Merge pull request #3348 from chengduoZH/develop
Add Conv3DLayer, DeConv3DLayerrevert-3824-remove_grad_op_type
chengduo
8 years ago
committed by
GitHub
commit
1e6c9926db
@ -0,0 +1,244 @@
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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 "Conv3DLayer.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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REGISTER_LAYER(conv3d, Conv3DLayer);
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bool Conv3DLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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if (!ConvBaseLayer::init(layerMap, parameterMap)) return false;
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int index = 0;
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for (auto &inputConfig : config_.inputs()) {
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const ConvConfig &conf = inputConfig.conv_conf();
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M_.push_back(numFilters_ / conf.groups());
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K_.push_back(filterPixels_[index] * filterChannels_[index]);
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// create a new weight
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size_t height, width;
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width = filterPixels_[index] * filterChannels_[index];
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height = numFilters_;
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CHECK_EQ(parameters_[index]->getSize(), width * height);
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Weight *w = new Weight(height, width, parameters_[index]);
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weights_.emplace_back(w);
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++index;
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}
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if (biasParameter_.get()) {
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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(1, numFilters_, biasParameter_));
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} else {
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biases_ =
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std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
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}
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}
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return true;
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}
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size_t Conv3DLayer::getSize() {
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CHECK_NE(inputLayers_.size(), 0UL);
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outputH_.clear();
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outputW_.clear();
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outputD_.clear();
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N_.clear();
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size_t layerSize = 0;
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for (size_t i = 0; i < inputLayers_.size(); ++i) {
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outputW_.push_back(outputSize(
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imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
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outputH_.push_back(outputSize(
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imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
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outputD_.push_back(outputSize(
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imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
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N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
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CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
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layerSize += N_[i] * numFilters_;
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}
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getOutput().setFrameHeight(outputH_[0]);
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getOutput().setFrameWidth(outputW_[0]);
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getOutput().setFrameDepth(outputD_[0]);
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return layerSize;
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}
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void Conv3DLayer::forward(PassType passType) {
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Layer::forward(passType);
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int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
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int outWidth = getSize();
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resetOutput(batchSize, outWidth);
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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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REGISTER_TIMER_INFO("FwdConv3D", getName().c_str());
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const MatrixPtr &inMat = getInputValue(i);
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const MatrixPtr &outMat = getOutputValue();
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int M = M_[i];
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int N = N_[i];
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int K = K_[i];
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Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
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MatrixPtr wMat = weights_[i]->getW();
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for (int n = 0; n < batchSize; ++n) {
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colBuf_->vol2Col(inMat->getData() + n * inMat->getStride(),
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channels_[i],
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imgSizeD_[i],
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imgSizeH_[i],
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imgSizeW_[i],
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filterSizeZ_[i],
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filterSizeY_[i],
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filterSize_[i],
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strideZ_[i],
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strideY_[i],
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stride_[i],
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paddingZ_[i],
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paddingY_[i],
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padding_[i]);
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real *outData = outMat->getData() + n * outMat->getStride();
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MatrixPtr outMatSub =
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Matrix::create(outData, groups_[i] * M, N, false, useGpu_);
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for (int g = 0; g < groups_[i]; g++) {
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MatrixPtr wMatSub = wMat->subMatrix(g * M, M);
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MatrixPtr in = colBuf_->subMatrix(g * K, K);
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MatrixPtr out = outMatSub->subMatrix(g * M, M);
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out->mul(*wMatSub, *in, 1.0, 1.0);
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}
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}
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}
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if (nullptr != this->biasParameter_) {
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REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
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this->addBias();
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}
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forwardActivation();
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}
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void Conv3DLayer::backward(const UpdateCallback &callback) {
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backwardActivation();
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if (biases_ && biases_->getWGrad()) {
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bpropBiases();
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biases_->getParameterPtr()->incUpdate(callback);
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}
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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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REGISTER_TIMER_INFO("BwdConv3D", getName().c_str());
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if (weights_[i]->getWGrad()) {
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bpropWeights(i);
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}
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if (getInputGrad(i)) {
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bpropData(i);
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}
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REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
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weights_[i]->getParameterPtr()->incUpdate(callback);
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}
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}
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void Conv3DLayer::bpropWeights(int i) {
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int M = M_[i];
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int N = N_[i];
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int K = K_[i];
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const MatrixPtr &inMat = getInputValue(i);
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Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
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MatrixPtr wGradMat = weights_[i]->getWGrad();
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int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
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for (int n = 0; n < batchSize; ++n) {
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colBuf_->vol2Col(inMat->getData() + n * inMat->getStride(),
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channels_[i],
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imgSizeD_[i],
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imgSizeH_[i],
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imgSizeW_[i],
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filterSizeZ_[i],
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filterSizeY_[i],
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filterSize_[i],
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strideZ_[i],
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strideY_[i],
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stride_[i],
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paddingZ_[i],
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paddingY_[i],
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padding_[i]);
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real *outGradData =
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getOutputGrad()->getData() + n * getOutputGrad()->getStride();
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MatrixPtr outGradSub =
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Matrix::create(outGradData, groups_[i] * M, N, false, useGpu_);
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for (int g = 0; g < groups_[i]; ++g) {
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MatrixPtr inMatSub = colBuf_->subMatrix(g * K, K);
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MatrixPtr outG = outGradSub->subMatrix(g * M, M);
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MatrixPtr wGradSub = wGradMat->subMatrix(g * M, M);
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wGradSub->mul(*outG, *(inMatSub->getTranspose()), 1.0, 1.0);
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}
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}
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}
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void Conv3DLayer::bpropData(int i) {
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int M = M_[i];
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int N = N_[i];
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int K = K_[i];
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Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
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MatrixPtr wMat = weights_[i]->getW();
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int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
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for (int n = 0; n < batchSize; ++n) {
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real *outGradData =
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getOutputGrad()->getData() + n * getOutputGrad()->getStride();
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real *preGradData =
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getInputGrad(i)->getData() + n * getInputGrad(i)->getStride();
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MatrixPtr outGradSub =
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Matrix::create(outGradData, M * groups_[i], N, false, useGpu_);
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for (int g = 0; g < groups_[i]; ++g) {
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MatrixPtr wMatSub = wMat->subMatrix(g * M, M);
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MatrixPtr outG = outGradSub->subMatrix(g * M, M);
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MatrixPtr inGradMatSub = colBuf_->subMatrix(g * K, K);
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inGradMatSub->mul(*(wMatSub->getTranspose()), *outG, 1.0, 0.0);
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}
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colBuf_->col2Vol(preGradData,
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channels_[i],
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imgSizeD_[i],
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imgSizeH_[i],
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imgSizeW_[i],
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filterSizeZ_[i],
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filterSizeY_[i],
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filterSize_[i],
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strideZ_[i],
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strideY_[i],
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stride_[i],
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paddingZ_[i],
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paddingY_[i],
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padding_[i],
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1.0,
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1.0);
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}
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}
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void Conv3DLayer::bpropBiases() {
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MatrixPtr outGradMat = getOutputGrad();
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if (this->sharedBiases_) {
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biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
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} else {
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biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
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}
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}
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void Conv3DLayer::addBias() {
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MatrixPtr outMat = getOutputValue();
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if (this->sharedBiases_) {
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outMat->addSharedBias(*(biases_->getW()), 1.0f);
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} else {
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outMat->addBias(*(biases_->getW()), 1.0f);
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}
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}
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} // namespace paddle
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@ -0,0 +1,51 @@
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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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#pragma once
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#include <vector>
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#include "ConvBaseLayer.h"
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#include "paddle/math/MathUtils.h"
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#include "paddle/math/Matrix.h"
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namespace paddle {
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/**
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* @brief A subclass of convolution layer.
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* This layer expands input and use matrix multiplication to
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* calculate convolution operation.
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*/
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class Conv3DLayer : public ConvBaseLayer {
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public:
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explicit Conv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
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~Conv3DLayer() {}
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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 addBias();
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void backward(const UpdateCallback& callback);
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void bpropBiases();
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void bpropData(int i);
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void bpropWeights(int i);
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size_t getSize();
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protected:
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// Figure out the dimensions for individual gemms.
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IntV M_; /// numFilters_ / filter_group_;
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IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_
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IntV K_; /// outputD_ * outputH_ * outputW_
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MatrixPtr colBuf_;
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};
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} // namespace paddle
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@ -0,0 +1,212 @@
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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 "DeConv3DLayer.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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REGISTER_LAYER(deconv3d, DeConv3DLayer);
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bool DeConv3DLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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if (!ConvBaseLayer::init(layerMap, parameterMap)) return false;
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// for Deconv, the dimension of Kernel is
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// channel * output * depth * height * weigth
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// Matrix storage format: (output * depth * height * weigth) x channel
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for (int index = 0; index < config_.inputs().size(); ++index) {
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M_.push_back(filterChannels_[index]);
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K_.push_back(filterPixels_[index] * (numFilters_ / groups_[index]));
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// create a new weight
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size_t height, width;
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height = filterPixels_[index] * numFilters_;
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width = filterChannels_[index];
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CHECK_EQ(parameters_[index]->getSize(), width * height);
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Weight *w = new Weight(height, width, parameters_[index]);
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weights_.emplace_back(w);
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}
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if (biasParameter_.get()) {
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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(1, numFilters_, biasParameter_));
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} else {
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biases_ =
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std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
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}
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}
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return true;
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}
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size_t DeConv3DLayer::getSize() {
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CHECK_NE(inputLayers_.size(), 0UL);
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outputH_.clear();
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outputW_.clear();
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outputD_.clear();
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N_.clear();
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NOut_.clear();
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size_t layerSize = 0;
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for (size_t i = 0; i < inputLayers_.size(); ++i) {
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outputW_.push_back(
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imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
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outputH_.push_back(imageSize(
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imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
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outputD_.push_back(imageSize(
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imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
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NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
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N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
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CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
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layerSize += NOut_[i] * numFilters_;
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}
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getOutput().setFrameHeight(outputH_[0]);
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getOutput().setFrameWidth(outputW_[0]);
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getOutput().setFrameDepth(outputD_[0]);
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return layerSize;
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}
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void DeConv3DLayer::forward(PassType passType) {
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Layer::forward(passType);
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int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
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int outWidth = getSize();
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resetOutput(batchSize, outWidth);
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const MatrixPtr outMat = getOutputValue();
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for (size_t i = 0; i != inputLayers_.size(); ++i) {
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REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str());
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const MatrixPtr &inMat = getInputValue(i);
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int M = M_[i];
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int N = N_[i];
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int K = K_[i];
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MatrixPtr wMat = weights_[i]->getW();
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Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
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for (int n = 0; n < batchSize; ++n) {
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real *inData = inMat->getData() + n * inMat->getStride();
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for (int g = 0; g < groups_[i]; ++g) {
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MatrixPtr inMatSub = Matrix::create(inData, M, N, false, useGpu_);
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MatrixPtr wMatSub = wMat->subMatrix(g * K, K);
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MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K);
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colBufDataSub->mul(*wMatSub, *inMatSub, 1.0, 0.0);
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inData += M * N;
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}
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colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(),
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numFilters_,
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outputD_[i],
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outputH_[i],
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outputW_[i],
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filterSizeZ_[i],
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filterSizeY_[i],
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filterSize_[i],
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strideZ_[i],
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strideY_[i],
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stride_[i],
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paddingZ_[i],
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paddingY_[i],
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padding_[i],
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1.0,
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1.0);
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}
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}
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if (nullptr != this->biasParameter_) {
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REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str());
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this->addBias();
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}
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forwardActivation();
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}
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void DeConv3DLayer::backward(const UpdateCallback &callback) {
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backwardActivation();
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int batchSize = getOutputGrad()->getHeight();
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if (biases_ && biases_->getWGrad()) {
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bpropBiases();
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biases_->getParameterPtr()->incUpdate(callback);
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}
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for (size_t i = 0; i < inputLayers_.size(); ++i) {
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if (weights_[i]->getWGrad() || this->needGradient_) {
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int M = M_[i];
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int N = N_[i];
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int K = K_[i];
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REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str());
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Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_);
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const MatrixPtr &inMat = getInputValue(i);
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for (int n = 0; n < batchSize; ++n) {
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colBuf_->vol2Col(
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getOutputGrad()->getData() + n * getOutputGrad()->getStride(),
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numFilters_,
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outputD_[i],
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outputH_[i],
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outputW_[i],
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filterSizeZ_[i],
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filterSizeY_[i],
|
||||
filterSize_[i],
|
||||
strideZ_[i],
|
||||
strideY_[i],
|
||||
stride_[i],
|
||||
paddingZ_[i],
|
||||
paddingY_[i],
|
||||
padding_[i]);
|
||||
if (weights_[i]->getWGrad()) {
|
||||
real *inData = inMat->getData() + n * inMat->getStride();
|
||||
for (int g = 0; g < groups_[i]; ++g) {
|
||||
MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K);
|
||||
MatrixPtr wGradMatSub =
|
||||
weights_[i]->getWGrad()->subMatrix(g * K, K);
|
||||
MatrixPtr inMatSub = Matrix::create(inData, M, N, false, useGpu_);
|
||||
wGradMatSub->mul(
|
||||
*colBufDataSub, *(inMatSub->getTranspose()), 1.0, 1.0);
|
||||
inData += M * N;
|
||||
}
|
||||
}
|
||||
if (getInputGrad(i)) {
|
||||
real *preGrad =
|
||||
getInputGrad(i)->getData() + n * getInputGrad(i)->getStride();
|
||||
for (int g = 0; g < groups_[i]; ++g) {
|
||||
MatrixPtr w = weights_[i]->getW()->subMatrix(g * K, K);
|
||||
MatrixPtr outGradMat = colBuf_->subMatrix(g * K, K);
|
||||
MatrixPtr inGradMatSub =
|
||||
Matrix::create(preGrad, M, N, false, useGpu_);
|
||||
inGradMatSub->mul(*(w->getTranspose()), *outGradMat, 1.0, 1.0);
|
||||
preGrad += M * N;
|
||||
}
|
||||
}
|
||||
}
|
||||
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
|
||||
weights_[i]->getParameterPtr()->incUpdate(callback);
|
||||
}
|
||||
}
|
||||
}
|
||||
void DeConv3DLayer::bpropWeights(int i) {}
|
||||
void DeConv3DLayer::bpropData(int i) {}
|
||||
|
||||
void DeConv3DLayer::bpropBiases() {
|
||||
const MatrixPtr &outGradMat = getOutputGrad();
|
||||
|
||||
if (this->sharedBiases_) {
|
||||
biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
|
||||
} else {
|
||||
biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
|
||||
}
|
||||
}
|
||||
|
||||
void DeConv3DLayer::addBias() {
|
||||
MatrixPtr outMat = getOutputValue();
|
||||
if (this->sharedBiases_) {
|
||||
outMat->addSharedBias(*(biases_->getW()), 1.0f);
|
||||
} else {
|
||||
outMat->addBias(*(biases_->getW()), 1.0f);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,52 @@
|
||||
/* Copyright (c) 2016 Baidu, Inc. 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. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <vector>
|
||||
#include "ConvBaseLayer.h"
|
||||
#include "paddle/math/MathUtils.h"
|
||||
#include "paddle/math/Matrix.h"
|
||||
|
||||
namespace paddle {
|
||||
|
||||
/**
|
||||
* @brief A subclass of deconvolution3D layer.
|
||||
* This layer expands input and use matrix multiplication to
|
||||
* calculate deconvolution3D operation.
|
||||
*/
|
||||
class DeConv3DLayer : public ConvBaseLayer {
|
||||
public:
|
||||
explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
|
||||
~DeConv3DLayer() {}
|
||||
bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
|
||||
|
||||
void forward(PassType passType);
|
||||
void addBias();
|
||||
void backward(const UpdateCallback& callback);
|
||||
void bpropBiases();
|
||||
void bpropData(int i);
|
||||
void bpropWeights(int i);
|
||||
size_t getSize();
|
||||
|
||||
protected:
|
||||
// Figure out the dimensions for individual gemms.
|
||||
IntV M_; /// numFilters_ / filter_group_;
|
||||
IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_
|
||||
IntV K_; /// outputD_ * outputH_ * outputW_
|
||||
IntV NOut_;
|
||||
MatrixPtr colBuf_;
|
||||
};
|
||||
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,132 @@
|
||||
type: "nn"
|
||||
layers {
|
||||
name: "data"
|
||||
type: "data"
|
||||
size: 36288
|
||||
active_type: ""
|
||||
height: 48
|
||||
width: 42
|
||||
depth: 6
|
||||
}
|
||||
layers {
|
||||
name: "conv3d_1"
|
||||
type: "conv3d"
|
||||
size: 24192
|
||||
active_type: ""
|
||||
inputs {
|
||||
input_layer_name: "data"
|
||||
input_parameter_name: "_conv3d_1.w0"
|
||||
conv_conf {
|
||||
filter_size: 3
|
||||
channels: 3
|
||||
stride: 2
|
||||
padding: 1
|
||||
groups: 1
|
||||
filter_channels: 3
|
||||
output_x: 21
|
||||
img_size: 42
|
||||
caffe_mode: true
|
||||
filter_size_y: 3
|
||||
padding_y: 1
|
||||
stride_y: 2
|
||||
output_y: 24
|
||||
img_size_y: 48
|
||||
filter_size_z: 3
|
||||
padding_z: 1
|
||||
stride_z: 2
|
||||
output_z: 3
|
||||
img_size_z: 6
|
||||
}
|
||||
}
|
||||
bias_parameter_name: "_conv3d_1.wbias"
|
||||
num_filters: 16
|
||||
shared_biases: true
|
||||
height: 24
|
||||
width: 21
|
||||
depth: 3
|
||||
}
|
||||
layers {
|
||||
name: "conv3d_2"
|
||||
type: "conv3d"
|
||||
size: 24192
|
||||
active_type: ""
|
||||
inputs {
|
||||
input_layer_name: "data"
|
||||
input_parameter_name: "_conv3d_2.w0"
|
||||
conv_conf {
|
||||
filter_size: 3
|
||||
channels: 3
|
||||
stride: 2
|
||||
padding: 1
|
||||
groups: 1
|
||||
filter_channels: 3
|
||||
output_x: 21
|
||||
img_size: 42
|
||||
caffe_mode: true
|
||||
filter_size_y: 3
|
||||
padding_y: 1
|
||||
stride_y: 2
|
||||
output_y: 24
|
||||
img_size_y: 48
|
||||
filter_size_z: 3
|
||||
padding_z: 1
|
||||
stride_z: 2
|
||||
output_z: 3
|
||||
img_size_z: 6
|
||||
}
|
||||
}
|
||||
bias_parameter_name: "_conv3d_2.wbias"
|
||||
num_filters: 16
|
||||
shared_biases: true
|
||||
height: 24
|
||||
width: 21
|
||||
depth: 3
|
||||
}
|
||||
parameters {
|
||||
name: "_conv3d_1.w0"
|
||||
size: 1296
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.272165526976
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
parameters {
|
||||
name: "_conv3d_1.wbias"
|
||||
size: 16
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.0
|
||||
dims: 16
|
||||
dims: 1
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
parameters {
|
||||
name: "_conv3d_2.w0"
|
||||
size: 1296
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.272165526976
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
parameters {
|
||||
name: "_conv3d_2.wbias"
|
||||
size: 16
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.0
|
||||
dims: 16
|
||||
dims: 1
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
input_layer_names: "data"
|
||||
output_layer_names: "conv3d_2"
|
||||
sub_models {
|
||||
name: "root"
|
||||
layer_names: "data"
|
||||
layer_names: "conv3d_1"
|
||||
layer_names: "conv3d_2"
|
||||
input_layer_names: "data"
|
||||
output_layer_names: "conv3d_2"
|
||||
is_recurrent_layer_group: false
|
||||
}
|
||||
|
||||
@ -0,0 +1,132 @@
|
||||
type: "nn"
|
||||
layers {
|
||||
name: "data"
|
||||
type: "data"
|
||||
size: 36288
|
||||
active_type: ""
|
||||
height: 48
|
||||
width: 42
|
||||
depth: 6
|
||||
}
|
||||
layers {
|
||||
name: "deconv3d_1"
|
||||
type: "deconv3d"
|
||||
size: 1387760
|
||||
active_type: ""
|
||||
inputs {
|
||||
input_layer_name: "data"
|
||||
input_parameter_name: "_deconv3d_1.w0"
|
||||
conv_conf {
|
||||
filter_size: 3
|
||||
channels: 3
|
||||
stride: 2
|
||||
padding: 1
|
||||
groups: 1
|
||||
filter_channels: 16
|
||||
output_x: 42
|
||||
img_size: 83
|
||||
caffe_mode: true
|
||||
filter_size_y: 3
|
||||
padding_y: 1
|
||||
stride_y: 2
|
||||
output_y: 48
|
||||
img_size_y: 95
|
||||
filter_size_z: 3
|
||||
padding_z: 1
|
||||
stride_z: 2
|
||||
output_z: 6
|
||||
img_size_z: 11
|
||||
}
|
||||
}
|
||||
bias_parameter_name: "_deconv3d_1.wbias"
|
||||
num_filters: 16
|
||||
shared_biases: true
|
||||
height: 95
|
||||
width: 83
|
||||
depth: 11
|
||||
}
|
||||
layers {
|
||||
name: "deconv3d_2"
|
||||
type: "deconv3d"
|
||||
size: 1387760
|
||||
active_type: ""
|
||||
inputs {
|
||||
input_layer_name: "data"
|
||||
input_parameter_name: "_deconv3d_2.w0"
|
||||
conv_conf {
|
||||
filter_size: 3
|
||||
channels: 3
|
||||
stride: 2
|
||||
padding: 1
|
||||
groups: 1
|
||||
filter_channels: 16
|
||||
output_x: 42
|
||||
img_size: 83
|
||||
caffe_mode: true
|
||||
filter_size_y: 3
|
||||
padding_y: 1
|
||||
stride_y: 2
|
||||
output_y: 48
|
||||
img_size_y: 95
|
||||
filter_size_z: 3
|
||||
padding_z: 1
|
||||
stride_z: 2
|
||||
output_z: 6
|
||||
img_size_z: 11
|
||||
}
|
||||
}
|
||||
bias_parameter_name: "_deconv3d_2.wbias"
|
||||
num_filters: 16
|
||||
shared_biases: true
|
||||
height: 95
|
||||
width: 83
|
||||
depth: 11
|
||||
}
|
||||
parameters {
|
||||
name: "_deconv3d_1.w0"
|
||||
size: 6912
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.272165526976
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
parameters {
|
||||
name: "_deconv3d_1.wbias"
|
||||
size: 16
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.0
|
||||
dims: 16
|
||||
dims: 1
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
parameters {
|
||||
name: "_deconv3d_2.w0"
|
||||
size: 6912
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.272165526976
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
parameters {
|
||||
name: "_deconv3d_2.wbias"
|
||||
size: 16
|
||||
initial_mean: 0.0
|
||||
initial_std: 0.0
|
||||
dims: 16
|
||||
dims: 1
|
||||
initial_strategy: 0
|
||||
initial_smart: false
|
||||
}
|
||||
input_layer_names: "data"
|
||||
output_layer_names: "deconv3d_2"
|
||||
sub_models {
|
||||
name: "root"
|
||||
layer_names: "data"
|
||||
layer_names: "deconv3d_1"
|
||||
layer_names: "deconv3d_2"
|
||||
input_layer_names: "data"
|
||||
output_layer_names: "deconv3d_2"
|
||||
is_recurrent_layer_group: false
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue