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108 lines
2.9 KiB
108 lines
2.9 KiB
/* 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 "Layer.h"
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#include "paddle/math/Matrix.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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/**
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* @brief A layer for circular convluation of two vectors,
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* which is used in NEURAL TURING MACHINE.
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* - Input: two vectors, the first is data (batchSize x dataDim)
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* the second is shift weights (batchSize x shiftDim)
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* - Output: a vector (batchSize x dataDim)
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* Assumed that:
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* - a[in]: contains M elements.
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* - b[in]: contains N elements (N should be odd).
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* - c[out]: contains M elements.
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*
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* \f[
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* c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}
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* \f]
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*
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* In this formula:
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* - a's index is computed modulo M.
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* - b's index is comupted modulo N.
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*
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* The config file api is conv_shift_layer.
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*/
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class ConvShiftLayer : public Layer {
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public:
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explicit ConvShiftLayer(const LayerConfig& config) : Layer(config) {}
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~ConvShiftLayer() {}
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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void forward(PassType passType);
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void backward(const UpdateCallback& callback = nullptr);
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};
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REGISTER_LAYER(conv_shift, ConvShiftLayer);
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bool ConvShiftLayer::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(), 2U);
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return true;
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}
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void ConvShiftLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV0 = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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size_t batchSize = inV0->getHeight();
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size_t dataDim = inV0->getWidth();
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CHECK_EQ(batchSize, inV1->getHeight());
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CHECK_EQ(dataDim, getSize());
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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resetOutput(batchSize, dataDim);
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}
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MatrixPtr outV = getOutputValue();
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REGISTER_TIMER_INFO("FwConvShiftTimer", getName().c_str());
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outV->circularConv(*inV0, *inV1);
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}
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void ConvShiftLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV0 = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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MatrixPtr outG = getOutputGrad();
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MatrixPtr inG0 = getInputGrad(0);
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MatrixPtr inG1 = getInputGrad(1);
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REGISTER_TIMER_INFO("BwConvShiftTimer", getName().c_str());
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if (inG0 && inG1) {
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outG->circularConvDerivative(*outG, *inV0, *inV1, *inG0, *inG1);
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} else {
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CHECK(!inG0 || !inG1) << "Not supported";
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}
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}
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} // namespace paddle
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