dangqingqing
7 years ago
commit
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## Evaluator Design
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### The Problem
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During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.
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### Evaluator Design
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Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
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1. Initialize the metric state and add it into the block.
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2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
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3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
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### Implementation
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This design is shown in python API.
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Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.
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```python
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class Evaluator(object):
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"""
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Evaluator Base class.
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"""
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def __init__(self, name, **kwargs):
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"""
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Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.
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Auc need four variables, `true_positives`,
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`true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program
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The initialization of Evaluator should be responsible for:
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create metric states and append to the main_program
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"""
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pass
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def _update_ops(self, input, label, **kwargs)
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"""
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Add mini-batch evaluator caculate operators to the main_program.
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Add increment operator to accumulate the metric states.
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"""
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def reset(self, executor, reset_program=None):
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"""
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Reset metric states at the begin of each pass/user specified batch number.
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Execute the reset_program to reset the states.
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"""
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def eval(self, executor, eval_program=None):
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"""
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Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
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Execute the eval_program and return the result.
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"""
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return eval_result
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```
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@ -0,0 +1,109 @@
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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 "MaxPoolWithMaskLayer.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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bool MaxPoolWithMaskLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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PoolLayer::init(layerMap, parameterMap);
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setOutput("mask", &mask_);
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return true;
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}
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size_t MaxPoolWithMaskLayer::getSize() {
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CHECK_EQ(inputLayers_.size(), 1UL);
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size_t layerSize = 0;
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outputY_ = outputSize(imgSizeY_,
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sizeY_,
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confPaddingY_,
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strideY_,
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/* caffeMode */ false);
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outputX_ = outputSize(imgSize_,
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sizeX_,
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confPadding_,
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stride_,
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/* caffeMode */ false);
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layerSize = outputX_ * outputY_ * channels_;
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getOutput().setFrameHeight(outputY_);
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getOutput().setFrameWidth(outputX_);
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return layerSize;
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}
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void MaxPoolWithMaskLayer::forward(PassType passType) {
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size_t size = getSize();
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MatrixPtr inputV = inputLayers_[0]->getOutputValue();
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int batchSize = inputV->getHeight();
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resetOutput(batchSize, size);
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MatrixPtr outV = getOutputValue();
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CHECK_EQ(size, outV->getWidth());
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resetSpecifyOutput(mask_,
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batchSize,
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size,
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/* isValueClean */ false,
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/* isGradClean */ true);
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MatrixPtr maskV = mask_.value;
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outV->maxPoolForward(*inputV,
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imgSizeY_,
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imgSize_,
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channels_,
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sizeX_,
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sizeY_,
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strideY_,
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stride_,
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outputY_,
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outputX_,
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confPaddingY_,
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confPadding_,
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maskV);
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}
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void MaxPoolWithMaskLayer::backward(const UpdateCallback& callback) {
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(void)callback;
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if (NULL == getInputGrad(0)) {
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return;
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}
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MatrixPtr outGrad = getOutputGrad();
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MatrixPtr inputV = inputLayers_[0]->getOutputValue();
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MatrixPtr outV = getOutputValue();
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MatrixPtr inputGrad = inputLayers_[0]->getOutputGrad();
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inputGrad->maxPoolBackward(*inputV,
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imgSizeY_,
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imgSize_,
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*outGrad,
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*outV,
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sizeX_,
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sizeY_,
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strideY_,
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stride_,
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outputY_,
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outputX_,
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1,
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1,
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confPaddingY_,
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confPadding_);
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}
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} // namespace paddle
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@ -0,0 +1,40 @@
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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 "PoolLayer.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 Basic parent layer of different kinds of pooling
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*/
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class MaxPoolWithMaskLayer : public PoolLayer {
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protected:
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Argument mask_;
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public:
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explicit MaxPoolWithMaskLayer(const LayerConfig& config)
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: PoolLayer(config) {}
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size_t getSize();
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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};
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} // namespace paddle
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