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116 lines
3.6 KiB
116 lines
3.6 KiB
/* Copyright (c) 2016 Baidu, Inc. 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 "CRFLayer.h"
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namespace paddle {
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REGISTER_LAYER(crf, CRFLayer);
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bool CRFLayer::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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if (config_.type() == "crf") {
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CHECK_GE(inputLayers_.size(), 2UL);
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// the third output is sequence weight. one weight for each sequence
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CHECK_LE(inputLayers_.size(), 3UL);
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}
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// coeff only affect bp, keep consistent with CostLayer
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coeff_ = config_.coeff();
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if (inputLayers_.size() == 3) {
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weightLayer_ = inputLayers_[2];
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}
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numClasses_ = inputLayers_[0]->getSize();
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CHECK_GE(numClasses_, 2UL);
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CHECK_EQ(parameters_[0]->getSize(), numClasses_ * (numClasses_ + 2));
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parameter_ = parameters_[0];
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// We don't need sequenceStartPositions because each sample of output_ is
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// for the cost of one sequence.
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setNeedSequenceInfo(false);
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return true;
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}
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void CRFLayer::forward(PassType passType) {
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Layer::forward(passType);
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CHECK(!useGpu_) << "GPU is not supported";
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const Argument& output = getInput(0);
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const Argument& label = getInput(1);
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CHECK(label.sequenceStartPositions);
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CHECK(label.ids);
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int batchSize = output.getBatchSize();
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size_t numSequences = label.sequenceStartPositions->getSize() - 1;
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resizeOutput(numSequences, 1);
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const int* starts = label.sequenceStartPositions->getData(false);
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CHECK_EQ(starts[numSequences], batchSize);
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for (size_t i = 0; i < numSequences; ++i) {
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if (i >= crfs_.size()) {
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crfs_.emplace_back(numClasses_,
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parameter_->getBuf(PARAMETER_VALUE)->getData(),
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parameter_->getBuf(PARAMETER_GRADIENT)
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? parameter_->getBuf(PARAMETER_GRADIENT)->getData()
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: nullptr);
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}
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output_.value->getData()[i] = crfs_[i].forward(
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output.value->getData() + numClasses_ * starts[i],
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label.ids->getData() + starts[i], starts[i + 1] - starts[i]);
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}
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if (weightLayer_) {
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const MatrixPtr& weight = getInputValue(*weightLayer_);
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getOutputValue()->dotMul(*getOutputValue(), *weight);
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}
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}
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void CRFLayer::backward(const UpdateCallback &callback) {
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const Argument& output = getInput(0);
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const Argument& label = getInput(1);
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const int* starts = label.sequenceStartPositions->getData(false);
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int numSequences = label.sequenceStartPositions->getSize() - 1;
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for (int i = 0; i < numSequences; ++i) {
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crfs_[i].backward(output.value->getData() + numClasses_ * starts[i],
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output.grad->getData() + numClasses_ * starts[i],
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label.ids->getData() + starts[i],
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starts[i + 1] - starts[i]);
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if (weightLayer_) {
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real weight = getInputValue(*weightLayer_)->getElement(i, 0);
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MatrixPtr grad = output.grad->subRowMatrix(starts[i], starts[i+1]);
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grad->mulScalar(weight);
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}
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}
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if (coeff_ != real(1.0f)) {
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output.grad->mulScalar(coeff_);
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}
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parameter_->incUpdate(callback);
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}
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} // namespace paddle
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