Merge remote-tracking branch 'upstream/dygraph' into dy3

doc/doc_en/quickstart_en.md

@@ -13,8 +13,8 @@ The detection and recognition models on the mobile and server sides are as follo
 
 | Model introduction     | Model name      | Recommended scene          | Detection model | Direction Classifier | Recognition model |
 | ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| Ultra-lightweight Chinese OCR model（8.1M） | ch_ppocr_mobile_v2.0_xx |Mobile-side/Server-side|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar)      |
-| Universal Chinese OCR model（155.1M）   | ch_ppocr_server_v2.0_xx |Server-side |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)          |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
+| Ultra-lightweight Chinese OCR model (8.1M) | ch_ppocr_mobile_v2.0_xx |Mobile-side/Server-side|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar)      |
+| Universal Chinese OCR model (143M)   | ch_ppocr_server_v2.0_xx |Server-side |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)          |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar)  |
 
 
 * If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.

doc/doc_en/recognition_en.md

@@ -120,6 +120,9 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
 
 `ppocr/utils/dict/german_dict.txt` is a German dictionary with 131 characters
 
+`ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
+
+
 You can use it on demand.
 
 The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
@@ -149,10 +152,10 @@ First download the pretrain model, you can download the trained model to finetun
 ```
 cd PaddleOCR/
 # Download the pre-trained model of MobileNetV3
-wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
 # Decompress model parameters
 cd pretrain_models
-tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
+tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
 ```
 
 Start training:
@@ -194,7 +197,6 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
 | rec_mv3_tps_bilstm_attn.yml |  RARE |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  attention  |
 | rec_r34_vd_none_bilstm_ctc.yml |  CRNN |   Resnet34_vd |  None   |  BiLSTM |  ctc  |
 | rec_r34_vd_none_none_ctc.yml |  Rosetta |   Resnet34_vd |  None   |  None |  ctc  |
-| rec_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
 | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
 
 For training Chinese data, it is recommended to use

paddleocr.py

@@ -35,44 +35,45 @@ __all__ = ['PaddleOCR']
 
 model_urls = {
     'det':
-        'https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar',
+    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
     'rec': {
         'ch': {
             'url':
-                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar',
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
         },
         'en': {
             'url':
-                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar',
-            'dict_path': './ppocr/utils/ic15_dict.txt'
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/en_dict.txt'
         },
         'french': {
             'url':
-                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar',
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/french_dict.txt'
         },
         'german': {
             'url':
-                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar',
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/german_dict.txt'
         },
         'korean': {
             'url':
-                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar',
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/korean_dict.txt'
         },
         'japan': {
             'url':
-                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar',
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/japan_dict.txt'
         }
     },
     'cls':
-        'https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar'
+    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
 }
 
 SUPPORT_DET_MODEL = ['DB']
+VERSION = 2.0
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
@@ -94,20 +95,24 @@ def download_with_progressbar(url, save_path):
 
 def maybe_download(model_storage_directory, url):
     # using custom model
-    if not os.path.exists(os.path.join(
-            model_storage_directory, 'model')) or not os.path.exists(
-        os.path.join(model_storage_directory, 'params')):
+    tar_file_name_list = [
+        'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel'
+    ]
+    if not os.path.exists(
+            os.path.join(model_storage_directory, 'inference.pdiparams')
+    ) or not os.path.exists(
+            os.path.join(model_storage_directory, 'inference.pdmodel')):
         tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
         print('download {} to {}'.format(url, tmp_path))
         os.makedirs(model_storage_directory, exist_ok=True)
         download_with_progressbar(url, tmp_path)
         with tarfile.open(tmp_path, 'r') as tarObj:
             for member in tarObj.getmembers():
-                if "model" in member.name:
-                    filename = 'model'
-                elif "params" in member.name:
-                    filename = 'params'
-                else:
+                filename = None
+                for tar_file_name in tar_file_name_list:
+                    if tar_file_name in member.name:
+                        filename = tar_file_name
+                if filename is None:
                     continue
                 file = tarObj.extractfile(member)
                 with open(
@@ -176,7 +181,8 @@ def parse_args(mMain=True, add_help=True):
         parser.add_argument("--use_angle_cls", type=str2bool, default=False)
         return parser.parse_args()
     else:
-        return argparse.Namespace(use_gpu=True,
+        return argparse.Namespace(
+            use_gpu=True,
             ir_optim=True,
             use_tensorrt=False,
             gpu_mem=8000,
@@ -211,8 +217,7 @@ def parse_args(mMain=True, add_help=True):
             lang='ch',
             det=True,
             rec=True,
-                                  use_angle_cls=False
-                                  )
+            use_angle_cls=False)
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -235,12 +240,14 @@ class PaddleOCR(predict_system.TextSystem):
 
         # init model dir
         if postprocess_params.det_model_dir is None:
-            postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
+            postprocess_params.det_model_dir = os.path.join(
+                BASE_DIR, '{}/det'.format(VERSION))
         if postprocess_params.rec_model_dir is None:
             postprocess_params.rec_model_dir = os.path.join(
-                BASE_DIR, 'rec/{}'.format(lang))
+                BASE_DIR, '{}/rec/{}'.format(VERSION, lang))
         if postprocess_params.cls_model_dir is None:
-            postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
+            postprocess_params.cls_model_dir = os.path.join(
+                BASE_DIR, '{}/cls'.format(VERSION))
         print(postprocess_params)
         # download model
         maybe_download(postprocess_params.det_model_dir, model_urls['det'])

ppocr/data/imaug/rec_img_aug.py

@@ -35,12 +35,13 @@ from .text_image_aug import tia_perspective, tia_stretch, tia_distort
 
 
 class RecAug(object):
-    def __init__(self, use_tia=True, **kwargsz):
+    def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
         self.use_tia = use_tia
+        self.aug_prob = aug_prob
 
     def __call__(self, data):
         img = data['image']
-        img = warp(img, 10, self.use_tia)
+        img = warp(img, 10, self.use_tia, self.aug_prob)
         data['image'] = img
         return data
 
@@ -329,7 +330,7 @@ def get_warpAffine(config):
     return rz
 
 
-def warp(img, ang, use_tia=True):
+def warp(img, ang, use_tia=True, prob=0.4):
     """
     warp
     """
@@ -338,8 +339,6 @@ def warp(img, ang, use_tia=True):
     config.make(w, h, ang)
     new_img = img
 
-    prob = 0.4
-
     if config.distort:
         img_height, img_width = img.shape[0:2]
         if random.random() <= prob and img_height >= 20 and img_width >= 20:

ppocr/optimizer/__init__.py

@@ -16,8 +16,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
-
 import copy
+import paddle
 
 __all__ = ['build_optimizer']
 
@@ -49,7 +49,13 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
 
     # step3 build optimizer
     optim_name = config.pop('name')
+    if 'clip_norm' in config:
+        clip_norm = config.pop('clip_norm')
+        grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
+    else:
+        grad_clip = None
     optim = getattr(optimizer, optim_name)(learning_rate=lr,
                                            weight_decay=reg,
+                                           grad_clip=grad_clip,
                                            **config)
     return optim(parameters), lr

ppocr/optimizer/optimizer.py

@@ -30,18 +30,25 @@ class Momentum(object):
         regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
     """
 
-    def __init__(self, learning_rate, momentum, weight_decay=None, **args):
+    def __init__(self,
+                 learning_rate,
+                 momentum,
+                 weight_decay=None,
+                 grad_clip=None,
+                 **args):
         super(Momentum, self).__init__()
         self.learning_rate = learning_rate
         self.momentum = momentum
         self.weight_decay = weight_decay
+        self.grad_clip = grad_clip
 
     def __call__(self, parameters):
         opt = optim.Momentum(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
-            parameters=parameters,
-            weight_decay=self.weight_decay)
+            weight_decay=self.weight_decay,
+            grad_clip=self.grad_clip,
+            parameters=parameters)
         return opt
 
 
@@ -96,10 +103,11 @@ class RMSProp(object):
 
     def __init__(self,
                  learning_rate,
-                 momentum,
+                 momentum=0.0,
                  rho=0.95,
                  epsilon=1e-6,
                  weight_decay=None,
+                 grad_clip=None,
                  **args):
         super(RMSProp, self).__init__()
         self.learning_rate = learning_rate
@@ -107,6 +115,7 @@ class RMSProp(object):
         self.rho = rho
         self.epsilon = epsilon
         self.weight_decay = weight_decay
+        self.grad_clip = grad_clip
 
     def __call__(self, parameters):
         opt = optim.RMSProp(
@@ -115,5 +124,6 @@ class RMSProp(object):
             rho=self.rho,
             epsilon=self.epsilon,
             weight_decay=self.weight_decay,
+            grad_clip=self.grad_clip,
             parameters=parameters)
         return opt

ppocr/postprocess/db_postprocess.py

@@ -40,7 +40,7 @@ class DBPostProcess(object):
         self.max_candidates = max_candidates
         self.unclip_ratio = unclip_ratio
         self.min_size = 3
-        self.dilation_kernel = None if not use_dilation else [[1, 1], [1, 1]]
+        self.dilation_kernel = None if not use_dilation else np.array([[1, 1], [1, 1]])
 
     def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
         '''

tools/infer/predict_det.py

@@ -63,6 +63,7 @@ class TextDetector(object):
             postprocess_params["box_thresh"] = args.det_db_box_thresh
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+            postprocess_params["use_dilation"] = True
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
@@ -111,7 +112,7 @@ class TextDetector(object):
             box = self.clip_det_res(box, img_height, img_width)
             rect_width = int(np.linalg.norm(box[0] - box[1]))
             rect_height = int(np.linalg.norm(box[0] - box[3]))
-            if rect_width <= 10 or rect_height <= 10:
+            if rect_width <= 3 or rect_height <= 3:
                 continue
             dt_boxes_new.append(box)
         dt_boxes = np.array(dt_boxes_new)
@@ -186,4 +187,4 @@ if __name__ == "__main__":
         cv2.imwrite(img_path, src_im)
         logger.info("The visualized image saved in {}".format(img_path))
     if count > 1:
-        logger.info("Avg Time:", total_time / (count - 1))
+        logger.info("Avg Time: {}".format(total_time / (count - 1)))