pool_op.cc

/* Copyright (c) 2016 PaddlePaddle 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 "paddle/fluid/operators/pool_op.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif

namespace paddle {
namespace operators {

int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
                   bool ceil_mode) {
  int output_size;
  if (!ceil_mode) {
    output_size = (input_size - filter_size + 2 * padding) / stride + 1;
  } else {
    output_size =
        (input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
  }
  PADDLE_ENFORCE(output_size > 0,
                 "Due to the settings of padding(%d), filter_size(%d) and "
                 "stride(%d), the output size is less than 0, please check "
                 "again. Input_size:%d",
                 padding, filter_size, stride, input_size);
  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");
  bool ceil_mode = ctx->Attrs().Get<bool>("ceil_mode");
  bool adaptive = ctx->Attrs().Get<bool>("adaptive");

  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]});
  if (adaptive) {
    output_shape.insert(output_shape.end(), ksize.begin(), ksize.end());
  } else {
    for (size_t i = 0; i < ksize.size(); ++i) {
      output_shape.push_back(PoolOutputSize(
          in_x_dims[i + 2], ksize[i], paddings[i], strides[i], ceil_mode));
    }
  }
  ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
  ctx->ShareLoD("X", "Out");
}

framework::OpKernelType PoolOp::GetExpectedKernelType(
    const framework::ExecutionContext& ctx) const {
  framework::LibraryType library_{framework::LibraryType::kPlain};
  std::string data_format = ctx.Attr<std::string>("data_format");
  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);

#ifdef PADDLE_WITH_CUDA
  if (platform::CanCUDNNBeUsed(ctx)) {
    library_ = framework::LibraryType::kCUDNN;
  }
#endif
#ifdef PADDLE_WITH_MKLDNN
  if (library_ == framework::LibraryType::kPlain &&
      platform::CanMKLDNNBeUsed(ctx)) {
    library_ = framework::LibraryType::kMKLDNN;
    layout_ = framework::DataLayout::kMKLDNN;
  }
#endif

  return framework::OpKernelType(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 {
  framework::LibraryType library_{framework::LibraryType::kPlain};
  std::string data_format = ctx.Attr<std::string>("data_format");
  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);

#ifdef PADDLE_WITH_CUDA
  if (platform::CanCUDNNBeUsed(ctx)) {
    library_ = framework::LibraryType::kCUDNN;
  }
#endif
#ifdef PADDLE_WITH_MKLDNN
  if (library_ == framework::LibraryType::kPlain &&
      platform::CanMKLDNNBeUsed(ctx)) {
    library_ = framework::LibraryType::kMKLDNN;
    layout_ = framework::DataLayout::kMKLDNN;
  }
#endif

  auto input_data_type = ctx.Input<Tensor>("X")->type();
  if (input_data_type == framework::proto::VarType::FP16) {
    PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
                      "float16 can only be used when CUDNN is used");
  }
  return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
                                 library_);
}

void Pool2dOpMaker::Make() {
  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>(
      "exclusive",
      "(bool, default True) When true, will exclude the zero-padding in the "
      "averaging calculating, otherwise, include the zero-padding. Note, it "
      "is only used when pooling_type is avg. The defalut is True.")
      .SetDefault(true);
  AddAttr<bool>(
      "adaptive",
      "(bool, default False) When true, will perform adaptive pooling instead, "
      "output shape in H and W dimensions will be same as ksize, input data "
      "will be divided into grids specify by ksize averagely and perform "
      "pooling in each grid area to get output pooling value.")
      .SetDefault(false);

  AddAttr<bool>(
      "use_cudnn",
      "(bool, default false) Only used in cudnn kernel, need install cudnn")
      .SetDefault(false);
  AddAttr<bool>(
      "ceil_mode",
      "(bool, default false) Wether to use the ceil function to calculate "
      "output height and width. False is the default. If it is set to False, "
      "the floor function will be used.")
      .SetDefault(false);
  AddAttr<bool>("use_mkldnn",
                "(bool, default false) Only used in mkldnn kernel")
      .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");
  AddAttr<bool>("is_test",
                "(bool, default false) Set to true for inference only, false "
                "for training. Some layers may run faster when this is true.")
      .SetDefault(false);

  // TODO(dzhwinter): need to registered layout transform function

  AddComment(R"DOC(
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: $(N, C, H_{out}, W_{out})$

  For ceil_mode = false:
       $$
       H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1
       $$
       $$
       W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
       $$
  For ceil_mode = true:
       $$
       H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1
       $$
       $$
       W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
       $$

  For exclusive = false:
       $$
       hstart = i * strides[0] - paddings[0]
       $$
       $$
       hend = hstart + ksize[0]
       $$
       $$
       wstart = j * strides[1] - paddings[1]
       $$
       $$
       wend = wstart + ksize[1]
       $$
       $$
       Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
       $$
  For exclusive = true:
       $$
       hstart = max(0, i * strides[0] - paddings[0])
       $$
       $$
       hend = min(H, hstart + ksize[0])
       $$
       $$
       wstart = max(0, j * strides[1] - paddings[1])
       $$
       $$
       wend = min(W, wstart + ksize[1])
       $$
       $$
       Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
       $$

  For adaptive = true:
       $$
       hstart = floor(i * H_{in} / H_{out})
       $$
       $$
       hend = ceil((i + 1) * H_{in} / H_{out})
       $$
       $$
       wstart = floor(j * W_{in} / W_{out})
       $$
       $$
       wend = ceil((j + 1) * W_{in} / W_{out})
       $$
       $$
       Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
       $$
)DOC");
}

class PoolOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
 protected:
  std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
      const override {
    return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
  }
};

void Pool3dOpMaker::Make() {
  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>(
      "exclusive",
      "(bool, default True) When true, will exclude the zero-padding in the "
      "averaging calculating, otherwise, include the zero-padding. Note, it "
      "is only used when pooling_type is avg. The defalut is True.")
      .SetDefault(true);
  AddAttr<bool>(
      "adaptive",
      "(bool, default False) When true, will perform adaptive pooling instead, "
      "output shape in H and W dimensions will be same as ksize, input data "
      "will be divided into grids specify by ksize averagely and perform "
      "pooling in each grid area to get output pooling value.")
      .SetDefault(false);

  AddAttr<bool>(
      "use_cudnn",
      "(bool, default false) Only used in cudnn kernel, need install cudnn")
      .SetDefault(false);
  AddAttr<bool>(
      "ceil_mode",
      "(bool, default false) Wether to use the ceil function to calculate "
      "output height and width. False is the default. If it is set to False, "
      "the floor function will be used.")
      .SetDefault(false);
  AddAttr<bool>("use_mkldnn",
                "(bool, default false) Only used in mkldnn kernel")
      .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: $(N, C, D_{out}, H_{out}, W_{out})$
  For ceil_mode = false:
  $$
       D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
       H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
       W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
  $$
  For ceil_mode = true:
  $$
       D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1 \\
       H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
       W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
  $$

  For exclusive = false:
  $$
  dstart = i * strides[0] - paddings[0]
  $$
  $$
  dend = dstart + ksize[0]
  $$
  $$
  hstart = j * strides[1] - paddings[1]
  $$
  $$
  hend = hstart + ksize[1]
  $$
  $$
  wstart = k * strides[2] - paddings[2]
  $$
  $$
  wend = wstart + ksize[2]
  $$
  $$
  Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
  $$
  For exclusive = true:
  $$
  dstart = max(0, i * strides[0] - paddings[0])
  $$
  $$
  dend = min(D, dstart + ksize[0])
  $$
  $$
  hstart = max(0, j * strides[1] - paddings[1])
  $$
  $$
  hend = min(H, hstart + ksize[1])
  $$
  $$
  wstart = max(0, k * strides[2] - paddings[2])
  $$
  $$
  wend = min(W, wstart + ksize[2])
  $$
  $$
  Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
  $$

  For adaptive = true:
  $$
  dstart = floor(i * D_{in} / D_{out})
  $$
  $$
  dend = ceil((i + 1) * D_{in} / D_{out})
  $$
  $$
  hstart = floor(j * H_{in} / H_{out})
  $$
  $$
  hend = ceil((j + 1) * H_{in} / H_{out})
  $$
  $$
  wstart = floor(k * W_{in} / W_{out})
  $$
  $$
  wend = ceil((k + 1) * W_{in} / W_{out})
  $$
  $$
  Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
  $$

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

namespace ops = paddle::operators;

REGISTER_OPERATOR(pool2d, ops::PoolOp, ops::Pool2dOpMaker,
                  ops::PoolOpInferVarType,
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(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_OPERATOR(pool3d, ops::PoolOp, ops::Pool3dOpMaker,
                  ops::PoolOpInferVarType,
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(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>);