/* 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 #include #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/op_version_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 { OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "GridSampler"); OP_INOUT_CHECK(ctx->HasInput("Grid"), "Input", "Grid", "GridSampler"); OP_INOUT_CHECK(ctx->HasOutput("Output"), "Output", "Output", "GridSampler"); auto x_dims = ctx->GetInputDim("X"); auto grid_dims = ctx->GetInputDim("Grid"); PADDLE_ENFORCE_EQ(x_dims.size(), 4, platform::errors::InvalidArgument( "Input(X) of GridSampleOp should be 4-D Tensor, but " "received X dimension size(%d)", x_dims.size())); PADDLE_ENFORCE_EQ(grid_dims.size(), 4, platform::errors::InvalidArgument( "Input(Grid) of GridSampleOp should be 4-D Tensor, " "but received X dimension size(%d)", grid_dims.size())); if (ctx->IsRuntime() || grid_dims[3] > 0) { PADDLE_ENFORCE_EQ( grid_dims[3], 2, platform::errors::InvalidArgument( "Input(Grid) dimension[3] should be 2, but received %d", grid_dims[3])); } if (ctx->IsRuntime()) { PADDLE_ENFORCE_EQ( grid_dims[0], x_dims[0], platform::errors::InvalidArgument( "Input(X) and Input(Grid) dimension[0] should be equal, but " "received X dimension[0](%d) != Grid dimension[0](%d)", x_dims[0], grid_dims[0])); } ctx->SetOutputDim("Output", {x_dims[0], x_dims[1], grid_dims[1], grid_dims[2]}); 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( OperatorWithKernel::IndicateVarDataType(ctx, "X"), 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 dimension"); AddOutput("Output", "(Tensor) Output tensor with shape [N, C, H, W]"); AddAttr( "use_cudnn", "(bool, default true) Only used in cudnn kernel, need install cudnn") .SetDefault(true); AddAttr( "align_corners", "(bool, default true) If align_corners is true, it will project" "-1 and 1 to the centers of the corner pixels. Otherwise, it will " "project" "-1 and 1 to the image edges.") .SetDefault(true); AddAttr( "mode", "(bool, default true) The interpolation method which can be 'bilinear'" " or 'nearest'.") .SetDefault("bilinear"); AddAttr( "padding_mode", "(bool, default true) The padding method used when source" "index is out of input images. It can be 'zeros', 'reflection' and " "'border'.") .SetDefault("zeros"); AddComment(R"DOC( This operation samples input X by using bilinear or nearest interpolation based on flow field grid, which is usually generated 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 indexing the 3rd dimension (in height dimension), finally results is the bilinear interpolation value or nearest value of 4 nearest corner points. For bilinear interpolation mode: 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 { OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output", framework::GradVarName("X"), "grid_sampler"); 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( OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(), framework::DataLayout::kAnyLayout, library_); } }; template class GridSampleGradMaker : public framework::SingleGradOpMaker { public: using framework::SingleGradOpMaker::SingleGradOpMaker; protected: void Apply(GradOpPtr op) const override { op->SetType("grid_sampler_grad"); op->SetInput("X", this->Input("X")); op->SetInput("Grid", this->Input("Grid")); op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output")); op->SetAttrMap(this->Attrs()); op->SetOutput(framework::GradVarName("X"), this->InputGrad("X")); op->SetOutput(framework::GradVarName("Grid"), this->InputGrad("Grid")); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(grid_sampler, ops::GridSampleOp, ops::GridSampleOpMaker, ops::GridSampleGradMaker, ops::GridSampleGradMaker); REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad); REGISTER_OP_CPU_KERNEL( grid_sampler, ops::GridSampleOpKernel, ops::GridSampleOpKernel); REGISTER_OP_CPU_KERNEL( grid_sampler_grad, ops::GridSampleGradOpKernel, ops::GridSampleGradOpKernel); REGISTER_OP_VERSION(grid_sampler) .AddCheckpoint( R"ROC( Upgrade grid_sampler add a new attribute [mode]. )ROC", paddle::framework::compatible::OpVersionDesc().NewAttr( "mode", "In order to specify interpolation mode", "bilinear"));