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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 <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 {
template <ArrayDataType Type>
void Stack(Model* model, StackOperator const& op) {
auto& output_array = model->GetArray(op.outputs[0]);
CHECK(output_array.data_type == Type);
// Create a buffer for the output array
std::vector<DataType<Type>>& output_data =
output_array.GetMutableBuffer<Type>().data;
output_data.resize(RequiredBufferSizeForShape(output_array.shape()));
// Stack inputs into buffer
CHECK_EQ(op.axis, 0) << "Stacking only supported along first axis";
int dst_offset = 0;
for (int i = 0; i < op.inputs.size(); i++) {
// Append array data to output for each input array
const auto& input_array = model->GetArray(op.inputs[i]);
int input_size = RequiredBufferSizeForShape(input_array.shape());
memcpy(&output_data[dst_offset], &input_array.GetBuffer<Type>().data[0],
input_size * ElementSize(Type));
dst_offset += input_size;
}
CHECK_EQ(dst_offset, output_data.size());
}
} // namespace
bool ResolveConstantStack::Run(Model* model, std::size_t op_index) {
auto it = model->operators.begin() + op_index;
const auto* base_op = it->get();
if (base_op->type != OperatorType::kStack) {
return false;
}
const auto* op = static_cast<const StackOperator*>(base_op);
CHECK_GE(op->inputs.size(), 1);
CHECK_EQ(op->outputs.size(), 1);
auto& output_array = model->GetArray(op->outputs[0]);
if (output_array.data_type == ArrayDataType::kNone) {
// Yield until the output type has been set by PropagateArrayDataTypes
return false;
}
if (!output_array.has_shape()) {
// Yield until the output shape has been set by PropagateFixedShapes
return false;
}
for (const auto& input : op->inputs) {
if (!IsConstantParameterArray(*model, input)) {
// Yield if any input is mutable
return false;
}
}
int axis = op->axis;
if (axis < 0) {
// Handle negative axis
axis += model->GetArray(op->inputs[0]).shape().dims().size();
}
CHECK_EQ(axis, 0) << "Stacking only supported along 0th axis";
CHECK(!output_array.buffer);
switch (output_array.data_type) {
case ArrayDataType::kFloat:
Stack<ArrayDataType::kFloat>(model, *op);
break;
case ArrayDataType::kUint8:
Stack<ArrayDataType::kUint8>(model, *op);
break;
case ArrayDataType::kInt32:
Stack<ArrayDataType::kInt32>(model, *op);
break;
case ArrayDataType::kInt64:
Stack<ArrayDataType::kInt64>(model, *op);
break;
default:
LOG(FATAL) << "Unsupported data type given to Stack op with output \""
<< op->outputs[0] << "\"";
break;
}
// Erase input arrays if no longer used
for (const auto& input : op->inputs) {
toco::DeleteArrayIfUsedOnce(input, model);
}
// Erase the operator
model->operators.erase(it);
return true;
}
} // namespace toco
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