conv_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/conv_op.h"

namespace paddle {
namespace operators {

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

  auto in_dims = ctx->GetInputDim("Input");
  auto filter_dims = ctx->GetInputDim("Filter");
  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
  int groups = ctx->Attrs().Get<int>("groups");
  int input_channels = in_dims[1];
  int output_channels = filter_dims[0];

  PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
                 "Conv intput should be 4-D or 5-D tensor.");
  PADDLE_ENFORCE_EQ(
      in_dims.size(), filter_dims.size(),
      "Conv input dimension and filter dimension should be the same.");
  PADDLE_ENFORCE(
      in_dims.size() - strides.size() == 2U,
      "Conv input dimension and strides dimension should be consistent.");
  PADDLE_ENFORCE_EQ(
      paddings.size(), strides.size(),
      "Conv paddings dimension and Conv strides dimension should be the same.");
  PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
                    "The number of input channels should be equal to filter "
                    "channels * groups.");
  PADDLE_ENFORCE_EQ(
      output_channels % groups, 0,
      "The number of output channels should be divided by groups.");

  std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
  for (size_t i = 0; i < paddings.size(); ++i) {
    output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
                                      paddings[i], strides[i]));
  }
  ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}

Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
                             framework::OpAttrChecker* op_checker)
    : OpProtoAndCheckerMaker(proto, op_checker) {
  AddInput(
      "Input",
      "(Tensor), the input tensor of convolution operator. "
      "The format of input tensor is NCHW. Where N is batch size, C is the "
      "number of channels, H and W is the height and width of image.");
  AddInput("Filter",
           "(Tensor), the filter tensor of convolution operator."
           "The format of the filter tensor is MCHW, where M is the number of "
           "output image channels, C is the number of input image channels, "
           "H and W is height and width of filter. "
           "If the groups attribute is greater than 1, C equal the number of "
           "input image channels divided by the groups.");
  AddOutput("Output",
            "(Tensor), the output tensor of convolution operator."
            "The format of output tensor is also NCHW. Where N is batch size, "
            "C is the "
            "number of channels, H and W is the height and width of image.");
  AddAttr<std::vector<int>>(
      "strides", "(vector default:{1, 1}), strides of convolution operator.")
      .SetDefault({1, 1});
  AddAttr<std::vector<int>>(
      "paddings", "(vector default:{0, 0}), paddings of convolution operator.")
      .SetDefault({0, 0});
  AddAttr<int>(
      "groups",
      "(int, default:1), group size of convolution operator. "
      "Refer to grouped convolution in Alex Krizhevsky's paper: "
      "when group=2, the first half of the filters are only connected to the "
      "first half of the input channels, and the second half only connected "
      "to the second half.")
      .SetDefault(1);
  AddComment(R"DOC(
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H and W is the height and
width of 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:
       Input shape: (N, C_in, H_in, W_in)
       Filter shape: (C_out, C_in, H_f, W_f)
  Output:
       Output shape: (N, C_out, H_out, W_out)
  where
       H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
       W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
)DOC");
}

Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
                             framework::OpAttrChecker* op_checker)
    : OpProtoAndCheckerMaker(proto, op_checker) {
  AddInput(
      "Input",
      "(Tensor), the input tensor of convolution operator. "
      "The format of input tensor is NCDHW. Where N is batch size, C is the "
      "number of channels, D, H and W is the depth, height and width of "
      "image.");
  AddInput("Filter",
           "(Tensor), the filter tensor of convolution operator."
           "The format of the filter tensor is MCDHW, where M is the number of "
           "output image channels, C is the number of input image channels, "
           "D, H and W is depth, height and width of filter. "
           "If the groups attribute is greater than 1, C equal the number of "
           "input image channels divided by the groups.");
  AddOutput("Output",
            "(Tensor), the output tensor of convolution operator."
            "The format of output tensor is also NCDHW.");
  AddAttr<std::vector<int>>(
      "strides",
      "(vector, default:{0, 0, 0}), the strides of convolution operator.")
      .SetDefault({1, 1, 1});
  AddAttr<std::vector<int>>(
      "paddings",
      "(vector, default:{0, 0, 0}), the paddings of convolution operator.")
      .SetDefault({0, 0, 0});
  AddAttr<int>(
      "groups",
      "(int, default:1) the group size of convolution operator. "
      "Refer to grouped convolution in Alex Krizhevsky's paper: "
      "when group=2, the first half of the filters are only connected to the "
      "first half of the input channels, and the second half only connected "
      "to the second half.")
      .SetDefault(1);
  AddComment(R"DOC(
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
size, C is the number of channels, D, H and W is the depth, height and
width of feature. 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:
       Input shape: (N, C_in, D_in, H_in, W_in)
       Filter shape: (C_out, C_in, D_f, H_f, W_f)
  Output:
       Output shape: (N, C_out, D_out, H_out, W_out)
  where
       D_out = (D_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
       H_out = (H_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
       W_out = (W_in - filter_size[2] + 2 * paddings[2]) / strides[2] + 1;
)DOC");
}

void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {
  auto in_dims = ctx->GetInputDim("Input");
  auto filter_dims = ctx->GetInputDim("Filter");
  if (ctx->HasOutput(framework::GradVarName("Input"))) {
    ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
  }
  if (ctx->HasOutput(framework::GradVarName("Filter"))) {
    ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
  }
}

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(conv2d, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
            ops::ConvOpGrad);
namespace ops = paddle::operators;
REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
            ops::ConvOpGrad);

REGISTER_OP_CPU_KERNEL(
    conv2d, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
    conv2d_grad, ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);

REGISTER_OP_CPU_KERNEL(
    conv3d, ops::GemmConv3DKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
    conv3d_grad, ops::GemmConvGrad3DKernel<paddle::platform::CPUPlace, float>);