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218 lines
9.6 KiB
218 lines
9.6 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_transpose_op.h"
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namespace paddle {
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namespace operators {
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void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of ConvTransposeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Filter"),
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"Input(Filter) of ConvTransposeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of ConvTransposeOp 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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PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
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"ConvTransposeOp intput should be 4-D or 5-D tensor.");
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PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
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"ConvTransposeOp input dimension and filter dimension "
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"should be the same.");
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PADDLE_ENFORCE(in_dims.size() - strides.size() == 2U,
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"ConvTransposeOp input dimension and strides dimension should "
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"be consistent.");
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PADDLE_ENFORCE_EQ(paddings.size(), strides.size(),
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"ConvTransposeOp paddings dimension and strides "
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"dimension should be the same.");
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PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
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"In ConvTransposeOp, The input channel should be the same "
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"as the number of filters.");
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std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
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for (size_t i = 0; i < strides.size(); ++i) {
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output_shape.push_back((in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] +
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filter_dims[i + 2]);
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}
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ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
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}
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Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
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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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"(Tensor) The input tensor of convolution transpose 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 input channels, H is the height of the feature, and "
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"W is the width of the feature.");
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AddInput(
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"Filter",
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"(Tensor) The filter tensor of convolution transpose operator. "
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"The format of the filter tensor is MCHW, where M is the number of "
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"input feature channels, C is the number of "
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"output feature channels,"
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"H is the height of the filter, and W is the width of the filter. "
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"We enforce groups number == 1 in the convolution transpose scenario.");
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AddOutput("Output",
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"(Tensor) The output tensor of convolution transpose operator. "
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"The format of output tensor is also NCHW.");
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AddAttr<std::vector<int>>(
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"strides",
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"(vector<int> default:{1, 1}), the strides(h_stride, w_stride) of "
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"convolution transpose operator.")
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.SetDefault({1, 1});
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AddAttr<std::vector<int>>(
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"paddings",
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"(vector<int> default:{0, 0}), the paddings(h_pad, w_pad) of convolution "
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"transpose operator.")
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.SetDefault({0, 0});
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AddComment(R"DOC(
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Convolution2D Transpose Operator.
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The convolution transpose 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) and output(Output) are in NCHW format. Where N is batchsize, C is the
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number of channels, H is the height of the feature, and W is the width of the feature.
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Filter(Input) is in MCHW format. Where M is the number of input feature channels,
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C is the number of output feature channels, H is the height of the filter,
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and W is the width of the filter.
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Parameters(strides, paddings) are two elements. These two elements represent height
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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: $(C_{in}, C_{out}, H_f, W_f)$
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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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$$
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H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\
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W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f
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$$
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)DOC");
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}
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Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
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framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Input",
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"(Tensor) The input tensor of convolution transpose operator."
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"The format of input tensor is NCDHW. Where N is batch size, C is "
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"the number of channels, D is the depth of the feature, H is the "
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"height of the feature, and "
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"W is the width of the feature.");
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AddInput("Filter",
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"(Tensor) The filter tensor of convolution transpose operator."
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"The format of the filter tensor is MCDHW, where M is the number of "
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"input feature channels, C is the number of "
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"output feature channels, D "
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"is the depth of the filter, H is the height of the filter, and "
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"W is the width of the filter."
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"We enforce groups number == 1 and padding == 0 in "
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"the convolution3d transpose scenario.");
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AddOutput("Output",
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"(Tensor) The output tensor of convolution transpose operator."
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"The format of output tensor is also NCDHW."
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"Where N is batch size, C is "
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"the number of channels, D is the depth of the feature, H is the "
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"height of the feature, and W is the width of the feature.");
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AddAttr<std::vector<int>>("strides",
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"(vector<int> default:{1, 1, 1}), the "
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"strides{d_stride, h_stride, w_stride} of "
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"convolution transpose operator.")
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.SetDefault({1, 1, 1});
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AddAttr<std::vector<int>>("paddings",
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"(vector<int> default:{0, 0, 0}), paddings(d_pad, "
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"h_pad, w_pad) of convolution transpose operator.")
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.SetDefault({0, 0, 0});
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AddComment(R"DOC(
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Convolution3D Transpose Operator.
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The convolution transpose 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) and output(Output) are in NCDHW format. 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 the feature,
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and W is the width of the feature.
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Filter(Input) is in MCDHW format. Where M is the number of input feature channels,
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C is the number of output feature channels, D is the depth of the filter,H is the
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height of the filter, and W is the width of the filter.
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Parameters(strides, paddings) are three elements. These three elements represent
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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: $(C_{in}, C_{out}, D_f, H_f, W_f)$
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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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$$
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D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\
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H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\
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W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f
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$$
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)DOC");
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}
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void ConvTransposeOpGrad::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_transpose, ops::ConvTransposeOp, ops::Conv2DTransposeOpMaker,
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conv2d_transpose_grad, ops::ConvTransposeOpGrad);
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REGISTER_OP_CPU_KERNEL(
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conv2d_transpose,
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ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
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ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
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REGISTER_OP_CPU_KERNEL(
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conv2d_transpose_grad,
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ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
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ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
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REGISTER_OP(conv3d_transpose, ops::ConvTransposeOp, ops::Conv3DTransposeOpMaker,
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conv3d_transpose_grad, ops::ConvTransposeOpGrad);
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REGISTER_OP_CPU_KERNEL(
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conv3d_transpose,
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ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
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ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
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REGISTER_OP_CPU_KERNEL(
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conv3d_transpose_grad,
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ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
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ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
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