You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
91 lines
2.7 KiB
91 lines
2.7 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
|
|
|
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. */
|
|
|
|
#include "ExpandConvTransLayer.h"
|
|
#include "paddle/utils/Logging.h"
|
|
#include "paddle/utils/Stat.h"
|
|
|
|
/* The implementation of the convTransLayer is basically a swap of forward and
|
|
* backward of the original convLayer.
|
|
* The variable naming follows the convention of the convLayer.
|
|
* */
|
|
|
|
namespace paddle {
|
|
|
|
REGISTER_LAYER(exconvt, ExpandConvTransLayer);
|
|
|
|
bool ExpandConvTransLayer::init(const LayerMap &layerMap,
|
|
const ParameterMap ¶meterMap) {
|
|
/* Initialize the basic convolutional parent class */
|
|
ExpandConvBaseLayer::init(layerMap, parameterMap);
|
|
|
|
return true;
|
|
}
|
|
|
|
void ExpandConvTransLayer::forward(PassType passType) {
|
|
Layer::forward(passType);
|
|
|
|
/* malloc memory for the output_ if necessary */
|
|
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
|
|
resetOutput(batchSize, getOutputSize());
|
|
|
|
MatrixPtr output = nullptr;
|
|
for (size_t i = 0; i < inputLayers_.size(); ++i) {
|
|
LayerPtr prevLayer = getPrev(i);
|
|
output = prevLayer->getOutputValue();
|
|
REGISTER_TIMER_INFO("shrinkFwd", getName().c_str());
|
|
bpropActs(output, getOutputValue(), i);
|
|
}
|
|
|
|
/* add the bias-vector */
|
|
if (biases_.get()) {
|
|
if (sharedBiases_) {
|
|
addSharedBias();
|
|
} else {
|
|
addUnsharedBias();
|
|
}
|
|
}
|
|
|
|
/* activation */
|
|
forwardActivation();
|
|
}
|
|
|
|
void ExpandConvTransLayer::backward(const UpdateCallback &callback) {
|
|
backwardActivation();
|
|
|
|
MatrixPtr imageGrad = getOutputGrad();
|
|
if (biases_ && biases_->getWGrad()) {
|
|
bpropBiases(imageGrad);
|
|
/* Increasing the number of gradient */
|
|
biases_->getParameterPtr()->incUpdate(callback);
|
|
}
|
|
|
|
for (size_t i = 0; i < inputLayers_.size(); ++i) {
|
|
/* First, calculate the input layers error */
|
|
for (size_t off = 0; off < imageGrad->getHeight(); off++) {
|
|
if (getPrev(i)->getOutputGrad()) {
|
|
expandFwdOnce(imageGrad, getPrev(i)->getOutputGrad(), i, off);
|
|
}
|
|
}
|
|
if (weights_[i]->getWGrad()) {
|
|
/* Then, calculate the W-gradient for the current layer */
|
|
bpropWeights(imageGrad, getPrev(i)->getOutputValue(), i);
|
|
/* Increasing the number of gradient */
|
|
weights_[i]->getParameterPtr()->incUpdate(callback);
|
|
}
|
|
}
|
|
}
|
|
|
|
} // namespace paddle
|