wanghaox
7 years ago
commit
794117bb93
@ -0,0 +1,91 @@
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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 "L2DistanceLayer.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(l2_distance, L2DistanceLayer);
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bool L2DistanceLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 2UL) << "The L2DistanceLayer accepts two and "
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<< "only two inputs.";
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CHECK_EQ(getSize(), 1UL) << "The output dimensionality of L2DistanceLayer "
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<< "is fixed to be 1.";
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return true;
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}
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void L2DistanceLayer::forward(PassType passType) {
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Layer::forward(passType);
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const auto inV1 = getInputValue(0);
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const auto inV2 = getInputValue(1);
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CHECK(inV1 && inV2);
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CHECK_EQ(inV1->getHeight(), inV2->getHeight())
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<< "The height of two inputs of this layer must be the same.";
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CHECK_EQ(inV1->getWidth(), inV2->getWidth())
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<< "The width of two inputs of this layer must be the same.";
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int batchSize = inV1->getHeight();
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int output_dim = getSize();
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{
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REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
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reserveOutput(batchSize, output_dim);
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auto outV = getOutputValue();
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CHECK(outV) << "The output matrix should not be null.";
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Matrix::resizeOrCreate(
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inputSub_, inV1->getHeight(), inV1->getWidth(), false, useGpu_);
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inputSub_->assign(*inV1);
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inputSub_->sub(*inV2);
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outV->sumOfProducts(*inputSub_, *inputSub_, 1, 0);
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outV->sqrt2(*outV);
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}
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}
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void L2DistanceLayer::backward(const UpdateCallback& callback) {
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const auto outG = getOutputGrad();
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const auto outV = getOutputValue();
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CHECK(outG && outV);
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auto inGrad1 = getInputGrad(0);
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auto inGrad2 = getInputGrad(1);
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{
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REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
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if (inGrad1 || inGrad2) {
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outV->scalarDiv(*outV, 1.);
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outV->dotMul(*outG, *outV);
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}
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if (inGrad1) inGrad1->addRowScale(0, *inputSub_, *outV);
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if (inGrad2) {
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inputSub_->mulScalar(-1.);
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inGrad2->addRowScale(0, *inputSub_, *outV);
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}
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}
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}
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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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#pragma once
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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namespace paddle {
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/**
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* @brief The layer calculates the l2 distance between two input vectors.
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* \f[
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* f(\bf{x}, \bf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}
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* \f]
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*
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* - Input1: A vector (batchSize * dataDim)
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* - Input2: A vector (batchSize * dataDim)
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* - Output: A vector (batchSize * 1)
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*
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* The configuration api is: l2_distance_layer.
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*/
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class L2DistanceLayer : public Layer {
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public:
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explicit L2DistanceLayer(const LayerConfig& config) : Layer(config) {}
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~L2DistanceLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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private:
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// Store the result of subtracting Input2 from Input1 in forward computation,
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// which will be reused in backward computation.
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MatrixPtr inputSub_;
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};
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} // namespace paddle
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@ -0,0 +1,153 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/logical_op.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename OpComment>
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class BinaryLogicalOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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BinaryLogicalOpProtoMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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OpComment comment;
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AddInput("X",
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string::Sprintf("(LoDTensor) Left hand operand of %s operator",
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comment.type));
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AddInput("Y",
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string::Sprintf("(LoDTensor) Right hand operand of %s operator",
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comment.type));
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AddOutput("Out", string::Sprintf(
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"(LoDTensor) n-dim bool tensor. Each element is %s",
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comment.equation));
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AddComment(string::Sprintf(R"DOC(%s Operator
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It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.
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Each element of Out is calculated by %s
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)DOC",
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comment.type, comment.equation));
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}
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};
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template <typename OpComment>
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class UnaryLogicalOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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UnaryLogicalOpProtoMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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OpComment comment;
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AddInput("X", string::Sprintf("(LoDTensor) Operand of %s operator",
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comment.type));
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AddOutput("Out", string::Sprintf(
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"(LoDTensor) n-dim bool tensor. Each element is %s",
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comment.equation));
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AddComment(string::Sprintf(R"DOC(%s Operator
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It operates element-wise on X, and returns the Out. X and Out are N-dim boolean tensors.
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Each element of Out is calculated by %s
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)DOC",
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comment.type, comment.equation));
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}
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};
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template <typename OpComment>
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class BinaryLogicalOpInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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OpComment comment;
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PADDLE_ENFORCE(context->HasInput("X"),
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"Input(X) of %s operator must not be null", comment.type);
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PADDLE_ENFORCE(context->HasInput("Y"),
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"Input(Y) of %s operator must not be null", comment.type);
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auto dim_x = context->GetInputDim("X");
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auto dim_y = context->GetInputDim("Y");
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PADDLE_ENFORCE_EQ(framework::product(dim_x), framework::product(dim_y),
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"The number of elements in X and Y should be same");
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context->SetOutputDim("Out", context->GetInputDim("X"));
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context->ShareLoD("X", "Out");
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}
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};
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template <typename OpComment>
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class UnaryLogicalOpInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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OpComment comment;
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PADDLE_ENFORCE(context->HasInput("X"),
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"Input(X) of %s operator must not be null", comment.type);
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auto dim_x = context->GetInputDim("X");
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context->SetOutputDim("Out", context->GetInputDim("X"));
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context->ShareLoD("X", "Out");
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}
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};
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class LogicalOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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framework::OpKernelType GetKernelType(
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const framework::ExecutionContext &ctx) const override {
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framework::OpKernelType kt = OperatorWithKernel::GetKernelType(ctx);
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// LogicalOp kernel's device type is decided by input tensor place
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kt.place_ = ctx.Input<framework::LoDTensor>("X")->place();
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return kt;
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}
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};
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} // namespace operators
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} // namespace paddle
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#define REGISTER_BINARY_LOGICAL_OP(op_type, _equation) \
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struct _##op_type##Comment { \
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static char type[]; \
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static char equation[]; \
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}; \
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char _##op_type##Comment::type[]{#op_type}; \
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char _##op_type##Comment::equation[]{_equation}; \
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REGISTER_OPERATOR( \
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op_type, ::paddle::operators::LogicalOp, \
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::paddle::operators::BinaryLogicalOpProtoMaker<_##op_type##Comment>, \
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::paddle::operators::BinaryLogicalOpInferShape<_##op_type##Comment>, \
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::paddle::framework::EmptyGradOpMaker);
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#define REGISTER_UNARY_LOGICAL_OP(op_type, _equation) \
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struct _##op_type##Comment { \
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static char type[]; \
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static char equation[]; \
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}; \
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char _##op_type##Comment::type[]{#op_type}; \
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char _##op_type##Comment::equation[]{_equation}; \
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REGISTER_OPERATOR( \
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op_type, ::paddle::operators::LogicalOp, \
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::paddle::operators::UnaryLogicalOpProtoMaker<_##op_type##Comment>, \
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::paddle::operators::UnaryLogicalOpInferShape<_##op_type##Comment>, \
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::paddle::framework::EmptyGradOpMaker);
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REGISTER_BINARY_LOGICAL_OP(logical_and, "Out = X && Y");
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REGISTER_BINARY_LOGICAL_KERNEL(logical_and, CPU,
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paddle::operators::LogicalAndFunctor);
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REGISTER_BINARY_LOGICAL_OP(logical_or, "Out = X && Y");
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REGISTER_BINARY_LOGICAL_KERNEL(logical_or, CPU,
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paddle::operators::LogicalOrFunctor);
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REGISTER_UNARY_LOGICAL_OP(logical_not, "Out = !X");
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REGISTER_UNARY_LOGICAL_KERNEL(logical_not, CPU,
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paddle::operators::LogicalNotFunctor);
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REGISTER_BINARY_LOGICAL_OP(logical_xor, "Out = (X || Y) && !(X && Y)");
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REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, CPU,
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paddle::operators::LogicalXorFunctor);
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@ -0,0 +1,24 @@
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|
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
|
||||||
|
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. */
|
||||||
|
|
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|
#include "paddle/operators/logical_op.h"
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|
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|
REGISTER_BINARY_LOGICAL_KERNEL(logical_and, GPU,
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|
paddle::operators::LogicalAndFunctor);
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|
REGISTER_BINARY_LOGICAL_KERNEL(logical_or, GPU,
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|
paddle::operators::LogicalOrFunctor);
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|
REGISTER_UNARY_LOGICAL_KERNEL(logical_not, GPU,
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|
paddle::operators::LogicalNotFunctor);
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REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, GPU,
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|
paddle::operators::LogicalXorFunctor);
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@ -0,0 +1,93 @@
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|||||||
|
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
|
||||||
|
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. */
|
||||||
|
|
||||||
|
#pragma once
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|
#include <math.h>
|
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|
#include <type_traits>
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|
#include "paddle/framework/op_registry.h"
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#include "paddle/platform/transform.h"
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|
|
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|
namespace paddle {
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namespace operators {
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|
|
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|
template <typename T>
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|
struct LogicalAndFunctor {
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using ELEM_TYPE = T;
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|
HOSTDEVICE bool operator()(const T& a, const T& b) const { return a && b; }
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||||||
|
};
|
||||||
|
|
||||||
|
template <typename T>
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|
struct LogicalOrFunctor {
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|
using ELEM_TYPE = T;
|
||||||
|
HOSTDEVICE bool operator()(const T& a, const T& b) const { return a || b; }
|
||||||
|
};
|
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|
|
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|
template <typename T>
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|
struct LogicalNotFunctor {
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|
using ELEM_TYPE = T;
|
||||||
|
HOSTDEVICE bool operator()(const T& a) const { return !a; }
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename T>
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||||||
|
struct LogicalXorFunctor {
|
||||||
|
using ELEM_TYPE = T;
|
||||||
|
HOSTDEVICE bool operator()(const T& a, const T& b) const {
|
||||||
|
return (a || b) && !(a && b);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename Place, typename Functor>
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||||||
|
class BinaryLogicalOpKernel
|
||||||
|
: public framework::OpKernel<typename Functor::ELEM_TYPE> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& context) const override {
|
||||||
|
using T = typename Functor::ELEM_TYPE;
|
||||||
|
auto* x = context.Input<framework::Tensor>("X");
|
||||||
|
auto* y = context.Input<framework::Tensor>("Y");
|
||||||
|
auto* out = context.Output<framework::Tensor>("Out");
|
||||||
|
Functor binary_func;
|
||||||
|
platform::Transform<Place> trans;
|
||||||
|
trans(context.device_context(), x->data<T>(), x->data<T>() + x->numel(),
|
||||||
|
y->data<T>(), out->mutable_data<bool>(context.GetPlace()),
|
||||||
|
binary_func);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename Place, typename Functor>
|
||||||
|
class UnaryLogicalOpKernel
|
||||||
|
: public framework::OpKernel<typename Functor::ELEM_TYPE> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& context) const override {
|
||||||
|
using T = typename Functor::ELEM_TYPE;
|
||||||
|
auto* x = context.Input<framework::Tensor>("X");
|
||||||
|
auto* out = context.Output<framework::Tensor>("Out");
|
||||||
|
Functor unary_func;
|
||||||
|
platform::Transform<Place> trans;
|
||||||
|
trans(context.device_context(), x->data<T>(), x->data<T>() + x->numel(),
|
||||||
|
out->mutable_data<bool>(context.GetPlace()), unary_func);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
|
|
||||||
|
#define REGISTER_BINARY_LOGICAL_KERNEL(op_type, dev, functor) \
|
||||||
|
REGISTER_OP_##dev##_KERNEL( \
|
||||||
|
op_type, ::paddle::operators::BinaryLogicalOpKernel< \
|
||||||
|
::paddle::platform::dev##Place, functor<bool>>);
|
||||||
|
|
||||||
|
#define REGISTER_UNARY_LOGICAL_KERNEL(op_type, dev, functor) \
|
||||||
|
REGISTER_OP_##dev##_KERNEL( \
|
||||||
|
op_type, ::paddle::operators::UnaryLogicalOpKernel< \
|
||||||
|
::paddle::platform::dev##Place, functor<bool>>);
|
||||||
@ -0,0 +1,39 @@
|
|||||||
|
type: "nn"
|
||||||
|
layers {
|
||||||
|
name: "x"
|
||||||
|
type: "data"
|
||||||
|
size: 128
|
||||||
|
active_type: ""
|
||||||
|
}
|
||||||
|
layers {
|
||||||
|
name: "y"
|
||||||
|
type: "data"
|
||||||
|
size: 128
|
||||||
|
active_type: ""
|
||||||
|
}
|
||||||
|
layers {
|
||||||
|
name: "__l2_distance_layer_0__"
|
||||||
|
type: "l2_distance"
|
||||||
|
size: 1
|
||||||
|
active_type: ""
|
||||||
|
inputs {
|
||||||
|
input_layer_name: "x"
|
||||||
|
}
|
||||||
|
inputs {
|
||||||
|
input_layer_name: "y"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
input_layer_names: "x"
|
||||||
|
input_layer_names: "y"
|
||||||
|
output_layer_names: "__l2_distance_layer_0__"
|
||||||
|
sub_models {
|
||||||
|
name: "root"
|
||||||
|
layer_names: "x"
|
||||||
|
layer_names: "y"
|
||||||
|
layer_names: "__l2_distance_layer_0__"
|
||||||
|
input_layer_names: "x"
|
||||||
|
input_layer_names: "y"
|
||||||
|
output_layer_names: "__l2_distance_layer_0__"
|
||||||
|
is_recurrent_layer_group: false
|
||||||
|
}
|
||||||
|
|
||||||
@ -0,0 +1,7 @@
|
|||||||
|
from paddle.trainer_config_helpers import *
|
||||||
|
|
||||||
|
outputs(
|
||||||
|
l2_distance_layer(
|
||||||
|
x=data_layer(
|
||||||
|
name='x', size=128), y=data_layer(
|
||||||
|
name='y', size=128)))
|
||||||
@ -0,0 +1,35 @@
|
|||||||
|
import op_test
|
||||||
|
import unittest
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def create_test_class(op_type, callback, binary_op=True):
|
||||||
|
class Cls(op_test.OpTest):
|
||||||
|
def setUp(self):
|
||||||
|
a = np.random.choice(a=[True, False], size=(10, 7)).astype(bool)
|
||||||
|
if binary_op:
|
||||||
|
b = np.random.choice(a=[True, False], size=(10, 7)).astype(bool)
|
||||||
|
c = callback(a, b)
|
||||||
|
else:
|
||||||
|
c = callback(a)
|
||||||
|
self.outputs = {'Out': c}
|
||||||
|
self.op_type = op_type
|
||||||
|
if binary_op:
|
||||||
|
self.inputs = {'X': a, 'Y': b}
|
||||||
|
else:
|
||||||
|
self.inputs = {'X': a}
|
||||||
|
|
||||||
|
def test_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
Cls.__name__ = op_type
|
||||||
|
globals()[op_type] = Cls
|
||||||
|
|
||||||
|
|
||||||
|
create_test_class('logical_and', lambda _a, _b: np.logical_and(_a, _b))
|
||||||
|
create_test_class('logical_or', lambda _a, _b: np.logical_or(_a, _b))
|
||||||
|
create_test_class('logical_not', lambda _a: np.logical_not(_a), False)
|
||||||
|
create_test_class('logical_xor', lambda _a, _b: np.logical_xor(_a, _b))
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue