Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into crop_op
wanghaoshuang
8 years ago
commit
e2d75bd364
@ -1,124 +0,0 @@
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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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#include "ExpandConvBaseLayer.h"
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#include "paddle/utils/Logging.h"
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namespace paddle {
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bool ExpandConvBaseLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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/* Initialize the basic convolutional parent class */
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ConvBaseLayer::init(layerMap, parameterMap);
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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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/* Consistent caffe mode for multiple input */
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caffeMode_ = conf.caffe_mode();
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// create a new weight
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size_t height, width;
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height = filterPixels_[index] * filterChannels_[index];
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width = (!isDeconv_) ? numFilters_ : channels_[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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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(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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getOutputSize();
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return true;
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}
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size_t ExpandConvBaseLayer::getOutputSize() {
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CHECK_NE(inputLayers_.size(), 0UL);
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size_t layerSize = ConvBaseLayer::calOutputSize();
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return layerSize;
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}
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void ExpandConvBaseLayer::addSharedBias() {
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size_t mapW = getOutputSize() / numFilters_;
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size_t mapH = getOutputValue()->getElementCnt() / mapW;
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MatrixPtr out =
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Matrix::create(getOutputValue()->getData(), mapH, mapW, false, useGpu_);
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Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);
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out->transpose(transOutValue_, false); // false means no memory allocation
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transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
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numFilters_);
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MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
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1,
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biases_->getW()->getElementCnt(),
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false,
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useGpu_);
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transOutValue_->addBias(*bias, 1.0f);
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transOutValue_->reshape(mapW, mapH);
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transOutValue_->transpose(out, false); // false means no memory allocation
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out->clear();
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bias->clear();
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}
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void ExpandConvBaseLayer::addUnsharedBias() {
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MatrixPtr outValue = getOutputValue();
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MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
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1,
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biases_->getW()->getElementCnt(),
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false,
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useGpu_);
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outValue->addBias(*bias, 1.0f);
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}
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void ExpandConvBaseLayer::bpropSharedBias(MatrixPtr biases, MatrixPtr v) {
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size_t mapW = getOutputSize() / numFilters_;
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size_t mapH = v->getElementCnt() / mapW;
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MatrixPtr vTmp = Matrix::create(v->getData(), mapH, mapW, false, useGpu_);
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Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);
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vTmp->transpose(transOutValue_, false); // false means no memory allocation
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transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
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numFilters_);
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biases->collectBias(*transOutValue_, 1.0f);
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}
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void ExpandConvBaseLayer::bpropBiases(MatrixPtr v) {
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MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
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1,
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biases_->getWGrad()->getElementCnt(),
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false,
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useGpu_);
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if (sharedBiases_) {
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bpropSharedBias(biases, v);
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} else {
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biases->collectBias(*v, 1.0f);
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}
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biases->clear();
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}
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} // namespace paddle
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@ -1,57 +0,0 @@
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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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#include <vector>
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#include "ConvBaseLayer.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 ConvBaseLayer that is a superclass of both
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* ExpandConvLayer and ExpandConvTransLayer
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*/
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class ExpandConvBaseLayer : public ConvBaseLayer {
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protected:
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/// The transpose of output, which is an auxiliary matrix.
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MatrixPtr transOutValue_;
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public:
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explicit ExpandConvBaseLayer(const LayerConfig& config)
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: ConvBaseLayer(config) {}
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~ExpandConvBaseLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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size_t getOutputSize();
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/**
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* Add shared bias.
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*/
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void addSharedBias();
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/**
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* Add unshared bias.
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*/
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void addUnsharedBias();
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void bpropSharedBias(MatrixPtr biases, MatrixPtr v);
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void bpropBiases(MatrixPtr v);
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};
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} // namespace paddle
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@ -0,0 +1,51 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestSeqAvgPool1D(OpTest):
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def setUp(self):
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self.op_type = 'sequence_avg_pool'
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# one level, batch size is 4
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x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
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lod = [[0, 4, 5, 8, 11]]
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out = np.zeros((4, 23)).astype('float32')
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for i in range(4):
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sub_x = x[lod[0][i]:lod[0][i + 1], :]
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out[i] = sub_x.mean(axis=0)
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self.inputs = {'X': (x, lod)}
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestSeqAvgPool2D(OpTest):
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def setUp(self):
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self.op_type = 'sequence_avg_pool'
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# one level, batch size is 4
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x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32')
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lod = [[0, 4, 5, 8, 13]]
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out = np.zeros((4, 3, 17)).astype('float32')
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for i in range(4):
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sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
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out[i] = np.reshape(sub_x.mean(axis=0), (3, 17))
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self.inputs = {'X': (x, lod)}
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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if __name__ == '__main__':
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unittest.main()
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