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100 lines
4.0 KiB
100 lines
4.0 KiB
/* Copyright (c) 2016 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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Indicesou 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/spp_op.h"
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namespace paddle {
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namespace operators {
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class SppOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SppOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"(Tensor) The input tensor of spp operator. "
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"The format of input tensor is NCHW. Where N is batch size, C is the "
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"number of channels, H and W is the height and width of feature.");
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AddOutput("Out",
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"(Tensor) The output tensor of spp operator."
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"N * M."
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"M = C * H * W");
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AddAttr<int>("pyramid_height", "(int), multi level pooling");
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AddAttr<std::string>(
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"pooling_type",
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"(string), pooling type, can be \"max\" for max-pooling "
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"and \"avg\" for average-pooling.")
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.InEnum({"max", "avg"});
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AddComment(R"DOC(
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"With spatial pyramid pooling, the input image can
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be of any sizes. This not only allows arbitrary aspect
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ratios, but also allows arbitrary scales. We can resize
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the input image to any scale (e.g., min(w, h)=180, 224,
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...) and apply the same deep network. When the
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input image is at different scales, the network (with
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the same filter sizes) will extract features at different
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scales. The scales play important roles in traditional
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methods.
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Input shape: $(N, C_{in}, H_{in}, W_{in})$
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Output shape: $(H_{out}, W_{out})$
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Where
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$$
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H_{out} = N \\
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W_{out} = (((4^pyramid_height) - 1) / (4 - 1))$ * C_{in}
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$$
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paper https://arxiv.org/pdf/1406.4729v4.pdf
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)DOC");
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}
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};
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class SppOp : 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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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SppOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SppOp should not be null.");
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auto in_x_dims = ctx->GetInputDim("X");
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int pyramid_height = ctx->Attrs().Get<int>("pyramid_height");
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PADDLE_ENFORCE(in_x_dims.size() == 4,
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"Spping intput must be of 4-dimensional.");
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int outlen = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * in_x_dims[1];
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std::vector<int64_t> output_shape({in_x_dims[0], outlen});
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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}
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};
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class SppOpGrad : 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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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Input(X@GRAD) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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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_OP(spp, ops::SppOp, ops::SppOpMaker, spp_grad, ops::SppOpGrad);
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REGISTER_OP_CPU_KERNEL(
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spp, ops::SppKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SppKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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spp_grad, ops::SppGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SppGradKernel<paddle::platform::CPUDeviceContext, double>);
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