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STT-tensorflow/tensorflow/lite/toco/export_tensorflow.cc
Benoit Jacob 517381ecd9 Fix SplitV support. 
PiperOrigin-RevId: 253155146
2019-06-13 20:05:16 -07:00

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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "google/protobuf/map.h"
#include "google/protobuf/text_format.h"
#include "absl/memory/memory.h"
#include "absl/strings/string_view.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/lite/toco/model.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/runtime/types.h"
#include "tensorflow/lite/toco/tensorflow_util.h"
#include "tensorflow/lite/toco/tooling_util.h"
using tensorflow::DT_BOOL;
using tensorflow::DT_COMPLEX64;
using tensorflow::DT_FLOAT;
using tensorflow::DT_INT16;
using tensorflow::DT_INT32;
using tensorflow::DT_INT64;
using tensorflow::DT_UINT8;
using tensorflow::GraphDef;
using tensorflow::TensorProto;
namespace toco {
namespace {
tensorflow::DataType GetTensorFlowDataType(ArrayDataType data_type,
const string& error_location) {
switch (data_type) {
case ArrayDataType::kBool:
return tensorflow::DT_BOOL;
case ArrayDataType::kFloat:
return tensorflow::DT_FLOAT;
case ArrayDataType::kUint8:
return tensorflow::DT_UINT8;
case ArrayDataType::kInt32:
return tensorflow::DT_INT32;
case ArrayDataType::kInt64:
return tensorflow::DT_INT64;
case ArrayDataType::kString:
return tensorflow::DT_STRING;
case ArrayDataType::kComplex64:
return tensorflow::DT_COMPLEX64;
default:
case ArrayDataType::kNone:
LOG(FATAL) << "Unsupported data type '" << ArrayDataTypeName(data_type)
<< "' in " << error_location;
return tensorflow::DT_INVALID;
}
}
tensorflow::DataType GetTensorFlowDataTypeForOp(ArrayDataType data_type,
const string& op_name) {
return GetTensorFlowDataType(data_type, "op '" + op_name + "'");
}
tensorflow::DataType GetTensorFlowDataType(const Model& model,
const string& array_name) {
return GetTensorFlowDataType(model.GetArray(array_name).data_type,
"array '" + array_name + "'");
}
// TensorFlow sometimes forbids what it calls "legacy scalars",
// which are 1-D shapes where the unique shape size is 1.
// See OpKernel::IsLegacyScalar and OpKernel::allow_legacy_scalars.
// For that reason, we generally avoid creating legacy scalars,
// by detecting the case where a 1-D shape would be of size 1 and
// replacing that by a 0-D shape.
// However, there is a special circumstance where we must not do that
// and must unconditionally create a 1-D shape even if it is going to
// be of size 1: that is the case of bias vectors, with BiasAdd nodes.
// Indeed, TensorFlow requires bias vectors to be 1-D; in the case of
// a depth of 1, that would be a legacy scalar, so in that case we
// must go ahead and keep the shape 1-D, letting it be a legacy scalar.
enum class LegacyScalarPolicy { kAvoidLegacyScalars, kDoCreateLegacyScalars };
void ExportFloatArray(const Shape& input_shape, const float* input_data,
TensorProto* output_tensor,
LegacyScalarPolicy legacy_scalar_policy) {
output_tensor->set_dtype(DT_FLOAT);
const int input_flat_size = RequiredBufferSizeForShape(input_shape);
auto* shape = output_tensor->mutable_tensor_shape();
const int kDims = input_shape.dimensions_count();
if (legacy_scalar_policy == LegacyScalarPolicy::kDoCreateLegacyScalars ||
kDims > 1 || (kDims == 1 && input_shape.dims(0) > 1)) {
for (int i = 0; i < kDims; ++i) {
shape->add_dim()->set_size(input_shape.dims(i));
}
}
output_tensor->set_tensor_content(
string(reinterpret_cast<const char*>(input_data),
sizeof(*input_data) * input_flat_size));
}
void ExportFloatArray(AxesOrder input_axes_order, const Shape& input_shape,
const float* input_data, AxesOrder output_axes_order,
TensorProto* output_tensor,
LegacyScalarPolicy legacy_scalar_policy) {
CHECK_EQ(AxesCount(output_axes_order), AxesCount(input_axes_order));
output_tensor->set_dtype(DT_FLOAT);
CHECK_EQ(input_shape.dimensions_count(), AxesCount(input_axes_order));
const int input_flat_size = RequiredBufferSizeForShape(input_shape);
Shape shuffled_shape;
ShuffleDims(input_shape, input_axes_order, output_axes_order,
&shuffled_shape);
std::vector<float> shuffled_data(input_flat_size);
ShuffleArray(input_shape, input_axes_order, output_axes_order, shuffled_shape,
input_data, shuffled_data.data());
ExportFloatArray(shuffled_shape, shuffled_data.data(), output_tensor,
legacy_scalar_policy);
}
bool HasAlreadyExportedConst(const string& name,
const GraphDef& tensorflow_graph) {
for (const auto& node : tensorflow_graph.node()) {
if (node.op() == "Const" && node.name() == name) {
return true;
}
}
return false;
}
void ConvertFloatTensorConst(const string& name, const Shape& input_shape,
const float* input_data,
AxesOrder input_axes_order,
AxesOrder output_axes_order,
GraphDef* tensorflow_graph,
LegacyScalarPolicy legacy_scalar_policy) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_FLOAT);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
ExportFloatArray(input_axes_order, input_shape, input_data, output_axes_order,
tensor, legacy_scalar_policy);
}
void ConvertFloatTensorConst(const string& name, const Shape& input_shape,
const float* input_data,
AxesOrder input_axes_order,
AxesOrder output_axes_order,
GraphDef* tensorflow_graph) {
ConvertFloatTensorConst(name, input_shape, input_data, input_axes_order,
output_axes_order, tensorflow_graph,
LegacyScalarPolicy::kAvoidLegacyScalars);
}
void ConvertFloatTensorConst(const Model& model, const string& name,
AxesOrder input_axes_order,
AxesOrder output_axes_order,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
CHECK(model.HasArray(name));
const auto& input_array = model.GetArray(name);
const auto& input_shape = input_array.shape();
CHECK(input_array.buffer);
CHECK(input_array.buffer->type == ArrayDataType::kFloat);
const float* input_data =
input_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(name, input_shape, input_data, input_axes_order,
output_axes_order, tensorflow_graph);
}
void ConvertFloatTensorConst(const Model& model, const string& name,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_FLOAT);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
CHECK(model.HasArray(name));
const auto& input_array = model.GetArray(name);
const auto& input_shape = input_array.shape();
CHECK(input_array.buffer);
CHECK(input_array.buffer->type == ArrayDataType::kFloat);
const float* input_data =
input_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ExportFloatArray(input_shape, input_data, tensor,
LegacyScalarPolicy::kAvoidLegacyScalars);
}
void ConvertBoolTensorConst(const Model& model, const string& name,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
CHECK(model.HasArray(name));
const auto& array = model.GetArray(name);
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_BOOL);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_BOOL);
const auto& data = array.GetBuffer<ArrayDataType::kBool>().data;
for (auto index : data) {
tensor->add_bool_val(index);
}
const auto& array_shape = array.shape();
auto* shape = tensor->mutable_tensor_shape();
for (int i = 0; i < array_shape.dimensions_count(); i++) {
shape->add_dim()->set_size(array_shape.dims(i));
}
}
void ConvertIntTensorConst(const Model& model, const string& name,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
CHECK(model.HasArray(name));
const auto& array = model.GetArray(name);
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
const auto& data = array.GetBuffer<ArrayDataType::kInt32>().data;
for (auto index : data) {
tensor->add_int_val(index);
}
const auto& array_shape = array.shape();
auto* shape = tensor->mutable_tensor_shape();
for (int i = 0; i < array_shape.dimensions_count(); i++) {
shape->add_dim()->set_size(array_shape.dims(i));
}
}
void CreateIntTensorConst(const string& name, const std::vector<int32>& data,
const std::vector<int32>& shape,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
for (auto index : data) {
tensor->add_int_val(index);
}
auto* tensor_shape = tensor->mutable_tensor_shape();
int num_elements = 1;
for (int size : shape) {
tensor_shape->add_dim()->set_size(size);
num_elements *= size;
}
CHECK_EQ(num_elements, data.size());
}
void ConvertComplex64TensorConst(const Model& model, const string& name,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
CHECK(model.HasArray(name));
const auto& array = model.GetArray(name);
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_COMPLEX64);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_COMPLEX64);
const auto& data = array.GetBuffer<ArrayDataType::kComplex64>().data;
for (auto index : data) {
tensor->add_scomplex_val(std::real(index));
tensor->add_scomplex_val(std::imag(index));
}
const auto& array_shape = array.shape();
auto* shape = tensor->mutable_tensor_shape();
for (int i = 0; i < array_shape.dimensions_count(); i++) {
shape->add_dim()->set_size(array_shape.dims(i));
}
}
void CreateMatrixShapeTensorConst(const string& name, int rows, int cols,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
const int32 data[2] = {cols, rows};
tensor->set_tensor_content(
string(reinterpret_cast<const char*>(data), sizeof(data)));
auto* shape = tensor->mutable_tensor_shape();
shape->add_dim()->set_size(2);
}
void CreateDummyConcatDimTensorConst(const string& name, int dim,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
tensor->add_int_val(dim);
}
void CreateReshapeShapeTensorConst(const string& name,
const std::vector<int32>& shape,
GraphDef* tensorflow_graph) {
if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
return;
}
tensorflow::NodeDef* const_op = tensorflow_graph->add_node();
const_op->set_op("Const");
const_op->set_name(name);
(*const_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
for (auto s : shape) {
tensor->add_int_val(s);
}
// TensorFlow sometimes forbids what it calls "legacy scalars",
// which are shapes of size 1 where the unique shape size is 1.
// See OpKernel::IsLegacyScalar and OpKernel::allow_legacy_scalars.
if (shape.size() > 1) {
auto* tensor_shape = tensor->mutable_tensor_shape();
tensor_shape->add_dim()->set_size(shape.size());
}
}
string WalkUpToConstantArray(const Model& model, const string& name) {
const Array& original_array = model.GetArray(name);
if (original_array.buffer) {
return name;
}
const auto* op = GetOpWithOutput(model, name);
CHECK(op);
CHECK(op->type == OperatorType::kFakeQuant);
const string& input_of_fakequant_name = op->inputs[0];
const Array& input_of_fakequant = model.GetArray(input_of_fakequant_name);
CHECK(input_of_fakequant.buffer);
return input_of_fakequant_name;
}
void ConvertConvOperator(const Model& model, const ConvOperator& src_op,
GraphDef* tensorflow_graph) {
const bool has_bias = src_op.inputs.size() >= 3;
string conv_output = src_op.outputs[0];
if (has_bias) {
conv_output += "/conv";
}
tensorflow::NodeDef* conv2d_op = tensorflow_graph->add_node();
conv2d_op->set_op("Conv2D");
conv2d_op->set_name(conv_output);
*conv2d_op->add_input() = src_op.inputs[0];
*conv2d_op->add_input() = src_op.inputs[1];
(*conv2d_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string& weights_array_name =
WalkUpToConstantArray(model, src_op.inputs[1]);
const auto& weights_array = model.GetArray(weights_array_name);
CHECK(weights_array.buffer->type == ArrayDataType::kFloat);
ConvertFloatTensorConst(model, weights_array_name, AxesOrder::kOHWI,
AxesOrder::kHWIO, tensorflow_graph);
auto& strides = (*conv2d_op->mutable_attr())["strides"];
strides.mutable_list()->add_i(1);
strides.mutable_list()->add_i(src_op.stride_height);
strides.mutable_list()->add_i(src_op.stride_width);
strides.mutable_list()->add_i(1);
if ((src_op.dilation_width_factor != 1) ||
(src_op.dilation_height_factor != 1)) {
auto& dilations = (*conv2d_op->mutable_attr())["dilations"];
dilations.mutable_list()->add_i(1);
dilations.mutable_list()->add_i(src_op.dilation_height_factor);
dilations.mutable_list()->add_i(src_op.dilation_width_factor);
dilations.mutable_list()->add_i(1);
}
string padding;
if (src_op.padding.type == PaddingType::kSame) {
padding = "SAME";
} else if (src_op.padding.type == PaddingType::kValid) {
padding = "VALID";
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
}
(*conv2d_op->mutable_attr())["padding"].set_s(padding);
if (has_bias) {
tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node();
biasadd_op->set_op("BiasAdd");
biasadd_op->set_name(src_op.outputs[0]);
biasadd_op->add_input(conv_output);
biasadd_op->add_input(src_op.inputs[2]);
(*biasadd_op->mutable_attr())["T"].set_type(DT_FLOAT);
CHECK(model.HasArray(src_op.inputs[2]));
const string& bias_array_name =
WalkUpToConstantArray(model, src_op.inputs[2]);
const auto& bias_array = model.GetArray(bias_array_name);
// TODO(b/62904716) Bias arrays should be 1-D, and used directly.
Shape bias_shape_1d = bias_array.shape();
UnextendShape(&bias_shape_1d, 1);
CHECK(bias_array.buffer->type == ArrayDataType::kFloat);
const float* bias_data =
bias_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(bias_array_name, bias_shape_1d, bias_data,
AxesOrder::kOneAxis, AxesOrder::kOneAxis,
tensorflow_graph,
LegacyScalarPolicy::kDoCreateLegacyScalars);
}
}
void ConvertDepthwiseConvOperator(const Model& model,
const DepthwiseConvOperator& src_op,
GraphDef* tensorflow_graph) {
const bool has_bias = src_op.inputs.size() >= 3;
string conv_output = src_op.outputs[0];
if (has_bias) {
conv_output += "/conv";
}
tensorflow::NodeDef* dc2d_op = tensorflow_graph->add_node();
dc2d_op->set_op("DepthwiseConv2dNative");
dc2d_op->set_name(conv_output);
*dc2d_op->add_input() = src_op.inputs[0];
*dc2d_op->add_input() = src_op.inputs[1];
(*dc2d_op->mutable_attr())["T"].set_type(DT_FLOAT);
// Our internal DepthwiseConv weights are 1 x H x W x OutputDepth.
// We need to convert that to H x W x InputDepth x Multiplier.
// That's only a matter of constructing a Dims object; the actual
// array layout is the same.
CHECK(model.HasArray(src_op.inputs[1]));
const string& src_weights_name =
WalkUpToConstantArray(model, src_op.inputs[1]);
const auto& src_weights_array = model.GetArray(src_weights_name);
const auto& src_weights_shape = src_weights_array.shape();
CHECK_EQ(src_weights_shape.dimensions_count(), 4);
const Shape dst_weights_shape =
Shape({src_weights_shape.dims(1), src_weights_shape.dims(2),
src_weights_shape.dims(3) / src_op.depth_multiplier,
src_op.depth_multiplier});
CHECK_EQ(src_weights_shape.dims(3) % src_op.depth_multiplier, 0);
CHECK(dst_weights_shape.dims(2) * dst_weights_shape.dims(3) ==
src_weights_shape.dims(3));
CHECK_EQ(src_weights_shape.dims(0), 1);
CHECK(src_weights_array.buffer->type == ArrayDataType::kFloat);
const float* src_weights_data =
src_weights_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(src_weights_name, dst_weights_shape, src_weights_data,
AxesOrder::kHWIM, AxesOrder::kHWIM, tensorflow_graph);
auto& strides = (*dc2d_op->mutable_attr())["strides"];
strides.mutable_list()->add_i(1);
strides.mutable_list()->add_i(src_op.stride_height);
strides.mutable_list()->add_i(src_op.stride_width);
strides.mutable_list()->add_i(1);
// TODO(b/116063589): To return a working TF GraphDef, we should be returning
// the correct SpaceToBatchNd and BatchToSpaceND operation before and after
// the conv since TF doesn't support dilations.
if ((src_op.dilation_width_factor != 1) ||
(src_op.dilation_height_factor != 1)) {
auto& dilations = (*dc2d_op->mutable_attr())["dilations"];
dilations.mutable_list()->add_i(1);
dilations.mutable_list()->add_i(src_op.dilation_height_factor);
dilations.mutable_list()->add_i(src_op.dilation_width_factor);
dilations.mutable_list()->add_i(1);
}
string padding;
if (src_op.padding.type == PaddingType::kSame) {
padding = "SAME";
} else if (src_op.padding.type == PaddingType::kValid) {
padding = "VALID";
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
}
(*dc2d_op->mutable_attr())["padding"].set_s(padding);
if (has_bias) {
tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node();
biasadd_op->set_op("BiasAdd");
biasadd_op->set_name(src_op.outputs[0]);
biasadd_op->add_input(conv_output);
biasadd_op->add_input(src_op.inputs[2]);
(*biasadd_op->mutable_attr())["T"].set_type(DT_FLOAT);
CHECK(model.HasArray(src_op.inputs[2]));
const string& bias_name = WalkUpToConstantArray(model, src_op.inputs[2]);
const auto& bias_array = model.GetArray(bias_name);
// TODO(b/62904716) Bias arrays should be 1-D, and used directly.
Shape bias_shape_1d = bias_array.shape();
UnextendShape(&bias_shape_1d, 1);
CHECK(bias_array.buffer->type == ArrayDataType::kFloat);
const float* bias_data =
bias_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(bias_name, bias_shape_1d, bias_data,
AxesOrder::kOneAxis, AxesOrder::kOneAxis,
tensorflow_graph,
LegacyScalarPolicy::kDoCreateLegacyScalars);
}
}
void ConvertTransposeConvOperator(const Model& model,
const TransposeConvOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* conv2d_op = tensorflow_graph->add_node();
conv2d_op->set_op("Conv2DBackpropInput");
conv2d_op->set_name(src_op.outputs[0]);
*conv2d_op->add_input() = src_op.inputs[0];
*conv2d_op->add_input() = src_op.inputs[1];
*conv2d_op->add_input() = src_op.inputs[2];
(*conv2d_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string& weights_array_name = WalkUpToConstantArray(
model, src_op.inputs[TransposeConvOperator::WEIGHTS]);
const auto& weights_array = model.GetArray(weights_array_name);
CHECK(weights_array.buffer->type == ArrayDataType::kFloat);
ConvertFloatTensorConst(model, weights_array_name, AxesOrder::kOHWI,
AxesOrder::kHWOI, tensorflow_graph);
auto& strides = (*conv2d_op->mutable_attr())["strides"];
strides.mutable_list()->add_i(1);
strides.mutable_list()->add_i(src_op.stride_height);
strides.mutable_list()->add_i(src_op.stride_width);
strides.mutable_list()->add_i(1);
string padding;
if (src_op.padding.type == PaddingType::kSame) {
padding = "SAME";
} else if (src_op.padding.type == PaddingType::kValid) {
padding = "VALID";
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
}
(*conv2d_op->mutable_attr())["padding"].set_s(padding);
}
void ConvertDepthToSpaceOperator(const Model& model,
const DepthToSpaceOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* op = tensorflow_graph->add_node();
op->set_op("DepthToSpace");
op->set_name(src_op.outputs[0]);
*op->add_input() = src_op.inputs[0];
(*op->mutable_attr())["T"].set_type(DT_FLOAT);
(*op->mutable_attr())["block_size"].set_i(src_op.block_size);
}
void ConvertSpaceToDepthOperator(const Model& model,
const SpaceToDepthOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* op = tensorflow_graph->add_node();
op->set_op("SpaceToDepth");
op->set_name(src_op.outputs[0]);
*op->add_input() = src_op.inputs[0];
(*op->mutable_attr())["T"].set_type(DT_FLOAT);
(*op->mutable_attr())["block_size"].set_i(src_op.block_size);
}
void ConvertFullyConnectedOperator(const Model& model,
const FullyConnectedOperator& src_op,
GraphDef* tensorflow_graph) {
// Reshape input activations to have the shape expected by the MatMul.
const string reshape_output =
AvailableArrayName(model, src_op.outputs[0] + "/reshape");
const string reshape_shape =
AvailableArrayName(model, reshape_output + "/shape");
const auto& fc_weights_array = model.GetArray(src_op.inputs[1]);
const auto& fc_weights_shape = fc_weights_array.shape();
CHECK_EQ(fc_weights_shape.dimensions_count(), 2);
CreateMatrixShapeTensorConst(reshape_shape, fc_weights_shape.dims(1), -1,
tensorflow_graph);
tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node();
reshape_op->set_op("Reshape");
reshape_op->set_name(reshape_output);
reshape_op->add_input(src_op.inputs[0]);
reshape_op->add_input(reshape_shape);
(*reshape_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
const bool has_bias = src_op.inputs.size() >= 3;
string matmul_output = src_op.outputs[0];
if (has_bias) {
matmul_output += "/matmul";
}
// Transpose the RHS input from column-major to row-major to match TensorFlow
// expectations. This is the inverse of the transpose we do during
// ResolveTensorFlowMatMul.
const string transpose_output =
AvailableArrayName(model, matmul_output + "/transpose_weights");
const string transpose_perm =
AvailableArrayName(model, transpose_output + "/perm");
CreateIntTensorConst(transpose_perm, {1, 0}, {2}, tensorflow_graph);
tensorflow::NodeDef* transpose_op = tensorflow_graph->add_node();
transpose_op->set_op("Transpose");
transpose_op->set_name(transpose_output);
*transpose_op->add_input() = src_op.inputs[1];
*transpose_op->add_input() = transpose_perm;
(*transpose_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
(*transpose_op->mutable_attr())["Tperm"].set_type(DT_INT32);
tensorflow::NodeDef* matmul_op = tensorflow_graph->add_node();
matmul_op->set_op("MatMul");
matmul_op->set_name(matmul_output);
*matmul_op->add_input() = reshape_output;
*matmul_op->add_input() = transpose_op->name();
(*matmul_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*matmul_op->mutable_attr())["transpose_a"].set_b(false);
(*matmul_op->mutable_attr())["transpose_b"].set_b(false);
CHECK(model.HasArray(src_op.inputs[1]));
// Add the bias, if it exists.
if (has_bias) {
tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node();
biasadd_op->set_op("BiasAdd");
biasadd_op->set_name(src_op.outputs[0]);
biasadd_op->add_input(matmul_output);
biasadd_op->add_input(src_op.inputs[2]);
(*biasadd_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
CHECK(model.HasArray(src_op.inputs[2]));
const auto& bias_array = model.GetArray(src_op.inputs[2]);
// TODO(b/62904716) Bias arrays should be 1-D, and used directly.
Shape bias_shape_1d = bias_array.shape();
UnextendShape(&bias_shape_1d, 1);
CHECK(bias_array.buffer);
CHECK(bias_array.buffer->type == ArrayDataType::kFloat);
const float* bias_data =
bias_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(WalkUpToConstantArray(model, src_op.inputs[2]),
bias_shape_1d, bias_data, AxesOrder::kOneAxis,
AxesOrder::kOneAxis, tensorflow_graph,
LegacyScalarPolicy::kDoCreateLegacyScalars);
}
}
void ConvertAddOperator(const Model& model, const AddOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* add_op = tensorflow_graph->add_node();
add_op->set_op("Add");
add_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*add_op->add_input() = src_op.inputs[0];
*add_op->add_input() = src_op.inputs[1];
(*add_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertAddNOperator(const Model& model, const AddNOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* add_op = tensorflow_graph->add_node();
add_op->set_op("AddN");
add_op->set_name(src_op.outputs[0]);
for (const auto& input : src_op.inputs) {
*add_op->add_input() = input;
}
(*add_op->mutable_attr())["N"].set_i(src_op.inputs.size());
(*add_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertMulOperator(const Model& model, const MulOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* mul_op = tensorflow_graph->add_node();
mul_op->set_op("Mul");
mul_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*mul_op->add_input() = src_op.inputs[0];
*mul_op->add_input() = src_op.inputs[1];
(*mul_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertDivOperator(const Model& model, const DivOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* div_op = tensorflow_graph->add_node();
div_op->set_op("Div");
div_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*div_op->add_input() = src_op.inputs[0];
*div_op->add_input() = src_op.inputs[1];
(*div_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertReluOperator(const Model& model, const ReluOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* relu_op = tensorflow_graph->add_node();
relu_op->set_op("Relu");
relu_op->set_name(src_op.outputs[0]);
*relu_op->add_input() = src_op.inputs[0];
(*relu_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertRelu1Operator(const Relu1Operator& src_op,
GraphDef* tensorflow_graph) {
const string max_bounds = src_op.outputs[0] + "/max_bounds";
const string min_bounds = src_op.outputs[0] + "/min_bounds";
const string max_output = src_op.outputs[0] + "/max_output";
tensorflow::NodeDef* max_bounds_const_op = tensorflow_graph->add_node();
max_bounds_const_op->set_op("Const");
max_bounds_const_op->set_name(max_bounds);
(*max_bounds_const_op->mutable_attr())["dtype"].set_type(DT_FLOAT);
auto* max_bounds_const_op_tensor =
(*max_bounds_const_op->mutable_attr())["value"].mutable_tensor();
max_bounds_const_op_tensor->set_dtype(DT_FLOAT);
max_bounds_const_op_tensor->add_float_val(-1.0f);
tensorflow::NodeDef* min_bounds_const_op = tensorflow_graph->add_node();
min_bounds_const_op->set_op("Const");
min_bounds_const_op->set_name(min_bounds);
(*min_bounds_const_op->mutable_attr())["dtype"].set_type(DT_FLOAT);
auto* min_bounds_const_op_tensor =
(*min_bounds_const_op->mutable_attr())["value"].mutable_tensor();
min_bounds_const_op_tensor->set_dtype(DT_FLOAT);
min_bounds_const_op_tensor->add_float_val(1.0f);
tensorflow::NodeDef* max_op = tensorflow_graph->add_node();
max_op->set_op("Maximum");
max_op->set_name(max_output);
*max_op->add_input() = src_op.inputs[0];
*max_op->add_input() = max_bounds;
(*max_op->mutable_attr())["T"].set_type(DT_FLOAT);
tensorflow::NodeDef* min_op = tensorflow_graph->add_node();
min_op->set_op("Minimum");
min_op->set_name(src_op.outputs[0]);
*min_op->add_input() = max_output;
*min_op->add_input() = min_bounds;
(*min_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertRelu6Operator(const Relu6Operator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* relu_op = tensorflow_graph->add_node();
relu_op->set_op("Relu6");
relu_op->set_name(src_op.outputs[0]);
*relu_op->add_input() = src_op.inputs[0];
(*relu_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertLogOperator(const LogOperator& src_op, GraphDef* tensorflow_graph) {
tensorflow::NodeDef* op = tensorflow_graph->add_node();
op->set_op("Log");
op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*op->add_input() = src_op.inputs[0];
(*op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertLogisticOperator(const LogisticOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* relu_op = tensorflow_graph->add_node();
relu_op->set_op("Sigmoid");
relu_op->set_name(src_op.outputs[0]);
*relu_op->add_input() = src_op.inputs[0];
(*relu_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertTanhOperator(const TanhOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* tanh_op = tensorflow_graph->add_node();
tanh_op->set_op("Tanh");
tanh_op->set_name(src_op.outputs[0]);
*tanh_op->add_input() = src_op.inputs[0];
(*tanh_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op,
GraphDef* tensorflow_graph) {
string softmax_input;
Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]);
if (providing_op != nullptr && providing_op->type == OperatorType::kReshape) {
softmax_input = src_op.inputs[0];
} else {
// Insert a reshape operator that reduces the dimensions down to the 2 that
// are required for TensorFlow Logits.
const string reshape_output = src_op.outputs[0] + "/softmax_insert_reshape";
const string softmax_size = src_op.outputs[0] + "/softmax_insert_size";
softmax_input = reshape_output;
tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node();
reshape_op->set_op("Reshape");
reshape_op->set_name(reshape_output);
*reshape_op->add_input() = src_op.inputs[0];
*reshape_op->add_input() = softmax_size;
(*reshape_op->mutable_attr())["T"].set_type(DT_FLOAT);
const auto& input_shape = model.GetArray(src_op.inputs[0]).shape();
int32 flattened_size = 1;
for (int i = 0; i < input_shape.dimensions_count() - 1; ++i) {
flattened_size *= input_shape.dims(i);
}
const std::vector<int32> shape_data = {
flattened_size, input_shape.dims(input_shape.dimensions_count() - 1)};
CreateReshapeShapeTensorConst(softmax_size, shape_data, tensorflow_graph);
}
tensorflow::NodeDef* softmax_op = tensorflow_graph->add_node();
softmax_op->set_op("Softmax");
softmax_op->set_name(src_op.outputs[0]);
*softmax_op->add_input() = softmax_input;
// TensorFlow's Softmax doesn't seem to admit a 'beta' parameter
CHECK_EQ(src_op.beta, 1.f);
(*softmax_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertLogSoftmaxOperator(const Model& model,
const LogSoftmaxOperator& src_op,
GraphDef* tensorflow_graph) {
string softmax_input;
Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]);
if (providing_op != nullptr && providing_op->type == OperatorType::kReshape) {
softmax_input = src_op.inputs[0];
} else {
// Insert a reshape operator that reduces the dimensions down to the 2 that
// are required for TensorFlow Logits.
const string reshape_output =
src_op.outputs[0] + "/log_softmax_insert_reshape";
const string softmax_size = src_op.outputs[0] + "/log_softmax_insert_size";
softmax_input = reshape_output;
tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node();
reshape_op->set_op("Reshape");
reshape_op->set_name(reshape_output);
*reshape_op->add_input() = src_op.inputs[0];
*reshape_op->add_input() = softmax_size;
(*reshape_op->mutable_attr())["T"].set_type(DT_FLOAT);
const auto& input_shape = model.GetArray(src_op.inputs[0]).shape();
int32 flattened_size = 1;
for (int i = 0; i < input_shape.dimensions_count() - 1; ++i) {
flattened_size *= input_shape.dims(i);
}
const std::vector<int32> shape_data = {
flattened_size, input_shape.dims(input_shape.dimensions_count() - 1)};
CreateReshapeShapeTensorConst(softmax_size, shape_data, tensorflow_graph);
}
tensorflow::NodeDef* log_softmax_op = tensorflow_graph->add_node();
log_softmax_op->set_op("LogSoftmax");
log_softmax_op->set_name(src_op.outputs[0]);
*log_softmax_op->add_input() = softmax_input;
(*log_softmax_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertL2NormalizationOperator(const L2NormalizationOperator& src_op,
GraphDef* tensorflow_graph) {
const string square_output = src_op.outputs[0] + "/square";
const string sum_reduction_indices = src_op.outputs[0] + "/reduction_indices";
const string sum_output = src_op.outputs[0] + "/sum";
const string rsqrt_output = src_op.outputs[0] + "/rsqrt";
const string rsqrt_tiled_output = src_op.outputs[0] + "/rsqrt_tiled";
tensorflow::NodeDef* sum_reduction_indices_op = tensorflow_graph->add_node();
sum_reduction_indices_op->set_op("Const");
sum_reduction_indices_op->set_name(sum_reduction_indices);
(*sum_reduction_indices_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* sum_reduction_indices_tensor =
(*sum_reduction_indices_op->mutable_attr())["value"].mutable_tensor();
sum_reduction_indices_tensor->set_dtype(DT_INT32);
auto* sum_reduction_indices_shape =
sum_reduction_indices_tensor->mutable_tensor_shape();
auto* sum_reduction_indices_dim = sum_reduction_indices_shape->add_dim();
sum_reduction_indices_dim->set_size(2);
sum_reduction_indices_tensor->add_int_val(0);
sum_reduction_indices_tensor->add_int_val(1);
tensorflow::NodeDef* square_op = tensorflow_graph->add_node();
square_op->set_op("Square");
square_op->set_name(square_output);
*square_op->add_input() = src_op.inputs[0];
(*square_op->mutable_attr())["T"].set_type(DT_FLOAT);
tensorflow::NodeDef* sum_op = tensorflow_graph->add_node();
sum_op->set_op("Sum");
sum_op->set_name(sum_output);
*sum_op->add_input() = square_output;
*sum_op->add_input() = sum_reduction_indices;
(*sum_op->mutable_attr())["T"].set_type(DT_FLOAT);
tensorflow::NodeDef* rsqrt_op = tensorflow_graph->add_node();
rsqrt_op->set_op("Rsqrt");
rsqrt_op->set_name(rsqrt_output);
*rsqrt_op->add_input() = sum_output;
(*rsqrt_op->mutable_attr())["T"].set_type(DT_FLOAT);
tensorflow::NodeDef* mul_op = tensorflow_graph->add_node();
mul_op->set_op("Mul");
mul_op->set_name(src_op.outputs[0]);
*mul_op->add_input() = src_op.inputs[0];
*mul_op->add_input() = rsqrt_output;
(*mul_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertLocalResponseNormalizationOperator(
const LocalResponseNormalizationOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* lrn_op = tensorflow_graph->add_node();
lrn_op->set_op("LRN");
lrn_op->set_name(src_op.outputs[0]);
*lrn_op->add_input() = src_op.inputs[0];
(*lrn_op->mutable_attr())["depth_radius"].set_i(src_op.range);
(*lrn_op->mutable_attr())["bias"].set_f(src_op.bias);
(*lrn_op->mutable_attr())["alpha"].set_f(src_op.alpha);
(*lrn_op->mutable_attr())["beta"].set_f(src_op.beta);
}
void ConvertFakeQuantOperator(const FakeQuantOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* fakequant_op = tensorflow_graph->add_node();
fakequant_op->set_op("FakeQuantWithMinMaxArgs");
fakequant_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*fakequant_op->add_input() = src_op.inputs[0];
CHECK(src_op.minmax);
(*fakequant_op->mutable_attr())["min"].set_f(src_op.minmax->min);
(*fakequant_op->mutable_attr())["max"].set_f(src_op.minmax->max);
if (src_op.num_bits) {
(*fakequant_op->mutable_attr())["num_bits"].set_i(src_op.num_bits);
}
if (src_op.narrow_range) {
(*fakequant_op->mutable_attr())["narrow_range"].set_b(src_op.narrow_range);
}
}
void ConvertMaxPoolOperator(const MaxPoolOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* maxpool_op = tensorflow_graph->add_node();
maxpool_op->set_op("MaxPool");
maxpool_op->set_name(src_op.outputs[0]);
*maxpool_op->add_input() = src_op.inputs[0];
auto& strides = (*maxpool_op->mutable_attr())["strides"];
strides.mutable_list()->add_i(1);
strides.mutable_list()->add_i(src_op.stride_height);
strides.mutable_list()->add_i(src_op.stride_width);
strides.mutable_list()->add_i(1);
string padding;
if (src_op.padding.type == PaddingType::kSame) {
padding = "SAME";
} else if (src_op.padding.type == PaddingType::kValid) {
padding = "VALID";
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
}
(*maxpool_op->mutable_attr())["padding"].set_s(padding);
(*maxpool_op->mutable_attr())["T"].set_type(DT_FLOAT);
auto& ksize = (*maxpool_op->mutable_attr())["ksize"];
ksize.mutable_list()->add_i(1);
ksize.mutable_list()->add_i(src_op.kheight);
ksize.mutable_list()->add_i(src_op.kwidth);
ksize.mutable_list()->add_i(1);
}
void ConvertAveragePoolOperator(const AveragePoolOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* avgpool_op = tensorflow_graph->add_node();
avgpool_op->set_op("AvgPool");
avgpool_op->set_name(src_op.outputs[0]);
*avgpool_op->add_input() = src_op.inputs[0];
auto& strides = (*avgpool_op->mutable_attr())["strides"];
strides.mutable_list()->add_i(1);
strides.mutable_list()->add_i(src_op.stride_height);
strides.mutable_list()->add_i(src_op.stride_width);
strides.mutable_list()->add_i(1);
string padding;
if (src_op.padding.type == PaddingType::kSame) {
padding = "SAME";
} else if (src_op.padding.type == PaddingType::kValid) {
padding = "VALID";
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
}
(*avgpool_op->mutable_attr())["padding"].set_s(padding);
(*avgpool_op->mutable_attr())["T"].set_type(DT_FLOAT);
auto& ksize = (*avgpool_op->mutable_attr())["ksize"];
ksize.mutable_list()->add_i(1);
ksize.mutable_list()->add_i(src_op.kheight);
ksize.mutable_list()->add_i(src_op.kwidth);
ksize.mutable_list()->add_i(1);
}
void ConvertConcatenationOperator(const Model& model,
const ConcatenationOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* dc_op = tensorflow_graph->add_node();
dc_op->set_op("ConcatV2");
dc_op->set_name(src_op.outputs[0]);
const string dummy_axis = src_op.outputs[0] + "/axis";
CreateDummyConcatDimTensorConst(dummy_axis, src_op.axis, tensorflow_graph);
for (const auto& input : src_op.inputs) {
*dc_op->add_input() = input;
}
*dc_op->add_input() = dummy_axis;
(*dc_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*dc_op->mutable_attr())["Tidx"].set_type(DT_INT32);
(*dc_op->mutable_attr())["N"].set_i(src_op.inputs.size());
}
void ConvertTensorFlowReshapeOperator(const Model& model,
const TensorFlowReshapeOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* reshape_op = tensorflow_graph->add_node();
reshape_op->set_op("Reshape");
reshape_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*reshape_op->add_input() = src_op.inputs[0];
*reshape_op->add_input() = src_op.inputs[1];
(*reshape_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
const auto& shape_array = model.GetArray(src_op.inputs[1]);
QCHECK(shape_array.data_type == ArrayDataType::kInt32)
<< "Only int32 shape is supported.";
QCHECK(shape_array.buffer != nullptr)
<< "Shape inferred at runtime is not supported.";
const auto& shape_data = shape_array.GetBuffer<ArrayDataType::kInt32>().data;
CreateReshapeShapeTensorConst(src_op.inputs[1], shape_data, tensorflow_graph);
}
void ConvertL2PoolOperator(const L2PoolOperator& src_op,
GraphDef* tensorflow_graph) {
const string square_output = src_op.outputs[0] + "/square";
const string avgpool_output = src_op.outputs[0] + "/avgpool";
tensorflow::NodeDef* square_op = tensorflow_graph->add_node();
square_op->set_op("Square");
square_op->set_name(square_output);
*square_op->add_input() = src_op.inputs[0];
(*square_op->mutable_attr())["T"].set_type(DT_FLOAT);
string padding;
if (src_op.padding.type == PaddingType::kSame) {
padding = "SAME";
} else if (src_op.padding.type == PaddingType::kValid) {
padding = "VALID";
} else {
LOG(FATAL) << "Bad padding (only SAME and VALID are supported)";
}
tensorflow::NodeDef* avgpool_op = tensorflow_graph->add_node();
avgpool_op->set_op("AvgPool");
avgpool_op->set_name(avgpool_output);
*avgpool_op->add_input() = square_output;
auto& strides = (*avgpool_op->mutable_attr())["strides"];
strides.mutable_list()->add_i(1);
strides.mutable_list()->add_i(src_op.stride_height);
strides.mutable_list()->add_i(src_op.stride_width);
strides.mutable_list()->add_i(1);
(*avgpool_op->mutable_attr())["padding"].set_s(padding);
(*avgpool_op->mutable_attr())["T"].set_type(DT_FLOAT);
auto& ksize = (*avgpool_op->mutable_attr())["ksize"];
ksize.mutable_list()->add_i(1);
ksize.mutable_list()->add_i(src_op.kheight);
ksize.mutable_list()->add_i(src_op.kwidth);
ksize.mutable_list()->add_i(1);
tensorflow::NodeDef* sqrt_op = tensorflow_graph->add_node();
sqrt_op->set_op("Sqrt");
sqrt_op->set_name(src_op.outputs[0]);
*sqrt_op->add_input() = avgpool_output;
(*sqrt_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertSquareOperator(const TensorFlowSquareOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* square_op = tensorflow_graph->add_node();
square_op->set_op("Square");
square_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*square_op->add_input() = src_op.inputs[0];
(*square_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertSqrtOperator(const TensorFlowSqrtOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* sqrt_op = tensorflow_graph->add_node();
sqrt_op->set_op("Sqrt");
sqrt_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*sqrt_op->add_input() = src_op.inputs[0];
(*sqrt_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertRsqrtOperator(const Model& model,
const TensorFlowRsqrtOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* rsqrt_op = tensorflow_graph->add_node();
rsqrt_op->set_op("Rsqrt");
rsqrt_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*rsqrt_op->add_input() = src_op.inputs[0];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*rsqrt_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertSplitOperator(const Model& model,
const TensorFlowSplitOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* split_op = tensorflow_graph->add_node();
split_op->set_op("Split");
split_op->set_name(src_op.outputs[0]);
for (const auto& input : src_op.inputs) {
*split_op->add_input() = input;
}
(*split_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
(*split_op->mutable_attr())["num_split"].set_i(src_op.num_split);
const auto& split_dim_array = model.GetArray(src_op.inputs[0]);
CHECK(split_dim_array.buffer);
CHECK(split_dim_array.data_type == ArrayDataType::kInt32);
const auto& split_dim_data =
split_dim_array.GetBuffer<ArrayDataType::kInt32>().data;
CHECK_EQ(split_dim_data.size(), 1);
const int split_dim = split_dim_data[0];
CreateDummyConcatDimTensorConst(src_op.inputs[0], split_dim,
tensorflow_graph);
}
void ConvertSplitVOperator(const Model& model,
const TensorFlowSplitVOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* split_v_op = tensorflow_graph->add_node();
split_v_op->set_op("SplitV");
split_v_op->set_name(src_op.outputs[0]);
for (const auto& input : src_op.inputs) {
*split_v_op->add_input() = input;
}
(*split_v_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
(*split_v_op->mutable_attr())["Tlen"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
(*split_v_op->mutable_attr())["num_split"].set_i(src_op.num_split);
ConvertIntTensorConst(model, src_op.inputs[1], tensorflow_graph);
}
void ConvertCastOperator(const Model& model, const CastOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* cast_op = tensorflow_graph->add_node();
cast_op->set_op("Cast");
cast_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*cast_op->add_input() = src_op.inputs[0];
(*cast_op->mutable_attr())["DstT"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
(*cast_op->mutable_attr())["SrcT"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
}
void ConvertFloorOperator(const Model& model, const FloorOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* floor_op = tensorflow_graph->add_node();
floor_op->set_op("Floor");
floor_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*floor_op->add_input() = src_op.inputs[0];
(*floor_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertCeilOperator(const Model& model, const CeilOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* ceil_op = tensorflow_graph->add_node();
ceil_op->set_op("Ceil");
ceil_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*ceil_op->add_input() = src_op.inputs[0];
(*ceil_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertRoundOperator(const Model& model, const RoundOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* round_op = tensorflow_graph->add_node();
round_op->set_op("Round");
round_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*round_op->add_input() = src_op.inputs[0];
(*round_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertGatherOperator(const Model& model, const GatherOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* gather_op = tensorflow_graph->add_node();
gather_op->set_op("GatherV2");
gather_op->set_name(src_op.outputs[0]);
*gather_op->add_input() = src_op.inputs[0];
*gather_op->add_input() = src_op.inputs[1];
if (!src_op.axis) {
// Dynamic axis.
CHECK_EQ(src_op.inputs.size(), 3);
*gather_op->add_input() = src_op.inputs[2];
} else {
// Constant axis.
CHECK_EQ(src_op.inputs.size(), 2);
const string gather_axis =
AvailableArrayName(model, gather_op->name() + "/axis");
CreateIntTensorConst(gather_axis, {src_op.axis.value()}, {},
tensorflow_graph);
*gather_op->add_input() = gather_axis;
}
(*gather_op->mutable_attr())["Tindices"].set_type(DT_INT32);
(*gather_op->mutable_attr())["Taxis"].set_type(DT_INT32);
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*gather_op->mutable_attr())["Tparams"].set_type(params_type);
}
void ConvertArgMaxOperator(const Model& model, const ArgMaxOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* argmax_op = tensorflow_graph->add_node();
argmax_op->set_op("ArgMax");
argmax_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*argmax_op->add_input() = src_op.inputs[0];
*argmax_op->add_input() = src_op.inputs[1];
(*argmax_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*argmax_op->mutable_attr())["Tidx"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
(*argmax_op->mutable_attr())["output_type"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertArgMinOperator(const Model& model, const ArgMinOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* argmin_op = tensorflow_graph->add_node();
argmin_op->set_op("ArgMin");
argmin_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*argmin_op->add_input() = src_op.inputs[0];
*argmin_op->add_input() = src_op.inputs[1];
(*argmin_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*argmin_op->mutable_attr())["Tidx"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
(*argmin_op->mutable_attr())["output_type"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertTransposeOperator(const Model& model,
const TransposeOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* transpose_op = tensorflow_graph->add_node();
transpose_op->set_op("Transpose");
transpose_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*transpose_op->add_input() = src_op.inputs[0];
*transpose_op->add_input() = src_op.inputs[1];
(*transpose_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*transpose_op->mutable_attr())["Tperm"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
}
void ConvertTensorFlowShapeOperator(const Model& model,
const TensorFlowShapeOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* shape_op = tensorflow_graph->add_node();
shape_op->set_op("Shape");
shape_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*shape_op->add_input() = src_op.inputs[0];
(*shape_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*shape_op->mutable_attr())["out_type"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
}
void ConvertRankOperator(const Model& model,
const TensorFlowRankOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* rank_op = tensorflow_graph->add_node();
rank_op->set_op("Rank");
rank_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*rank_op->add_input() = src_op.inputs[0];
(*rank_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
}
void ConvertRangeOperator(const Model& model, const RangeOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* range_op = tensorflow_graph->add_node();
range_op->set_op("Range");
range_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 3);
*range_op->add_input() = src_op.inputs[0];
*range_op->add_input() = src_op.inputs[1];
*range_op->add_input() = src_op.inputs[2];
(*range_op->mutable_attr())["Tidx"].set_type(
GetTensorFlowDataTypeForOp(src_op.dtype, /*op_name=*/src_op.outputs[0]));
}
void ConvertPackOperator(const Model& model, const PackOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* pack_op = tensorflow_graph->add_node();
pack_op->set_op("Pack");
pack_op->set_name(src_op.outputs[0]);
for (const auto& input : src_op.inputs) {
*pack_op->add_input() = input;
}
(*pack_op->mutable_attr())["axis"].set_i(src_op.axis);
(*pack_op->mutable_attr())["N"].set_i(src_op.inputs.size());
(*pack_op->mutable_attr())["T"].set_type(
GetTensorFlowDataTypeForOp(src_op.dtype, src_op.outputs[0]));
}
void ConvertFillOperator(const Model& model, const FillOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* fill_op = tensorflow_graph->add_node();
fill_op->set_op("Fill");
fill_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*fill_op->add_input() = src_op.inputs[0];
*fill_op->add_input() = src_op.inputs[1];
(*fill_op->mutable_attr())["index_type"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*fill_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
}
void ConvertFloorDivOperator(const Model& model, const FloorDivOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* floor_div_op = tensorflow_graph->add_node();
floor_div_op->set_op("FloorDiv");
floor_div_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*floor_div_op->add_input() = src_op.inputs[0];
*floor_div_op->add_input() = src_op.inputs[1];
(*floor_div_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
}
void ConvertFloorModOperator(const Model& model, const FloorModOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* floor_mod_op = tensorflow_graph->add_node();
floor_mod_op->set_op("FloorMod");
floor_mod_op->set_name(src_op.outputs[0]);
DCHECK_EQ(src_op.inputs.size(), 2);
*floor_mod_op->add_input() = src_op.inputs[0];
*floor_mod_op->add_input() = src_op.inputs[1];
(*floor_mod_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
}
void ConvertExpandDimsOperator(const Model& model,
const ExpandDimsOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* expand_dims_op = tensorflow_graph->add_node();
expand_dims_op->set_op("ExpandDims");
expand_dims_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*expand_dims_op->add_input() = src_op.inputs[0];
*expand_dims_op->add_input() = src_op.inputs[1];
(*expand_dims_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.inputs[0]));
(*expand_dims_op->mutable_attr())["Tdim"].set_type(
GetTensorFlowDataType(model, src_op.inputs[1]));
}
void ConvertResizeBilinearOperator(const Model& model,
const ResizeBilinearOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* resize_op = tensorflow_graph->add_node();
resize_op->set_op("ResizeBilinear");
resize_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*resize_op->add_input() = src_op.inputs[0];
*resize_op->add_input() = src_op.inputs[1];
(*resize_op->mutable_attr())["T"].set_type(DT_FLOAT);
(*resize_op->mutable_attr())["align_corners"].set_b(src_op.align_corners);
}
void ConvertOneHotOperator(const Model& model, const OneHotOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* onehot_op = tensorflow_graph->add_node();
onehot_op->set_op("OneHot");
onehot_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 4);
for (const auto& input : src_op.inputs) {
*onehot_op->add_input() = input;
}
(*onehot_op->mutable_attr())["T"].set_type(
GetTensorFlowDataType(model, src_op.outputs[0]));
(*onehot_op->mutable_attr())["axis"].set_i(src_op.axis);
}
namespace {
// TODO(aselle): Remove when available in absl
absl::string_view FindLongestCommonPrefix(absl::string_view a,
absl::string_view b) {
if (a.empty() || b.empty()) return absl::string_view();
const char* pa = a.data();
const char* pb = b.data();
string::difference_type count = 0;
const string::difference_type limit = std::min(a.size(), b.size());
while (count < limit && *pa == *pb) {
++pa;
++pb;
++count;
}
return absl::string_view(a.data(), count);
}
} // namespace
void ConvertLstmCellOperator(const Model& model, const LstmCellOperator& src_op,
GraphDef* tensorflow_graph) {
// Find the base name
const string base(
FindLongestCommonPrefix(src_op.outputs[LstmCellOperator::STATE_OUTPUT],
src_op.outputs[LstmCellOperator::ACTIV_OUTPUT]));
// Concatenate inputs
const string concat_output = base + "basic_lstm_cell/concat";
// Op names have been chosen to match the tf.slim LSTM naming
// as closely as possible.
const int axis =
model.GetArray(src_op.inputs[LstmCellOperator::PREV_ACTIV_INPUT])
.shape()
.dimensions_count() -
1;
// Note that DATA_INPUT may have extra size 1 dimensions, but TF concat
// works the same since the tensor has the same underlying data layout.
const string axis_output = concat_output + "/axis";
CreateDummyConcatDimTensorConst(axis_output, axis, tensorflow_graph);
tensorflow::NodeDef* concat_op = tensorflow_graph->add_node();
concat_op->set_op("ConcatV2");
concat_op->set_name(concat_output);
*concat_op->add_input() = src_op.inputs[LstmCellOperator::DATA_INPUT];
*concat_op->add_input() = src_op.inputs[LstmCellOperator::PREV_ACTIV_INPUT];
*concat_op->add_input() = axis_output;
(*concat_op->mutable_attr())["T"].set_type(DT_FLOAT);
(*concat_op->mutable_attr())["Tidx"].set_type(DT_INT32);
(*concat_op->mutable_attr())["N"].set_i(2); // Number of inputs
// Write weights
const string weights_output = base + "weights";
CHECK(model.HasArray(src_op.inputs[LstmCellOperator::WEIGHTS_INPUT]));
const string weights_name = WalkUpToConstantArray(
model, src_op.inputs[LstmCellOperator::WEIGHTS_INPUT]);
const auto& weights_array = model.GetArray(weights_name);
// Convert 4D FullyConnected weights into 2D matrix
const auto& weights_shape = weights_array.shape();
CHECK_EQ(weights_shape.dimensions_count(), 2);
CHECK(weights_array.buffer);
CHECK(weights_array.buffer->type == ArrayDataType::kFloat);
const float* weights_data =
weights_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(weights_output, weights_shape, weights_data,
AxesOrder::kCR, AxesOrder::kRC, tensorflow_graph);
// Fully connected matrix multiply
const string matmul_output = base + "MatMul";
tensorflow::NodeDef* matmul_op = tensorflow_graph->add_node();
matmul_op->set_op("MatMul");
matmul_op->set_name(matmul_output);
*matmul_op->add_input() = concat_output;
*matmul_op->add_input() = weights_output;
(*matmul_op->mutable_attr())["transpose_a"].set_b(false);
(*matmul_op->mutable_attr())["transpose_b"].set_b(false);
(*matmul_op->mutable_attr())["T"].set_type(DT_FLOAT);
// Write biases
const string biases_output = base + "biases";
CHECK(model.HasArray(src_op.inputs[LstmCellOperator::BIASES_INPUT]));
const string bias_name = WalkUpToConstantArray(
model, src_op.inputs[LstmCellOperator::BIASES_INPUT]);
const auto& bias_array = model.GetArray(bias_name);
// TODO(b/62904716) Bias arrays should be 1-D, and used directly.
Shape bias_shape_1d = bias_array.shape();
UnextendShape(&bias_shape_1d, 1);
CHECK(bias_array.buffer);
CHECK(bias_array.buffer->type == ArrayDataType::kFloat);
const float* bias_data =
bias_array.GetBuffer<ArrayDataType::kFloat>().data.data();
ConvertFloatTensorConst(biases_output, bias_shape_1d, bias_data,
AxesOrder::kOneAxis, AxesOrder::kOneAxis,
tensorflow_graph,
LegacyScalarPolicy::kDoCreateLegacyScalars);
// Add biases
string biasadd_output = base + "BiasAdd";
tensorflow::NodeDef* biasadd_op = tensorflow_graph->add_node();
biasadd_op->set_op("BiasAdd");
biasadd_op->set_name(biasadd_output);
biasadd_op->add_input(matmul_output);
biasadd_op->add_input(biases_output);
(*biasadd_op->mutable_attr())["data_format"].set_s("NHWC");
(*biasadd_op->mutable_attr())["T"].set_type(DT_FLOAT);
// Split
string split_dim_output = base + "split/split_dim";
// The dimension is the same as the concatenation dimension
CreateDummyConcatDimTensorConst(split_dim_output, axis, tensorflow_graph);
string split_output = base + "split";
tensorflow::NodeDef* split_op = tensorflow_graph->add_node();
split_op->set_op("Split");
split_op->set_name(split_output);
*split_op->add_input() = split_dim_output;
*split_op->add_input() = biasadd_output;
(*split_op->mutable_attr())["T"].set_type(DT_FLOAT);
(*split_op->mutable_attr())["num_split"].set_i(4); // Split into four outputs
// Activation functions and memory computations
const string tanh_0_output = base + "Tanh";
tensorflow::NodeDef* tanh_0_op = tensorflow_graph->add_node();
tanh_0_op->set_op("Tanh");
tanh_0_op->set_name(tanh_0_output);
*tanh_0_op->add_input() = split_output + ":1";
(*tanh_0_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string sigmoid_1_output = base + "Sigmoid_1";
tensorflow::NodeDef* logistic_1_op = tensorflow_graph->add_node();
logistic_1_op->set_op("Sigmoid");
logistic_1_op->set_name(sigmoid_1_output);
*logistic_1_op->add_input() = split_output;
(*logistic_1_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string mul_1_output = base + "mul_1";
tensorflow::NodeDef* mul_1_op = tensorflow_graph->add_node();
mul_1_op->set_op("Mul");
mul_1_op->set_name(mul_1_output);
*mul_1_op->add_input() = sigmoid_1_output;
*mul_1_op->add_input() = tanh_0_output;
(*mul_1_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string sigmoid_0_output = base + "Sigmoid";
tensorflow::NodeDef* logistic_2_op = tensorflow_graph->add_node();
logistic_2_op->set_op("Sigmoid");
logistic_2_op->set_name(sigmoid_0_output);
*logistic_2_op->add_input() = split_output + ":2";
(*logistic_2_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string sigmoid_2_output = base + "Sigmoid_2";
tensorflow::NodeDef* logistic_3_op = tensorflow_graph->add_node();
logistic_3_op->set_op("Sigmoid");
logistic_3_op->set_name(sigmoid_2_output);
*logistic_3_op->add_input() = split_output + ":3";
(*logistic_3_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string mul_0_output = base + "mul";
tensorflow::NodeDef* mul_0_op = tensorflow_graph->add_node();
mul_0_op->set_op("Mul");
mul_0_op->set_name(mul_0_output);
*mul_0_op->add_input() = src_op.inputs[LstmCellOperator::PREV_STATE_INPUT];
*mul_0_op->add_input() = sigmoid_0_output;
(*mul_0_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string add_1_output = src_op.outputs[LstmCellOperator::STATE_OUTPUT];
tensorflow::NodeDef* add_1_op = tensorflow_graph->add_node();
add_1_op->set_op("Add");
add_1_op->set_name(add_1_output);
*add_1_op->add_input() = mul_0_output;
*add_1_op->add_input() = mul_1_output;
(*add_1_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string tanh_1_output = base + "Tanh_1";
tensorflow::NodeDef* tanh_1_op = tensorflow_graph->add_node();
tanh_1_op->set_op("Tanh");
tanh_1_op->set_name(tanh_1_output);
*tanh_1_op->add_input() = add_1_output;
(*tanh_1_op->mutable_attr())["T"].set_type(DT_FLOAT);
const string mul_2_output = src_op.outputs[LstmCellOperator::ACTIV_OUTPUT];
tensorflow::NodeDef* mul_2_op = tensorflow_graph->add_node();
mul_2_op->set_op("Mul");
mul_2_op->set_name(mul_2_output);
*mul_2_op->add_input() = tanh_1_output;
*mul_2_op->add_input() = sigmoid_2_output;
(*mul_2_op->mutable_attr())["T"].set_type(DT_FLOAT);
}
void ConvertSpaceToBatchNDOperator(const Model& model,
const SpaceToBatchNDOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("SpaceToBatchND");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 3);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
*new_op->add_input() = src_op.inputs[2];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
(*new_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32);
(*new_op->mutable_attr())["Tpaddings"].set_type(DT_INT32);
}
void ConvertBatchToSpaceNDOperator(const Model& model,
const BatchToSpaceNDOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("BatchToSpaceND");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 3);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
*new_op->add_input() = src_op.inputs[2];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
(*new_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32);
(*new_op->mutable_attr())["Tcrops"].set_type(DT_INT32);
}
void ConvertPadOperator(const Model& model, const PadOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("Pad");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
// Create the params tensor.
tensorflow::NodeDef* params_op = tensorflow_graph->add_node();
params_op->set_op("Const");
params_op->set_name(src_op.inputs[1]);
(*params_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*params_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
CHECK_EQ(src_op.left_padding.size(), src_op.right_padding.size());
for (int i = 0; i < src_op.left_padding.size(); ++i) {
tensor->add_int_val(src_op.left_padding[i]);
tensor->add_int_val(src_op.right_padding[i]);
}
auto* shape = tensor->mutable_tensor_shape();
shape->add_dim()->set_size(src_op.left_padding.size());
shape->add_dim()->set_size(2);
}
void ConvertPadV2Operator(const Model& model, const PadV2Operator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("PadV2");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
*new_op->add_input() = src_op.inputs[2];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
// Create the params tensor.
tensorflow::NodeDef* params_op = tensorflow_graph->add_node();
params_op->set_op("Const");
params_op->set_name(src_op.inputs[1]);
(*params_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*params_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
CHECK_EQ(src_op.left_padding.size(), src_op.right_padding.size());
for (int i = 0; i < src_op.left_padding.size(); ++i) {
tensor->add_int_val(src_op.left_padding[i]);
tensor->add_int_val(src_op.right_padding[i]);
}
auto* shape = tensor->mutable_tensor_shape();
shape->add_dim()->set_size(src_op.left_padding.size());
shape->add_dim()->set_size(2);
}
void CreateSliceInput(const string& input_name, const std::vector<int>& values,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* params_op = tensorflow_graph->add_node();
params_op->set_op("Const");
params_op->set_name(input_name);
(*params_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*params_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
for (int i = 0; i < values.size(); ++i) {
tensor->add_int_val(values[i]);
}
auto* shape = tensor->mutable_tensor_shape();
shape->add_dim()->set_size(values.size());
}
void ConvertStridedSliceOperator(const Model& model,
const StridedSliceOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("StridedSlice");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 4);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
*new_op->add_input() = src_op.inputs[2];
*new_op->add_input() = src_op.inputs[3];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
(*new_op->mutable_attr())["Index"].set_type(DT_INT32);
(*new_op->mutable_attr())["begin_mask"].set_i(src_op.begin_mask);
(*new_op->mutable_attr())["ellipsis_mask"].set_i(src_op.ellipsis_mask);
(*new_op->mutable_attr())["end_mask"].set_i(src_op.end_mask);
(*new_op->mutable_attr())["new_axis_mask"].set_i(src_op.new_axis_mask);
(*new_op->mutable_attr())["shrink_axis_mask"].set_i(src_op.shrink_axis_mask);
// Create tensors for start/stop indices and strides.
CreateSliceInput(src_op.inputs[1], src_op.start_indices, tensorflow_graph);
CreateSliceInput(src_op.inputs[2], src_op.stop_indices, tensorflow_graph);
CreateSliceInput(src_op.inputs[3], src_op.strides, tensorflow_graph);
}
void ConvertSliceOperator(const Model& model, const SliceOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("Slice");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 3);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
*new_op->add_input() = src_op.inputs[2];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
(*new_op->mutable_attr())["Index"].set_type(DT_INT32);
// Create tensors for begin and size inputs.
CreateSliceInput(src_op.inputs[1], src_op.begin, tensorflow_graph);
CreateSliceInput(src_op.inputs[2], src_op.size, tensorflow_graph);
}
template <typename T>
void ConvertReduceOperator(const Model& model, const T& src_op,
GraphDef* tensorflow_graph, const string& op_name) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op(op_name);
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*new_op->add_input() = src_op.inputs[0];
*new_op->add_input() = src_op.inputs[1];
if (src_op.type != OperatorType::kAny) {
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
}
const tensorflow::DataType indices_type =
GetTensorFlowDataType(model, src_op.inputs[1]);
(*new_op->mutable_attr())["Tidx"].set_type(indices_type);
if (src_op.keep_dims) {
(*new_op->mutable_attr())["keep_dims"].set_b(true);
}
// Create the params tensor.
tensorflow::NodeDef* params_op = tensorflow_graph->add_node();
params_op->set_op("Const");
params_op->set_name(src_op.inputs[1]);
(*params_op->mutable_attr())["dtype"].set_type(DT_INT32);
auto* tensor = (*params_op->mutable_attr())["value"].mutable_tensor();
tensor->set_dtype(DT_INT32);
for (int i = 0; i < src_op.axis.size(); ++i) {
tensor->add_int_val(src_op.axis[i]);
}
auto* shape = tensor->mutable_tensor_shape();
shape->add_dim()->set_size(src_op.axis.size());
}
void ConvertSqueezeOperator(const Model& model, const SqueezeOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("Squeeze");
new_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*new_op->add_input() = src_op.inputs[0];
const tensorflow::DataType params_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(params_type);
if (!src_op.squeeze_dims.empty()) {
auto& squeeze_dims = (*new_op->mutable_attr())["squeeze_dims"];
for (int i : src_op.squeeze_dims) {
squeeze_dims.mutable_list()->add_i(i);
}
}
}
void ConvertSubOperator(const Model& model, const SubOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* sub_op = tensorflow_graph->add_node();
sub_op->set_op("Sub");
sub_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*sub_op->add_input() = src_op.inputs[0];
*sub_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*sub_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertTensorFlowMinimumOperator(const Model& model,
const TensorFlowMinimumOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* min_op = tensorflow_graph->add_node();
min_op->set_op("Minimum");
min_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*min_op->add_input() = src_op.inputs[0];
*min_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*min_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertTensorFlowMaximumOperator(const Model& model,
const TensorFlowMaximumOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* max_op = tensorflow_graph->add_node();
max_op->set_op("Maximum");
max_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*max_op->add_input() = src_op.inputs[0];
*max_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*max_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertSelectOperator(const Model& model, const SelectOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* select_op = tensorflow_graph->add_node();
select_op->set_op("Select");
select_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 3);
*select_op->add_input() = src_op.inputs[0];
*select_op->add_input() = src_op.inputs[1];
*select_op->add_input() = src_op.inputs[2];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[1]);
(*select_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertTileOperator(const Model& model,
const TensorFlowTileOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* tile_op = tensorflow_graph->add_node();
tile_op->set_op("Tile");
tile_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*tile_op->add_input() = src_op.inputs[0];
*tile_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*tile_op->mutable_attr())["T"].set_type(data_type);
const tensorflow::DataType multiples_data_type =
GetTensorFlowDataType(model, src_op.inputs[1]);
(*tile_op->mutable_attr())["Tmultiples"].set_type(multiples_data_type);
}
void ConvertTopKV2Operator(const Model& model, const TopKV2Operator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* topk_op = tensorflow_graph->add_node();
topk_op->set_op("TopKV2");
topk_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*topk_op->add_input() = src_op.inputs[0];
*topk_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*topk_op->mutable_attr())["T"].set_type(data_type);
(*topk_op->mutable_attr())["sorted"].set_b(true);
}
void ConvertRandomUniformOperator(const Model& model,
const RandomUniformOperator& src_op,
GraphDef* tensorflow_graph) {
CHECK(tensorflow_graph != nullptr);
tensorflow::NodeDef* new_op = tensorflow_graph->add_node();
new_op->set_op("RandomUniform");
CHECK_EQ(src_op.inputs.size(), 1);
new_op->set_name(src_op.outputs[0]);
*new_op->add_input() = src_op.inputs[0];
const tensorflow::DataType shape_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(shape_type);
(*new_op->mutable_attr())["dtype"].set_type(
GetTensorFlowDataTypeForOp(src_op.dtype, src_op.outputs[0]));
(*new_op->mutable_attr())["seed"].set_i(src_op.seed);
(*new_op->mutable_attr())["seed2"].set_i(src_op.seed2);
}
void ConvertComparisonOperator(const Model& model, const Operator& src_op,
const char* op_name,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* comparison_op = tensorflow_graph->add_node();
comparison_op->set_op(op_name);
comparison_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*comparison_op->add_input() = src_op.inputs[0];
*comparison_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*comparison_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertSparseToDenseOperator(const Model& model,
const SparseToDenseOperator& src_op,
const char* op_name,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* sparse_to_dense_op = tensorflow_graph->add_node();
sparse_to_dense_op->set_op(op_name);
sparse_to_dense_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 4);
for (int i = 0; i < 4; ++i) {
*sparse_to_dense_op->add_input() = src_op.inputs[i];
}
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[3]);
(*sparse_to_dense_op->mutable_attr())["T"].set_type(data_type);
const tensorflow::DataType index_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*sparse_to_dense_op->mutable_attr())["Tindices"].set_type(index_type);
(*sparse_to_dense_op->mutable_attr())["Tindices"].set_b(
src_op.validate_indices);
}
void ConvertPowOperator(const Model& model, const PowOperator& src_op,
const char* op_name, GraphDef* tensorflow_graph) {
tensorflow::NodeDef* pow_op = tensorflow_graph->add_node();
pow_op->set_op(op_name);
pow_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
for (int i = 0; i < 2; ++i) {
*pow_op->add_input() = src_op.inputs[i];
}
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*pow_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertLogicalAndOperator(const Model& model,
const LogicalAndOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* logical_op = tensorflow_graph->add_node();
logical_op->set_op("LogicalAnd");
logical_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
for (int i = 0; i < 2; ++i) {
*logical_op->add_input() = src_op.inputs[i];
}
}
void ConvertLogicalNotOperator(const Model& model,
const LogicalNotOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* logical_op = tensorflow_graph->add_node();
logical_op->set_op("LogicalNot");
logical_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 1);
*logical_op->add_input() = src_op.inputs[0];
}
void ConvertLogicalOrOperator(const Model& model,
const LogicalOrOperator& src_op,
const char* op_name, GraphDef* tensorflow_graph) {
tensorflow::NodeDef* logical_or_op = tensorflow_graph->add_node();
logical_or_op->set_op(op_name);
logical_or_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
for (int i = 0; i < 2; ++i) {
*logical_or_op->add_input() = src_op.inputs[i];
}
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*logical_or_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertCTCBeamSearchDecoderOperator(
const Model& model, const CTCBeamSearchDecoderOperator& src_op,
const char* op_name, GraphDef* tensorflow_graph) {
auto* op = tensorflow_graph->add_node();
op->set_op(op_name);
op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
for (int i = 0; i < 2; ++i) {
*op->add_input() = src_op.inputs[i];
}
(*op->mutable_attr())["beam_width"].set_i(src_op.beam_width);
(*op->mutable_attr())["top_paths"].set_i(src_op.top_paths);
(*op->mutable_attr())["merge_repeated"].set_b(src_op.merge_repeated);
}
void ConvertUnpackOperator(const Model& model, const UnpackOperator& src_op,
const char* op_name, GraphDef* tensorflow_graph) {
tensorflow::NodeDef* unpack_op = tensorflow_graph->add_node();
unpack_op->set_op(op_name);
unpack_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*unpack_op->add_input() = src_op.inputs[0];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*unpack_op->mutable_attr())["T"].set_type(data_type);
(*unpack_op->mutable_attr())["num"].set_i(src_op.num);
(*unpack_op->mutable_attr())["axis"].set_i(src_op.axis);
}
void ConvertZerosLikeOperator(const Model& model,
const TensorFlowZerosLikeOperator& src_op,
const char* op_name, GraphDef* tensorflow_graph) {
tensorflow::NodeDef* zeros_like_op = tensorflow_graph->add_node();
zeros_like_op->set_op(op_name);
zeros_like_op->set_name(src_op.outputs[0]);
DCHECK_EQ(src_op.inputs.size(), 1);
*zeros_like_op->add_input() = src_op.inputs[0];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*zeros_like_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertReverseV2Operator(const Model& model,
const ReverseV2Operator& src_op,
const char* op_name, GraphDef* tensorflow_graph) {
tensorflow::NodeDef* reverse_v2_op = tensorflow_graph->add_node();
reverse_v2_op->set_op(op_name);
reverse_v2_op->set_name(src_op.outputs[0]);
DCHECK_EQ(src_op.inputs.size(), 2);
*reverse_v2_op->add_input() = src_op.inputs[0];
*reverse_v2_op->add_input() = src_op.inputs[1];
const tensorflow::DataType data_type =
GetTensorFlowDataType(model, src_op.inputs[0]);
(*reverse_v2_op->mutable_attr())["T"].set_type(data_type);
}
void ConvertReverseSequenceOperator(const Model& model,
const ReverseSequenceOperator& src_op,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* reverse_seq_op = tensorflow_graph->add_node();
reverse_seq_op->set_op("ReverseSequence");
reverse_seq_op->set_name(src_op.outputs[0]);
CHECK_EQ(src_op.inputs.size(), 2);
*reverse_seq_op->add_input() = src_op.inputs[0];
*reverse_seq_op->add_input() = src_op.inputs[1];
(*reverse_seq_op->mutable_attr())["seq_dim"].set_i(src_op.seq_dim);
(*reverse_seq_op->mutable_attr())["batch_dim"].set_i(src_op.batch_dim);
}
void ConvertOperator(const Model& model, const Operator& src_op,
GraphDef* tensorflow_graph) {
if (src_op.fused_activation_function != FusedActivationFunctionType::kNone) {
LOG(FATAL)
<< "Unsupported: the input model has a fused activation function";
}
if (src_op.type == OperatorType::kConv) {
ConvertConvOperator(model, static_cast<const ConvOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kDepthwiseConv) {
ConvertDepthwiseConvOperator(
model, static_cast<const DepthwiseConvOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kDepthToSpace) {
ConvertDepthToSpaceOperator(
model, static_cast<const DepthToSpaceOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSpaceToDepth) {
ConvertSpaceToDepthOperator(
model, static_cast<const SpaceToDepthOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kFullyConnected) {
ConvertFullyConnectedOperator(
model, static_cast<const FullyConnectedOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kAdd) {
ConvertAddOperator(model, static_cast<const AddOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kAddN) {
ConvertAddNOperator(model, static_cast<const AddNOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kMul) {
ConvertMulOperator(model, static_cast<const MulOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kDiv) {
ConvertDivOperator(model, static_cast<const DivOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRelu) {
ConvertReluOperator(model, static_cast<const ReluOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRelu1) {
ConvertRelu1Operator(static_cast<const Relu1Operator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRelu6) {
ConvertRelu6Operator(static_cast<const Relu6Operator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLog) {
ConvertLogOperator(static_cast<const LogOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLogistic) {
ConvertLogisticOperator(static_cast<const LogisticOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kTanh) {
ConvertTanhOperator(static_cast<const TanhOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kL2Normalization) {
ConvertL2NormalizationOperator(
static_cast<const L2NormalizationOperator&>(src_op), tensorflow_graph);
} else if (src_op.type == OperatorType::kSoftmax) {
ConvertSoftmaxOperator(model, static_cast<const SoftmaxOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLogSoftmax) {
ConvertLogSoftmaxOperator(model,
static_cast<const LogSoftmaxOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLocalResponseNormalization) {
ConvertLocalResponseNormalizationOperator(
static_cast<const LocalResponseNormalizationOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLstmCell) {
ConvertLstmCellOperator(model, static_cast<const LstmCellOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kMaxPool) {
ConvertMaxPoolOperator(static_cast<const MaxPoolOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kAveragePool) {
ConvertAveragePoolOperator(static_cast<const AveragePoolOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kConcatenation) {
ConvertConcatenationOperator(
model, static_cast<const ConcatenationOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kReshape) {
ConvertTensorFlowReshapeOperator(
model, static_cast<const TensorFlowReshapeOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kL2Pool) {
ConvertL2PoolOperator(static_cast<const L2PoolOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSquare) {
ConvertSquareOperator(static_cast<const TensorFlowSquareOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSqrt) {
ConvertSqrtOperator(static_cast<const TensorFlowSqrtOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRsqrt) {
ConvertRsqrtOperator(model,
static_cast<const TensorFlowRsqrtOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSplit) {
ConvertSplitOperator(model,
static_cast<const TensorFlowSplitOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSplitV) {
ConvertSplitVOperator(model,
static_cast<const TensorFlowSplitVOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kFakeQuant) {
ConvertFakeQuantOperator(static_cast<const FakeQuantOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kCast) {
ConvertCastOperator(model, static_cast<const CastOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kFloor) {
ConvertFloorOperator(model, static_cast<const FloorOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kCeil) {
ConvertCeilOperator(model, static_cast<const CeilOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRound) {
ConvertRoundOperator(model, static_cast<const RoundOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kGather) {
ConvertGatherOperator(model, static_cast<const GatherOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kResizeBilinear) {
ConvertResizeBilinearOperator(
model, static_cast<const ResizeBilinearOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSpaceToBatchND) {
ConvertSpaceToBatchNDOperator(
model, static_cast<const SpaceToBatchNDOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kBatchToSpaceND) {
ConvertBatchToSpaceNDOperator(
model, static_cast<const BatchToSpaceNDOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kPad) {
ConvertPadOperator(model, static_cast<const PadOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kPadV2) {
ConvertPadV2Operator(model, static_cast<const PadV2Operator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kStridedSlice) {
ConvertStridedSliceOperator(
model, static_cast<const StridedSliceOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kMean) {
ConvertReduceOperator(model, static_cast<const MeanOperator&>(src_op),
tensorflow_graph, "Mean");
} else if (src_op.type == OperatorType::kSum) {
ConvertReduceOperator(model,
static_cast<const TensorFlowSumOperator&>(src_op),
tensorflow_graph, "Sum");
} else if (src_op.type == OperatorType::kReduceProd) {
ConvertReduceOperator(model,
static_cast<const TensorFlowProdOperator&>(src_op),
tensorflow_graph, "Prod");
} else if (src_op.type == OperatorType::kReduceMin) {
ConvertReduceOperator(model,
static_cast<const TensorFlowMinOperator&>(src_op),
tensorflow_graph, "Min");
} else if (src_op.type == OperatorType::kReduceMax) {
ConvertReduceOperator(model,
static_cast<const TensorFlowMaxOperator&>(src_op),
tensorflow_graph, "Max");
} else if (src_op.type == OperatorType::kSub) {
ConvertSubOperator(model, static_cast<const SubOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kMinimum) {
ConvertTensorFlowMinimumOperator(
model, static_cast<const TensorFlowMinimumOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kMaximum) {
ConvertTensorFlowMaximumOperator(
model, static_cast<const TensorFlowMaximumOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSqueeze) {
ConvertSqueezeOperator(model, static_cast<const SqueezeOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kSlice) {
ConvertSliceOperator(model, static_cast<const SliceOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kArgMax) {
ConvertArgMaxOperator(model, static_cast<const ArgMaxOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kArgMin) {
ConvertArgMinOperator(model, static_cast<const ArgMinOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kTopK_V2) {
ConvertTopKV2Operator(model, static_cast<const TopKV2Operator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kTranspose) {
ConvertTransposeOperator(
model, static_cast<const TransposeOperator&>(src_op), tensorflow_graph);
} else if (src_op.type == OperatorType::kShape) {
ConvertTensorFlowShapeOperator(
model, static_cast<const TensorFlowShapeOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRank) {
ConvertRankOperator(model,
static_cast<const TensorFlowRankOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRange) {
ConvertRangeOperator(model, static_cast<const RangeOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kPack) {
ConvertPackOperator(model, static_cast<const PackOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kFill) {
ConvertFillOperator(model, static_cast<const FillOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kFloorDiv) {
ConvertFloorDivOperator(model, static_cast<const FloorDivOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kFloorMod) {
ConvertFloorModOperator(model, static_cast<const FloorModOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kExpandDims) {
ConvertExpandDimsOperator(model,
static_cast<const ExpandDimsOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kTransposeConv) {
ConvertTransposeConvOperator(
model, static_cast<const TransposeConvOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kRandomUniform) {
ConvertRandomUniformOperator(
model, static_cast<const RandomUniformOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kEqual) {
ConvertComparisonOperator(model, src_op, "Equal", tensorflow_graph);
} else if (src_op.type == OperatorType::kNotEqual) {
ConvertComparisonOperator(model, src_op, "NotEqual", tensorflow_graph);
} else if (src_op.type == OperatorType::kGreater) {
ConvertComparisonOperator(model, src_op, "Greater", tensorflow_graph);
} else if (src_op.type == OperatorType::kGreaterEqual) {
ConvertComparisonOperator(model, src_op, "GreaterEqual", tensorflow_graph);
} else if (src_op.type == OperatorType::kLess) {
ConvertComparisonOperator(model, src_op, "Less", tensorflow_graph);
} else if (src_op.type == OperatorType::kLessEqual) {
ConvertComparisonOperator(model, src_op, "LessEqual", tensorflow_graph);
} else if (src_op.type == OperatorType::kSelect) {
ConvertSelectOperator(model, static_cast<const SelectOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kTile) {
ConvertTileOperator(model,
static_cast<const TensorFlowTileOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kPow) {
ConvertPowOperator(model, static_cast<const PowOperator&>(src_op), "Pow",
tensorflow_graph);
} else if (src_op.type == OperatorType::kAny) {
ConvertReduceOperator(model,
static_cast<const TensorFlowAnyOperator&>(src_op),
tensorflow_graph, "Any");
} else if (src_op.type == OperatorType::kLogicalAnd) {
ConvertLogicalAndOperator(model,
static_cast<const LogicalAndOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLogicalNot) {
ConvertLogicalNotOperator(model,
static_cast<const LogicalNotOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kOneHot) {
ConvertOneHotOperator(model, static_cast<const OneHotOperator&>(src_op),
tensorflow_graph);
} else if (src_op.type == OperatorType::kLogicalOr) {
ConvertLogicalOrOperator(model,
static_cast<const LogicalOrOperator&>(src_op),
"LogicalOr", tensorflow_graph);
} else if (src_op.type == OperatorType::kCTCBeamSearchDecoder) {
ConvertCTCBeamSearchDecoderOperator(
model, static_cast<const CTCBeamSearchDecoderOperator&>(src_op),
"CTCBeamSearchDecoder", tensorflow_graph);
} else if (src_op.type == OperatorType::kUnpack) {
ConvertUnpackOperator(model, static_cast<const UnpackOperator&>(src_op),
"Unpack", tensorflow_graph);
} else if (src_op.type == OperatorType::kZerosLike) {
ConvertZerosLikeOperator(
model, static_cast<const TensorFlowZerosLikeOperator&>(src_op),
"ZerosLike", tensorflow_graph);
} else if (src_op.type == OperatorType::kReverseV2) {
ConvertReverseV2Operator(model,
static_cast<const ReverseV2Operator&>(src_op),
"Reverse_V2", tensorflow_graph);
} else if (src_op.type == OperatorType::kReverseSequence) {
ConvertReverseSequenceOperator(
model, static_cast<const ReverseSequenceOperator&>(src_op),
tensorflow_graph);
} else {
LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type);
}
}
void AddPlaceholder(const string& name, ArrayDataType type,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* placeholder = tensorflow_graph->add_node();
placeholder->set_op("Placeholder");
switch (type) {
case ArrayDataType::kBool:
(*placeholder->mutable_attr())["dtype"].set_type(DT_BOOL);
break;
case ArrayDataType::kFloat:
(*placeholder->mutable_attr())["dtype"].set_type(DT_FLOAT);
break;
case ArrayDataType::kUint8:
(*placeholder->mutable_attr())["dtype"].set_type(DT_UINT8);
break;
case ArrayDataType::kInt32:
(*placeholder->mutable_attr())["dtype"].set_type(DT_INT32);
break;
case ArrayDataType::kInt64:
(*placeholder->mutable_attr())["dtype"].set_type(DT_INT64);
break;
case ArrayDataType::kInt16:
(*placeholder->mutable_attr())["dtype"].set_type(DT_INT16);
break;
case ArrayDataType::kComplex64:
(*placeholder->mutable_attr())["dtype"].set_type(DT_COMPLEX64);
break;
default:
LOG(FATAL) << "Unexpected data type in array \"" << name << "\"";
}
placeholder->set_name(name);
}
void AddPlaceholderForRNNState(const Model& model, const string& name, int size,
GraphDef* tensorflow_graph) {
tensorflow::NodeDef* placeholder = tensorflow_graph->add_node();
placeholder->set_op("Placeholder");
placeholder->set_name(name);
(*placeholder->mutable_attr())["dtype"].set_type(DT_FLOAT);
auto* shape = (*placeholder->mutable_attr())["shape"].mutable_shape();
const auto& state_array = model.GetArray(name);
if (state_array.has_shape()) {
const auto& state_shape = state_array.shape();
const int kDims = state_shape.dimensions_count();
for (int i = 0; i < kDims; ++i) {
shape->add_dim()->set_size(state_shape.dims(i));
}
} else {
shape->add_dim()->set_size(1);
shape->add_dim()->set_size(size);
}
}
void ExportTensorFlowGraphDefImplementation(const Model& model,
GraphDef* tensorflow_graph) {
for (const auto& input_array : model.flags.input_arrays()) {
AddPlaceholder(input_array.name(),
model.GetArray(input_array.name()).data_type,
tensorflow_graph);
}
for (const auto& rnn_state : model.flags.rnn_states()) {
AddPlaceholderForRNNState(model, rnn_state.state_array(), rnn_state.size(),
tensorflow_graph);
}
for (const auto& op : model.operators) {
ConvertOperator(model, *op, tensorflow_graph);
}
// Generically export arrays that haven't been exported already
// by the above operators export. It's important that this comes
// after, as some operators need to export arrays that they reference
// in a specific way, rather than in the generic way done below.
for (const auto& array_pair : model.GetArrayMap()) {
const string& array_name = array_pair.first;
const auto& array = *array_pair.second;
if (array.buffer) {
switch (array.data_type) {
case ArrayDataType::kBool:
ConvertBoolTensorConst(model, array_name, tensorflow_graph);
break;
case ArrayDataType::kFloat:
ConvertFloatTensorConst(model, array_name, tensorflow_graph);
break;
case ArrayDataType::kInt32:
ConvertIntTensorConst(model, array_name, tensorflow_graph);
break;
case ArrayDataType::kComplex64:
ConvertComplex64TensorConst(model, array_name, tensorflow_graph);
break;
default:
break;
}
}
}
}
} // namespace
void EncodeConstantArraysMinMaxByWrappingThemInFakeQuantNodes(Model* model) {
for (const auto& array_kv : model->GetArrayMap()) {
const string& array_name = array_kv.first;
Array& array = *array_kv.second;
if (!array.buffer || !array.minmax) {
continue;
}
const string& wrapped_array_name =
AvailableArrayName(*model, array_name + "/data");
Array& wrapped_array = model->GetOrCreateArray(wrapped_array_name);
wrapped_array.data_type = array.data_type;
wrapped_array.copy_shape(array.shape());
wrapped_array.buffer = std::move(array.buffer);
FakeQuantOperator* fakequant_op = new FakeQuantOperator;
fakequant_op->inputs = {wrapped_array_name};
fakequant_op->outputs = {array_name};
fakequant_op->minmax.reset(new MinMax);
*fakequant_op->minmax = *array.minmax;
const auto& it = FindOpWithInput(*model, array_name);
model->operators.emplace(it, fakequant_op);
}
CheckInvariants(*model);
}
void ExportTensorFlowGraphDef(const Model& model,
string* output_file_contents) {
CHECK(output_file_contents->empty());
GraphDef tensorflow_graph;
ExportTensorFlowGraphDefImplementation(model, &tensorflow_graph);
LogDumpGraphDef(kLogLevelModelChanged, "AT EXPORT", tensorflow_graph);
CHECK(tensorflow_graph.SerializeToString(output_file_contents));
}
} // namespace toco
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