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252 lines
9.5 KiB
252 lines
9.5 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/grid_sampler_op.h"
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#include <memory>
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#include <string>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/op_version_registry.h"
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#ifdef PADDLE_WITH_CUDA
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#include "paddle/fluid/platform/cudnn_helper.h"
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#endif
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#ifdef PADDLE_WITH_HIP
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#include "paddle/fluid/platform/miopen_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class GridSampleOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "GridSampler");
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OP_INOUT_CHECK(ctx->HasInput("Grid"), "Input", "Grid", "GridSampler");
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OP_INOUT_CHECK(ctx->HasOutput("Output"), "Output", "Output", "GridSampler");
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auto x_dims = ctx->GetInputDim("X");
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auto grid_dims = ctx->GetInputDim("Grid");
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PADDLE_ENFORCE_EQ(x_dims.size(), 4,
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platform::errors::InvalidArgument(
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"Input(X) of GridSampleOp should be 4-D Tensor, but "
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"received X dimension size(%d)",
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x_dims.size()));
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PADDLE_ENFORCE_EQ(grid_dims.size(), 4,
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platform::errors::InvalidArgument(
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"Input(Grid) of GridSampleOp should be 4-D Tensor, "
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"but received X dimension size(%d)",
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grid_dims.size()));
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if (ctx->IsRuntime() || grid_dims[3] > 0) {
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PADDLE_ENFORCE_EQ(
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grid_dims[3], 2,
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platform::errors::InvalidArgument(
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"Input(Grid) dimension[3] should be 2, but received %d",
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grid_dims[3]));
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}
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(
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grid_dims[0], x_dims[0],
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platform::errors::InvalidArgument(
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"Input(X) and Input(Grid) dimension[0] should be equal, but "
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"received X dimension[0](%d) != Grid dimension[0](%d)",
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x_dims[0], grid_dims[0]));
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}
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ctx->SetOutputDim("Output",
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{x_dims[0], x_dims[1], grid_dims[1], grid_dims[2]});
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ctx->ShareLoD("X", "Output");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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if (platform::CanCUDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kCUDNN;
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}
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#endif
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(),
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framework::DataLayout::kAnyLayout, library_);
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}
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};
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class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor) The input data of GridSampleOp, "
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"This is a 4-D tensor with shape of [N, C, H, W]");
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AddInput(
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"Grid",
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"(Tensor) The input grid of GridSampleOp generated by AffineGridOp, "
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"This is a 4-D tensor with shape of [N, H, W, 2] is the concatenation "
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"of x and y coordinates with shape [N, H, W] in last dimension");
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AddOutput("Output", "(Tensor) Output tensor with shape [N, C, H, W]");
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AddAttr<bool>(
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"use_cudnn",
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"(bool, default true) Only used in cudnn kernel, need install cudnn")
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.SetDefault(true);
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AddAttr<bool>(
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"align_corners",
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"(bool, default true) If align_corners is true, it will project"
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"-1 and 1 to the centers of the corner pixels. Otherwise, it will "
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"project"
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"-1 and 1 to the image edges.")
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.SetDefault(true);
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AddAttr<std::string>(
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"mode",
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"(bool, default true) The interpolation method which can be 'bilinear'"
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" or 'nearest'.")
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.SetDefault("bilinear");
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AddAttr<std::string>(
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"padding_mode",
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"(bool, default true) The padding method used when source"
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"index is out of input images. It can be 'zeros', 'reflection' and "
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"'border'.")
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.SetDefault("zeros");
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AddComment(R"DOC(
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This operation samples input X by using bilinear or nearest interpolation based on
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flow field grid, which is usually generated by affine_grid. The grid of
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shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
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with shape [N, H, W] each, where grid_x is indexing the 4th dimension
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(in width dimension) of input data x and grid_y is indexing the 3rd
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dimension (in height dimension), finally results is the bilinear
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interpolation value or nearest value of 4 nearest corner points.
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For bilinear interpolation mode:
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Step 1:
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Get (x, y) grid coordinates and scale to [0, H-1/W-1].
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grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
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grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
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Step 2:
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Indices input data X with grid (x, y) in each [H, W] area, and bilinear
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interpolate point value by 4 nearest points.
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wn ------- y_n ------- en
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| | |
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| d_n |
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x_w --d_w-- grid--d_e-- x_e
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| d_s |
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ws ------- y_s ------- wn
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x_w = floor(x) // west side x coord
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x_e = x_w + 1 // east side x coord
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y_n = floor(y) // north side y coord
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y_s = y_s + 1 // south side y coord
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d_w = grid_x - x_w // distance to west side
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d_e = x_e - grid_x // distance to east side
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d_n = grid_y - y_n // distance to north side
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d_s = y_s - grid_y // distance to south side
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wn = X[:, :, y_n, x_w] // north-west point value
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en = X[:, :, y_n, x_e] // north-east point value
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ws = X[:, :, y_s, x_w] // south-east point value
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es = X[:, :, y_s, x_w] // north-east point value
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output = wn * d_e * d_s + en * d_w * d_s
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+ ws * d_e * d_n + es * d_w * d_n
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)DOC");
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}
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};
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class GridSampleOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
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framework::GradVarName("X"), "grid_sampler");
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auto input_dims = ctx->GetInputDim("X");
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auto grid_dims = ctx->GetInputDim("Grid");
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), input_dims);
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}
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if (ctx->HasOutput(framework::GradVarName("Grid"))) {
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ctx->SetOutputDim(framework::GradVarName("Grid"), grid_dims);
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}
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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if (platform::CanCUDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kCUDNN;
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}
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#endif
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(),
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framework::DataLayout::kAnyLayout, library_);
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}
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};
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template <typename T>
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class GridSampleGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("grid_sampler_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Grid", this->Input("Grid"));
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op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output"));
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op->SetAttrMap(this->Attrs());
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetOutput(framework::GradVarName("Grid"), this->InputGrad("Grid"));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(grid_sampler, ops::GridSampleOp, ops::GridSampleOpMaker,
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ops::GridSampleGradMaker<paddle::framework::OpDesc>,
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ops::GridSampleGradMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad);
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REGISTER_OP_CPU_KERNEL(
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grid_sampler,
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ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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grid_sampler_grad,
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ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_VERSION(grid_sampler)
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.AddCheckpoint(
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R"ROC(
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Upgrade grid_sampler add a new attribute [mode].
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)ROC",
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paddle::framework::compatible::OpVersionDesc().NewAttr(
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"mode", "In order to specify interpolation mode", "bilinear"));
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