grid_sampler_op.cc

/* Copyright (c) 2018 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/grid_sampler_op.h"
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

class GridSampleOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("X"),
                   "Input(X) of GridSampleOp should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Grid"),
                   "Input(Grid) of GridSampleOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Output"),
                   "Output(Output) of GridSampleOp should not be null.");

    auto x_dims = ctx->GetInputDim("X");
    auto grid_dims = ctx->GetInputDim("Grid");
    PADDLE_ENFORCE(x_dims.size() == 4,
                   "Input(X) of GridSampleOp should be 4-D Tensor.");
    PADDLE_ENFORCE(grid_dims.size() == 4,
                   "Input(Grid) of GridSampleOp should be 4-D Tensor.");
    PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
    PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
                      "Input(X) and Input(Grid) dims[0] should be equal.");
    if (ctx->IsRuntime()) {
      PADDLE_ENFORCE_EQ(
          grid_dims[1], x_dims[2],
          "Input(X) dims[2] and Input(Grid) dims[1] should be equal.");
      PADDLE_ENFORCE_EQ(
          grid_dims[2], x_dims[3],
          "Input(X) dims[3] and Input(Grid) dims[2] should be equal.");
    }

    ctx->SetOutputDim("Output", x_dims);
    ctx->ShareLoD("X", "Output");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
    if (platform::CanCUDNNBeUsed(ctx)) {
      library_ = framework::LibraryType::kCUDNN;
    }
#endif
    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
                                   ctx.GetPlace(),
                                   framework::DataLayout::kAnyLayout, library_);
  }
};

class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "(Tensor) The input data of GridSampleOp, "
             "This is a 4-D tensor with shape of [N, C, H, W]");
    AddInput(
        "Grid",
        "(Tensor) The input grid of GridSampleOp generated by AffineGridOp, "
        "This is a 4-D tensor with shape of [N, H, W, 2] is the concatenation "
        "of x and y coordinates with shape [N, H, W] in last dimention");
    AddOutput("Output", "(Tensor) Output tensor with shape [N, C, H, W]");
    AddAttr<bool>(
        "use_cudnn",
        "(bool, default true) Only used in cudnn kernel, need install cudnn")
        .SetDefault(true);

    AddComment(R"DOC(
      This operation samples input X by using bilinear interpolation based on 
      flow field grid, which is usually gennerated by affine_grid. The grid of
      shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates 
      with shape [N, H, W] each, where grid_x is indexing the 4th dimension 
      (in width dimension) of input data x and grid_y is indexng the 3rd 
      dimention (in height dimension), finally results is the bilinear 
      interpolation value of 4 nearest corner points.

      Step 1:
        Get (x, y) grid coordinates and scale to [0, H-1/W-1].

        grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
        grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)

      Step 2:
        Indices input data X with grid (x, y) in each [H, W] area, and bilinear 
        interpolate point value by 4 nearest points.

          wn ------- y_n ------- en
          |           |           |
          |          d_n          |
          |           |           |
         x_w --d_w-- grid--d_e-- x_e
          |           |           |
          |          d_s          |
          |           |           |
          ws ------- y_s ------- wn

        x_w = floor(x)              // west side x coord
        x_e = x_w + 1               // east side x coord
        y_n = floor(y)              // north side y coord
        y_s = y_s + 1               // south side y coord

        d_w = grid_x - x_w          // distance to west side
        d_e = x_e - grid_x          // distance to east side
        d_n = grid_y - y_n          // distance to north side
        d_s = y_s - grid_y          // distance to south side

        wn = X[:, :, y_n, x_w]      // north-west point value
        en = X[:, :, y_n, x_e]      // north-east point value
        ws = X[:, :, y_s, x_w]      // south-east point value
        es = X[:, :, y_s, x_w]      // north-east point value

        output = wn * d_e * d_s + en * d_w * d_s
               + ws * d_e * d_n + es * d_w * d_n
        )DOC");
  }
};

class GridSampleOpGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext* ctx) const override {
    auto input_dims = ctx->GetInputDim("X");
    auto grid_dims = ctx->GetInputDim("Grid");
    if (ctx->HasOutput(framework::GradVarName("X"))) {
      ctx->SetOutputDim(framework::GradVarName("X"), input_dims);
    }
    if (ctx->HasOutput(framework::GradVarName("Grid"))) {
      ctx->SetOutputDim(framework::GradVarName("Grid"), grid_dims);
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
    if (platform::CanCUDNNBeUsed(ctx)) {
      library_ = framework::LibraryType::kCUDNN;
    }
#endif
    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
                                   ctx.GetPlace(),
                                   framework::DataLayout::kAnyLayout, library_);
  }
};

class GridSampleGradMaker : public framework::SingleGradOpDescMaker {
 public:
  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;

 protected:
  std::unique_ptr<framework::OpDesc> Apply() const override {
    auto* op = new framework::OpDesc();
    op->SetType("grid_sampler_grad");
    op->SetInput("X", Input("X"));
    op->SetInput("Grid", Input("Grid"));
    op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));

    op->SetAttrMap(Attrs());

    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
    op->SetOutput(framework::GradVarName("Grid"), InputGrad("Grid"));
    return std::unique_ptr<framework::OpDesc>(op);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(grid_sampler, ops::GridSampleOp, ops::GridSampleOpMaker,
                  ops::GridSampleGradMaker);
REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad);

REGISTER_OP_CPU_KERNEL(
    grid_sampler,
    ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    grid_sampler_grad,
    ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, double>);