Generating random numbers with given batch size (#8337)
* Generating random numbers with given batch size uniform_random_batch_size_like_op gaussian_random_batch_size_like_op * More comments about random seed. * Move test_*_random_batch_size_like_op to unittestsemailweixu-patch-1
emailweixu
7 years ago
committed by
Yi Wang
parent
118d950e74
commit
6752b06f8c
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/* Copyright (c) 2018 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/fluid/operators/batch_size_like.h"
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namespace paddle {
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namespace operators {
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void BatchSizeLikeOp::InferShape(framework::InferShapeContext *ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of %s should not be null.", Type());
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PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of %s should not be null.",
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Type());
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auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
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PADDLE_ENFORCE_GT(shape.size(), 0);
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto output_dim = framework::make_ddim(shape_int64);
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int input_dim_idx = ctx->Attrs().Get<int>("input_dim_idx");
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PADDLE_ENFORCE_GE(input_dim_idx, 0);
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PADDLE_ENFORCE_GT(ctx->GetInputDim("Input").size(), input_dim_idx);
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int output_dim_idx = ctx->Attrs().Get<int>("output_dim_idx");
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PADDLE_ENFORCE_GE(output_dim_idx, 0);
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PADDLE_ENFORCE_GT(static_cast<int>(shape.size()), output_dim_idx);
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output_dim[output_dim_idx] = ctx->GetInputDim("Input")[input_dim_idx];
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ctx->SetOutputDim("Out", output_dim);
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}
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BatchSizeLikeOpMaker::BatchSizeLikeOpMaker(OpProto *proto,
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OpAttrChecker *op_checker)
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: framework::OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Input",
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"(Tensor) Tensor "
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"whose input_dim_idx'th dimension specifies the batch_size");
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AddOutput("Out",
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"(Tensor) Tensor of specified shape will be filled "
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"with the specified value");
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AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
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AddAttr<int>("input_dim_idx",
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"(int, default 0) The index of input's batch size dimension")
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.SetDefault(0);
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AddAttr<int>("output_dim_idx",
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"(int, default 0) The index of output's batch size dimension")
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.SetDefault(0);
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}
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,36 @@
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/* Copyright (c) 2018 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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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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class BatchSizeLikeOp : 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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};
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class BatchSizeLikeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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BatchSizeLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker);
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};
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} // namespace operators
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} // namespace paddle
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/* 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/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/operators/batch_size_like.h"
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namespace paddle {
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namespace operators {
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class GaussianRandomBatchSizeLikeOp : public BatchSizeLikeOp {
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protected:
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using BatchSizeLikeOp::BatchSizeLikeOp;
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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static_cast<framework::proto::DataType>(ctx.Attr<int>("dtype")),
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ctx.GetPlace());
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}
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};
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class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
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public:
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GaussianRandomBatchSizeLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: BatchSizeLikeOpMaker(proto, op_checker) {
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AddAttr<float>("mean",
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"(float, default 0.0) "
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"mean of random tensor.")
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.SetDefault(.0f);
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AddAttr<float>("std",
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"(float, default 1.0) "
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"std of random tensor.")
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.SetDefault(1.0f);
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AddAttr<int>("seed",
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"(int, default 0) "
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"Random seed of generator."
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"0 means use system wide seed."
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"Note that if seed is not 0, this operator will always "
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"generate the same random numbers every time.")
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.SetDefault(0);
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AddAttr<int>("dtype",
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"(int, default 5(FP32)) "
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"Output data type.")
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.SetDefault(framework::proto::DataType::FP32);
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AddComment(R"DOC(
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GaussianRandom Operator.
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Used to initialize tensors with gaussian random generator.
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_WITHOUT_GRADIENT(
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gaussian_random_batch_size_like,
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paddle::operators::GaussianRandomBatchSizeLikeOp,
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paddle::operators::GaussianRandomBatchSizeLikeOpMaker);
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// Kernels are registered in gaussian_random_op.cc and gaussian_random_op.cu
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@ -0,0 +1,72 @@
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/* 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/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/operators/batch_size_like.h"
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namespace paddle {
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namespace operators {
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class UniformRandomBatchSizeLikeOp : public BatchSizeLikeOp {
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protected:
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using BatchSizeLikeOp::BatchSizeLikeOp;
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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static_cast<framework::proto::DataType>(ctx.Attr<int>("dtype")),
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ctx.GetPlace());
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}
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};
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class UniformRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
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public:
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UniformRandomBatchSizeLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: BatchSizeLikeOpMaker(proto, op_checker) {
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AddComment(R"DOC(
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Uniform random operator
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This operator initializes a tensor with the same batch_size as the Input tensor
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with random values sampled from a uniform distribution.
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)DOC");
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AddAttr<float>("min",
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"(float, default -1.0) "
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"Minimum value of uniform random")
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.SetDefault(-1.0f);
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AddAttr<float>("max",
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"(float, default 1.0) "
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"Maximun value of uniform random")
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.SetDefault(1.0f);
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AddAttr<int>("seed",
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"(int, default 0) "
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"Random seed used for generating samples. "
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"0 means use a seed generated by the system."
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"Note that if seed is not 0, this operator will always "
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"generate the same random numbers every time.")
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.SetDefault(0);
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AddAttr<int>("dtype", "(int, default 5(FP32)) Output tensor data type")
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.SetDefault(framework::proto::DataType::FP32);
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_WITHOUT_GRADIENT(
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uniform_random_batch_size_like,
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paddle::operators::UniformRandomBatchSizeLikeOp,
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paddle::operators::UniformRandomBatchSizeLikeOpMaker);
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// Kernels are registered in uniform_random_op.cc and uniform_random_op.cu
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@ -0,0 +1,46 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import unittest
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import numpy as np
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from op_test import OpTest
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class TestGaussianRandomBatchSizeLike(OpTest):
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def setUp(self):
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self.op_type = "gaussian_random_batch_size_like"
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self.inputs = {'Input': np.zeros((500, 2000), dtype="float32")}
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self.attrs = {'mean': 1., 'std': 2., 'shape': [-1, 2000]}
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self.outputs = {'Out': np.zeros((500, 2000), dtype='float32')}
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def test_check_output(self):
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self.check_output_customized(self.verify_output)
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def verify_output(self, outs):
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self.assertEqual(outs[0].shape, (500, 2000))
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hist, _ = np.histogram(outs[0], range=(-3, 5))
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hist = hist.astype("float32")
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hist /= float(outs[0].size)
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data = np.random.normal(size=(500, 2000), loc=1, scale=2)
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hist2, _ = np.histogram(data, range=(-3, 5))
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hist2 = hist2.astype("float32")
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hist2 /= float(outs[0].size)
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self.assertTrue(
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np.allclose(
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hist, hist2, rtol=0, atol=0.01),
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"hist: " + str(hist) + " hist2: " + str(hist2))
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if __name__ == "__main__":
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unittest.main()
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@ -0,0 +1,42 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import unittest
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import numpy as np
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from op_test import OpTest
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class TestUniformRandomBatchSizeLike(OpTest):
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def setUp(self):
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self.op_type = "uniform_random_batch_size_like"
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self.inputs = {'Input': np.zeros((500, 2000), dtype="float32")}
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self.attrs = {'min': 1., 'max': 2., 'shape': [-1, 2000]}
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self.outputs = {'Out': np.zeros((500, 2000), dtype='float32')}
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def test_check_output(self):
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self.check_output_customized(self.verify_output)
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def verify_output(self, outs):
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self.assertEqual(outs[0].shape, (500, 2000))
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hist, _ = np.histogram(outs[0], range=(1, 2))
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hist = hist.astype("float32")
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hist /= float(outs[0].size)
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prob = 0.1 * np.ones((10))
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self.assertTrue(
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np.allclose(
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hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
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if __name__ == "__main__":
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unittest.main()
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