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121 lines
4.4 KiB
121 lines
4.4 KiB
/* 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 "ConvBaseLayer.h"
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#include "paddle/math/MathUtils.h"
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#include "paddle/utils/Logging.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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isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv")
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? false
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: true;
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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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dilation_.push_back(conf.dilation());
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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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dilationY_.push_back(conf.dilation_y());
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filterSizeY_.push_back(conf.filter_size_y());
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channels_.push_back(conf.channels());
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imgSizeH_.push_back(conf.has_img_size_y() ? conf.img_size_y()
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: conf.img_size());
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imgSizeW_.push_back(conf.img_size());
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groups_.push_back(conf.groups());
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filterChannels_.push_back(conf.filter_channels());
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outputH_.push_back(conf.has_output_y() ? conf.output_y() : conf.output_x());
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outputW_.push_back(conf.output_x());
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paddingZ_.push_back(conf.padding_z());
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strideZ_.push_back(conf.stride_z());
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filterSizeZ_.push_back(conf.filter_size_z());
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imgSizeD_.push_back(conf.img_size_z());
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outputD_.push_back(conf.output_z());
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filterPixels_.push_back(filterSize_.back() * filterSizeY_.back() *
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filterSizeZ_.back());
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}
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CHECK(inputLayers_.size() == parameters_.size());
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// create new weights_ in derived class
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// create new biases_ in derived class
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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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size_t ConvBaseLayer::calOutputSize() {
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auto clearAndReserve = [this](IntV* vec) {
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vec->clear();
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vec->reserve(this->inputLayers_.size());
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};
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clearAndReserve(&imgSizeH_);
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clearAndReserve(&imgSizeW_);
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clearAndReserve(&outputH_);
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clearAndReserve(&outputW_);
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size_t layerSize = 0;
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auto setLayerSize = [&](IntV& inH, IntV& inW, IntV& outH, IntV& outW) {
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size_t filterSizeY;
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size_t filterSize;
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for (size_t i = 0; i < inputLayers_.size(); i++) {
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filterSizeY = (filterSizeY_[i] - 1) * dilationY_[i] + 1;
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filterSize = (filterSize_[i] - 1) * dilation_[i] + 1;
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inH.push_back(inputLayers_[i]->getOutput().getFrameHeight());
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inW.push_back(inputLayers_[i]->getOutput().getFrameWidth());
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const ConvConfig& conf = config_.inputs(i).conv_conf();
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if (isDeconv_) {
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if (inH[i] == 0)
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inH[i] = conf.has_output_y() ? conf.output_y() : conf.output_x();
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if (inW[i] == 0) inW[i] = conf.output_x();
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outH.push_back(imageSize(
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inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_));
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outW.push_back(
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imageSize(inW[i], filterSize, padding_[i], stride_[i], caffeMode_));
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} else {
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if (inH[i] == 0)
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inH[i] = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
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if (inW[i] == 0) inW[i] = conf.img_size();
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outH.push_back(outputSize(
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inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_));
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outW.push_back(outputSize(
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inW[i], filterSize, padding_[i], stride_[i], caffeMode_));
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}
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CHECK_EQ(outH[i], outH[0]);
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CHECK_EQ(outW[i], outW[0]);
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}
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getOutput().setFrameHeight(outH[0]);
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getOutput().setFrameWidth(outW[0]);
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layerSize = outH[0] * outW[0] * size_t(numFilters_);
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};
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setLayerSize(imgSizeH_, imgSizeW_, outputH_, outputW_);
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return layerSize;
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}
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} // namespace paddle
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