Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-3736
yangyaming
8 years ago
commit
e9cc32820d
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Debian Package installation guide
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=================================
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PaddlePaddle supports :code:`deb` pacakge. The installation of this :code:`deb` package is tested in ubuntu 14.04, but it should be support other debian based linux, too.
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There are four versions of debian package, :code:`cpu`, :code:`gpu`, :code:`cpu-noavx`, :code:`gpu-noavx`. And :code:`noavx` version is used to support CPU which does not contain :code:`AVX` instructions. The download url of :code:`deb` package is \: https://github.com/baidu/Paddle/releases/
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After downloading PaddlePaddle deb packages, you can use :code:`gdebi` install.
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.. code-block:: bash
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gdebi paddle-*.deb
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If :code:`gdebi` is not installed, you can use :code:`sudo apt-get install gdebi` to install it.
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Or you can use following commands to install PaddlePaddle.
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.. code-block:: bash
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dpkg -i paddle-*.deb
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apt-get install -f
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And if you use GPU version deb package, you need to install CUDA toolkit and cuDNN, and set related environment variables(such as LD_LIBRARY_PATH) first. It is normal when `dpkg -i` get errors. `apt-get install -f` will continue install paddle, and install dependences.
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/* Copyright (c) 2016 PaddlePaddle Authors. 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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#if defined(__ARM_NEON__) || defined(__ARM_NEON)
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#include <arm_neon.h>
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namespace paddle {
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namespace neon {
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inline float32x4_t vld1q_f32_aligned(const float* p) {
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return vld1q_f32(
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(const float*)__builtin_assume_aligned(p, sizeof(float32x4_t)));
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}
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#ifndef __aarch64__
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inline float32_t vaddvq_f32(float32x4_t a) {
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float32x2_t v = vadd_f32(vget_high_f32(a), vget_low_f32(a));
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return vget_lane_f32(vpadd_f32(v, v), 0);
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}
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inline float32x4_t vmlaq_laneq_f32(float32x4_t a,
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float32x4_t b,
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float32x4_t v,
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const int lane) {
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return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane));
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}
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#endif
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} // namespace neon
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} // namespace paddle
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#endif
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@ -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.
|
||||
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. */
|
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|
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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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