channel_shuffle_op.cc

/* Copyright (c) 2022 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/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/unary.h"

namespace paddle {
namespace operators {

class ChannelShuffleOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
};

class ChannelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "(Tensor, default Tensor<float>), "
             "the input feature data of ChannelShuffleOp, the layout is "
             "[N, C, H, W] or [N, H, W, C].");
    AddOutput("Out",
              "(Tensor, default Tensor<float>), the output of "
              "ChannelShuffleOp. The layout is also [N, C, "
              "H, W] or [N, H, W, C].");
    AddAttr<int>("groups", "number of groups to divide channels in.");
    AddAttr<std::string>(
        "data_format",
        "An optional string from: \"NHWC\", \"NCHW\". "
        "Defaults to \"NHWC\", Specify the data format of the input data.")
        .SetDefault("NCHW");

    AddComment(R"DOC(
    Channel Shuffle operator
    This operator divides channels in a tensor of shape :math:`(*, C, H, W)`
        into :math:`g` groups and rearranges them as :math:`(*, C/g, g, H, W)`
        while keeping the original tensor shape.

    Please refer to the paper:
        `ShuffleNet: An Extremely Efficient Convolutional Neural Network for 
        Mobile Devices <https://arxiv.org/abs/1707.01083>`_
        by Zhang et. al (2017) for more details. 

        )DOC");
  }
};

class ChannelShuffleGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE_EQ(
        ctx->HasInput(framework::GradVarName("Out")), true,
        platform::errors::NotFound("Input(Out@Grad) should not be null"));
    PADDLE_ENFORCE_EQ(
        ctx->HasOutput(framework::GradVarName("X")), true,
        platform::errors::NotFound("Output(X@Grad) should not be null"));

    auto do_dims = ctx->GetInputDim(framework::GradVarName("Out"));
    PADDLE_ENFORCE_EQ(do_dims.size(), 4,
                      platform::errors::InvalidArgument(
                          "Input should be a 4-D tensor of format [N, C, "
                          "H, W] or [N, H, W, C], but got %u.",
                          do_dims.size()));

    auto dx_dims = do_dims;
    ctx->SetOutputDim(framework::GradVarName("X"), dx_dims);
  }
};

template <typename T>
class ChannelShuffleGradOpMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("channel_shuffle_grad");
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    op->SetAttrMap(this->Attrs());
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(channel_shuffle, ChannelShuffleInferShapeFunctor,
                            PD_INFER_META(phi::ChannelShuffleInferMeta));

REGISTER_OPERATOR(channel_shuffle, ops::ChannelShuffleOp,
                  ops::ChannelShuffleOpMaker,
                  ops::ChannelShuffleGradOpMaker<paddle::framework::OpDesc>,
                  ops::ChannelShuffleGradOpMaker<paddle::imperative::OpBase>,
                  ChannelShuffleInferShapeFunctor);

REGISTER_OPERATOR(channel_shuffle_grad, ops::ChannelShuffleGradOp);