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210 lines
9.1 KiB
210 lines
9.1 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/conv_op.h"
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namespace paddle {
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namespace operators {
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void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of ConvOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Filter"),
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"Input(Filter) of ConvOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of ConvOp 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(in_dims.size() == 4 || in_dims.size() == 5,
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"Conv intput should be 4-D or 5-D tensor.");
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PADDLE_ENFORCE_EQ(
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in_dims.size(), filter_dims.size(),
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"Conv input dimension and filter dimension should be the same.");
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PADDLE_ENFORCE(
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in_dims.size() - strides.size() == 2U,
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"Conv input dimension and strides dimension should be consistent.");
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PADDLE_ENFORCE_EQ(
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paddings.size(), strides.size(),
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"Conv paddings dimension and Conv strides dimension should be the same.");
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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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std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
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for (size_t i = 0; i < paddings.size(); ++i) {
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output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
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paddings[i], strides[i]));
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}
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ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
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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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"(Tensor) 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 is the height of the feature, "
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"and W is the width of the feature.");
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AddInput("Filter",
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"(Tensor) 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 is the height of the filter, and W is the width of the filter. "
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"If the groups attribute is greater than 1, C equals the number of "
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"input image channels divided by the groups.");
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AddOutput("Output",
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"(Tensor) 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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"(int default:1), the group size of 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 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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AddComment(R"DOC(
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Convolution Operator.
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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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Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
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size, C is the number of channels, H is the height of the feature, and W is
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the width of the feature. Parameters(ksize, strides, paddings) are two elements.
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These two elements represent height and width, respectively.
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The input(X) size and output(Out) size may be different.
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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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Output:
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Output shape: (N, C_out, H_out, W_out)
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where
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H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
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W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
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)DOC");
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}
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Conv3DOpMaker::Conv3DOpMaker(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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"(Tensor) The input tensor of convolution operator. "
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"The format of input tensor is NCDHW. Where N is batch size, C is the "
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"number of channels, D is the depth of the feature, H is the height of "
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"the feature, "
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"and W is the width of the feature.");
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AddInput("Filter",
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"(Tensor) The filter tensor of convolution operator. "
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"The format of the filter tensor is MCDHW, 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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"D is the depth of the filter, H is the height of the filter, and W "
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"is the width of the filter."
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"If the groups attribute is greater than 1, C equals the number of "
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"input image channels divided by the groups.");
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AddOutput("Output",
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"(Tensor) The output tensor of convolution operator."
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"The format of output tensor is also NCDHW.");
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AddAttr<std::vector<int>>(
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"strides",
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"(vector, default:{0, 0, 0}), the strides of convolution operator.")
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.SetDefault({1, 1, 1});
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AddAttr<std::vector<int>>(
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"paddings",
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"(vector, default:{0, 0, 0}), the paddings of convolution operator.")
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.SetDefault({0, 0, 0});
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AddAttr<int>(
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"groups",
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"(int default:1), the group size of 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 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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AddComment(R"DOC(
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Convolution3D Operator.
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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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Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
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size, C is the number of channels,D is the depth of the feature, H is the height of
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the feature, and W is the width of the feature. Parameters(ksize, strides, paddings)
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are three elements. These three elements represent depth, height and width, respectively.
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The input(X) size and output(Out) size may be different.
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Example:
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Input:
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Input shape: (N, C_in, D_in, H_in, W_in)
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Filter shape: (C_out, C_in, D_f, H_f, W_f)
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Output:
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Output shape: (N, C_out, D_out, H_out, W_out)
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where
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D_out = (D_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
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H_out = (H_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
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W_out = (W_in - filter_size[2] + 2 * paddings[2]) / strides[2] + 1;
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)DOC");
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}
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void ConvOpGrad::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::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
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ops::ConvOpGrad);
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namespace ops = paddle::operators;
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REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
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ops::ConvOpGrad);
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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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REGISTER_OP_CPU_KERNEL(conv3d,
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ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
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