;
connect(layer_id, dstNet, inpId, mvnId, 0);
inpId = Pin(mvnName);
}
else
{
blobFromTensor(getConstBlob(layer, value_id, 3), mean);
blobFromTensor(getConstBlob(layer, value_id, 4), std);
}
layerParams.blobs[0] = mean;
layerParams.blobs[1] = std;
layerParams.blobs[2] = gamma;
layerParams.blobs[3] = beta;
if (hasLayerAttr(layer, "epsilon"))
layerParams.set("eps", getLayerAttr(layer, "epsilon").f());
layerParams.set("has_weight", true);
layerParams.set("has_bias", true);
int id = dstNet.addLayer(name, "BatchNorm", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, inpId, id, 0);
}
else if (type == "Conv2DBackpropInput")
{
// op: "Conv2DBackpropInput"
// input: "conv2d_transpose/output_shape"
// input: "weights"
// input: "input"
if (layer.input_size() != 3)
CV_Error(Error::StsNotImplemented,
"Expected output shape, weights and input nodes");
layerParams.set("bias_term", false);
layerParams.blobs.resize(1);
StrIntVector next_layers = getNextLayers(net, name, "BiasAdd");
if (next_layers.size() == 1)
{
layerParams.set("bias_term", true);
layerParams.blobs.resize(2);
int weights_layer_index = next_layers[0].second;
blobFromTensor(getConstBlob(net.node(weights_layer_index), value_id), layerParams.blobs[1]);
ExcludeLayer(net, weights_layer_index, 0, false);
layers_to_ignore.insert(next_layers[0].first);
}
kernelFromTensor(getConstBlob(layer, value_id, 1), layerParams.blobs[0]);
const int* kshape = layerParams.blobs[0].size.p;
const int kernelH = kshape[2];
const int kernelW = kshape[3];
layerParams.set("kernel_h", kernelH);
layerParams.set("kernel_w", kernelW);
layerParams.set("num_output", kshape[1]);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
// For convolution layer, output shape computes as
// o = 1 + (i - k + 2*p) / s
// i - input size, o - output size, k - kernel size, p - pad, s - stride
// In TensorFlow, p == 0 is padMode == 'VALID' or p == (k - 1) / 2
// considering that k is odd.
// SAME: o = 1 + (i - 1) / s
// VALID: o = 1 + i / s
// Deconvolution's layer output shape computes as
// SAME: o = 1 + (i - 1)*s
// VALID: o = (i - 1)*s
// If output_shape differs from formulas above then adjust padding is applied.
const int strideY = layerParams.get<int>("stride_h");
const int strideX = layerParams.get<int>("stride_w");
Mat outShape = getTensorContent(getConstBlob(layer, value_id, 0));
const int outH = outShape.at<int>(2);
const int outW = outShape.at<int>(1);
if (layerParams.get<String>("pad_mode") == "SAME")
{
layerParams.set("adj_w", (outW - 1) % strideX);
layerParams.set("adj_h", (outH - 1) % strideY);
}
else if (layerParams.get<String>("pad_mode") == "VALID")
{
layerParams.set("adj_w", (outW - kernelW) % strideX);
layerParams.set("adj_h", (outH - kernelH) % strideY);
}
int id = dstNet.addLayer(name, "Deconvolution", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(2)), id, 0);
}
else if (type == "BlockLSTM")
{
// op: "BlockLSTM"
// input: "lstm_block_wrapper/ToInt64/x" (ignore, number of time stamps)
// input: "input"
// input: "lstm_block_wrapper/zeros" (ignore)
// input: "lstm_block_wrapper/zeros" (ignore)
// input: "lstm_block_wrapper/kernel"
// input: "lstm_block_wrapper/w_i_diag"
// input: "lstm_block_wrapper/w_f_diag"
// input: "lstm_block_wrapper/w_o_diag"
// input: "lstm_block_wrapper/bias"
if (layer.input_size() != 9)
CV_Error(Error::StsNotImplemented, "Unexpected number of input nodes");
if (hasLayerAttr(layer, "forget_bias"))
layerParams.set("forget_bias", getLayerAttr(layer, "forget_bias").f());
if (hasLayerAttr(layer, "forget_bias"))
{
float cellClip = getLayerAttr(layer, "cell_clip").f();
// Cell clip disabled if it's negative.
if (cellClip >= 0)
{
layerParams.set("use_cell_clip", true);
layerParams.set("cell_clip", cellClip);
}
}
Mat W, Wh, Wx, b;
blobFromTensor(getConstBlob(layer, value_id, 4), W);
blobFromTensor(getConstBlob(layer, value_id, 8), b);
const int outSize = W.cols / 4;
// IGFO->IFOG
float* weightData = (float*)W.data;
for (int i = 0; i < W.rows; ++i)
for (int j = 0; j < outSize; ++j)
{
std::swap(weightData[i * W.cols + 1 * outSize + j],
weightData[i * W.cols + 2 * outSize + j]);
std::swap(weightData[i * W.cols + 2 * outSize + j],
weightData[i * W.cols + 3 * outSize + j]);
}
Wx = W.rowRange(0, W.rows - outSize).t();
Wh = W.rowRange(W.rows - outSize, W.rows).t();
layerParams.blobs.resize(3);
layerParams.blobs[0] = Wh;
layerParams.blobs[1] = Wx;
layerParams.blobs[2] = b;
if (hasLayerAttr(layer, "use_peephole"))
{
bool usePeephole = getLayerAttr(layer, "use_peephole").b();
if (usePeephole)
{
layerParams.set("use_peephole", true);
layerParams.blobs.resize(6);
for (int i = 0; i < 3; ++i)
{
Mat w;
blobFromTensor(getConstBlob(layer, value_id, 5 + i), w);
w = w.reshape(1, w.total()); // Single column.
w = Mat::diag(w); // Make a diagonal matrix.
layerParams.blobs[3 + i] = w;
}
}
}
int id = dstNet.addLayer(name, "LSTM", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "ResizeNearestNeighbor")
{
Mat outSize = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(outSize.type() == CV_32SC1, outSize.total() == 2);
layerParams.set("height", outSize.at<int>(0, 0));
layerParams.set("width", outSize.at<int>(0, 1));
if (hasLayerAttr(layer, "align_corners"))
layerParams.set("align_corners", getLayerAttr(layer, "align_corners").b());
int id = dstNet.addLayer(name, "ResizeNearestNeighbor", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "L2Normalize")
{
// op: "L2Normalize"
// input: "input"
CV_Assert(layer.input_size() == 1);
layerParams.set("across_spatial", false);
layerParams.set("channel_shared", false);
int id = dstNet.addLayer(name, "Normalize", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "PriorBox")
{
if (hasLayerAttr(layer, "min_size"))
layerParams.set("min_size", getLayerAttr(layer, "min_size").i());
if (hasLayerAttr(layer, "max_size"))
layerParams.set("max_size", getLayerAttr(layer, "max_size").i());
if (hasLayerAttr(layer, "flip"))
layerParams.set("flip", getLayerAttr(layer, "flip").b());
if (hasLayerAttr(layer, "clip"))
layerParams.set("clip", getLayerAttr(layer, "clip").b());
if (hasLayerAttr(layer, "offset"))
layerParams.set("offset", getLayerAttr(layer, "offset").f());
if (hasLayerAttr(layer, "step"))
layerParams.set("step", getLayerAttr(layer, "step").f());
const std::string paramNames[] = {"variance", "aspect_ratio", "scales",
"width", "height"};
for (int i = 0; i < 5; ++i)
{
if (hasLayerAttr(layer, paramNames[i]))
{
Mat values = getTensorContent(getLayerAttr(layer, paramNames[i]).tensor());
layerParams.set(paramNames[i],
DictValue::arrayReal<float*>((float*)values.data, values.total()));
}
}
int id = dstNet.addLayer(name, "PriorBox", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 1);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "DetectionOutput")
{
// op: "DetectionOutput"
// input_0: "locations"
// input_1: "classifications"
// input_2: "prior_boxes"
if (hasLayerAttr(layer, "num_classes"))
layerParams.set("num_classes", getLayerAttr(layer, "num_classes").i());
if (hasLayerAttr(layer, "share_location"))
layerParams.set("share_location", getLayerAttr(layer, "share_location").b());
if (hasLayerAttr(layer, "background_label_id"))
layerParams.set("background_label_id", getLayerAttr(layer, "background_label_id").i());
if (hasLayerAttr(layer, "nms_threshold"))
layerParams.set("nms_threshold", getLayerAttr(layer, "nms_threshold").f());
if (hasLayerAttr(layer, "top_k"))
layerParams.set("top_k", getLayerAttr(layer, "top_k").i());
if (hasLayerAttr(layer, "code_type"))
layerParams.set("code_type", getLayerAttr(layer, "code_type").s());
if (hasLayerAttr(layer, "keep_top_k"))
layerParams.set("keep_top_k", getLayerAttr(layer, "keep_top_k").i());
if (hasLayerAttr(layer, "confidence_threshold"))
layerParams.set("confidence_threshold", getLayerAttr(layer, "confidence_threshold").f());
if (hasLayerAttr(layer, "loc_pred_transposed"))
layerParams.set("loc_pred_transposed", getLayerAttr(layer, "loc_pred_transposed").b());
int id = dstNet.addLayer(name, "DetectionOutput", layerParams);
layer_id[name] = id;
for (int i = 0; i < 3; ++i)
connect(layer_id, dstNet, parsePin(layer.input(i)), id, i);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
else if (type == "Softmax")
{
if (hasLayerAttr(layer, "axis"))
layerParams.set("axis", getLayerAttr(layer, "axis").i());
int id = dstNet.addLayer(name, "Softmax", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "Abs" || type == "Tanh" || type == "Sigmoid" ||
type == "Relu" || type == "Elu" ||
type == "Identity" || type == "Relu6")
{
std::string dnnType = type;
if (type == "Abs") dnnType = "AbsVal";
else if (type == "Tanh") dnnType = "TanH";
else if (type == "Relu") dnnType = "ReLU";
else if (type == "Relu6") dnnType = "ReLU6";
else if (type == "Elu") dnnType = "ELU";
int id = dstNet.addLayer(name, dnnType, layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else
{
printLayerAttr(layer);
CV_Error_(Error::StsError, ("Unknown layer type %s in op %s", type.c_str(), name.c_str()));
}
}
}
} // namespace
#endif //HAVE_PROTOBUF
Net readNetFromTensorflow(const String &model, const String &config)
{
TFImporter importer(model.c_str(), config.c_str());
Net net;
importer.populateNet(net);
return net;
}
Net readNetFromTensorflow(const char* bufferModel, size_t lenModel,
const char* bufferConfig, size_t lenConfig)
{
TFImporter importer(bufferModel, lenModel, bufferConfig, lenConfig);
Net net;
importer.populateNet(net);
return net;
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace