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78 lines
2.6 KiB
78 lines
2.6 KiB
9 years ago
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/* 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 "paddle/utils/Logging.h"
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#include "ConvBaseLayer.h"
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namespace paddle {
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bool ConvBaseLayer::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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/* Initialize the convolutional layer parameter */
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numFilters_ = config_.num_filters();
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sharedBiases_ = config_.shared_biases();
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for (auto& inputConfig : config_.inputs()) {
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const ConvConfig& conf = inputConfig.conv_conf();
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padding_.push_back(conf.padding());
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stride_.push_back(conf.stride());
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filterSize_.push_back(conf.filter_size());
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paddingY_.push_back(conf.padding_y());
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strideY_.push_back(conf.stride_y());
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filterSizeY_.push_back(conf.filter_size_y());
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filterPixels_.push_back(filterSize_.back() * filterSizeY_.back());
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channels_.push_back(conf.channels());
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imgSize_.push_back(conf.img_size());
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imgPixels_.push_back(imgSize_.back() * imgSize_.back());
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groups_.push_back(conf.groups());
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filterChannels_.push_back(conf.filter_channels());
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outputX_.push_back(conf.output_x());
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outputs_.push_back(outputX_.back() * outputX_.back());
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}
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/* initialize the weightList */
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CHECK(inputLayers_.size() == parameters_.size());
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for (size_t i = 0; i < inputLayers_.size(); i++) {
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size_t height, width;
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height = filterPixels_[i] * filterChannels_[i];
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width = numFilters_;
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// create a new weight
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CHECK_EQ(parameters_[i]->getSize(), width * height);
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Weight* w = new Weight(height, width, parameters_[i]);
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weights_.emplace_back(w);
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}
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/* initialize the biases_ */
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if (biasParameter_.get() != NULL) {
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if (sharedBiases_) {
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CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
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biases_ =
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std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
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} else {
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biases_ =
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std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
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}
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}
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// default caffe model
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caffeMode_ = true;
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return true;
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}
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} // namespace paddle
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