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8 years ago
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/* 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/conv2d_op.h"
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namespace paddle {
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namespace operators {
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void Conv2DOp::InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of Conv2DOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Filter"),
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"Input(Filter) of Conv2DOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of Conv2DOp should not be null.");
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auto in_dims = ctx->GetInputDim("Input");
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auto filter_dims = ctx->GetInputDim("Filter");
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std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
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std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
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int groups = ctx->Attrs().Get<int>("groups");
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int input_channels = in_dims[1];
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int output_channels = filter_dims[0];
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PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
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PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
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PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
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"The number of input channels should be equal to filter "
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"channels * groups.");
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PADDLE_ENFORCE_EQ(
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output_channels % groups, 0,
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"The number of output channels should be divided by groups.");
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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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ctx->SetOutputDim("Output",
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{in_dims[0], filter_dims[0], output_height, output_width});
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}
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Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
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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("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 image channels, C is the number of input image channels, "
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"H and W is height and width of filter. "
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"If the groups attribute is greater than 1, C equal the number of "
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"input image channels divided by the groups.");
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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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AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
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.SetDefault({1, 1});
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AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
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.SetDefault({0, 0});
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AddAttr<int>(
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"groups",
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"group size of convolution operator. "
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"Refer to grouped convolution in Alex Krizhevsky's paper: "
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"when group=2, the first half of the filters are only connected to the "
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"first half of the input channels, and the second half only connected "
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"to the second half.")
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.SetDefault(1);
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AddComment(R"DOC(
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The convolution operation calculates the output based on the input, filter
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and strides, paddings, groups parameters. The size of each dimension of the
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parameters is checked in the infer-shape.
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)DOC");
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}
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void Conv2DOpGrad::InferShape(framework::InferShapeContext* ctx) const {
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auto in_dims = ctx->GetInputDim("Input");
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auto filter_dims = ctx->GetInputDim("Filter");
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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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}
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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(
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conv2d, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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conv2d_grad, ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);
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@ -1,22 +0,0 @@
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/* 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/conv3d_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(
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conv3d, ops::GemmConv3DKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(
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conv3d_grad, ops::GemmConvGrad3DKernel<paddle::platform::GPUPlace, float>);
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