/* 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/gaussian_random_op.h" namespace paddle { namespace operators { class GaussianRandomOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; protected: void InferShape(const framework::InferShapeContext& context) const override { auto* tensor = context.Output("Out"); auto dims = GetAttr>("dims"); PADDLE_ENFORCE(dims.size() > 0UL, "dims can be one int or array. dims must be set."); tensor->Resize(framework::make_ddim(dims)); } }; class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker { public: GaussianRandomOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) : framework::OpProtoAndCheckerMaker(proto, op_checker) { AddOutput("Out", "output matrix of random op"); AddComment(R"DOC( GaussianRandom operator. Use to initialize tensor with gaussian random generator. )DOC"); AddAttr>("dims", "The dimension of random tensor."); AddAttr("mean", "mean value of random.").SetDefault(.0f); AddAttr("std", "minimum value of random value.").SetDefault(1.0f); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker); REGISTER_OP_CPU_KERNEL( gaussian_random, ops::GaussianRandomKernel);