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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 <unistd.h>
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <limits>
#include "tensorflow/contrib/lite/builtin_op_data.h"
#include "tensorflow/contrib/lite/context.h"
#include "tensorflow/contrib/lite/kernels/eigen_support.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
#include "tensorflow/contrib/lite/kernels/op_macros.h"
#include "tensorflow/contrib/lite/kernels/padding.h"
namespace tflite {
namespace ops {
namespace builtin {
namespace transpose_conv {
constexpr int kOutputShapeTensor = 0;
constexpr int kWeightsTensor = 1;
constexpr int kDataInputTensor = 2;
constexpr int kOutputTensor = 0;
const int kTensorNotAllocated = -1;
struct OpData {
// IDs are the arbitrary identifiers used by TF Lite to identify and access
// memory buffers.
int im2col_id = kTensorNotAllocated;
// im2col is the only temporary currently tracked, therefore always index 0.
// If more temporaries are added, they should be properly tracked.
int32_t im2col_index = 0;
};
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
// This is a builtin op, so we don't use the contents in 'buffer', if any.
// Instead, we allocate a new object to use as scratch space for im2col, and
// to carry information from Prepare() to Eval().
auto* data = new OpData;
eigen_support::IncrementUsageCounter(context);
return data;
}
void Free(TfLiteContext* context, void* buffer) {
eigen_support::DecrementUsageCounter(context);
delete reinterpret_cast<OpData*>(buffer);
}
TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
const TfLiteTensor* output_shape,
TfLiteTensor* output) {
// Currently only support int32 for output shape.
if (output_shape->type != kTfLiteInt32) {
context->ReportError(context, "Output shape is %d, not int32.",
output_shape->type);
return kTfLiteError;
}
const int output_dimensions = NumElements(output_shape);
TfLiteIntArray* output_shape_array = TfLiteIntArrayCreate(output_dimensions);
for (int i = 0; i < output_dimensions; ++i) {
output_shape_array->data[i] = GetTensorData<int32_t>(output_shape)[i];
}
return context->ResizeTensor(context, output, output_shape_array);
}
// Allocate temporary im2col tensor.
static TfLiteStatus AllocateIm2colTensor(TfLiteContext* context,
TfLiteNode* node) {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
if (data->im2col_id == kTensorNotAllocated) {
context->AddTensors(context, 1, &data->im2col_id);
}
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(1);
node->temporaries->data[data->im2col_index] = data->im2col_id;
return kTfLiteOk;
}
TfLiteStatus ResizeIm2ColTensor(TfLiteContext* context,
const TfLiteTensor* output_shape,
const TfLiteTensor* weights,
const TfLiteTensor* input,
TfLiteTensor* im2col) {
if (output_shape->type != kTfLiteInt32) {
context->ReportError(context, "im2col shape is %d, not int32.",
output_shape->type);
return kTfLiteError;
}
TF_LITE_ENSURE_EQ(context, NumElements(output_shape), 4);
TfLiteIntArray* im2col_shape_array = TfLiteIntArrayCreate(4);
im2col_shape_array->data[0] = output_shape->data.i32[0];
im2col_shape_array->data[1] = output_shape->data.i32[1];
im2col_shape_array->data[2] = output_shape->data.i32[2];
const int input_depth = SizeOfDimension(input, 3);
const int filter_width = SizeOfDimension(weights, 1);
const int filter_height = SizeOfDimension(weights, 2);
im2col_shape_array->data[3] = input_depth * filter_height * filter_width;
im2col->type = input->type;
im2col->allocation_type = kTfLiteArenaRw;
return context->ResizeTensor(context, im2col, im2col_shape_array);
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_STATUS(AllocateIm2colTensor(context, node));
const TfLiteTensor* output_shape =
GetInput(context, node, kOutputShapeTensor);
const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* input = GetInput(context, node, kDataInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
OpData* user_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* im2col =
&context->tensors[node->temporaries->data[user_data->im2col_index]];
TF_LITE_ENSURE_EQ(context, NumDimensions(output_shape), 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 4);
// Currenlty only supports float32.
const TfLiteType data_type = input->type;
TF_LITE_ENSURE(context, data_type == kTfLiteFloat32);
TF_LITE_ENSURE_EQ(context, output->type, data_type);
TF_LITE_ENSURE_EQ(context, weights->type, data_type);
// Ensure that weights and inputs have the same channel dimension.
// Note: TOCO will reorder weights in the following format: OHWI.
TF_LITE_ENSURE_EQ(context, SizeOfDimension(input, 3),
SizeOfDimension(weights, 3));
if (IsConstantTensor(output_shape)) {
TF_LITE_ENSURE_STATUS(ResizeOutputTensor(context, output_shape, output));
TF_LITE_ENSURE_STATUS(
ResizeIm2ColTensor(context, output_shape, weights, input, im2col));
} else {
// Defer resizing until Eval().
SetTensorToDynamic(output);
}
return kTfLiteOk;
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* output_shape =
GetInput(context, node, kOutputShapeTensor);
const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor);
const TfLiteTensor* input = GetInput(context, node, kDataInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
OpData* user_data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* im2col =
&context->tensors[node->temporaries->data[user_data->im2col_index]];
const auto* params =
reinterpret_cast<TfLiteTransposeConvParams*>(node->builtin_data);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context,
ResizeOutputTensor(context, output_shape, output));
}
if (IsDynamicTensor(im2col)) {
TF_LITE_ENSURE_OK(context, ResizeIm2ColTensor(context, output_shape,
weights, input, im2col));
}
// Get height and width of the output image.
const int width = SizeOfDimension(output, 2);
const int height = SizeOfDimension(output, 1);
const int filter_width = SizeOfDimension(weights, 1);
const int filter_height = SizeOfDimension(weights, 2);
const int stride_width = params->stride_width;
const int stride_height = params->stride_height;
const TfLitePaddingValues& padding_size =
ComputePaddingHeightWidth(stride_height, stride_width, 1, height, width,
filter_height, filter_width, params->padding);
// Currently only support float32.
switch (input->type) {
case kTfLiteFloat32:
optimized_ops::TransposeConv(
GetTensorData<float>(input), GetTensorDims(input),
GetTensorData<float>(weights), GetTensorDims(weights), stride_width,
stride_height, padding_size.width, padding_size.height,
GetTensorData<float>(output), GetTensorDims(output),
GetTensorData<float>(im2col), GetTensorDims(im2col));
break;
default:
context->ReportError(context, "Type %d, not currently supported.",
input->type);
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace transpose_conv
TfLiteRegistration* Register_TRANSPOSE_CONV() {
static TfLiteRegistration r = {transpose_conv::Init, transpose_conv::Free,
transpose_conv::Prepare, transpose_conv::Eval};
return &r;
}
} // namespace builtin
} // namespace ops
} // namespace tflite
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