Merge pull request #1657 from baiyfbupt/dygraph
Add dygraph quantization
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## 介绍
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复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
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模型量化可以在基本不损失模型的精度的情况下，将FP32精度的模型参数转换为Int8精度，减小模型参数大小并加速计算，使用量化后的模型在移动端等部署时更具备速度优势。
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本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
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[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。
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在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
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## 快速开始
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量化多适用于轻量模型在移动端的部署，当训练出一个模型后，如果希望进一步的压缩模型大小并加速预测，可使用量化的方法压缩模型。
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模型量化主要包括五个步骤：
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1. 安装 PaddleSlim
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2. 准备训练好的模型
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3. 量化训练
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4. 导出量化推理模型
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5. 量化模型预测部署
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### 1. 安装PaddleSlim
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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cd Paddleslim
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python setup.py install
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```
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### 2. 准备训练好的模型
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PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.md)，如果待量化的模型不在列表中，需要按照[常规训练](../../../doc/doc_ch/quickstart.md)方法得到训练好的模型。
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### 3. 量化训练
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量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。
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量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下：
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```bash
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python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model
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# 比如下载提供的训练模型
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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tar -xf ch_ppocr_mobile_v2.0_det_train.tar
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python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
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```
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如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
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### 4. 导出模型
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在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
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```bash
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python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
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```
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### 5. 量化模型部署
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上述步骤导出的量化模型，参数精度仍然是FP32，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。
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量化模型部署的可参考 [移动端模型部署](../../lite/readme.md)
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## Introduction
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Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
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Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
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so as to reduce model calculation complexity and improve model inference performance.
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This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.
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It is recommended that you could understand following pages before reading this example：
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- [The training strategy of OCR model](../../../doc/doc_en/quickstart_en.md)
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- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)
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## Quick Start
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Quantization is mostly suitable for the deployment of lightweight models on mobile terminals.
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After training, if you want to further compress the model size and accelerate the prediction, you can use quantization methods to compress the model according to the following steps.
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1. Install PaddleSlim
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2. Prepare trained model
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3. Quantization-Aware Training
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4. Export inference model
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5. Deploy quantization inference model
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### 1. Install PaddleSlim
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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cd Paddleslim
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python setup.py install
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```
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### 2. Download Pretrain Model
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PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md).
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If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
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### 3. Quant-Aware Training
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Quantization training includes offline quantization training and online quantization training.
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Online quantization training is more effective. It is necessary to load the pre-training model.
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After the quantization strategy is defined, the model can be quantified.
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The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
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```bash
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python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model
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# download provided model
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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tar -xf ch_ppocr_mobile_v2.0_det_train.tar
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python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
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```
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### 4. Export inference model
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After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
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```bash
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python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
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```
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### 5. Deploy
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The numerical range of the quantized model parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8.
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The derived model can be converted through the `opt tool` of PaddleLite.
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For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
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sys.path.append(
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os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
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import argparse
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import paddle
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from paddle.jit import to_static
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from ppocr.modeling.architectures import build_model
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from ppocr.postprocess import build_post_process
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from ppocr.utils.save_load import init_model
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from ppocr.utils.logging import get_logger
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from tools.program import load_config, merge_config, ArgsParser
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from ppocr.metrics import build_metric
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import tools.program as program
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from paddleslim.dygraph.quant import QAT
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from ppocr.data import build_dataloader
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def main():
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############################################################################################################
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# 1. quantization configs
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############################################################################################################
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quant_config = {
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# weight preprocess type, default is None and no preprocessing is performed.
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'weight_preprocess_type': None,
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# activation preprocess type, default is None and no preprocessing is performed.
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'activation_preprocess_type': None,
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# weight quantize type, default is 'channel_wise_abs_max'
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'weight_quantize_type': 'channel_wise_abs_max',
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# activation quantize type, default is 'moving_average_abs_max'
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'activation_quantize_type': 'moving_average_abs_max',
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# weight quantize bit num, default is 8
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'weight_bits': 8,
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# activation quantize bit num, default is 8
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'activation_bits': 8,
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# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
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'dtype': 'int8',
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# window size for 'range_abs_max' quantization. default is 10000
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'window_size': 10000,
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# The decay coefficient of moving average, default is 0.9
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'moving_rate': 0.9,
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# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
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'quantizable_layer_type': ['Conv2D', 'Linear'],
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}
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FLAGS = ArgsParser().parse_args()
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config = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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logger = get_logger()
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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config['Global'])
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# build model
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# for rec algorithm
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if hasattr(post_process_class, 'character'):
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char_num = len(getattr(post_process_class, 'character'))
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config['Architecture']["Head"]['out_channels'] = char_num
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model = build_model(config['Architecture'])
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# get QAT model
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quanter = QAT(config=quant_config)
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quanter.quantize(model)
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init_model(config, model, logger)
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model.eval()
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# build metric
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eval_class = build_metric(config['Metric'])
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# build dataloader
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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# start eval
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metirc = program.eval(model, valid_dataloader, post_process_class,
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eval_class)
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logger.info('metric eval ***************')
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for k, v in metirc.items():
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logger.info('{}:{}'.format(k, v))
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save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
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infer_shape = [3, 32, 100] if config['Architecture'][
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'model_type'] != "det" else [3, 640, 640]
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quanter.save_quantized_model(
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model,
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save_path,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None] + infer_shape, dtype='float32')
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])
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logger.info('inference QAT model is saved to {}'.format(save_path))
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if __name__ == "__main__":
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config, device, logger, vdl_writer = program.preprocess()
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main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
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sys.path.append(
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os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
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import yaml
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import paddle
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import paddle.distributed as dist
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paddle.seed(2)
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from ppocr.data import build_dataloader
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from ppocr.modeling.architectures import build_model
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from ppocr.losses import build_loss
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from ppocr.optimizer import build_optimizer
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from ppocr.postprocess import build_post_process
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from ppocr.metrics import build_metric
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from ppocr.utils.save_load import init_model
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import tools.program as program
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from paddleslim.dygraph.quant import QAT
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dist.get_world_size()
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class PACT(paddle.nn.Layer):
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def __init__(self):
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super(PACT, self).__init__()
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alpha_attr = paddle.ParamAttr(
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name=self.full_name() + ".pact",
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initializer=paddle.nn.initializer.Constant(value=20),
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learning_rate=1.0,
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regularizer=paddle.regularizer.L2Decay(2e-5))
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self.alpha = self.create_parameter(
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shape=[1], attr=alpha_attr, dtype='float32')
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def forward(self, x):
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out_left = paddle.nn.functional.relu(x - self.alpha)
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out_right = paddle.nn.functional.relu(-self.alpha - x)
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x = x - out_left + out_right
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return x
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quant_config = {
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# weight preprocess type, default is None and no preprocessing is performed.
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'weight_preprocess_type': None,
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# activation preprocess type, default is None and no preprocessing is performed.
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'activation_preprocess_type': None,
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# weight quantize type, default is 'channel_wise_abs_max'
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'weight_quantize_type': 'channel_wise_abs_max',
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# activation quantize type, default is 'moving_average_abs_max'
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'activation_quantize_type': 'moving_average_abs_max',
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# weight quantize bit num, default is 8
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'weight_bits': 8,
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# activation quantize bit num, default is 8
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'activation_bits': 8,
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# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
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'dtype': 'int8',
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# window size for 'range_abs_max' quantization. default is 10000
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'window_size': 10000,
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# The decay coefficient of moving average, default is 0.9
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'moving_rate': 0.9,
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# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
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'quantizable_layer_type': ['Conv2D', 'Linear'],
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}
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def main(config, device, logger, vdl_writer):
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# init dist environment
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if config['Global']['distributed']:
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dist.init_parallel_env()
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global_config = config['Global']
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# build dataloader
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train_dataloader = build_dataloader(config, 'Train', device, logger)
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if config['Eval']:
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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else:
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valid_dataloader = None
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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global_config)
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# build model
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# for rec algorithm
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if hasattr(post_process_class, 'character'):
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char_num = len(getattr(post_process_class, 'character'))
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config['Architecture']["Head"]['out_channels'] = char_num
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model = build_model(config['Architecture'])
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# prepare to quant
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quanter = QAT(config=quant_config, act_preprocess=PACT)
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quanter.quantize(model)
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if config['Global']['distributed']:
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model = paddle.DataParallel(model)
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# build loss
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loss_class = build_loss(config['Loss'])
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# build optim
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optimizer, lr_scheduler = build_optimizer(
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config['Optimizer'],
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epochs=config['Global']['epoch_num'],
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step_each_epoch=len(train_dataloader),
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parameters=model.parameters())
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# build metric
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eval_class = build_metric(config['Metric'])
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# load pretrain model
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pre_best_model_dict = init_model(config, model, logger, optimizer)
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logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
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format(len(train_dataloader), len(valid_dataloader)))
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# start train
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program.train(config, train_dataloader, valid_dataloader, device, model,
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loss_class, optimizer, lr_scheduler, post_process_class,
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eval_class, pre_best_model_dict, logger, vdl_writer)
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def test_reader(config, device, logger):
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loader = build_dataloader(config, 'Train', device, logger)
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import time
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starttime = time.time()
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count = 0
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try:
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for data in loader():
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count += 1
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if count % 1 == 0:
|
||||
batch_time = time.time() - starttime
|
||||
starttime = time.time()
|
||||
logger.info("reader: {}, {}, {}".format(
|
||||
count, len(data[0]), batch_time))
|
||||
except Exception as e:
|
||||
logger.info(e)
|
||||
logger.info("finish reader: {}, Success!".format(count))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
config, device, logger, vdl_writer = program.preprocess(is_train=True)
|
||||
main(config, device, logger, vdl_writer)
|
||||
# test_reader(config, device, logger)
|
||||
Loading…
Reference in New Issue