wehub-resource-sync
195 lines
7.5 KiB
C++
195 lines
7.5 KiB
C++
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <memory>
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#include "paddle/fluid/framework/infershape_utils.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/phi/core/infermeta_utils.h"
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#include "paddle/phi/infermeta/multiary.h"
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namespace paddle {
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namespace operators {
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class DeformableConvV1OpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Input",
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"(Tensor) The input of deformable conv op. "
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"The shape of input is "
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"[N, channel_in, H, W]");
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AddInput("Offset",
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"(Tensor) The input offset. "
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"The shape of the offset is "
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"[N, deformable_groups * kernel_w * kernel_h * 2, H, W");
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AddInput("Filter",
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"(Tensor) The Input Filter "
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"The shape of the weight is "
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"[num_filters, channel_in, kernel_h, kernel_w.");
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AddOutput("Output",
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"(Tensor) The output. "
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"The shape of the output tensor is "
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"[N, num_filters, out_height, out_width]].");
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AddAttr<std::vector<int>>("strides",
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"(vector<int> default:{1, 1}), the "
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"strides(h_stride, w_stride) of "
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"convolution operator.")
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.SetDefault({1, 1});
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AddAttr<std::vector<int>>("paddings",
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"(vector<int> default:{0,0}), the "
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"paddings(h_pad, w_pad) of "
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"convolution operator. ")
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.SetDefault({0, 0});
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AddAttr<std::vector<int>>("dilations",
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"(vector<int> default:{1, 1}), the "
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"dilations(h_dilation, w_dilation) of "
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"convolution operator.")
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.SetDefault({1, 1});
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AddAttr<int>(
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"groups",
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"(int default:1), the groups number of the convolution operator. "
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"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
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"when group=2, the first half of the filters is only connected to the "
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"first half of the input channels, while the second half of the "
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"filters "
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"is only connected to the second half of the input channels.")
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.SetDefault(1);
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AddAttr<int>("deformable_groups",
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"(int default:1), the number of the deformable groups.")
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.SetDefault(1);
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AddAttr<int>("im2col_step",
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"im2col maximum number of image per computation")
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.SetDefault(64);
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AddComment(R"DOC(
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**Deformable Convolution v1 Operator**
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Deformable Convolution is a new method based Convolution which feature has offset
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in spatial location.
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1. Get offset of each pixel in feature map with convolution layers which number
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of channels should be double of weight size.
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2. Add offset to pixel to get new location and the new value which are computed
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directly through bilinear interpolation with four nearest pixel.
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3. Get the product of pixel and weight as result
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Compute 2-D deformable convolution on 4-D input.
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Given input image x, output feature map y, the deformable convolution operation can be expressed as follow:
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$$
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y(p) = \\sum_{k=1}^{K}{w_k * x(p + p_k + \\Delta p_k)}
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$$
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Where $$\\Delta p_k$$ is the learnable offset for the k-th location, respectively.
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Refer to 'https://arxiv.org/abs/1703.06211 '<https://arxiv.org/abs/1703.06211>
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Example:
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Input:
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Input shape: $(N, C_{in}, H_{in}, W_{in})$
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Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
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Offset shape: $(N, 2 * deformable_groups, * H_f * W_f, H_{out}, W_{out})$
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Output:
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Output shape: $(N, C_{out}, H_{out}, W_{out})$
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where $H_{out}, W_{out}$ must be equal to $H_{in}, W_{in}$ respectively.
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Where
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$$
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H_{out}= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
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W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
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$$
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)DOC");
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}
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};
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class DeformableConvV1Op : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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phi::KernelKey GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "Input"),
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ctx.device_context().GetPlace());
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}
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};
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template <typename T>
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class DeformableConvV1GradOpMaker : 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("deformable_conv_v1_grad");
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op->SetInput("Input", this->Input("Input"));
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op->SetInput("Filter", this->Input("Filter"));
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op->SetInput("Offset", this->Input("Offset"));
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op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output"));
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op->SetOutput(framework::GradVarName("Input"), this->InputGrad("Input"));
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op->SetOutput(framework::GradVarName("Filter"), this->InputGrad("Filter"));
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op->SetOutput(framework::GradVarName("Offset"), this->InputGrad("Offset"));
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op->SetAttrMap(this->Attrs());
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}
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};
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class DeformableConvV1GradOp : 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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auto in_dims = ctx->GetInputDim("Input");
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auto filter_dims = ctx->GetInputDim("Filter");
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auto offset_dims = ctx->GetInputDim("Offset");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Output")),
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"Input",
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"Output@Grad",
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"deformable_conv_v1_grad");
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if (ctx->HasOutput(framework::GradVarName("Input"))) {
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ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
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}
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if (ctx->HasOutput(framework::GradVarName("Filter"))) {
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ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
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}
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if (ctx->HasOutput(framework::GradVarName("Offset"))) {
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ctx->SetOutputDim(framework::GradVarName("Offset"), offset_dims);
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}
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}
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protected:
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phi::KernelKey GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "Input"),
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ctx.device_context().GetPlace());
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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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DECLARE_INFER_SHAPE_FUNCTOR(deformable_conv,
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DeformableConvV1InferShapeFunctor,
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PD_INFER_META(phi::DeformableConvInferMeta));
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REGISTER_OPERATOR(deformable_conv_v1,
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ops::DeformableConvV1Op,
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ops::DeformableConvV1OpMaker,
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ops::DeformableConvV1GradOpMaker<paddle::framework::OpDesc>,
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ops::DeformableConvV1GradOpMaker<paddle::imperative::OpBase>,
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DeformableConvV1InferShapeFunctor);
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REGISTER_OPERATOR(deformable_conv_v1_grad, ops::DeformableConvV1GradOp);
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