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126 lines
4.5 KiB
126 lines
4.5 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/transpose_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class TransposeOp : 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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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Input"),
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"Input(Input) should not be null");
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auto input_dim = ctx.Input<Tensor>("Input")->dims();
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auto axis = ctx.Attr<std::vector<int>>("axis");
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size_t input_dim_size = input_dim.size();
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size_t axis_size = axis.size();
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PADDLE_ENFORCE_EQ(input_dim_size, axis_size,
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"the input tensor's dimension(%d) "
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"should be equal to the axis's size(%d)",
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input_dim_size, axis_size);
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std::vector<int> axis_sorted(axis);
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std::sort(axis_sorted.begin(), axis_sorted.end());
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for (size_t i = 0; i < axis_sorted.size(); i++) {
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PADDLE_ENFORCE_EQ(axis_sorted[i], static_cast<int>(i),
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"the sorted axis should be [0, 1, ... dims - 1], "
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"where the dims is the axis's size");
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}
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framework::DDim output_dim(input_dim);
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for (size_t i = 0; i < axis.size(); i++) {
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output_dim[i] = input_dim[axis[i]];
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}
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ctx.Output<framework::LoDTensor>("Output")->Resize(output_dim);
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}
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};
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class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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TransposeOpMaker(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, tensors with rank at most 7 are supported");
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AddOutput("Output", "(Tensor)The output tensor");
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AddAttr<std::vector<int>>(
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"axis",
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"(vector<int>)a list of values, and the size of the list should be "
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"the same with the input tensor dimensions, the tensor will "
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"permute the axes according the the values given");
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AddComment(R"DOC(
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The Tensor will be permuted according to the axis values given.
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The op is very much like the numpy.transpose function in python
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For example:
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>> input = numpy.arange(6).reshape((2,3))
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>> input
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array([[0, 1, 2],
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[3, 4, 5]])
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>> axis = [1, 0]
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>> output = input.transpose(axis)
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>> output
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array([[0, 3],
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[1, 4],
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[2, 5]])
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So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
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the output tensor shape will be (N, H, W, C)
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)DOC");
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}
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};
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class TransposeOpGrad : 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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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Input"),
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"Input(Input) should not be null");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Output")),
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"Input(Output@GRAD) should not be null");
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auto input_dims = ctx.Input<Tensor>("Input")->dims();
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auto *input_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("Input"));
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auto output_grad_dims =
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ctx.Input<Tensor>(framework::GradVarName("Output"))->dims();
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auto output_dims = ctx.Input<Tensor>("Output")->dims();
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PADDLE_ENFORCE(output_grad_dims == output_dims,
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"Output@GRAD dims must equal to Input(Input) dims");
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input_grad->Resize(input_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(transpose, ops::TransposeOp, ops::TransposeOpMaker, transpose_grad,
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ops::TransposeOpGrad);
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REGISTER_OP_CPU_KERNEL(transpose,
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ops::TransposeKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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transpose_grad,
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ops::TransposeGradKernel<paddle::platform::CPUPlace, float>);
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