update faq

ppocr/data/__init__.py

@@ -33,7 +33,7 @@ import paddle.distributed as dist
 
 from ppocr.data.imaug import transform, create_operators
 from ppocr.data.simple_dataset import SimpleDataSet
-from ppocr.data.lmdb_dataset import LMDBDateSet
+from ppocr.data.lmdb_dataset import LMDBDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -51,20 +51,21 @@ signal.signal(signal.SIGINT, term_mp)
 signal.signal(signal.SIGTERM, term_mp)
 
 
-def build_dataloader(config, mode, device, logger):
+def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDateSet']
+    support_dict = ['SimpleDataSet', 'LMDBDataSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))
     assert mode in ['Train', 'Eval', 'Test'
                     ], "Mode should be Train, Eval or Test."
 
-    dataset = eval(module_name)(config, mode, logger)
+    dataset = eval(module_name)(config, mode, logger, seed)
     loader_config = config[mode]['loader']
     batch_size = loader_config['batch_size_per_card']
     drop_last = loader_config['drop_last']
+    shuffle = loader_config['shuffle']
     num_workers = loader_config['num_workers']
     if 'use_shared_memory' in loader_config.keys():
         use_shared_memory = loader_config['use_shared_memory']
@@ -75,14 +76,14 @@ def build_dataloader(config, mode, device, logger):
         batch_sampler = DistributedBatchSampler(
             dataset=dataset,
             batch_size=batch_size,
-            shuffle=False,
+            shuffle=shuffle,
             drop_last=drop_last)
     else:
         #Distribute data to single card
         batch_sampler = BatchSampler(
             dataset=dataset,
             batch_size=batch_size,
-            shuffle=False,
+            shuffle=shuffle,
             drop_last=drop_last)
 
     data_loader = DataLoader(

ppocr/data/imaug/__init__.py

@@ -21,7 +21,7 @@ from .make_border_map import MakeBorderMap
 from .make_shrink_map import MakeShrinkMap
 from .random_crop_data import EastRandomCropData, PSERandomCrop
 
-from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg
+from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg
 from .randaugment import RandAugment
 from .operators import *
 from .label_ops import *

ppocr/data/imaug/label_ops.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import numpy as np
+import string
 
 
 class ClsLabelEncode(object):
@@ -92,18 +93,28 @@ class BaseRecLabelEncode(object):
                  character_type='ch',
                  use_space_char=False):
         support_character_type = [
-            'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean'
+            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
+            'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs',
+            'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi',
+            'mr', 'ne'
         ]
         assert character_type in support_character_type, "Only {} are supported now but get {}".format(
             support_character_type, character_type)
 
         self.max_text_len = max_text_length
+        self.beg_str = "sos"
+        self.end_str = "eos"
         if character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
-        elif character_type in ["ch", "french", "german", "japan", "korean"]:
+        elif character_type == "EN_symbol":
+            # same with ASTER setting (use 94 char).
+            self.character_str = string.printable[:-6]
+            dict_character = list(self.character_str)
+        elif character_type in support_character_type:
             self.character_str = ""
-            assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch"
+            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
+                character_type)
             with open(character_dict_path, "rb") as fin:
                 lines = fin.readlines()
                 for line in lines:
@@ -112,11 +123,6 @@ class BaseRecLabelEncode(object):
             if use_space_char:
                 self.character_str += " "
             dict_character = list(self.character_str)
-        elif character_type == "en_sensitive":
-            # same with ASTER setting (use 94 char).
-            import string
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
         self.character_type = character_type
         dict_character = self.add_special_char(dict_character)
         self.dict = {}
@@ -213,3 +219,49 @@ class AttnLabelEncode(BaseRecLabelEncode):
             assert False, "Unsupport type %s in get_beg_end_flag_idx" \
                           % beg_or_end
         return idx
+
+
+class SRNLabelEncode(BaseRecLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length=25,
+                 character_dict_path=None,
+                 character_type='en',
+                 use_space_char=False,
+                 **kwargs):
+        super(SRNLabelEncode,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+
+    def add_special_char(self, dict_character):
+        dict_character = dict_character + [self.beg_str, self.end_str]
+        return dict_character
+
+    def __call__(self, data):
+        text = data['label']
+        text = self.encode(text)
+        char_num = len(self.character_str)
+        if text is None:
+            return None
+        if len(text) > self.max_text_len:
+            return None
+        data['length'] = np.array(len(text))
+        text = text + [char_num] * (self.max_text_len - len(text))
+        data['label'] = np.array(text)
+        return data
+
+    def get_ignored_tokens(self):
+        beg_idx = self.get_beg_end_flag_idx("beg")
+        end_idx = self.get_beg_end_flag_idx("end")
+        return [beg_idx, end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if beg_or_end == "beg":
+            idx = np.array(self.dict[self.beg_str])
+        elif beg_or_end == "end":
+            idx = np.array(self.dict[self.end_str])
+        else:
+            assert False, "Unsupport type %s in get_beg_end_flag_idx" \
+                          % beg_or_end
+        return idx

ppocr/data/imaug/rec_img_aug.py

@@ -12,20 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# 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 math
 import cv2
 import numpy as np
@@ -77,6 +63,26 @@ class RecResizeImg(object):
         return data
 
 
+class SRNRecResizeImg(object):
+    def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
+        self.image_shape = image_shape
+        self.num_heads = num_heads
+        self.max_text_length = max_text_length
+
+    def __call__(self, data):
+        img = data['image']
+        norm_img = resize_norm_img_srn(img, self.image_shape)
+        data['image'] = norm_img
+        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
+            srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)
+
+        data['encoder_word_pos'] = encoder_word_pos
+        data['gsrm_word_pos'] = gsrm_word_pos
+        data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1
+        data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2
+        return data
+
+
 def resize_norm_img(img, image_shape):
     imgC, imgH, imgW = image_shape
     h = img.shape[0]
@@ -103,7 +109,7 @@ def resize_norm_img(img, image_shape):
 def resize_norm_img_chinese(img, image_shape):
     imgC, imgH, imgW = image_shape
     # todo: change to 0 and modified image shape
-    max_wh_ratio = 0
+    max_wh_ratio = imgW * 1.0 / imgH
     h, w = img.shape[0], img.shape[1]
     ratio = w * 1.0 / h
     max_wh_ratio = max(max_wh_ratio, ratio)
@@ -126,6 +132,60 @@ def resize_norm_img_chinese(img, image_shape):
     return padding_im
 
 
+def resize_norm_img_srn(img, image_shape):
+    imgC, imgH, imgW = image_shape
+
+    img_black = np.zeros((imgH, imgW))
+    im_hei = img.shape[0]
+    im_wid = img.shape[1]
+
+    if im_wid <= im_hei * 1:
+        img_new = cv2.resize(img, (imgH * 1, imgH))
+    elif im_wid <= im_hei * 2:
+        img_new = cv2.resize(img, (imgH * 2, imgH))
+    elif im_wid <= im_hei * 3:
+        img_new = cv2.resize(img, (imgH * 3, imgH))
+    else:
+        img_new = cv2.resize(img, (imgW, imgH))
+
+    img_np = np.asarray(img_new)
+    img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+    img_black[:, 0:img_np.shape[1]] = img_np
+    img_black = img_black[:, :, np.newaxis]
+
+    row, col, c = img_black.shape
+    c = 1
+
+    return np.reshape(img_black, (c, row, col)).astype(np.float32)
+
+
+def srn_other_inputs(image_shape, num_heads, max_text_length):
+
+    imgC, imgH, imgW = image_shape
+    feature_dim = int((imgH / 8) * (imgW / 8))
+
+    encoder_word_pos = np.array(range(0, feature_dim)).reshape(
+        (feature_dim, 1)).astype('int64')
+    gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
+        (max_text_length, 1)).astype('int64')
+
+    gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
+    gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
+        [1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
+                                  [num_heads, 1, 1]) * [-1e9]
+
+    gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
+        [1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
+                                  [num_heads, 1, 1]) * [-1e9]
+
+    return [
+        encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+        gsrm_slf_attn_bias2
+    ]
+
+
 def flag():
     """
     flag

ppocr/data/lmdb_dataset.py

@@ -20,9 +20,9 @@ import cv2
 from .imaug import transform, create_operators
 
 
-class LMDBDateSet(Dataset):
-    def __init__(self, config, mode, logger):
-        super(LMDBDateSet, self).__init__()
+class LMDBDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(LMDBDataSet, self).__init__()
 
         global_config = config['Global']
         dataset_config = config[mode]['dataset']

ppocr/data/simple_dataset.py

@@ -20,7 +20,7 @@ from .imaug import transform, create_operators
 
 
 class SimpleDataSet(Dataset):
-    def __init__(self, config, mode, logger):
+    def __init__(self, config, mode, logger, seed=None):
         super(SimpleDataSet, self).__init__()
         self.logger = logger
 
@@ -41,6 +41,7 @@ class SimpleDataSet(Dataset):
         self.data_dir = dataset_config['data_dir']
         self.do_shuffle = loader_config['shuffle']
 
+        self.seed = seed
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
         self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
         self.data_idx_order_list = list(range(len(self.data_lines)))
@@ -55,6 +56,7 @@ class SimpleDataSet(Dataset):
         for idx, file in enumerate(file_list):
             with open(file, "rb") as f:
                 lines = f.readlines()
+                random.seed(self.seed)
                 lines = random.sample(lines,
                                       round(len(lines) * ratio_list[idx]))
                 data_lines.extend(lines)
@@ -62,6 +64,7 @@ class SimpleDataSet(Dataset):
 
     def shuffle_data_random(self):
         if self.do_shuffle:
+            random.seed(self.seed)
             random.shuffle(self.data_lines)
         return
 

ppocr/losses/__init__.py

@@ -23,11 +23,14 @@ def build_loss(config):
 
     # rec loss
     from .rec_ctc_loss import CTCLoss
+    from .rec_srn_loss import SRNLoss
 
     # cls loss
     from .cls_loss import ClsLoss
 
-    support_dict = ['DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss']
+    support_dict = [
+        'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'SRNLoss'
+    ]
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/rec_srn_loss.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+
+
+class SRNLoss(nn.Layer):
+    def __init__(self, **kwargs):
+        super(SRNLoss, self).__init__()
+        self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="sum")
+
+    def forward(self, predicts, batch):
+        predict = predicts['predict']
+        word_predict = predicts['word_out']
+        gsrm_predict = predicts['gsrm_out']
+        label = batch[1]
+
+        casted_label = paddle.cast(x=label, dtype='int64')
+        casted_label = paddle.reshape(x=casted_label, shape=[-1, 1])
+
+        cost_word = self.loss_func(word_predict, label=casted_label)
+        cost_gsrm = self.loss_func(gsrm_predict, label=casted_label)
+        cost_vsfd = self.loss_func(predict, label=casted_label)
+
+        cost_word = paddle.reshape(x=paddle.sum(cost_word), shape=[1])
+        cost_gsrm = paddle.reshape(x=paddle.sum(cost_gsrm), shape=[1])
+        cost_vsfd = paddle.reshape(x=paddle.sum(cost_vsfd), shape=[1])
+
+        sum_cost = cost_word * 3.0 + cost_vsfd + cost_gsrm * 0.15
+
+        return {'loss': sum_cost, 'word_loss': cost_word, 'img_loss': cost_vsfd}

ppocr/metrics/rec_metric.py

@@ -33,8 +33,6 @@ class RecMetric(object):
             if pred == target:
                 correct_num += 1
             all_num += 1
-            # if all_num < 10 and kwargs.get('show_str', False):
-            #     print('{} -> {}'.format(pred, target))
         self.correct_num += correct_num
         self.all_num += all_num
         self.norm_edit_dis += norm_edit_dis
@@ -50,7 +48,7 @@ class RecMetric(object):
                  'norm_edit_dis': 0,
             }
         """
-        acc = self.correct_num / self.all_num
+        acc = 1.0 * self.correct_num / self.all_num
         norm_edit_dis = 1 - self.norm_edit_dis / self.all_num
         self.reset()
         return {'acc': acc, 'norm_edit_dis': norm_edit_dis}

ppocr/modeling/architectures/base_model.py

@@ -68,11 +68,14 @@ class BaseModel(nn.Layer):
         config["Head"]['in_channels'] = in_channels
         self.head = build_head(config["Head"])
 
-    def forward(self, x):
+    def forward(self, x, data=None):
         if self.use_transform:
             x = self.transform(x)
         x = self.backbone(x)
         if self.use_neck:
             x = self.neck(x)
-        x = self.head(x)
+        if data is None:
+            x = self.head(x)
+        else:
+            x = self.head(x, data)
         return x

ppocr/modeling/backbones/__init__.py

@@ -24,7 +24,8 @@ def build_backbone(config, model_type):
     elif model_type == 'rec' or model_type == 'cls':
         from .rec_mobilenet_v3 import MobileNetV3
         from .rec_resnet_vd import ResNet
-        support_dict = ['MobileNetV3', 'ResNet', 'ResNet_FPN']
+        from .rec_resnet_fpn import ResNetFPN
+        support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
     else:
         raise NotImplementedError