conv3d_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/conv3d_op.h"

namespace paddle {
namespace operators {

int OutputSizeConv3d(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 Conv3DOp::InferShape(framework::InferShapeContext* ctx) const {
  PADDLE_ENFORCE(ctx->HasInput("Input"),
                 "Input(Input) of Conv3DOp should not be null.");
  PADDLE_ENFORCE(ctx->HasInput("Filter"),
                 "Input(Filter) of Conv3DOp should not be null.");
  PADDLE_ENFORCE(ctx->HasOutput("Output"),
                 "Output(Output) of Conv3DOp 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_EQ(in_dims.size(), 5, "Conv3DOp input should be 5-D.");
  PADDLE_ENFORCE_EQ(filter_dims.size(), 5, "Conv3DOp filter should be 5-D.");
  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(OutputSizeConv3d(in_dims[i + 2], filter_dims[i + 2],
                                            paddings[i], strides[i]));
  }
  ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}

void Conv3DOpGrad::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);
  }
}

Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
                             framework::OpAttrChecker* op_checker)
    : OpProtoAndCheckerMaker(proto, op_checker) {
  AddInput(
      "Input",
      "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",
           "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",
            "The output tensor of convolution operator."
            "The format of output tensor is also NCDHW.");
  AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
      .SetDefault({1, 1, 1});
  AddAttr<std::vector<int>>("paddings", "The paddings of convolution operator.")
      .SetDefault({0, 0, 0});
  AddAttr<int>(
      "groups",
      "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.
)DOC");
}

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(conv3d, ops::Conv3DOp, ops::Conv3DOpMaker, conv3d_grad,
            ops::Conv3DOpGrad);

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