Merge remote-tracking branch 'upstream/dygraph' into dy1

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

@@ -21,7 +21,7 @@ from .imaug import transform, create_operators
 
 
 class LMDBDateSet(Dataset):
-    def __init__(self, config, mode, logger):
+    def __init__(self, config, mode, logger, seed=None):
         super(LMDBDateSet, self).__init__()
 
         global_config = config['Global']

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)
+        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
 

ppocr/modeling/backbones/det_mobilenet_v3.py

@@ -58,15 +58,15 @@ class MobileNetV3(nn.Layer):
                 [5, 72, 40, True, 'relu', 2],
                 [5, 120, 40, True, 'relu', 1],
                 [5, 120, 40, True, 'relu', 1],
-                [3, 240, 80, False, 'hard_swish', 2],
-                [3, 200, 80, False, 'hard_swish', 1],
-                [3, 184, 80, False, 'hard_swish', 1],
-                [3, 184, 80, False, 'hard_swish', 1],
-                [3, 480, 112, True, 'hard_swish', 1],
-                [3, 672, 112, True, 'hard_swish', 1],
-                [5, 672, 160, True, 'hard_swish', 2],
-                [5, 960, 160, True, 'hard_swish', 1],
-                [5, 960, 160, True, 'hard_swish', 1],
+                [3, 240, 80, False, 'hardswish', 2],
+                [3, 200, 80, False, 'hardswish', 1],
+                [3, 184, 80, False, 'hardswish', 1],
+                [3, 184, 80, False, 'hardswish', 1],
+                [3, 480, 112, True, 'hardswish', 1],
+                [3, 672, 112, True, 'hardswish', 1],
+                [5, 672, 160, True, 'hardswish', 2],
+                [5, 960, 160, True, 'hardswish', 1],
+                [5, 960, 160, True, 'hardswish', 1],
             ]
             cls_ch_squeeze = 960
         elif model_name == "small":
@@ -75,14 +75,14 @@ class MobileNetV3(nn.Layer):
                 [3, 16, 16, True, 'relu', 2],
                 [3, 72, 24, False, 'relu', 2],
                 [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hard_swish', 2],
-                [5, 240, 40, True, 'hard_swish', 1],
-                [5, 240, 40, True, 'hard_swish', 1],
-                [5, 120, 48, True, 'hard_swish', 1],
-                [5, 144, 48, True, 'hard_swish', 1],
-                [5, 288, 96, True, 'hard_swish', 2],
-                [5, 576, 96, True, 'hard_swish', 1],
-                [5, 576, 96, True, 'hard_swish', 1],
+                [5, 96, 40, True, 'hardswish', 2],
+                [5, 240, 40, True, 'hardswish', 1],
+                [5, 240, 40, True, 'hardswish', 1],
+                [5, 120, 48, True, 'hardswish', 1],
+                [5, 144, 48, True, 'hardswish', 1],
+                [5, 288, 96, True, 'hardswish', 2],
+                [5, 576, 96, True, 'hardswish', 1],
+                [5, 576, 96, True, 'hardswish', 1],
             ]
             cls_ch_squeeze = 576
         else:
@@ -102,7 +102,7 @@ class MobileNetV3(nn.Layer):
             padding=1,
             groups=1,
             if_act=True,
-            act='hard_swish',
+            act='hardswish',
             name='conv1')
 
         self.stages = []
@@ -112,7 +112,8 @@ class MobileNetV3(nn.Layer):
         inplanes = make_divisible(inplanes * scale)
         for (k, exp, c, se, nl, s) in cfg:
             se = se and not self.disable_se
-            if s == 2 and i > 2:
+            start_idx = 2 if model_name == 'large' else 0
+            if s == 2 and i > start_idx:
                 self.out_channels.append(inplanes)
                 self.stages.append(nn.Sequential(*block_list))
                 block_list = []
@@ -137,7 +138,7 @@ class MobileNetV3(nn.Layer):
                 padding=0,
                 groups=1,
                 if_act=True,
-                act='hard_swish',
+                act='hardswish',
                 name='conv_last'))
         self.stages.append(nn.Sequential(*block_list))
         self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
@@ -191,10 +192,11 @@ class ConvBNLayer(nn.Layer):
         if self.if_act:
             if self.act == "relu":
                 x = F.relu(x)
-            elif self.act == "hard_swish":
-                x = F.activation.hard_swish(x)
+            elif self.act == "hardswish":
+                x = F.hardswish(x)
             else:
-                print("The activation function is selected incorrectly.")
+                print("The activation function({}) is selected incorrectly.".
+                      format(self.act))
                 exit()
         return x
 
@@ -281,5 +283,5 @@ class SEModule(nn.Layer):
         outputs = self.conv1(outputs)
         outputs = F.relu(outputs)
         outputs = self.conv2(outputs)
-        outputs = F.activation.hard_sigmoid(outputs)
+        outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
         return inputs * outputs

ppocr/modeling/backbones/rec_mobilenet_v3.py

@@ -51,15 +51,15 @@ class MobileNetV3(nn.Layer):
                 [5, 72, 40, True, 'relu', (large_stride[2], 1)],
                 [5, 120, 40, True, 'relu', 1],
                 [5, 120, 40, True, 'relu', 1],
-                [3, 240, 80, False, 'hard_swish', 1],
-                [3, 200, 80, False, 'hard_swish', 1],
-                [3, 184, 80, False, 'hard_swish', 1],
-                [3, 184, 80, False, 'hard_swish', 1],
-                [3, 480, 112, True, 'hard_swish', 1],
-                [3, 672, 112, True, 'hard_swish', 1],
-                [5, 672, 160, True, 'hard_swish', (large_stride[3], 1)],
-                [5, 960, 160, True, 'hard_swish', 1],
-                [5, 960, 160, True, 'hard_swish', 1],
+                [3, 240, 80, False, 'hardswish', 1],
+                [3, 200, 80, False, 'hardswish', 1],
+                [3, 184, 80, False, 'hardswish', 1],
+                [3, 184, 80, False, 'hardswish', 1],
+                [3, 480, 112, True, 'hardswish', 1],
+                [3, 672, 112, True, 'hardswish', 1],
+                [5, 672, 160, True, 'hardswish', (large_stride[3], 1)],
+                [5, 960, 160, True, 'hardswish', 1],
+                [5, 960, 160, True, 'hardswish', 1],
             ]
             cls_ch_squeeze = 960
         elif model_name == "small":
@@ -68,14 +68,14 @@ class MobileNetV3(nn.Layer):
                 [3, 16, 16, True, 'relu', (small_stride[0], 1)],
                 [3, 72, 24, False, 'relu', (small_stride[1], 1)],
                 [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hard_swish', (small_stride[2], 1)],
-                [5, 240, 40, True, 'hard_swish', 1],
-                [5, 240, 40, True, 'hard_swish', 1],
-                [5, 120, 48, True, 'hard_swish', 1],
-                [5, 144, 48, True, 'hard_swish', 1],
-                [5, 288, 96, True, 'hard_swish', (small_stride[3], 1)],
-                [5, 576, 96, True, 'hard_swish', 1],
-                [5, 576, 96, True, 'hard_swish', 1],
+                [5, 96, 40, True, 'hardswish', (small_stride[2], 1)],
+                [5, 240, 40, True, 'hardswish', 1],
+                [5, 240, 40, True, 'hardswish', 1],
+                [5, 120, 48, True, 'hardswish', 1],
+                [5, 144, 48, True, 'hardswish', 1],
+                [5, 288, 96, True, 'hardswish', (small_stride[3], 1)],
+                [5, 576, 96, True, 'hardswish', 1],
+                [5, 576, 96, True, 'hardswish', 1],
             ]
             cls_ch_squeeze = 576
         else:
@@ -96,7 +96,7 @@ class MobileNetV3(nn.Layer):
             padding=1,
             groups=1,
             if_act=True,
-            act='hard_swish',
+            act='hardswish',
             name='conv1')
         i = 0
         block_list = []
@@ -124,7 +124,7 @@ class MobileNetV3(nn.Layer):
             padding=0,
             groups=1,
             if_act=True,
-            act='hard_swish',
+            act='hardswish',
             name='conv_last')
 
         self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)

ppocr/modeling/backbones/rec_resnet_fpn.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
+
+from paddle import nn, ParamAttr
+from paddle.nn import functional as F
+import paddle.fluid as fluid
+import paddle
+import numpy as np
+
+__all__ = ["ResNetFPN"]
+
+
+class ResNetFPN(nn.Layer):
+    def __init__(self, in_channels=1, layers=50, **kwargs):
+        super(ResNetFPN, self).__init__()
+        supported_layers = {
+            18: {
+                'depth': [2, 2, 2, 2],
+                'block_class': BasicBlock
+            },
+            34: {
+                'depth': [3, 4, 6, 3],
+                'block_class': BasicBlock
+            },
+            50: {
+                'depth': [3, 4, 6, 3],
+                'block_class': BottleneckBlock
+            },
+            101: {
+                'depth': [3, 4, 23, 3],
+                'block_class': BottleneckBlock
+            },
+            152: {
+                'depth': [3, 8, 36, 3],
+                'block_class': BottleneckBlock
+            }
+        }
+        stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)]
+        num_filters = [64, 128, 256, 512]
+        self.depth = supported_layers[layers]['depth']
+        self.F = []
+        self.conv = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=7,
+            stride=2,
+            act="relu",
+            name="conv1")
+        self.block_list = []
+        in_ch = 64
+        if layers >= 50:
+            for block in range(len(self.depth)):
+                for i in range(self.depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    block_list = self.add_sublayer(
+                        "bottleneckBlock_{}_{}".format(block, i),
+                        BottleneckBlock(
+                            in_channels=in_ch,
+                            out_channels=num_filters[block],
+                            stride=stride_list[block] if i == 0 else 1,
+                            name=conv_name))
+                    in_ch = num_filters[block] * 4
+                    self.block_list.append(block_list)
+                self.F.append(block_list)
+        else:
+            for block in range(len(self.depth)):
+                for i in range(self.depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    if i == 0 and block != 0:
+                        stride = (2, 1)
+                    else:
+                        stride = (1, 1)
+                    basic_block = self.add_sublayer(
+                        conv_name,
+                        BasicBlock(
+                            in_channels=in_ch,
+                            out_channels=num_filters[block],
+                            stride=stride_list[block] if i == 0 else 1,
+                            is_first=block == i == 0,
+                            name=conv_name))
+                    in_ch = basic_block.out_channels
+                    self.block_list.append(basic_block)
+        out_ch_list = [in_ch // 4, in_ch // 2, in_ch]
+        self.base_block = []
+        self.conv_trans = []
+        self.bn_block = []
+        for i in [-2, -3]:
+            in_channels = out_ch_list[i + 1] + out_ch_list[i]
+
+            self.base_block.append(
+                self.add_sublayer(
+                    "F_{}_base_block_0".format(i),
+                    nn.Conv2D(
+                        in_channels=in_channels,
+                        out_channels=out_ch_list[i],
+                        kernel_size=1,
+                        weight_attr=ParamAttr(trainable=True),
+                        bias_attr=ParamAttr(trainable=True))))
+            self.base_block.append(
+                self.add_sublayer(
+                    "F_{}_base_block_1".format(i),
+                    nn.Conv2D(
+                        in_channels=out_ch_list[i],
+                        out_channels=out_ch_list[i],
+                        kernel_size=3,
+                        padding=1,
+                        weight_attr=ParamAttr(trainable=True),
+                        bias_attr=ParamAttr(trainable=True))))
+            self.base_block.append(
+                self.add_sublayer(
+                    "F_{}_base_block_2".format(i),
+                    nn.BatchNorm(
+                        num_channels=out_ch_list[i],
+                        act="relu",
+                        param_attr=ParamAttr(trainable=True),
+                        bias_attr=ParamAttr(trainable=True))))
+        self.base_block.append(
+            self.add_sublayer(
+                "F_{}_base_block_3".format(i),
+                nn.Conv2D(
+                    in_channels=out_ch_list[i],
+                    out_channels=512,
+                    kernel_size=1,
+                    bias_attr=ParamAttr(trainable=True),
+                    weight_attr=ParamAttr(trainable=True))))
+        self.out_channels = 512
+
+    def __call__(self, x):
+        x = self.conv(x)
+        fpn_list = []
+        F = []
+        for i in range(len(self.depth)):
+            fpn_list.append(np.sum(self.depth[:i + 1]))
+
+        for i, block in enumerate(self.block_list):
+            x = block(x)
+            for number in fpn_list:
+                if i + 1 == number:
+                    F.append(x)
+        base = F[-1]
+
+        j = 0
+        for i, block in enumerate(self.base_block):
+            if i % 3 == 0 and i < 6:
+                j = j + 1
+                b, c, w, h = F[-j - 1].shape
+                if [w, h] == list(base.shape[2:]):
+                    base = base
+                else:
+                    base = self.conv_trans[j - 1](base)
+                    base = self.bn_block[j - 1](base)
+                base = paddle.concat([base, F[-j - 1]], axis=1)
+            base = block(base)
+        return base
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 groups=1,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+        self.conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=2 if stride == (1, 1) else kernel_size,
+            dilation=2 if stride == (1, 1) else 1,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'),
+            bias_attr=False, )
+
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name=name + '.output.1.w_0'),
+            bias_attr=ParamAttr(name=name + '.output.1.b_0'),
+            moving_mean_name=bn_name + "_mean",
+            moving_variance_name=bn_name + "_variance")
+
+    def __call__(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class ShortCut(nn.Layer):
+    def __init__(self, in_channels, out_channels, stride, name, is_first=False):
+        super(ShortCut, self).__init__()
+        self.use_conv = True
+
+        if in_channels != out_channels or stride != 1 or is_first == True:
+            if stride == (1, 1):
+                self.conv = ConvBNLayer(
+                    in_channels, out_channels, 1, 1, name=name)
+            else:  # stride==(2,2)
+                self.conv = ConvBNLayer(
+                    in_channels, out_channels, 1, stride, name=name)
+        else:
+            self.use_conv = False
+
+    def forward(self, x):
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class BottleneckBlock(nn.Layer):
+    def __init__(self, in_channels, out_channels, stride, name):
+        super(BottleneckBlock, self).__init__()
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+
+        self.conv2 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels * 4,
+            kernel_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        self.short = ShortCut(
+            in_channels=in_channels,
+            out_channels=out_channels * 4,
+            stride=stride,
+            is_first=False,
+            name=name + "_branch1")
+        self.out_channels = out_channels * 4
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        y = self.conv2(y)
+        y = y + self.short(x)
+        y = F.relu(y)
+        return y
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self, in_channels, out_channels, stride, name, is_first):
+        super(BasicBlock, self).__init__()
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            act='relu',
+            stride=stride,
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            act=None,
+            name=name + "_branch2b")
+        self.short = ShortCut(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            stride=stride,
+            is_first=is_first,
+            name=name + "_branch1")
+        self.out_channels = out_channels
+
+    def forward(self, x):
+        y = self.conv0(x)
+        y = self.conv1(y)
+        y = y + self.short(x)
+        return F.relu(y)