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/* Copyright 2018 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 "tensorflow/contrib/lite/builtin_op_data.h"
#include "tensorflow/contrib/lite/context.h"
#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
namespace tflite {
namespace ops {
namespace builtin {
namespace pack {
namespace {
constexpr int kOutputTensor = 0;
// Op data for pack op.
struct OpData {
int values_count;
int axis;
};
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
auto* data = new OpData;
data->axis = 0;
return data;
}
void Free(TfLiteContext* context, void* buffer) {
delete reinterpret_cast<OpData*>(buffer);
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const OpData* data = reinterpret_cast<OpData*>(node->builtin_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), data->values_count);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input0 = GetInput(context, node, 0);
TF_LITE_ENSURE(context, NumDimensions(input0) < 4);
TF_LITE_ENSURE(context, NumDimensions(input0) >= data->axis);
// TODO(renjieliu): Support negative axis.
TF_LITE_ENSURE(context, data->axis >= 0);
if (input0->type != kTfLiteInt32 && input0->type != kTfLiteFloat32) {
context->ReportError(context,
"Currently pack only supports int32 and float32.");
return kTfLiteError;
}
// Make sure all inputs have the same shape and type.
for (int i = 1; i < data->values_count; ++i) {
const TfLiteTensor* input = GetInput(context, node, i);
TF_LITE_ENSURE(context, HaveSameShapes(input0, input));
TF_LITE_ENSURE_EQ(context, input0->type, input->type);
}
// Resize output. rank R will become rank R + 1
const int dimension_size = NumDimensions(input0) + 1;
const TfLiteIntArray* input_shape = input0->dims;
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(dimension_size);
int i = 0;
for (int index = 0; index < dimension_size; ++index) {
if (index == data->axis) {
output_shape->data[index] = data->values_count;
} else {
output_shape->data[index] = input_shape->data[i++];
}
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, output->type, input0->type);
return context->ResizeTensor(context, output, output_shape);
}
template <typename T>
void PackImpl(TfLiteContext* context, TfLiteNode* node, TfLiteTensor* output,
int values_count, int axis) {
VectorOfTensors<T> all_inputs(*context, *node->inputs);
reference_ops::Pack<T>(RemapDim(NumDimensions(output), axis),
all_inputs.data(), all_inputs.dims(), values_count,
GetTensorData<T>(output), GetTensorDims(output));
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const OpData* data = reinterpret_cast<OpData*>(node->builtin_data);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (output->type) {
case kTfLiteFloat32: {
PackImpl<float>(context, node, output, data->values_count, data->axis);
break;
}
case kTfLiteInt32: {
PackImpl<int32_t>(context, node, output, data->values_count, data->axis);
break;
}
default: {
context->ReportError(context,
"Currently pack only supports int32 and float32.");
return kTfLiteError;
}
}
return kTfLiteOk;
}
} // namespace
} // namespace pack
TfLiteRegistration* Register_PACK() {
static TfLiteRegistration r = {pack::Init, pack::Free, pack::Prepare,
pack::Eval};
return &r;
}
} // namespace builtin
} // namespace ops
} // namespace tflite
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