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119 lines
4.1 KiB
119 lines
4.1 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/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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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
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size_t x_rank = x_dims.size();
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size_t axis_size = axis.size();
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PADDLE_ENFORCE_EQ(x_rank, axis_size,
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"The input tensor's rank(%d) "
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"should be equal to the axis's size(%d)",
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x_rank, axis_size);
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std::vector<int> count(axis_size, 0);
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for (size_t i = 0; i < axis_size; i++) {
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PADDLE_ENFORCE(
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axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1,
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"Each element of Attribute axis should be a unique value "
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"range from 0 to (dims - 1), "
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"where the dims is the axis's size");
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}
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framework::DDim out_dims(x_dims);
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for (size_t i = 0; i < axis_size; i++) {
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out_dims[i] = x_dims[axis[i]];
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}
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ctx->SetOutputDim("Out", out_dims);
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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(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"(Tensor)The input tensor, tensors with rank at most 6 are supported");
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AddOutput("Out", "(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 rank, 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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Transpose Operator.
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The input tensor will be permuted according to the axis values given.
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The op functions similar to how numpy.transpose works 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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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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}
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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(
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transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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transpose_grad,
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ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
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