/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/operators/reshape_op.h" namespace paddle { namespace operators { class ReshapeOp : public framework::OperatorWithKernel { public: ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, const framework::AttributeMap &attrs) : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: void InferShape(const framework::InferShapeContext &ctx) const override { // input check PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) of ReshapeOp should not be null."); PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"), "Output(Out) of ReshapeOp should not be null."); auto shape = ctx.Attr<std::vector<int>>("shape"); PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty."); for (auto dim : shape) { PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive."); } // capacity check int64_t capacity = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()); auto *in = ctx.Input<framework::Tensor>("X"); int64_t in_size = framework::product(in->dims()); PADDLE_ENFORCE_EQ(capacity, in_size, "The size of Input(X) mismatches with Attr(shape)."); // resize output std::vector<int64_t> shape_int64(shape.size(), 0); std::transform(shape.begin(), shape.end(), shape_int64.begin(), [](int a) { return static_cast<int64_t>(a); }); auto out_dims = framework::make_ddim(shape_int64); ctx.Output<framework::LoDTensor>("Out")->Resize(out_dims); } }; class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker { public: ReshapeOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The input tensor of reshape operator."); AddOutput("Out", "The output tensor of reshape operator."); AddAttr<std::vector<int>>("shape", "Target shape of reshape operator."); AddComment(R"DOC(Reshape operator Reshape Input(X) into the shape specified by Attr(shape). An example: Given a 2-D tensor X with 2 rows and 2 columns [[1, 2], [3, 4]] with target shape = [1, 4], the reshape operator will transform the tensor X into a 1-D tensor: [1, 2, 3, 4] )DOC"); } }; class ReshapeGradOp : public framework::OperatorWithKernel { public: ReshapeGradOp(const std::string &type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, const framework::AttributeMap &attrs) : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: void InferShape(const framework::InferShapeContext &ctx) const override { PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null."); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), "Input(Out@GRAD) shouldn't be null."); auto dims = ctx.Input<framework::Tensor>("X")->dims(); auto *d_in = ctx.Output<framework::LoDTensor>(framework::GradVarName("X")); d_in->Resize(dims); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad, ops::ReshapeGradOp); REGISTER_OP_CPU_KERNEL(reshape, ops::ReshapeKernel<paddle::platform::CPUPlace, float>); REGISTER_OP_CPU_KERNEL( reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);