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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 "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/compiler/xla/client/xla_builder.h"
#include "tensorflow/core/util/tensor_format.h"
namespace tensorflow {
namespace {
class SpaceToDepthOp : public XlaOpKernel {
public:
explicit SpaceToDepthOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
string data_format_str;
OP_REQUIRES_OK(ctx, ctx->GetAttr("data_format", &data_format_str));
OP_REQUIRES(ctx, FormatFromString(data_format_str, &data_format_),
errors::InvalidArgument("Invalid data format"));
OP_REQUIRES(ctx, data_format_ == FORMAT_NCHW || data_format_ == FORMAT_NHWC,
errors::InvalidArgument("Unsupported data format ",
ToString(data_format_),
"; expected formats NHWC or NCHW"));
OP_REQUIRES_OK(ctx, ctx->GetAttr("block_size", &block_size_));
OP_REQUIRES(
ctx, block_size_ > 1,
errors::InvalidArgument("Block size should be > 1: ", block_size_));
}
void Compile(XlaOpKernelContext* ctx) override {
const TensorShape input_tensor_shape = ctx->InputShape(0);
int input_rank = input_tensor_shape.dims();
static const int kRequiredDims = 4;
OP_REQUIRES(ctx, kRequiredDims == input_rank,
errors::InvalidArgument("Input rank should be ", kRequiredDims,
"; got ", input_rank));
const absl::InlinedVector<int64, 4> input_shape =
input_tensor_shape.dim_sizes();
xla::XlaOp input = ctx->Input(0);
int feature_dim = GetTensorFeatureDimIndex(input_rank, data_format_);
int num_spatial_dims = GetTensorSpatialDims(input_rank, data_format_);
std::vector<int64> reshaped_shape;
std::vector<int64> transpose_order;
std::vector<int64> output_shape;
reshaped_shape.reserve(input_rank);
transpose_order.reserve(input_rank);
output_shape.reserve(input_rank);
if (data_format_ == FORMAT_NHWC) {
int64 block_elems = 1;
for (int i = 0; i < num_spatial_dims; ++i) {
OP_REQUIRES(ctx, input_shape[1 + i] % block_size_ == 0,
errors::InvalidArgument(
"input shape[", 1 + i, "]=", input_shape[1 + i],
" is not divisible by block_size=", block_size_));
block_elems *= block_size_;
}
reshaped_shape.push_back(input_shape[0]);
for (int i = 0; i < num_spatial_dims; ++i) {
reshaped_shape.push_back(input_shape[1 + i] / block_size_);
reshaped_shape.push_back(block_size_);
}
reshaped_shape.push_back(input_shape[feature_dim]);
transpose_order.push_back(0);
for (int i = 0; i < num_spatial_dims; ++i) {
transpose_order.push_back(i * 2 + 1);
}
for (int i = 0; i < num_spatial_dims; ++i) {
transpose_order.push_back(i * 2 + 2);
}
transpose_order.push_back(feature_dim + num_spatial_dims);
output_shape.push_back(input_shape[0]);
for (int i = 0; i < num_spatial_dims; ++i) {
output_shape.push_back(input_shape[1 + i] / block_size_);
}
output_shape.push_back(input_shape[feature_dim] * block_elems);
} else {
// FORMAT_NCHW
int64 block_elems = 1;
for (int i = 0; i < num_spatial_dims; ++i) {
OP_REQUIRES(ctx, input_shape[2 + i] % block_size_ == 0,
errors::InvalidArgument(
"input shape[", 2 + i, "]=", input_shape[2 + i],
" is not divisible by block_size=", block_size_));
block_elems *= block_size_;
}
reshaped_shape.push_back(input_shape[0]);
reshaped_shape.push_back(input_shape[feature_dim]);
for (int i = 0; i < num_spatial_dims; ++i) {
reshaped_shape.push_back(input_shape[2 + i] / block_size_);
reshaped_shape.push_back(block_size_);
}
transpose_order.push_back(0);
for (int i = 0; i < num_spatial_dims; ++i) {
transpose_order.push_back(i * 2 + 3);
}
transpose_order.push_back(feature_dim);
for (int i = 0; i < num_spatial_dims; ++i) {
transpose_order.push_back(i * 2 + 2);
}
output_shape.push_back(input_shape[0]);
output_shape.push_back(input_shape[feature_dim] * block_elems);
for (int i = 0; i < num_spatial_dims; ++i) {
output_shape.push_back(input_shape[2 + i] / block_size_);
}
}
// Note: comments are given in NHWC format; NCHW is similar with a different
// dimension order.
// 1. Reshape `input` to `reshaped` of shape:
//
// [batch,
// input_shape[1] / block_size_, block_size_,
// input_shape[2] / block_size_, block_size_,
// depth]
xla::XlaOp reshaped = xla::Reshape(input, reshaped_shape);
// 2. Permute dimensions of `reshaped` to produce
// `permuted_reshaped` of shape:
//
// [batch,
// input_shape[1] / block_size_,
// input_shape[2] / block_size_,
// block_size_, block_size_,
// depth]
xla::XlaOp permuted_reshaped = xla::Transpose(reshaped, transpose_order);
// 3. Reshape `permuted_reshaped` to flatten `block_shape` into the
// batch dimension, producing an output tensor of shape:
//
// [batch,
// input_shape[1] / block_size_,
// input_shape[2] / block_size_,
// block_size_ * block_size_ * depth]
//
xla::XlaOp output = xla::Reshape(permuted_reshaped, output_shape);
ctx->SetOutput(0, output);
}
private:
TensorFormat data_format_;
int block_size_;
};
REGISTER_XLA_OP(Name("SpaceToDepth"), SpaceToDepthOp);
} // namespace
} // namespace tensorflow
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