Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph_rc

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(
@@ -176,43 +181,43 @@ 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,
-                                  ir_optim=True,
-                                  use_tensorrt=False,
-                                  gpu_mem=8000,
-                                  image_dir='',
-                                  det_algorithm='DB',
-                                  det_model_dir=None,
-                                  det_limit_side_len=960,
-                                  det_limit_type='max',
-                                  det_db_thresh=0.3,
-                                  det_db_box_thresh=0.5,
-                                  det_db_unclip_ratio=2.0,
-                                  det_east_score_thresh=0.8,
-                                  det_east_cover_thresh=0.1,
-                                  det_east_nms_thresh=0.2,
-                                  rec_algorithm='CRNN',
-                                  rec_model_dir=None,
-                                  rec_image_shape="3, 32, 320",
-                                  rec_char_type='ch',
-                                  rec_batch_num=30,
-                                  max_text_length=25,
-                                  rec_char_dict_path=None,
-                                  use_space_char=True,
-                                  drop_score=0.5,
-                                  cls_model_dir=None,
-                                  cls_image_shape="3, 48, 192",
-                                  label_list=['0', '180'],
-                                  cls_batch_num=30,
-                                  cls_thresh=0.9,
-                                  enable_mkldnn=False,
-                                  use_zero_copy_run=False,
-                                  use_pdserving=False,
-                                  lang='ch',
-                                  det=True,
-                                  rec=True,
-                                  use_angle_cls=False
-                                  )
+        return argparse.Namespace(
+            use_gpu=True,
+            ir_optim=True,
+            use_tensorrt=False,
+            gpu_mem=8000,
+            image_dir='',
+            det_algorithm='DB',
+            det_model_dir=None,
+            det_limit_side_len=960,
+            det_limit_type='max',
+            det_db_thresh=0.3,
+            det_db_box_thresh=0.5,
+            det_db_unclip_ratio=2.0,
+            det_east_score_thresh=0.8,
+            det_east_cover_thresh=0.1,
+            det_east_nms_thresh=0.2,
+            rec_algorithm='CRNN',
+            rec_model_dir=None,
+            rec_image_shape="3, 32, 320",
+            rec_char_type='ch',
+            rec_batch_num=30,
+            max_text_length=25,
+            rec_char_dict_path=None,
+            use_space_char=True,
+            drop_score=0.5,
+            cls_model_dir=None,
+            cls_image_shape="3, 48, 192",
+            label_list=['0', '180'],
+            cls_batch_num=30,
+            cls_thresh=0.9,
+            enable_mkldnn=False,
+            use_zero_copy_run=False,
+            use_pdserving=False,
+            lang='ch',
+            det=True,
+            rec=True,
+            use_angle_cls=False)
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -228,19 +233,21 @@ class PaddleOCR(predict_system.TextSystem):
         lang = postprocess_params.lang
         assert lang in model_urls[
             'rec'], 'param lang must in {}, but got {}'.format(
-            model_urls['rec'].keys(), lang)
+                model_urls['rec'].keys(), lang)
         if postprocess_params.rec_char_dict_path is None:
             postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
                 'dict_path']
 
         # 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/__init__.py

@@ -67,6 +67,7 @@ def build_dataloader(config, mode, device, logger):
     drop_last = loader_config['drop_last']
     num_workers = loader_config['num_workers']
 
+    use_shared_memory = False
     if mode == "Train":
         #Distribute data to multiple cards
         batch_sampler = DistributedBatchSampler(
@@ -74,6 +75,7 @@ def build_dataloader(config, mode, device, logger):
             batch_size=batch_size,
             shuffle=False,
             drop_last=drop_last)
+        use_shared_memory = True
     else:
         #Distribute data to single card
         batch_sampler = BatchSampler(
@@ -87,6 +89,7 @@ def build_dataloader(config, mode, device, logger):
         batch_sampler=batch_sampler,
         places=device,
         num_workers=num_workers,
-        return_list=True)
+        return_list=True,
+        use_shared_memory=use_shared_memory)
 
     return data_loader

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/metrics/cls_metric.py

@@ -32,9 +32,8 @@ class ClsMetric(object):
 
     def get_metric(self):
         """
-        return metircs {
-                 'acc': 0,
-                 'norm_edit_dis': 0,
+        return metrics {
+                 'acc': 0
             }
         """
         acc = self.correct_num / self.all_num

ppocr/metrics/det_metric.py

@@ -57,7 +57,7 @@ class DetMetric(object):
 
     def get_metric(self):
         """
-        return metircs {
+        return metrics {
                  'precision': 0,
                  'recall': 0,
                  'hmean': 0

ppocr/metrics/rec_metric.py

@@ -43,7 +43,7 @@ class RecMetric(object):
 
     def get_metric(self):
         """
-        return metircs {
+        return metrics {
                  'acc': 0,
                  'norm_edit_dis': 0,
             }

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):
         '''

ppocr/utils/gen_label.py

+#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+import os
+import argparse
+import json
+
+
+def gen_rec_label(input_path, out_label):
+    with open(out_label, 'w') as out_file:
+        with open(input_path, 'r') as f:
+            for line in f.readlines():
+                tmp = line.strip('\n').replace(" ", "").split(',')
+                img_path, label = tmp[0], tmp[1]
+                label = label.replace("\"", "")
+                out_file.write(img_path + '\t' + label + '\n')
+
+
+def gen_det_label(root_path, input_dir, out_label):
+    with open(out_label, 'w') as out_file:
+        for label_file in os.listdir(input_dir):
+            img_path = root_path + label_file[3:-4] + ".jpg"
+            label = []
+            with open(os.path.join(input_dir, label_file), 'r') as f:
+                for line in f.readlines():
+                    tmp = line.strip("\n\r").replace("\xef\xbb\xbf",
+                                                     "").split(',')
+                    points = tmp[:8]
+                    s = []
+                    for i in range(0, len(points), 2):
+                        b = points[i:i + 2]
+                        b = [int(t) for t in b]
+                        s.append(b)
+                    result = {"transcription": tmp[8], "points": s}
+                    label.append(result)
+
+            out_file.write(img_path + '\t' + json.dumps(
+                label, ensure_ascii=False) + '\n')
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--mode',
+        type=str,
+        default="rec",
+        help='Generate rec_label or det_label, can be set rec or det')
+    parser.add_argument(
+        '--root_path',
+        type=str,
+        default=".",
+        help='The root directory of images.Only takes effect when mode=det ')
+    parser.add_argument(
+        '--input_path',
+        type=str,
+        default=".",
+        help='Input_label or input path to be converted')
+    parser.add_argument(
+        '--output_label',
+        type=str,
+        default="out_label.txt",
+        help='Output file name')
+
+    args = parser.parse_args()
+    if args.mode == "rec":
+        print("Generate rec label")
+        gen_rec_label(args.input_path, args.output_label)
+    elif args.mode == "det":
+        gen_det_label(args.root_path, args.input_path, args.output_label)

ppocr/utils/ic15_dict.txt

@@ -33,4 +33,4 @@ v
 w
 x
 y
-z
+z
\ No newline at end of file

tools/export_model.py

@@ -28,20 +28,13 @@ from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.utils.save_load import init_model
 from ppocr.utils.logging import get_logger
-from tools.program import load_config
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-c", "--config", help="configuration file to use")
-    parser.add_argument(
-        "-o", "--output_path", type=str, default='./output/infer/')
-    return parser.parse_args()
+from tools.program import load_config, merge_config, ArgsParser
 
 
 def main():
-    FLAGS = parse_args()
+    FLAGS = ArgsParser().parse_args()
     config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
     logger = get_logger()
     # build post process
 
@@ -57,8 +50,7 @@ def main():
     init_model(config, model, logger)
     model.eval()
 
-    save_path = '{}/{}/inference'.format(FLAGS.output_path,
-                                         config['Architecture']['model_type'])
+    save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
     infer_shape = [3, 32, 100] if config['Architecture'][
         'model_type'] != "det" else [3, 640, 640]
     model = to_static(

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)))

tools/infer/utility.py

@@ -100,8 +100,8 @@ def create_predictor(args, mode, logger):
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
         sys.exit(0)
-    model_file_path = model_dir + ".pdmodel"
-    params_file_path = model_dir + ".pdiparams"
+    model_file_path = model_dir + "/inference.pdmodel"
+    params_file_path = model_dir + "/inference.pdiparams"
     if not os.path.exists(model_file_path):
         logger.info("not find model file path {}".format(model_file_path))
         sys.exit(0)

tools/program.py

@@ -113,7 +113,6 @@ def merge_config(config):
                 global_config.keys(), sub_keys[0])
             cur = global_config[sub_keys[0]]
             for idx, sub_key in enumerate(sub_keys[1:]):
-                assert (sub_key in cur)
                 if idx == len(sub_keys) - 2:
                     cur[sub_key] = value
                 else: