fix doc and init model before qat
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LDOUBLEV
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@ -23,13 +23,13 @@
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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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git checkout develop
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cd Paddleslim
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python3 setup.py install
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```
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### 2. 获取预训练模型
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模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列(模型)[../../../doc/doc_ch/models_list.md]，开发者可根据需要自行选择模型或使用自己的模型。
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模型裁剪需要加载事先训练好的模型，PaddleOCR也提供了一系列[模型](../../../doc/doc_ch/models_list.md)，开发者可根据需要自行选择模型或使用自己的模型。
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### 3. 敏感度分析训练
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@ -49,14 +49,14 @@ python3 setup.py install
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进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
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```bash
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python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
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python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
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```
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### 4. 导出模型、预测部署
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在得到裁剪训练保存的模型后，我们可以将其导出为inference_model：
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```bash
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pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=inference_model
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pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=./prune/prune_inference_model
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```
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inference model的预测和部署参考：
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@ -22,15 +22,15 @@ Five steps for OCR model prune:
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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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git checkout develop
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cd Paddleslim
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python3 setup.py install
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```
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### 2. Download Pretrain Model
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Model prune needs to load pre-trained models.
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PaddleOCR also provides a series of (models)[../../../doc/doc_en/models_list_en.md]. Developers can choose their own models or use their own models according to their needs.
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PaddleOCR also provides a series of [models](../../../doc/doc_en/models_list_en.md). Developers can choose their own models or use their own models according to their needs.
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### 3. Pruning sensitivity analysis
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@ -54,7 +54,7 @@ Enter the PaddleOCR root directory，perform sensitivity analysis on the model w
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```bash
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python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
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python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
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```
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@ -63,7 +63,7 @@ python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_
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We can export the pruned model as inference_model for deployment:
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```bash
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python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=inference_model
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python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=./prune/prune_inference_model
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```
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Reference for prediction and deployment of inference model:
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@ -23,7 +23,7 @@
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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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cd PaddleSlim
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python setup.py install
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```
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@ -37,12 +37,12 @@ PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.
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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.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
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python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='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.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./output/quant_inference_model
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python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_inference_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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@ -52,7 +52,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
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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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python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
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```
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### 5. 量化模型部署
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@ -26,7 +26,7 @@ After training, if you want to further compress the model size and accelerate th
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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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cd PaddlSlim
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python setup.py install
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```
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@ -43,12 +43,12 @@ 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.pretrained_model='your trained model' Global.save_model_dir=./output/quant_model
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python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model='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.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
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python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
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```
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@ -57,7 +57,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
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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_inference_dir=./output/quant_inference_model
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python deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
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```
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### 5. Deploy
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@ -112,10 +112,6 @@ def main(config, device, logger, vdl_writer):
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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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@ -136,31 +132,15 @@ def main(config, device, logger, vdl_writer):
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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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quanter = QAT(config=quant_config, act_preprocess=PACT)
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quanter.quantize(model)
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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:
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batch_time = time.time() - starttime
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starttime = time.time()
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logger.info("reader: {}, {}, {}".format(
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count, len(data[0]), batch_time))
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except Exception as e:
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logger.info(e)
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logger.info("finish reader: {}, Success!".format(count))
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if __name__ == '__main__':
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config, device, logger, vdl_writer = program.preprocess(is_train=True)
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main(config, device, logger, vdl_writer)
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# test_reader(config, device, logger)
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