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103 lines
3.9 KiB
103 lines
3.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/gemm_conv_op.h"
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namespace paddle {
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namespace operators {
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int outputSize(int input_size, int filter_size, int padding, int stride) {
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int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
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return output_size;
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}
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class Conv2DOp : 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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void InferShape(const framework::InferShapeContext &ctx) const override {
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auto in = ctx.Input<Tensor>("Input");
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auto filter = ctx.Input<Tensor>("Filter");
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auto out = ctx.Output<Tensor>("Output");
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PADDLE_ENFORCE_EQ(in->dims().size(), 4, "Conv2DOp intput should be 4-D.");
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PADDLE_ENFORCE_EQ(filter->dims().size(), 4,
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"Conv2DOp filter should be 4-D.");
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std::vector<int> strides = Attr<std::vector<int>>("strides");
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std::vector<int> paddings = Attr<std::vector<int>>("paddings");
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auto output_height =
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outputSize(in->dims()[2], filter->dims()[2], paddings[0], strides[0]);
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auto output_width =
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outputSize(in->dims()[3], filter->dims()[3], paddings[1], strides[1]);
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out->Resize(
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{in->dims()[0], filter->dims()[0], output_height, output_width});
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}
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};
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class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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Conv2DOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"Input",
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"The input tensor of convolution 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 image.");
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AddInput(
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"Filter",
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"The filter tensor of convolution operator."
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"The format of the filter tensor is MCHW, where M is the number of "
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"output "
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"image channels, C is the number of input image channels, H and W is "
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"height and width of filter.");
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AddOutput("Output",
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"The output tensor of convolution operator."
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"The format of output tensor is also NCHW.");
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AddComment(R"DOC(
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The convolution operation calculates the output based on
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the input, filter and strides, paddings parameters.
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)DOC");
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AddAttr<std::vector<int>>("strides", "strides of convolution operator.");
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AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.");
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}
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};
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class Conv2DOpGrad : 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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void InferShape(const framework::InferShapeContext &ctx) const override {
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auto in = ctx.Input<Tensor>("Input");
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auto filter = ctx.Input<Tensor>("Filter");
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auto d_in = ctx.Output<Tensor>(framework::GradVarName("Input"));
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auto d_filter = ctx.Output<Tensor>(framework::GradVarName("Filter"));
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d_in->Resize(in->dims());
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d_filter->Resize(filter->dims());
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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(conv2d, ops::Conv2DOp, ops::Conv2DOpMaker, conv2d_grad,
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ops::Conv2DOpGrad);
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REGISTER_OP_CPU_KERNEL(conv2d,
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ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
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