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maxpooling_op.cc
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maxpooling_op.cc
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/* Copyright 2015 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.
==============================================================================*/
// See docs in ../ops/nn_ops.cc.
#define EIGEN_USE_THREADS
#include "tensorflow/core/kernels/maxpooling_op.h"
#include <vector>
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/framework/bounds_check.h"
#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/kernels/conv_2d.h"
#include "tensorflow/core/kernels/eigen_pooling.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/kernels/pooling_ops_common.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/util/env_var.h"
#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"
#include "tensorflow/core/util/use_cudnn.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#if GOOGLE_CUDA
#include "cuda/include/cudnn.h"
#include "tensorflow/core/kernels/maxpooling_op_gpu.h"
#include "tensorflow/core/kernels/pooling_ops_common_gpu.h"
#include "tensorflow/core/platform/stream_executor.h"
#endif // GOOGLE_CUDA
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
const int kInvalidMaxPoolingIndex = -1;
template <typename Device, typename T>
static void SpatialMaxPoolWithArgMaxHelper(
OpKernelContext* context, Tensor* output, Tensor* output_arg_max,
Tensor* input_backprop, const Tensor& tensor_in, const Tensor& out_backprop,
const PoolParameters& params, const bool include_batch_in_index) {
if (input_backprop != nullptr) {
OP_REQUIRES(
context, include_batch_in_index,
errors::Internal(
"SpatialMaxPoolWithArgMaxHelper requires include_batch_in_index "
"to be True when when input_backprop != nullptr"));
}
typedef Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
ConstEigenMatrixMap;
typedef Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
EigenMatrixMap;
typedef Eigen::Map<Eigen::Matrix<int64, Eigen::Dynamic, Eigen::Dynamic>>
EigenIndexMatrixMap;
ConstEigenMatrixMap in_mat(
tensor_in.flat<T>().data(), params.depth,
params.tensor_in_cols * params.tensor_in_rows * params.tensor_in_batch);
EigenMatrixMap out_mat(
output->flat<T>().data(), params.depth,
params.out_width * params.out_height * params.tensor_in_batch);
EigenIndexMatrixMap out_arg_max_mat(
output_arg_max->flat<int64>().data(), params.depth,
params.out_width * params.out_height * params.tensor_in_batch);
const DeviceBase::CpuWorkerThreads& worker_threads =
*(context->device()->tensorflow_cpu_worker_threads());
// The following code basically does the following:
// 1. Flattens the input and output tensors into two dimensional arrays.
// tensor_in_as_matrix:
// depth by (tensor_in_cols * tensor_in_rows * tensor_in_batch)
// output_as_matrix:
// depth by (out_width * out_height * tensor_in_batch)
//
// 2. Walks through the set of columns in the flattened tensor_in_as_matrix,
// and updates the corresponding column(s) in output_as_matrix with the
// max value.
auto shard = [¶ms, &in_mat, &out_mat, &out_arg_max_mat, &input_backprop,
&output_arg_max, &out_backprop,
include_batch_in_index](int64 start, int64 limit) {
const int32 depth = params.depth;
const int32 in_rows = params.tensor_in_rows;
const int32 in_cols = params.tensor_in_cols;
const int32 pad_rows = params.pad_rows;
const int32 pad_cols = params.pad_cols;
const int32 window_rows = params.window_rows;
const int32 window_cols = params.window_cols;
const int32 row_stride = params.row_stride;
const int32 col_stride = params.col_stride;
const int32 out_height = params.out_height;
const int32 out_width = params.out_width;
{
// Initializes the output tensor with MIN<T>.
const int32 output_image_size = out_height * out_width * depth;
EigenMatrixMap out_shard(out_mat.data() + start * output_image_size, 1,
(limit - start) * output_image_size);
out_shard.setConstant(Eigen::NumTraits<T>::lowest());
EigenIndexMatrixMap out_arg_max_shard(
out_arg_max_mat.data() + start * output_image_size, 1,
(limit - start) * output_image_size);
out_arg_max_shard.setConstant(kInvalidMaxPoolingIndex);
}
for (int64 b = start; b < limit; ++b) {
for (int h = 0; h < in_rows; ++h) {
for (int w = 0; w < in_cols; ++w) {
// (h_start, h_end) * (w_start, w_end) is the range that the input
// vector projects to.
const int hpad = h + pad_rows;
const int wpad = w + pad_cols;
const int h_start =
(hpad < window_rows) ? 0 : (hpad - window_rows) / row_stride + 1;
const int h_end = std::min(hpad / row_stride + 1, out_height);
const int w_start =
(wpad < window_cols) ? 0 : (wpad - window_cols) / col_stride + 1;
const int w_end = std::min(wpad / col_stride + 1, out_width);
// compute elementwise max
const int64 in_index = (b * in_rows + h) * in_cols + w;
for (int ph = h_start; ph < h_end; ++ph) {
const int64 out_index_base = (b * out_height + ph) * out_width;
for (int pw = w_start; pw < w_end; ++pw) {
const int64 out_index = out_index_base + pw;
/// NOTES(zhengxq): not using the eigen matrix operation for
/// now.
for (int d = 0; d < depth; ++d) {
const T& input_ref = in_mat.coeffRef(d, in_index);
T& output_ref = out_mat.coeffRef(d, out_index);
int64& out_arg_max_ref = out_arg_max_mat.coeffRef(d, out_index);
if (output_ref < input_ref ||
out_arg_max_ref == kInvalidMaxPoolingIndex) {
output_ref = input_ref;
if (include_batch_in_index) {
out_arg_max_ref = in_index * depth + d;
} else {
out_arg_max_ref = (h * in_cols + w) * depth + d;
}
}
}
}
}
}
}
}
if (input_backprop != nullptr) {
auto input_backprop_flat = input_backprop->flat<T>();
auto out_arg_max_flat = output_arg_max->flat<int64>();
auto out_backprop_flat = out_backprop.flat<T>();
// Initialize output to 0.
const int64 in_size = in_rows * in_cols * depth;
const int64 in_start = start * in_size;
const int64 in_end = limit * in_size;
EigenMatrixMap in_shard(input_backprop_flat.data() + in_start, 1,
in_end - in_start);
in_shard.setConstant(T(0));
// Backpropagate.
const int out_size = out_height * out_width * depth;
const int out_start = start * out_size;
const int out_end = limit * out_size;
for (int index = out_start; index < out_end; ++index) {
int input_backprop_index = out_arg_max_flat(index);
// Although this check is in the inner loop, it is worth its value
// so we don't end up with memory corruptions. Our benchmark shows that
// the performance impact is quite small
// CHECK(input_backprop_index >= in_start && input_backprop_index <
// in_end)
FastBoundsCheck(input_backprop_index - in_start, in_end - in_start);
input_backprop_flat(input_backprop_index) += out_backprop_flat(index);
}
}
};
const int64 shard_cost = params.tensor_in_rows * params.tensor_in_cols *
params.depth * params.window_rows *
params.window_cols;
Shard(worker_threads.num_threads, worker_threads.workers,
params.tensor_in_batch, shard_cost, shard);
}
// The operation to compute MaxPool gradients.
// It takes three inputs:
// - The original input tensor
// - The original output tensor
// - Backprop tensor for output
// It produces one output: backprop tensor for input.
template <class Device, class T>
class MaxPoolingGradOp : public OpKernel {
public:
explicit MaxPoolingGradOp(OpKernelConstruction* context) : OpKernel(context) {
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
OP_REQUIRES(
context, data_format_ == FORMAT_NHWC,
errors::InvalidArgument("Default MaxPoolingGradOp only supports NHWC ",
"on device type ",
DeviceTypeString(context->device_type())));
if (context->num_inputs() == 3) {
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
OP_REQUIRES(context, ksize_[0] == 1 && stride_[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
OP_REQUIRES(
context, ksize_[3] == 1 && stride_[3] == 1,
errors::Unimplemented(
"MaxPoolingGrad is not yet supported on the depth dimension."));
}
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
const Tensor& tensor_out = context->input(1);
const Tensor& out_backprop = context->input(2);
// For maxpooling, tensor_in should have 4 dimensions.
OP_REQUIRES(context, tensor_in.dims() == 4,
errors::InvalidArgument("tensor_in must be 4-dimensional"));
OP_REQUIRES(context, tensor_out.dims() == 4,
errors::InvalidArgument("tensor_out must be 4-dimensional"));
// For maxpooling, out_backprop should have 4 dimensions.
OP_REQUIRES(context, out_backprop.dims() == 4,
errors::InvalidArgument("out_backprop must be 4-dimensional"));
const TensorShape& output_shape = tensor_in.shape();
Tensor tensor_out_dup;
OP_REQUIRES_OK(context, context->forward_input_or_allocate_temp(
{1}, DataTypeToEnum<T>::v(), tensor_out.shape(),
&tensor_out_dup));
Tensor tensor_out_arg_max;
OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum<int64>::v(),
tensor_out.shape(),
&tensor_out_arg_max));
std::vector<int32> ksize = ksize_;
std::vector<int32> stride = stride_;
if (context->num_inputs() == 5) {
const Tensor& tensor_ksize = context->input(3);
auto value_ksize = tensor_ksize.flat<int32>();
ksize.resize(tensor_ksize.shape().num_elements());
std::copy_n(&value_ksize(0), ksize.size(), ksize.begin());
const Tensor& tensor_stride = context->input(4);
auto value_stride = tensor_stride.flat<int32>();
stride.resize(tensor_stride.shape().num_elements());
std::copy_n(&value_stride(0), stride.size(), stride.begin());
}
OP_REQUIRES(context, ksize.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES(context, stride.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
OP_REQUIRES(context, ksize[0] == 1 && stride[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
OP_REQUIRES(
context, ksize[3] == 1 && stride[3] == 1,
errors::Unimplemented(
"MaxPoolingGrad is not yet supported on the depth dimension."));
PoolParameters params{context, ksize, stride,
padding_, FORMAT_NHWC, tensor_in.shape()};
if (!context->status().ok()) {
return;
}
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
{0}, 0, output_shape, &output));
SpatialMaxPoolWithArgMaxHelper<CPUDevice, T>(
context, &tensor_out_dup, &tensor_out_arg_max, output, tensor_in,
out_backprop, params, true);
}
private:
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
TensorFormat data_format_;
};
#ifdef GOOGLE_CUDA
template <typename T>
static void MaxPoolingBackwardCustomKernel(
OpKernelContext* context, const std::vector<int32>& size,
const std::vector<int32>& stride, Padding padding, const Tensor* tensor_in,
const Tensor& out_backprop, const TensorShape& tensor_in_shape) {
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
{0}, 0, tensor_in_shape, &output));
PoolParameters params{context, size, stride,
padding, FORMAT_NHWC, tensor_in_shape};
if (!context->status().ok()) {
return;
}
functor::MaxPoolBackwardNoMask<T>()(
tensor_in->flat<T>().data(), params.tensor_in_batch,
params.tensor_in_rows, params.tensor_in_cols, params.depth,
params.out_height, params.out_width, params.window_rows,
params.window_cols, params.row_stride, params.col_stride, params.pad_rows,
params.pad_cols, out_backprop.flat<T>().data(), output->flat<T>().data(),
context->eigen_device<Eigen::GpuDevice>());
}
template <class T>
class MaxPoolingGradOp<Eigen::GpuDevice, T> : public OpKernel {
public:
typedef Eigen::GpuDevice Device;
explicit MaxPoolingGradOp(OpKernelConstruction* context) : OpKernel(context) {
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
if (context->num_inputs() == 3) {
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
const int32 ksize_n = GetTensorDim(ksize_, data_format_, 'N');
const int32 stride_n = GetTensorDim(stride_, data_format_, 'N');
OP_REQUIRES(context, ksize_n == 1 && stride_n == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
}
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
use_dnn_ = CanUseCudnn();
TF_CHECK_OK(ReadBoolFromEnvVar("TF_ENABLE_MAXPOOL_NANPROP", false,
&propagate_nans_));
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
const Tensor& tensor_out = context->input(1);
const Tensor& out_backprop = context->input(2);
// For maxpooling, tensor_in should have 4 dimensions.
OP_REQUIRES(context, tensor_in.dims() == 4,
errors::InvalidArgument("tensor_in must be 4-dimensional 4"));
OP_REQUIRES(context, tensor_out.dims() == 4,
errors::InvalidArgument("tensor_out must be 4-dimensional"));
// For maxpooling, out_backprop should have 4 dimensions.
OP_REQUIRES(context, out_backprop.dims() == 4,
errors::InvalidArgument("out_backprop must be 4-dimensional"));
TensorShape output_shape = tensor_in.shape();
std::vector<int32> ksize = ksize_;
std::vector<int32> stride = stride_;
if (context->num_inputs() == 5) {
const Tensor& tensor_ksize = context->input(3);
auto value_ksize = tensor_ksize.flat<int32>();
ksize.resize(tensor_ksize.shape().num_elements());
std::copy_n(&value_ksize(0), ksize.size(), ksize.begin());
const Tensor& tensor_stride = context->input(4);
auto value_stride = tensor_stride.flat<int32>();
stride.resize(tensor_stride.shape().num_elements());
std::copy_n(&value_stride(0), stride.size(), stride.begin());
}
OP_REQUIRES(context, ksize.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES(context, stride.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
const int32 ksize_n = GetTensorDim(ksize, data_format_, 'N');
const int32 stride_n = GetTensorDim(stride, data_format_, 'N');
OP_REQUIRES(context, ksize_n == 1 && stride_n == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
if (use_dnn_) {
DnnPoolingGradOp<T>::Compute(context, se::dnn::PoolingMode::kMaximum,
ksize, stride, padding_, data_format_,
&tensor_in, &tensor_out, out_backprop,
output_shape, propagate_nans_);
} else {
CHECK(data_format_ == FORMAT_NHWC)
<< "Non-Cudnn MaxPoolGrad only supports NHWC format";
MaxPoolingBackwardCustomKernel<T>(context, ksize, stride, padding_,
&tensor_in, out_backprop, output_shape);
}
}
private:
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
TensorFormat data_format_;
bool use_dnn_;
bool propagate_nans_;
};
#endif // GOOGLE_CUDA
// The operation to compute gradient of MaxPool gradients.
// It takes three inputs:
// - The original input tensor
// - The original output tensor
// - Backprop tensor for output gradients
// It produces one output: backprop tensor for output gradient.
template <class Device, class T>
class MaxPoolingGradGradOp : public OpKernel {
public:
explicit MaxPoolingGradGradOp(OpKernelConstruction* context)
: OpKernel(context) {
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
OP_REQUIRES(
context, data_format_ == FORMAT_NHWC,
errors::InvalidArgument(
"Default MaxPoolingGradGradOp only supports NHWC ",
"on device type ", DeviceTypeString(context->device_type())));
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
if (context->num_inputs() == 3) {
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
OP_REQUIRES(context, ksize_[0] == 1 && stride_[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
OP_REQUIRES(context, ksize_[3] == 1 && stride_[3] == 1,
errors::Unimplemented("MaxPoolingGradGrad is not yet "
"supported on the depth dimension."));
}
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
const Tensor& tensor_out = context->input(1);
const Tensor& out_grad_backprop = context->input(2);
// For maxpooling, tensor_in should have 4 dimensions.
OP_REQUIRES(context, tensor_in.dims() == 4,
errors::InvalidArgument("tensor_in must be 4-dimensional"));
OP_REQUIRES(context, tensor_out.dims() == 4,
errors::InvalidArgument("tensor_out must be 4-dimensional"));
// For maxpooling, out_grad_backprop should have 4 dimensions.
OP_REQUIRES(
context, out_grad_backprop.dims() == 4,
errors::InvalidArgument("out_grad_backprop must be 4-dimensional"));
std::vector<int32> ksize = ksize_;
std::vector<int32> stride = stride_;
if (context->num_inputs() == 5) {
const Tensor& tensor_ksize = context->input(3);
auto value_ksize = tensor_ksize.flat<int32>();
ksize.resize(tensor_ksize.shape().num_elements());
std::copy_n(&value_ksize(0), ksize.size(), ksize.begin());
const Tensor& tensor_stride = context->input(4);
auto value_stride = tensor_stride.flat<int32>();
stride.resize(tensor_stride.shape().num_elements());
std::copy_n(&value_stride(0), stride.size(), stride.begin());
}
OP_REQUIRES(context, ksize.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES(context, stride.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
OP_REQUIRES(context, ksize[0] == 1 && stride[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
OP_REQUIRES(
context, ksize[3] == 1 && stride[3] == 1,
errors::Unimplemented(
"MaxPoolingGrad is not yet supported on the depth dimension."));
PoolParameters params{context, ksize, stride,
padding_, FORMAT_NHWC, tensor_in.shape()};
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
{2}, 0, tensor_out.shape(), &output));
SpatialMaxPoolGradGrad(context, output, tensor_in, tensor_out,
out_grad_backprop, params, padding_);
}
private:
void SpatialMaxPoolGradGrad(OpKernelContext* context, Tensor* bottom_diff,
const Tensor& tensor_in, const Tensor& tensor_out,
const Tensor& top_diff,
const PoolParameters& params,
const Padding& padding) {
typedef Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
ConstEigenMatrixMap;
typedef Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
EigenMatrixMap;
ConstEigenMatrixMap in_mat(
tensor_in.flat<T>().data(), params.depth,
params.tensor_in_cols * params.tensor_in_rows * params.tensor_in_batch);
ConstEigenMatrixMap out_mat(
tensor_out.flat<T>().data(), params.depth,
params.out_width * params.out_height * params.tensor_in_batch);
ConstEigenMatrixMap top_diff_mat(
top_diff.flat<T>().data(), params.depth,
params.tensor_in_cols * params.tensor_in_rows * params.tensor_in_batch);
EigenMatrixMap bottom_diff_mat(
bottom_diff->flat<T>().data(), params.depth,
params.out_width * params.out_height * params.tensor_in_batch);
const DeviceBase::CpuWorkerThreads& worker_threads =
*(context->device()->tensorflow_cpu_worker_threads());
// The following code basically does the following:
// 1. Flattens the input, output, top_diff and bottom_diff tensors into
// two dimensional arrays.
// tensor_in_as_matrix:
// depth by (tensor_in_cols * tensor_in_rows * tensor_in_batch)
// tensor_out_as_matrix:
// depth by (out_width * out_height * tensor_in_batch)
// top_diff_as_matrix:
// depth by (tensor_in_cols * tensor_in_rows * tensor_in_batch)
// bottom_diff_as_matrix:
// depth by (out_width * out_height * tensor_in_batch)
//
// 2. Walks through the set of columns in the flattened
// tensor_in_as_matrix, tensor_out_as_matrix, top_diff_as_matrix
// and updates the column(s) corresponding to the maximum values in
// tensor_out_as_matrix with the corresponding values in
// top_diff_as_matrix.
auto shard = [¶ms, &in_mat, &out_mat, &top_diff_mat, &bottom_diff_mat](
int64 start, int64 limit) {
const int32 depth = params.depth;
const int32 in_rows = params.tensor_in_rows;
const int32 in_cols = params.tensor_in_cols;
const int32 pad_rows = params.pad_rows;
const int32 pad_cols = params.pad_cols;
const int32 window_rows = params.window_rows;
const int32 window_cols = params.window_cols;
const int32 row_stride = params.row_stride;
const int32 col_stride = params.col_stride;
const int32 out_height = params.out_height;
const int32 out_width = params.out_width;
{
// Initializes the output grad backprop tensor with 0.
const int32 output_image_size = out_height * out_width * params.depth;
EigenMatrixMap bottom_diff_shard(
bottom_diff_mat.data() + start * output_image_size, 1,
(limit - start) * output_image_size);
bottom_diff_shard.setZero();
}
for (int b = start; b < limit; ++b) {
for (int ph = 0; ph < out_height; ++ph) {
for (int pw = 0; pw < out_width; ++pw) {
// (h_start, h_end) * (w_start, w_end) is the range that the input
// vector projects to.
int h_start = ph * row_stride - pad_rows;
const int h_end = std::min(h_start + window_rows, in_rows);
int w_start = pw * col_stride - pad_cols;
const int w_end = std::min(w_start + window_cols, in_cols);
h_start = std::max(h_start, 0);
w_start = std::max(w_start, 0);
const int out_index = (b * out_height + ph) * out_width + pw;
// Find value corresponding to the input maximum in top_diff.
for (int d = 0; d < depth; ++d) {
const T& output_ref = out_mat.coeffRef(d, out_index);
bool should_stop = false;
for (int h = h_start; h < h_end && !should_stop; ++h) {
for (int w = w_start; w < w_end && !should_stop; ++w) {
const int in_index = (b * in_rows + h) * in_cols + w;
const T& input_ref = in_mat.coeffRef(d, in_index);
if (output_ref == input_ref) {
T& bottom_diff_ref = bottom_diff_mat.coeffRef(d, out_index);
bottom_diff_ref = top_diff_mat.coeffRef(d, in_index);
should_stop = true;
}
}
}
}
}
}
}
};
const int64 shard_cost = params.out_width * params.out_height *
params.depth * params.window_rows *
params.window_cols;
Shard(worker_threads.num_threads, worker_threads.workers,
params.tensor_in_batch, shard_cost, shard);
}
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
TensorFormat data_format_;
};
#ifdef GOOGLE_CUDA
template <class T>
class MaxPoolingGradGradOp<Eigen::GpuDevice, T> : public OpKernel {
public:
typedef Eigen::GpuDevice Device;
explicit MaxPoolingGradGradOp(OpKernelConstruction* context)
: OpKernel(context) {
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
if (context->num_inputs() == 3) {
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
const int32 ksize_n = GetTensorDim(ksize_, data_format_, 'N');
const int32 stride_n = GetTensorDim(stride_, data_format_, 'N');
OP_REQUIRES(context, ksize_n == 1 && stride_n == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
}
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
const Tensor& tensor_out = context->input(1);
const Tensor& out_grad_backprop = context->input(2);
// For maxpooling, tensor_in should have 4 dimensions.
OP_REQUIRES(context, tensor_in.dims() == 4,
errors::InvalidArgument("tensor_in must be 4-dimensional 4"));
OP_REQUIRES(context, tensor_out.dims() == 4,
errors::InvalidArgument("tensor_out must be 4-dimensional"));
// For maxpooling, out_grad_backprop should have 4 dimensions.
OP_REQUIRES(
context, out_grad_backprop.dims() == 4,
errors::InvalidArgument("out_grad_backprop must be 4-dimensional"));
Tensor* output = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, tensor_out.shape(), &output));
std::vector<int32> ksize = ksize_;
std::vector<int32> stride = stride_;
if (context->num_inputs() == 5) {
const Tensor& tensor_ksize = context->input(3);
auto value_ksize = tensor_ksize.flat<int32>();
ksize.resize(tensor_ksize.shape().num_elements());
std::copy_n(&value_ksize(0), ksize.size(), ksize.begin());
const Tensor& tensor_stride = context->input(4);
auto value_stride = tensor_stride.flat<int32>();
stride.resize(tensor_stride.shape().num_elements());
std::copy_n(&value_stride(0), stride.size(), stride.begin());
}
OP_REQUIRES(context, ksize.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES(context, stride.size() == 4,
errors::InvalidArgument("Sliding window strides field must "
"specify 4 dimensions"));
const int32 ksize_n = GetTensorDim(ksize, data_format_, 'N');
const int32 stride_n = GetTensorDim(stride, data_format_, 'N');
OP_REQUIRES(context, ksize_n == 1 && stride_n == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
PoolParameters params{context, ksize, stride,
padding_, data_format_, tensor_in.shape()};
functor::MaxPoolGradBackwardNoMask<T>()(
data_format_, tensor_in.flat<T>().data(), tensor_out.flat<T>().data(),
params.tensor_in_batch, params.out_height, params.out_width,
params.depth, params.tensor_in_rows, params.tensor_in_cols,
params.window_rows, params.window_cols, params.row_stride,
params.col_stride, params.pad_rows, params.pad_cols,
out_grad_backprop.flat<T>().data(), output->flat<T>().data(),
context->eigen_device<Eigen::GpuDevice>());
}
private:
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
TensorFormat data_format_;
bool use_dnn_;
};
#endif // GOOGLE_CUDA
template <typename Device, typename T>
struct LaunchMaxPoolingNoMask;
template <typename Device, typename T>
class MaxPoolingNoMaskOp : public OpKernel {
public:
explicit MaxPoolingNoMaskOp(OpKernelConstruction* context)
: OpKernel(context) {
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
OP_REQUIRES(
context, data_format_ == FORMAT_NHWC,
errors::InvalidArgument(
"Default MaxPoolingNoMaskOp only supports NHWC on device type ",
DeviceTypeString(context->device_type())));
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window stride field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
OP_REQUIRES(context, ksize_[0] == 1 && stride_[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
PoolParameters params{context, ksize_, stride_,
padding_, data_format_, tensor_in.shape()};
if (!context->status().ok()) {
return;
}
TensorShape out_shape({params.tensor_in_batch, params.out_height,
params.out_width, params.depth});
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
LaunchMaxPoolingNoMask<Device, T>::launch(context, params, tensor_in,
output);
}
private:
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
TensorFormat data_format_;
};
template <typename Device, typename T>
class MaxPoolingNoMaskV2Op : public OpKernel {
public:
explicit MaxPoolingNoMaskV2Op(OpKernelConstruction* context)
: OpKernel(context) {
string data_format;
OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
errors::InvalidArgument("Invalid data format"));
OP_REQUIRES(
context, data_format_ == FORMAT_NHWC,
errors::InvalidArgument(
"Default MaxPoolingNoMaskOp only supports NHWC on device type ",
DeviceTypeString(context->device_type())));
if (context->num_inputs() == 1) {
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window stride field must "
"specify 4 dimensions"));
OP_REQUIRES(context, ksize_[0] == 1 && stride_[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
}
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
std::vector<int32> ksize = ksize_;
std::vector<int32> stride = stride_;
if (context->num_inputs() != 1) {
const Tensor& tensor_ksize = context->input(1);
auto value_ksize = tensor_ksize.flat<int32>();
ksize.resize(tensor_ksize.shape().num_elements());
std::copy_n(&value_ksize(0), ksize.size(), ksize.begin());
const Tensor& tensor_stride = context->input(2);
auto value_stride = tensor_stride.flat<int32>();
stride.resize(tensor_stride.shape().num_elements());
std::copy_n(&value_stride(0), stride.size(), stride.begin());
}
OP_REQUIRES(context, ksize.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES(context, stride.size() == 4,
errors::InvalidArgument("Sliding window stride field must "
"specify 4 dimensions"));
OP_REQUIRES(context, ksize[0] == 1 && stride[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
PoolParameters params{context, ksize, stride,
padding_, data_format_, tensor_in.shape()};
if (!context->status().ok()) {
return;
}
TensorShape out_shape({params.tensor_in_batch, params.out_height,
params.out_width, params.depth});
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
LaunchMaxPoolingNoMask<Device, T>::launch(context, params, tensor_in,
output);
}
private:
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
TensorFormat data_format_;
};
template <typename Device, typename T>
struct LaunchMaxPoolingWithArgmax;
template <typename T>
struct LaunchMaxPoolingWithArgmax<CPUDevice, T> {
static void launch(OpKernelContext* context, const PoolParameters& params,
const Tensor& input, Tensor* output, Tensor* argmax,
bool propagate_nans, bool include_batch_in_index) {
Tensor unused;
SpatialMaxPoolWithArgMaxHelper<CPUDevice, T>(context, output, argmax,
nullptr, input, unused, params,
include_batch_in_index);
}
};
template <typename Device, typename T>
class MaxPoolingWithArgmaxOp : public OpKernel {
public:
explicit MaxPoolingWithArgmaxOp(OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("ksize", &ksize_));
OP_REQUIRES(context, ksize_.size() == 4,
errors::InvalidArgument("Sliding window ksize field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("strides", &stride_));
OP_REQUIRES(context, stride_.size() == 4,
errors::InvalidArgument("Sliding window stride field must "
"specify 4 dimensions"));
OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
OP_REQUIRES(context, ksize_[0] == 1 && stride_[0] == 1,
errors::Unimplemented(
"Pooling is not yet supported on the batch dimension."));
OP_REQUIRES_OK(context, context->GetAttr("include_batch_in_index",
&include_batch_in_index_));
TF_CHECK_OK(ReadBoolFromEnvVar("TF_ENABLE_MAXPOOL_NANPROP", false,
&propagate_nans_));
}
void Compute(OpKernelContext* context) override {
const Tensor& tensor_in = context->input(0);
PoolParameters params{context, ksize_, stride_,
padding_, FORMAT_NHWC, tensor_in.shape()};
if (!context->status().ok()) {
return;
}
TensorShape out_shape({params.tensor_in_batch, params.out_height,
params.out_width, params.depth});
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
Tensor* argmax = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(1, out_shape, &argmax));
LaunchMaxPoolingWithArgmax<Device, T>::launch(
context, params, tensor_in, output, argmax, propagate_nans_,
include_batch_in_index_);
}
private:
std::vector<int32> ksize_;
std::vector<int32> stride_;
Padding padding_;
bool propagate_nans_;
bool include_batch_in_index_;
};
template <typename Device, typename T>
struct LaunchMaxPoolingGradWithArgmax;
template <typename T>
struct LaunchMaxPoolingGradWithArgmax<CPUDevice, T> {
typedef Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
EigenMatrixMap;
static void launch(OpKernelContext* context, const PoolParameters& params,
const Tensor& grad_in, const Tensor& argmax,
Tensor* grad_out, const bool include_batch_in_index) {
const DeviceBase::CpuWorkerThreads& worker_threads =
*(context->device()->tensorflow_cpu_worker_threads());
auto shard = [&grad_in, &argmax, &grad_out, include_batch_in_index](
int64 start, int64 limit) {
const int64 batch_size =
GetTensorDim(grad_out->shape(), FORMAT_NHWC, 'N');
const int64 output_size_per_batch = grad_out->NumElements() / batch_size;
const int64 input_size_per_batch = grad_in.NumElements() / batch_size;
{
auto grad_out_flat = grad_out->flat<T>();
auto argmax_flat = argmax.flat<int64>();
auto grad_in_flat = grad_in.flat<T>();
const int64 output_start = start * output_size_per_batch;
const int64 output_end = limit * output_size_per_batch;
EigenMatrixMap inputShard(grad_out_flat.data() + output_start, 1,
output_end - output_start);
inputShard.setConstant(T(0));
const int input_start = start * input_size_per_batch;
const int input_end = limit * input_size_per_batch;
for (int64 index = input_start; index < input_end; index++) {
int64 grad_out_index = argmax_flat(index);
if (!include_batch_in_index) {
const int64 cur_batch = index / input_size_per_batch;
grad_out_index += cur_batch * output_size_per_batch;
}
CHECK(grad_out_index >= output_start && grad_out_index < output_end)
<< "Invalid output gradient index: " << grad_out_index << ", "
<< output_start << ", " << output_end;
grad_out_flat(grad_out_index) += grad_in_flat(index);
}
}
};
const int64 batch_size = GetTensorDim(grad_out->shape(), FORMAT_NHWC, 'N');
const int64 shard_cost = grad_out->NumElements() / batch_size;
Shard(worker_threads.num_threads, worker_threads.workers, batch_size,