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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 <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
#include "tensorflow/contrib/lite/toco/model.h"
#include "tensorflow/contrib/lite/toco/tooling_util.h"
#include "tensorflow/core/platform/logging.h"
namespace toco {
namespace {
int GetOutputDepthFromWeights(const Model& model, const Operator& op) {
const string& weights_name = op.inputs[1];
const auto& weights_shape = model.GetArray(weights_name).shape();
if (op.type == OperatorType::kConv ||
op.type == OperatorType::kFullyConnected) {
return weights_shape.dims(0);
}
if (op.type == OperatorType::kDepthwiseConv) {
return weights_shape.dims(3);
}
LOG(FATAL) << "Unhandled operator type";
return 0;
}
bool ProcessLinearOperator(Model* model, Operator* op) {
if (op->inputs.size() >= 3) {
return false;
}
const string& output_name = op->outputs[0];
const string& weights_name = op->inputs[1];
if (!model->GetArray(weights_name).has_shape()) {
return false;
}
const int depth = GetOutputDepthFromWeights(*model, *op);
const string& bias_name = AvailableArrayName(*model, output_name + "_bias");
op->inputs.push_back(bias_name);
DCHECK_EQ(op->inputs.size(), 3);
auto& bias_array = model->GetOrCreateArray(bias_name);
bias_array.data_type = ArrayDataType::kFloat;
bias_array.mutable_shape()->mutable_dims()->push_back(depth);
auto& bias_buffer = bias_array.GetMutableBuffer<ArrayDataType::kFloat>();
bias_buffer.data.resize(depth, 0.f);
return true;
}
} // namespace
::tensorflow::Status EnsureBiasVectors::Run(Model* model, std::size_t op_index,
bool* modified) {
*modified = false;
auto* op = model->operators[op_index].get();
if (op->type == OperatorType::kConv ||
op->type == OperatorType::kDepthwiseConv ||
op->type == OperatorType::kFullyConnected) {
if (ProcessLinearOperator(model, op)) {
AddMessageF("Added bias vector to %s as %s", LogName(*op), op->inputs[2]);
*modified = true;
return ::tensorflow::Status::OK();
}
}
return ::tensorflow::Status::OK();
}
} // namespace toco
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