pool_op.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 "paddle/operators/pool_op.h"

namespace paddle {
namespace operators {

int OutputSizePool(int input_size, int filter_size, int padding, int stride) {
  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
  return output_size;
}

void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
  PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null.");
  PADDLE_ENFORCE(ctx->HasOutput("Out"),
                 "Out(Output) of Pooling should not be null.");

  auto in_x_dims = ctx->GetInputDim("X");

  std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
  std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");

  PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
                 "Pooling intput should be 4-D or 5-D tensor.");

  if (ctx->Attrs().Get<bool>("global_pooling")) {
    ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
    for (size_t i = 0; i < ksize.size(); ++i) {
      paddings[i] = 0;
      ksize[i] = static_cast<int>(in_x_dims[i + 2]);
    }
  }

  PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
                 "Input size and pooling size should be consistent.");
  PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
                    "Strides size and pooling size should be the same.");
  PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
                    "Paddings size and pooling size should be the same.");

  std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
  for (size_t i = 0; i < ksize.size(); ++i) {
    output_shape.push_back(
        OutputSizePool(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
  }
  ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
  ctx->ShareLoD("X", "Out");
}

framework::OpKernelType PoolOp::GetExpectedKernelType(
    const framework::ExecutionContext &ctx) const {
  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
  if (paddle::platform::is_cpu_place(ctx.GetPlace())) {
    use_cudnn = false;
  }
  framework::LibraryType library_;
  if (use_cudnn) {
    library_ = framework::LibraryType::kCUDNN;
  } else {
    library_ = framework::LibraryType::kPlain;
  }

  std::string data_format = ctx.Attr<std::string>("data_format");
  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
  return framework::OpKernelType(
      framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
      layout_, library_);
}

void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
  PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
  PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
                 "Input(X@GRAD) should not be null.");
  ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}

framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
    const framework::ExecutionContext &ctx) const {
  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
  if (paddle::platform::is_cpu_place(ctx.GetPlace())) {
    use_cudnn = false;
  }
  framework::LibraryType library_;
  if (use_cudnn) {
    library_ = framework::LibraryType::kCUDNN;
  } else {
    library_ = framework::LibraryType::kPlain;
  }

  std::string data_format = ctx.Attr<std::string>("data_format");
  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
  return framework::OpKernelType(
      framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
      layout_, library_);
}

Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
    : OpProtoAndCheckerMaker(proto, op_checker) {
  AddInput(
      "X",
      "(Tensor) The input tensor of pooling operator. "
      "The format of input tensor is NCHW, where N is batch size, C is the "
      "number of channels, H is the height of the feature, "
      "and W is the width of the feature.");
  AddOutput("Out",
            "(Tensor) The output tensor of pooling operator. "
            "The format of output tensor is also NCHW, "
            "where N is batch size, C is the number of channels, "
            "H is the height of the feature, "
            "and W is the width of the feature.");

  AddAttr<std::string>("pooling_type",
                       "(string), pooling type, can be \"max\" for max-pooling "
                       "and \"avg\" for average-pooling.")
      .InEnum({"max", "avg"});
  AddAttr<std::vector<int>>("ksize",
                            "(vector<int>) The pooling window "
                            "size(height, width) of the pooling operator. "
                            "If global_pooling = true, ksize and paddings will "
                            "be ignored.");  // TODO(Chengduo): Add checker.
                                             // (Currently,
  // TypedAttrChecker don't support vector type.)
  AddAttr<bool>("global_pooling",
                "(bool, default false) Whether to use the global pooling. "
                "If global_pooling = true, ksize and paddings will be ignored.")
      .SetDefault(false);
  AddAttr<std::vector<int>>("strides",
                            "(vector<int>, default {1, 1}), strides(height, "
                            "width) of pooling operator.")
      .SetDefault({1, 1});
  // TODO(Chengduo): Add checker. (Currently,
  // TypedAttrChecker don't support vector type.)
  AddAttr<std::vector<int>>(
      "paddings",
      "(vector<int>, default {0,0}), paddings(height, width) of pooling "
      "operator."
      "If global_pooling = true, paddings and ksize will be ignored.")
      .SetDefault({0, 0});
  AddAttr<bool>(
      "use_cudnn",
      "(bool, default false) Only used in cudnn kernel, need install cudnn")
      .SetDefault(false);
  AddAttr<std::string>(
      "data_format",
      "(string, default NCHW) Only used in "
      "An optional string from: \"NHWC\", \"NCHW\". "
      "Defaults to \"NHWC\". Specify the data format of the output data, "
      "the input will be transformed automatically. ")
      .SetDefault("AnyLayout");
  // TODO(dzhwinter): need to registered layout transform function

  AddComment(R"DOC(
Pool2d Operator.

The pooling2d operation calculates the output based on
the input, pooling_type and ksize, strides, paddings parameters.
Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.

Example:   
  Input:
       X shape: $(N, C, H_{in}, W_{in})$
  Output:
       Out shape: $(N, C, H_{out}, W_{out})$
  Where
       $$ 
       H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
       W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
       $$

)DOC");
}

Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
    : OpProtoAndCheckerMaker(proto, op_checker) {
  AddInput("X",
           "(Tensor) The input tensor of pooling operator. "
           "The format of input tensor is NCDHW, where N is batch size, C is "
           "the number of channels, and D, H and W is the depth, height and "
           "width of "
           "the feature, respectively.");
  AddOutput("Out",
            "(Tensor) The output tensor of pooling operator."
            "The format of output tensor is also NCDHW, "
            "where N is batch size, C is "
            "the number of channels, and D, H and W is the depth, height and "
            "width of the feature, respectively.");

  AddAttr<std::string>("pooling_type",
                       "(string) Pooling type, can be \"max\" for max-pooling "
                       "and \"avg\" for average-pooling.")
      .InEnum({"max", "avg"});
  AddAttr<std::vector<int>>(
      "ksize",
      "(vector<int>) The pooling window size(depth, height, "
      "width) of pooling operator. "
      "If global_pooling = true, ksize and paddings will "
      "be ignored.");  // TODO(Chengduo): Add checker.
                       // (Currently,
  // TypedAttrChecker don't support vector type.)
  AddAttr<bool>(
      "global_pooling",
      "(bool, default false) Whether to use the global pooling. "
      "If global_pooling = true, ksize and paddings wille be ignored.")
      .SetDefault(false);
  AddAttr<std::vector<int>>(
      "strides",
      "(vector<int>, default {1,1,1}) Strides(depth, height, "
      "width) of the pooling operator.")
      .SetDefault({1, 1, 1});  // TODO(Chengduo): Add checker. (Currently,
                               // TypedAttrChecker don't support vector type.)
  AddAttr<std::vector<int>>(
      "paddings",
      "(vector<int>, default {0,0,0}), paddings(depth, height, "
      "width) of pooling operator. "
      "If global_pooling = true, ksize and paddings will be ignored.")
      .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
                               // TypedAttrChecker don't support vector type.)

  AddAttr<bool>(
      "use_cudnn",
      "(bool, default false) Only used in cudnn kernel, need install cudnn")
      .SetDefault(false);
  AddAttr<std::string>(
      "data_format",
      "(string, default NCHW) Only used in "
      "An optional string from: \"NHWC\", \"NCHW\". "
      "Defaults to \"NHWC\". Specify the data format of the output data, "
      "the input will be transformed automatically. ")
      .SetDefault("AnyLayout");
  // TODO(dzhwinter): need to registered layout transform function

  AddComment(R"DOC(
Pool3d Operator.

The pooling3d operation calculates the output based on
the input, pooling_type, ksize, strides, and paddings parameters.
Input(X) and output(Out) are in NCDHW format, where N is batch
size, C is the number of channels, and D, H and W are the depth, height and
width of the feature, respectively. Parameters(ksize, strides, paddings) 
are three elements. These three elements represent depth, height and 
width, respectively. The input(X) size and output(Out) size may be different.

Example:
  Input:
       X shape: $(N, C, D_{in}, H_{in}, W_{in})$
  Output:
       Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
  Where
  $$
       D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
       H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
       W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
  $$

)DOC");
}
}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
            ops::PoolOpGrad);

REGISTER_OP_CPU_KERNEL(
    pool2d, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
    ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    pool2d_grad, ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>)

REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad,
            ops::PoolOpGrad);

REGISTER_OP_CPU_KERNEL(
    pool3d, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
    ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    pool3d_grad, ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>);