dzhwinter
7 years ago
committed by
sneaxiy
parent
d292ad8501
commit
0153c21d83
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/fluid/operators/unstack_op.h"
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namespace plat = paddle::platform;
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namespace ops = paddle::operators;
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USE_OP(stack);
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REGISTER_OPERATOR(unstack, ops::UnStackOp, ops::UnStackOpMaker,
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ops::UnStackOpInferShape, ops::UnStackGradOpDescMaker);
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REGISTER_OPERATOR(unstack_grad, ops::UnStackGradOp,
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ops::UnStackOpGradInferShape);
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class UnStackOpInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist.");
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int axis = ctx->Attrs().Get<int>("axis");
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int num = ctx->Attrs().Get<int>("num");
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auto x_dim = ctx->GetInputDim("X");
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int rank = x_dim.size();
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PADDLE_ENFORCE(axis >= -rank && axis < rank,
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"Attr(axis) must be inside [-rank, rank), where rank = %d",
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rank);
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if (axis < 0) axis += rank;
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PADDLE_ENFORCE_EQ(ctx->Outputs("Y").size(), static_cast<size_t>(num),
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"Number of Outputs(Y) is wrong");
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if (x_dim[axis] > 0) {
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PADDLE_ENFORCE_EQ(num, x_dim[axis], "Number of Outputs(Y) is wrong");
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}
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auto vec = framework::vectorize2int(x_dim);
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vec.erase(vec.begin() + axis);
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ctx->SetOutputsDim("Y", std::vector<framework::DDim>( // NOLINT
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x_dim[axis], framework::make_ddim(vec)));
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}
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};
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class UnStackOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "The input of unstack op.");
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AddOutput("Y", "The output of unstack op.").AsDuplicable();
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AddAttr<int>("axis", "The axis along which Input(X) should be unstacked.")
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.SetDefault(0);
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AddAttr<int>("num", "The number of outputs(Y).").GreaterThan(0);
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AddComment(R"DOC(
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UnStack Operator.
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UnStack Input(X) into several tensors along Attr(axis).
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)DOC");
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}
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};
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class UnStackOp : public framework::OperatorBase {
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public:
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using OperatorBase::OperatorBase;
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &place) const override {
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auto stack_grad_op = framework::OpRegistry::CreateOp(
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"stack_grad", {{framework::GradVarName("Y"), {Input("X")}}},
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{{framework::GradVarName("X"), Outputs("Y")}}, Attrs());
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stack_grad_op->Run(scope, place);
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}
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};
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class UnStackOpGradInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE_GT(ctx->Inputs(framework::GradVarName("Y")).size(), 0,
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"Number of Inputs(Y@Grad) must be larger than 0");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Output(X@Grad) must exist.");
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auto input_dims = ctx->GetInputsDim(framework::GradVarName("Y"));
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for (size_t i = 1; i < input_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(input_dims[i], input_dims[0],
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"Dims of all Inputs(Y@Grad) must be the same");
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}
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int axis = ctx->Attrs().Get<int>("axis");
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int rank = input_dims[0].size();
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PADDLE_ENFORCE(
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axis >= -(rank + 1) && axis < rank + 1,
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"Attr(axis) must be inside [-(rank+1), rank+1), where rank = %d", rank);
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if (axis < 0) axis += (rank + 1);
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auto vec = framework::vectorize2int(input_dims[0]);
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vec.insert(vec.begin() + axis, input_dims.size());
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ctx->SetOutputDim(framework::GradVarName("X"), framework::make_ddim(vec));
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}
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};
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class UnStackGradOpDescMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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std::unique_ptr<framework::OpDesc> Apply() const override {
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std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
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op->SetType("unstack_grad");
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op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
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op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
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op->SetAttrMap(Attrs());
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return op;
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}
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};
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class UnStackGradOp : public framework::OperatorBase {
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public:
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using OperatorBase::OperatorBase;
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &place) const override {
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auto stack_op = framework::OpRegistry::CreateOp(
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"stack", {{"X", Inputs(framework::GradVarName("Y"))}},
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{{"Y", {Output(framework::GradVarName("X"))}}}, Attrs());
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stack_op->Run(scope, place);
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,81 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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from op_test import OpTest
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import numpy as np
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import unittest
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class TestUnStackOpBase(OpTest):
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def initDefaultParameters(self):
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self.input_dim = (5, 6, 7)
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self.axis = 0
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self.dtype = 'float32'
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def initParameters(self):
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pass
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def get_y_names(self):
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y_names = []
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for i in range(self.input_dim[self.axis]):
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y_names.append('y{}'.format(i))
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return y_names
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def setUp(self):
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self.initDefaultParameters()
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self.initParameters()
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self.op_type = 'unstack'
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self.x = np.random.random(size=self.input_dim).astype(self.dtype)
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outs = np.split(self.x, self.input_dim[self.axis], self.axis)
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new_shape = list(self.input_dim)
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del new_shape[self.axis]
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y_names = self.get_y_names()
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tmp = []
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for i in range(self.input_dim[self.axis]):
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tmp.append((y_names[i], np.reshape(outs[i], new_shape)))
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self.inputs = {'X': self.x}
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self.outputs = {'Y': tmp}
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self.attrs = {'axis': self.axis, 'num': self.input_dim[self.axis]}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad('X', self.get_y_names())
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class TestStackOp3(TestUnStackOpBase):
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def initParameters(self):
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self.axis = -1
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class TestStackOp4(TestUnStackOpBase):
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def initParameters(self):
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self.axis = -3
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class TestStackOp5(TestUnStackOpBase):
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def initParameters(self):
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self.axis = 1
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class TestStackOp6(TestUnStackOpBase):
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def initParameters(self):
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self.axis = 2
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if __name__ == '__main__':
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unittest.main()
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