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Merge remote-tracking branch 'origin/dygraph' into dygraph

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Leif 2021-08-26 10:09:31 +08:00
parent 6933f15a99 d77db841b0
commit e4d160e9e5
4 changed files with 14 additions and 12 deletions
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6
doc/doc_ch/recognition.md
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@ -185,11 +185,11 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
<a name="数据增强"></a> <a name="数据增强"></a>
#### 2.1 数据增强 #### 2.1 数据增强
PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。 PaddleOCR提供了多种数据增强方式，默认配置文件中已经添加了数据增广。
默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)。 默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)、TIA数据增广。
训练过程中每种扰动方式以50%的概率被选择，具体代码实现请参考：[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) 训练过程中每种扰动方式以40%的概率被选择，具体代码实现请参考：[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
*由于OpenCV的兼容性问题，扰动操作暂时只支持Linux* *由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*

6
doc/doc_en/recognition_en.md
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@ -177,11 +177,11 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
<a name="Data_Augmentation"></a> <a name="Data_Augmentation"></a>
#### 2.1 Data Augmentation #### 2.1 Data Augmentation
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file. PaddleOCR provides a variety of data augmentation methods. All the augmentation methods are enabled by default.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse. The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, TIA augmentation.
Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) Each disturbance method is selected with a 40% probability during the training process. For specific code implementation, please refer to: [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
<a name="Training"></a> <a name="Training"></a>
#### 2.2 Training #### 2.2 Training

12
ppocr/data/__init__.py
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@ -49,14 +49,12 @@ def term_mp(sig_num, frame):
os.killpg(pgid, signal.SIGKILL) os.killpg(pgid, signal.SIGKILL)
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
def build_dataloader(config, mode, device, logger, seed=None): def build_dataloader(config, mode, device, logger, seed=None):
config = copy.deepcopy(config) config = copy.deepcopy(config)
support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet'] support_dict = [
'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet'
]
module_name = config[mode]['dataset']['name'] module_name = config[mode]['dataset']['name']
assert module_name in support_dict, Exception( assert module_name in support_dict, Exception(
'DataSet only support {}'.format(support_dict)) 'DataSet only support {}'.format(support_dict))
@ -96,4 +94,8 @@ def build_dataloader(config, mode, device, logger, seed=None):
return_list=True, return_list=True,
use_shared_memory=use_shared_memory) use_shared_memory=use_shared_memory)
# support exit using ctrl+c
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
return data_loader return data_loader

2
tools/infer/predict_e2e.py
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@ -74,7 +74,7 @@ class TextE2E(object):
self.preprocess_op = create_operators(pre_process_list) self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params) self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor( self.predictor, self.input_tensor, self.output_tensors, _ = utility.create_predictor(
args, 'e2e', logger) # paddle.jit.load(args.det_model_dir) args, 'e2e', logger) # paddle.jit.load(args.det_model_dir)
# self.predictor.eval() # self.predictor.eval()