diff --git a/PPOCRLabel/PPOCRLabel.py b/PPOCRLabel/PPOCRLabel.py
index 4d9c52740a5ca5bcdd891bb55ff769f23e7a2499..8babac6558fa7c3b15779298630af03e47c97cb1 100644
--- a/PPOCRLabel/PPOCRLabel.py
+++ b/PPOCRLabel/PPOCRLabel.py
@@ -1031,7 +1031,7 @@ class MainWindow(QMainWindow, WindowMixin):
for box in self.result_dic:
trans_dic = {"label": box[1][0], "points": box[0], 'difficult': False}
- if trans_dic["label"] is "" and mode == 'Auto':
+ if trans_dic["label"] == "" and mode == 'Auto':
continue
shapes.append(trans_dic)
@@ -1450,7 +1450,7 @@ class MainWindow(QMainWindow, WindowMixin):
item = QListWidgetItem(closeicon, filename)
self.fileListWidget.addItem(item)
- print('dirPath in importDirImages is', dirpath)
+ print('DirPath in importDirImages is', dirpath)
self.iconlist.clear()
self.additems5(dirpath)
self.changeFileFolder = True
@@ -1459,7 +1459,6 @@ class MainWindow(QMainWindow, WindowMixin):
self.reRecogButton.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
self.actions.reRec.setEnabled(True)
- self.actions.saveLabel.setEnabled(True)
def openPrevImg(self, _value=False):
@@ -1764,7 +1763,7 @@ class MainWindow(QMainWindow, WindowMixin):
QMessageBox.information(self, "Information", msg)
return
result = self.ocr.ocr(img_crop, cls=True, det=False)
- if result[0][0] is not '':
+ if result[0][0] != '':
result.insert(0, box)
print('result in reRec is ', result)
self.result_dic.append(result)
@@ -1795,7 +1794,7 @@ class MainWindow(QMainWindow, WindowMixin):
QMessageBox.information(self, "Information", msg)
return
result = self.ocr.ocr(img_crop, cls=True, det=False)
- if result[0][0] is not '':
+ if result[0][0] != '':
result.insert(0, box)
print('result in reRec is ', result)
if result[1][0] == shape.label:
@@ -1862,6 +1861,8 @@ class MainWindow(QMainWindow, WindowMixin):
for each in states:
file, state = each.split('\t')
self.fileStatedict[file] = 1
+ self.actions.saveLabel.setEnabled(True)
+ self.actions.saveRec.setEnabled(True)
def saveFilestate(self):
@@ -1919,22 +1920,29 @@ class MainWindow(QMainWindow, WindowMixin):
rec_gt_dir = os.path.dirname(self.PPlabelpath) + '/rec_gt.txt'
crop_img_dir = os.path.dirname(self.PPlabelpath) + '/crop_img/'
+ ques_img = []
if not os.path.exists(crop_img_dir):
os.mkdir(crop_img_dir)
with open(rec_gt_dir, 'w', encoding='utf-8') as f:
for key in self.fileStatedict:
idx = self.getImglabelidx(key)
- for i, label in enumerate(self.PPlabel[idx]):
- if label['difficult']: continue
+ try:
img = cv2.imread(key)
- img_crop = get_rotate_crop_image(img, np.array(label['points'], np.float32))
- img_name = os.path.splitext(os.path.basename(idx))[0] + '_crop_'+str(i)+'.jpg'
- cv2.imwrite(crop_img_dir+img_name, img_crop)
- f.write('crop_img/'+ img_name + '\t')
- f.write(label['transcription'] + '\n')
-
- QMessageBox.information(self, "Information", "Cropped images has been saved in "+str(crop_img_dir))
+ for i, label in enumerate(self.PPlabel[idx]):
+ if label['difficult']: continue
+ img_crop = get_rotate_crop_image(img, np.array(label['points'], np.float32))
+ img_name = os.path.splitext(os.path.basename(idx))[0] + '_crop_'+str(i)+'.jpg'
+ cv2.imwrite(crop_img_dir+img_name, img_crop)
+ f.write('crop_img/'+ img_name + '\t')
+ f.write(label['transcription'] + '\n')
+ except Exception as e:
+ ques_img.append(key)
+ print("Can not read image ",e)
+ if ques_img:
+ QMessageBox.information(self, "Information", "The following images can not be saved, "
+ "please check the image path and labels.\n" + "".join(str(i)+'\n' for i in ques_img))
+ QMessageBox.information(self, "Information", "Cropped images have been saved in "+str(crop_img_dir))
def speedChoose(self):
if self.labelDialogOption.isChecked():
@@ -1991,7 +1999,7 @@ if __name__ == '__main__':
resource_file = './libs/resources.py'
if not os.path.exists(resource_file):
output = os.system('pyrcc5 -o libs/resources.py resources.qrc')
- assert output is 0, "operate the cmd have some problems ,please check whether there is a in the lib " \
+ assert output == 0, "operate the cmd have some problems ,please check whether there is a in the lib " \
"directory resources.py "
import libs.resources
sys.exit(main())
diff --git a/README.md b/README.md
index 27fbf06a7137669ccb99fb8b8b33312a687167bc..67d65e98e8d7b978e25d9582cf21d2b222858e69 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
## Notice
PaddleOCR supports both dynamic graph and static graph programming paradigm
-- Dynamic graph: dygraph branch (default), **supported by paddle 2.0rc1+ ([installation](./doc/doc_en/installation_en.md))**
+- Dynamic graph: dygraph branch (default), **supported by paddle 2.0.0 ([installation](./doc/doc_en/installation_en.md))**
- Static graph: develop branch
**Recent updates**
diff --git a/README_ch.md b/README_ch.md
index 335785db550c32343b0fd35d417a30ce47a8e034..d4870710a24a3c17a997a603b34d447ce8f9f0bd 100755
--- a/README_ch.md
+++ b/README_ch.md
@@ -4,12 +4,12 @@
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
## 注意
PaddleOCR同时支持动态图与静态图两种编程范式
-- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0rc1+（[快速安装](./doc/doc_ch/installation.md)）
+- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0.0（[快速安装](./doc/doc_ch/installation.md)）
- 静态图版本：develop分支
**近期更新**
+- 2021.2.1 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数162个，每周一都会更新，欢迎大家持续关注。
- 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802)
-- 2021.1.25 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数157个，每周一都会更新，欢迎大家持续关注。
- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，[多语言模型下载](./doc/doc_ch/models_list.md)，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)
- 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
diff --git a/configs/rec/rec_mv3_none_bilstm_ctc.yml b/configs/rec/rec_mv3_none_bilstm_ctc.yml
index 38f1e8691e6056ada01a2d5c19f70955e8117498..00c1db885e000d80ed3c3f42c2afbaa11c452ab5 100644
--- a/configs/rec/rec_mv3_none_bilstm_ctc.yml
+++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml
@@ -1,5 +1,5 @@
Global:
- use_gpu: true
+ use_gpu: True
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
@@ -59,7 +59,7 @@ Metric:
Train:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
@@ -78,7 +78,7 @@ Train:
Eval:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
diff --git a/configs/rec/rec_mv3_none_none_ctc.yml b/configs/rec/rec_mv3_none_none_ctc.yml
index 33079ad48c94c217ef86ef3f245492a540559350..6711b1d23f843551d72e1dffc003637734727754 100644
--- a/configs/rec/rec_mv3_none_none_ctc.yml
+++ b/configs/rec/rec_mv3_none_none_ctc.yml
@@ -58,7 +58,7 @@ Metric:
Train:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
@@ -77,7 +77,7 @@ Train:
Eval:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
diff --git a/configs/rec/rec_mv3_tps_bilstm_att.yml b/configs/rec/rec_mv3_tps_bilstm_att.yml
new file mode 100644
index 0000000000000000000000000000000000000000..0ce067343c0fbcff9bb204ed6902a2566d2b769c
--- /dev/null
+++ b/configs/rec/rec_mv3_tps_bilstm_att.yml
@@ -0,0 +1,102 @@
+Global:
+ use_gpu: True
+ epoch_num: 72
+ log_smooth_window: 20
+ print_batch_step: 10
+ save_model_dir: ./output/rec/rec_mv3_tps_bilstm_att/
+ save_epoch_step: 3
+ # evaluation is run every 5000 iterations after the 4000th iteration
+ eval_batch_step: [0, 2000]
+ # if pretrained_model is saved in static mode, load_static_weights must set to True
+ cal_metric_during_train: True
+ pretrained_model:
+ checkpoints:
+ save_inference_dir:
+ use_visualdl: False
+ infer_img: doc/imgs_words/ch/word_1.jpg
+ # for data or label process
+ character_dict_path:
+ character_type: en
+ max_text_length: 25
+ infer_mode: False
+ use_space_char: False
+
+
+Optimizer:
+ name: Adam
+ beta1: 0.9
+ beta2: 0.999
+ lr:
+ learning_rate: 0.0005
+ regularizer:
+ name: 'L2'
+ factor: 0.00001
+
+Architecture:
+ model_type: rec
+ algorithm: RARE
+ Transform:
+ name: TPS
+ num_fiducial: 20
+ loc_lr: 0.1
+ model_name: small
+ Backbone:
+ name: MobileNetV3
+ scale: 0.5
+ model_name: large
+ Neck:
+ name: SequenceEncoder
+ encoder_type: rnn
+ hidden_size: 96
+ Head:
+ name: AttentionHead
+ hidden_size: 96
+
+
+Loss:
+ name: AttentionLoss
+
+PostProcess:
+ name: AttnLabelDecode
+
+Metric:
+ name: RecMetric
+ main_indicator: acc
+
+Train:
+ dataset:
+ name: LMDBDataSet
+ data_dir: ../training/
+ transforms:
+ - DecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - AttnLabelEncode: # Class handling label
+ - RecResizeImg:
+ image_shape: [3, 32, 100]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: True
+ batch_size_per_card: 256
+ drop_last: True
+ num_workers: 8
+
+Eval:
+ dataset:
+ name: LMDBDataSet
+ data_dir: ../validation/
+ transforms:
+ - DecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - AttnLabelEncode: # Class handling label
+ - RecResizeImg:
+ image_shape: [3, 32, 100]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: False
+ drop_last: False
+ batch_size_per_card: 256
+ num_workers: 1
diff --git a/configs/rec/rec_mv3_tps_bilstm_ctc.yml b/configs/rec/rec_mv3_tps_bilstm_ctc.yml
index 08f68939d4f1e6de1c3688652bd86f6556a43384..4e86709942bcde410dc22df439fdd40e9a94fdef 100644
--- a/configs/rec/rec_mv3_tps_bilstm_ctc.yml
+++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml
@@ -1,5 +1,5 @@
Global:
- use_gpu: true
+ use_gpu: True
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
@@ -63,7 +63,7 @@ Metric:
Train:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
@@ -82,7 +82,7 @@ Train:
Eval:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
diff --git a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml
index 4ad2ff89ef1e72c58c426670742bc2ada27cfc4a..e4d301a6a173ea772898c0528c4b3082670870ff 100644
--- a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml
+++ b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml
@@ -58,7 +58,7 @@ Metric:
Train:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
@@ -77,7 +77,7 @@ Train:
Eval:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
diff --git a/configs/rec/rec_r34_vd_none_none_ctc.yml b/configs/rec/rec_r34_vd_none_none_ctc.yml
index 9c1eeb304f41d46e49cee350e5d659dd1e0c8b0e..4a17a004228185db7e52dd71aadcff36d407d2cf 100644
--- a/configs/rec/rec_r34_vd_none_none_ctc.yml
+++ b/configs/rec/rec_r34_vd_none_none_ctc.yml
@@ -56,7 +56,7 @@ Metric:
Train:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
@@ -75,7 +75,7 @@ Train:
Eval:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
diff --git a/configs/rec/rec_r34_vd_tps_bilstm_att.yml b/configs/rec/rec_r34_vd_tps_bilstm_att.yml
new file mode 100644
index 0000000000000000000000000000000000000000..02aeb8c522a62b9a3c6b90f818df5081428b652e
--- /dev/null
+++ b/configs/rec/rec_r34_vd_tps_bilstm_att.yml
@@ -0,0 +1,101 @@
+Global:
+ use_gpu: True
+ epoch_num: 400
+ log_smooth_window: 20
+ print_batch_step: 10
+ save_model_dir: ./output/rec/b3_rare_r34_none_gru/
+ save_epoch_step: 3
+ # evaluation is run every 5000 iterations after the 4000th iteration
+ eval_batch_step: [0, 2000]
+ # if pretrained_model is saved in static mode, load_static_weights must set to True
+ cal_metric_during_train: True
+ pretrained_model:
+ checkpoints:
+ save_inference_dir:
+ use_visualdl: False
+ infer_img: doc/imgs_words/ch/word_1.jpg
+ # for data or label process
+ character_dict_path:
+ character_type: en
+ max_text_length: 25
+ infer_mode: False
+ use_space_char: False
+
+
+Optimizer:
+ name: Adam
+ beta1: 0.9
+ beta2: 0.999
+ lr:
+ learning_rate: 0.0005
+ regularizer:
+ name: 'L2'
+ factor: 0.00000
+
+Architecture:
+ model_type: rec
+ algorithm: RARE
+ Transform:
+ name: TPS
+ num_fiducial: 20
+ loc_lr: 0.1
+ model_name: large
+ Backbone:
+ name: ResNet
+ layers: 34
+ Neck:
+ name: SequenceEncoder
+ encoder_type: rnn
+ hidden_size: 256 #96
+ Head:
+ name: AttentionHead # AttentionHead
+ hidden_size: 256 #
+ l2_decay: 0.00001
+
+Loss:
+ name: AttentionLoss
+
+PostProcess:
+ name: AttnLabelDecode
+
+Metric:
+ name: RecMetric
+ main_indicator: acc
+
+Train:
+ dataset:
+ name: LMDBDataSet
+ data_dir: ../training/
+ transforms:
+ - DecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - AttnLabelEncode: # Class handling label
+ - RecResizeImg:
+ image_shape: [3, 32, 100]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: True
+ batch_size_per_card: 256
+ drop_last: True
+ num_workers: 8
+
+Eval:
+ dataset:
+ name: LMDBDataSet
+ data_dir: ../validation/
+ transforms:
+ - DecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - AttnLabelEncode: # Class handling label
+ - RecResizeImg:
+ image_shape: [3, 32, 100]
+ - KeepKeys:
+ keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+ loader:
+ shuffle: False
+ drop_last: False
+ batch_size_per_card: 256
+ num_workers: 8
diff --git a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
index aeded4926a6d09cf30210f2d348d2933461a06b1..62edf84379ec1be9ef5f7155b240099f5fbb7b00 100644
--- a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
+++ b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
@@ -62,7 +62,7 @@ Metric:
Train:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
@@ -81,7 +81,7 @@ Train:
Eval:
dataset:
- name: LMDBDateSet
+ name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
diff --git a/configs/rec/rec_r50_fpn_srn.yml b/configs/rec/rec_r50_fpn_srn.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ec7f170560f5309818d537953a93c180b9de0bb7
--- /dev/null
+++ b/configs/rec/rec_r50_fpn_srn.yml
@@ -0,0 +1,107 @@
+Global:
+ use_gpu: True
+ epoch_num: 72
+ log_smooth_window: 20
+ print_batch_step: 5
+ save_model_dir: ./output/rec/srn_new
+ save_epoch_step: 3
+ # evaluation is run every 5000 iterations after the 4000th iteration
+ eval_batch_step: [0, 5000]
+ # if pretrained_model is saved in static mode, load_static_weights must set to True
+ cal_metric_during_train: True
+ pretrained_model:
+ checkpoints:
+ save_inference_dir:
+ use_visualdl: False
+ infer_img: doc/imgs_words/ch/word_1.jpg
+ # for data or label process
+ character_dict_path:
+ character_type: en
+ max_text_length: 25
+ num_heads: 8
+ infer_mode: False
+ use_space_char: False
+
+
+Optimizer:
+ name: Adam
+ beta1: 0.9
+ beta2: 0.999
+ clip_norm: 10.0
+ lr:
+ learning_rate: 0.0001
+
+Architecture:
+ model_type: rec
+ algorithm: SRN
+ in_channels: 1
+ Transform:
+ Backbone:
+ name: ResNetFPN
+ Head:
+ name: SRNHead
+ max_text_length: 25
+ num_heads: 8
+ num_encoder_TUs: 2
+ num_decoder_TUs: 4
+ hidden_dims: 512
+
+Loss:
+ name: SRNLoss
+
+PostProcess:
+ name: SRNLabelDecode
+
+Metric:
+ name: RecMetric
+ main_indicator: acc
+
+Train:
+ dataset:
+ name: LMDBDataSet
+ data_dir: ./train_data/srn_train_data_duiqi
+ transforms:
+ - DecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - SRNLabelEncode: # Class handling label
+ - SRNRecResizeImg:
+ image_shape: [1, 64, 256]
+ - KeepKeys:
+ keep_keys: ['image',
+ 'label',
+ 'length',
+ 'encoder_word_pos',
+ 'gsrm_word_pos',
+ 'gsrm_slf_attn_bias1',
+ 'gsrm_slf_attn_bias2'] # dataloader will return list in this order
+ loader:
+ shuffle: False
+ batch_size_per_card: 64
+ drop_last: False
+ num_workers: 4
+
+Eval:
+ dataset:
+ name: LMDBDataSet
+ data_dir: ./train_data/data_lmdb_release/evaluation
+ transforms:
+ - DecodeImage: # load image
+ img_mode: BGR
+ channel_first: False
+ - SRNLabelEncode: # Class handling label
+ - SRNRecResizeImg:
+ image_shape: [1, 64, 256]
+ - KeepKeys:
+ keep_keys: ['image',
+ 'label',
+ 'length',
+ 'encoder_word_pos',
+ 'gsrm_word_pos',
+ 'gsrm_slf_attn_bias1',
+ 'gsrm_slf_attn_bias2']
+ loader:
+ shuffle: False
+ drop_last: False
+ batch_size_per_card: 32
+ num_workers: 4
diff --git a/deploy/docker/hubserving/cpu/Dockerfile b/deploy/docker/hubserving/cpu/Dockerfile
index e46ca73be0a894631fe7e11cda3bdf7a20ad5526..ef1a7b7b16fe1ebe26b5681a72f64d404dd77608 100644
--- a/deploy/docker/hubserving/cpu/Dockerfile
+++ b/deploy/docker/hubserving/cpu/Dockerfile
@@ -1,5 +1,5 @@
# Version: 2.0.0
-FROM registry.baidubce.com/paddlepaddle/paddle:2.0.0rc1
+FROM registry.baidubce.com/paddlepaddle/paddle:2.0.0
# PaddleOCR base on Python3.7
RUN pip3.7 install --upgrade pip -i https://mirror.baidu.com/pypi/simple
diff --git a/deploy/docker/hubserving/gpu/Dockerfile b/deploy/docker/hubserving/gpu/Dockerfile
index b7fa6f4ca0467b43186073c82c9ac5afc4b1216e..b3f2d21ffec340a93894f089d72ae775c428ef33 100644
--- a/deploy/docker/hubserving/gpu/Dockerfile
+++ b/deploy/docker/hubserving/gpu/Dockerfile
@@ -1,5 +1,5 @@
# Version: 2.0.0
-FROM egistry.baidubce.com/paddlepaddle/paddle:2.0.0rc1-gpu-cuda10.0-cudnn7
+FROM registry.baidubce.com/paddlepaddle/paddle:2.0.0-gpu-cuda10.1-cudnn7
# PaddleOCR base on Python3.7
RUN pip3.7 install --upgrade pip -i https://mirror.baidu.com/pypi/simple
diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md
index ccd4d06b4f16165f968402751b63a8fe58773e0b..4ac3f7c3016c9ef53724ad6f7745507cef3580a8 100644
--- a/deploy/slim/quantization/README.md
+++ b/deploy/slim/quantization/README.md
@@ -42,7 +42,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
# 比如下载提供的训练模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
tar -xf ch_ppocr_mobile_v2.0_det_train.tar
-python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./output/quant_inference_model
```
如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md
index 7da0b3e7e7d5f72e45dc17864630b9725f6fc8ba..36407a2bb58ee3a36afc211ca7a8f0d786d1714f 100644
--- a/deploy/slim/quantization/README_en.md
+++ b/deploy/slim/quantization/README_en.md
@@ -58,7 +58,7 @@ python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global
After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
```bash
-python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
+python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_inference_dir=./output/quant_inference_model
```
### 5. Deploy
diff --git a/doc/doc_ch/FAQ.md b/doc/doc_ch/FAQ.md
index bb61689bfae403062ba77ca6bb39719ef7d93725..2026e7a087338c06fd2d951c23a8b91edd445e23 100755
--- a/doc/doc_ch/FAQ.md
+++ b/doc/doc_ch/FAQ.md
@@ -9,43 +9,38 @@
## PaddleOCR常见问题汇总(持续更新)
-* [近期更新（2021.1.25）](#近期更新)
+* [近期更新（2021.2.1）](#近期更新)
* [【精选】OCR精选10个问题](#OCR精选10个问题)
* [【理论篇】OCR通用32个问题](#OCR通用问题)
* [基础知识7题](#基础知识)
* [数据集7题](#数据集2)
* [模型训练调优18题](#模型训练调优2)
-* [【实战篇】PaddleOCR实战115个问题](#PaddleOCR实战问题)
+* [【实战篇】PaddleOCR实战120个问题](#PaddleOCR实战问题)
* [使用咨询38题](#使用咨询)
- * [数据集17题](#数据集3)
- * [模型训练调优28题](#模型训练调优3)
- * [预测部署32题](#预测部署3)
+ * [数据集18题](#数据集3)
+ * [模型训练调优30题](#模型训练调优3)
+ * [预测部署34题](#预测部署3)
-## 近期更新（2021.1.25）
+## 近期更新（2021.2.1）
-#### Q3.1.37: 小语种模型只有识别模型，没有检测模型吗？
+#### Q3.2.18: PaddleOCR动态图版本如何finetune？
+**A**：finetune需要将配置文件里的 Global.load_static_weights设置为false，如果没有此字段可以手动添加，然后将模型地址放到Global.pretrained_model字段下即可。
-**A**：小语种（包括纯英文数字）的检测模型和中文的检测模型是共用的，在训练中文检测模型时加入了多语言数据。https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/models_list_en.md#1-text-detection-model。
-#### Q3.1.38: module 'paddle.distributed' has no attribute ‘get_rank’。
+#### Q3.3.29: 微调v1.1预训练的模型，可以直接用文字垂直排列和上下颠倒的图片吗？还是必须要水平排列的？
+**A**：1.1和2.0的模型一样，微调时，垂直排列的文字需要逆时针旋转 90° 后加入训练，上下颠倒的需要旋转为水平的。
-**A**：Paddle版本问题，请安装2.0版本Paddle：pip install paddlepaddle==2.0.0rc1。
+#### Q3.3.30: 模型训练过程中如何得到 best_accuracy 模型？
+**A**：配置文件里的eval_batch_step字段用来控制多少次iter进行一次eval，在eval完成后会自动生成 best_accuracy 模型，所以如果希望很快就能拿到best_accuracy模型，可以将eval_batch_step改小一点(例如，10)。
-#### Q3.4.30: PaddleOCR是否支持在华为鲲鹏920CPU上部署？
+#### Q3.4.33: 如何多进程运行paddleocr？
+**A**：实例化多个paddleocr服务，然后将服务注册到注册中心，之后通过注册中心统一调度即可，关于注册中心，可以搜索eureka了解一下具体使用，其他的注册中心也行。
-**A**：目前Paddle的预测库是支持华为鲲鹏920CPU的，但是OCR还没在这些芯片上测试过，可以自己调试，有问题反馈给我们。
-#### Q3.4.31: 采用Paddle-Lite进行端侧部署，出现问题，环境没问题。
-
-**A**：如果你的预测库是自己编译的，那么你的nb文件也要自己编译，用同一个lite版本。不能直接用下载的nb文件，因为版本不同。
-
-#### Q3.4.32: PaddleOCR的模型支持onnx转换吗？
-
-**A**：我们目前已经通过Paddle2ONNX来支持各模型套件的转换，PaddleOCR基于PaddlePaddle 2.0的版本（dygraph分支）已经支持导出为ONNX，欢迎关注Paddle2ONNX，了解更多项目的进展：
-Paddle2ONNX项目：https://github.com/PaddlePaddle/Paddle2ONNX
-Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/model_zoo.md#%E5%9B%BE%E5%83%8Focr)
+#### Q3.4.34: 2.0训练出来的模型，能否在1.1版本上进行部署？
+**A**：这个是不建议的，2.0训练出来的模型建议使用dygraph分支里提供的部署代码。
## 【精选】OCR精选10个问题
@@ -397,13 +392,13 @@ Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2
**A**：动态图版本正在紧锣密鼓开发中，将于2020年12月16日发布，敬请关注。
#### Q3.1.22：ModuleNotFoundError: No module named 'paddle.nn'，
-**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0rc1的版本，安装方式为
+**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0的版本，安装方式为
```
-python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
```
#### Q3.1.23： ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so)
-**A**：这个问题是glibc版本不足导致的，Paddle2.0rc1版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。
+**A**：这个问题是glibc版本不足导致的，Paddle2.0.0版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。
如果您的环境不满足这个要求，或者使用的docker镜像为:
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`
`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`，安装Paddle2.0rc版本可能会出现上述错误，2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`。
@@ -415,7 +410,7 @@ python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/py
- develop：基于Paddle静态图开发的分支，推荐使用paddle1.8 或者2.0版本，该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能，领先于release/1.1分支。
- release/1.1：PaddleOCR 发布的第一个稳定版本，基于静态图开发，具备完善的训练、预测、推理部署、量化裁剪等功能。
-- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0rc1版本，目前仍在开发中。
+- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0.0版本。
- release/2.0-rc1-0：PaddleOCR发布的第二个稳定版本，基于动态图和paddle2.0版本开发，动态图开发的工程更易于调试，目前支，支持模型训练、预测，暂不支持移动端部署。
如果您已经上手过PaddleOCR，并且希望在各种环境上部署PaddleOCR，目前建议使用静态图分支，develop或者release/1.1分支。如果您是初学者，想快速训练，调试PaddleOCR中的算法，建议尝鲜PaddleOCR dygraph分支。
@@ -432,7 +427,7 @@ python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/py
#### Q3.1.27: 如何可视化acc,loss曲线图,模型网络结构图等？
-**A**：在配置文件里有`use_visualdl`的参数，设置为True即可，更多的使用命令可以参考：[VisualDL使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc1/guides/03_VisualDL/visualdl.html)。
+**A**：在配置文件里有`use_visualdl`的参数，设置为True即可，更多的使用命令可以参考：[VisualDL使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/03_VisualDL/visualdl.html)。
#### Q3.1.28: 在使用StyleText数据合成工具的时候，报错`ModuleNotFoundError: No module named 'utils.config'`，这是为什么呢？
@@ -451,7 +446,7 @@ https://github.com/PaddlePaddle/PaddleOCR/blob/de3e2e7cd3b8b65ee02d7a41e570fa5b5
#### Q3.1.31: 怎么输出网络结构以及每层的参数信息？
-**A**：可以使用 `paddle.summary`， 具体参考:https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc1/api/paddle/hapi/model_summary/summary_cn.html#summary。
+**A**：可以使用 `paddle.summary`， 具体参考:https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/hapi/model_summary/summary_cn.html。
#### Q3.1.32 能否修改StyleText配置文件中的分辨率？
@@ -485,7 +480,7 @@ StyleText的用途主要是：提取style_image中的字体、背景等style信
#### Q3.1.38: module 'paddle.distributed' has no attribute ‘get_rank’。
-**A**：Paddle版本问题，请安装2.0版本Paddle：pip install paddlepaddle==2.0.0rc1。
+**A**：Paddle版本问题，请安装2.0版本Paddle：pip install paddlepaddle==2.0.0。
### 数据集
@@ -578,6 +573,9 @@ StyleText的用途主要是：提取style_image中的字体、背景等style信
**A**：PPOCRLabel可运行于Linux、Windows、MacOS等多种系统。操作步骤可以参考文档，https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/README.md
+#### Q3.2.18: PaddleOCR动态图版本如何finetune？
+**A**：finetune需要将配置文件里的 Global.load_static_weights设置为false，如果没有此字段可以手动添加，然后将模型地址放到Global.pretrained_model字段下即可。
+
### 模型训练调优
@@ -723,6 +721,12 @@ ps -axu | grep train.py | awk '{print $2}' | xargs kill -9
**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4.由于tia数据增强特殊性，默认不采用，可以通过添加use_tia设置，使tia数据增强生效。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。
+#### Q3.3.29: 微调v1.1预训练的模型，可以直接用文字垂直排列和上下颠倒的图片吗？还是必须要水平排列的？
+**A**：1.1和2.0的模型一样，微调时，垂直排列的文字需要逆时针旋转 90°后加入训练，上下颠倒的需要旋转为水平的。
+
+#### Q3.3.30: 模型训练过程中如何得到 best_accuracy 模型？
+**A**：配置文件里的eval_batch_step字段用来控制多少次iter进行一次eval，在eval完成后会自动生成 best_accuracy 模型，所以如果希望很快就能拿到best_accuracy模型，可以将eval_batch_step改小一点，如改为[10,10]，这样表示第10次迭代后，以后没隔10个迭代就进行一次模型的评估。
+
### 预测部署
@@ -877,4 +881,11 @@ img = cv.imdecode(img_array, -1)
**A**：我们目前已经通过Paddle2ONNX来支持各模型套件的转换，PaddleOCR基于PaddlePaddle 2.0的版本（dygraph分支）已经支持导出为ONNX，欢迎关注Paddle2ONNX，了解更多项目的进展：
Paddle2ONNX项目：https://github.com/PaddlePaddle/Paddle2ONNX
-Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/model_zoo.md#%E5%9B%BE%E5%83%8Focr)
\ No newline at end of file
+Paddle2ONNX支持转换的[模型列表](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/docs/zh/model_zoo.md#%E5%9B%BE%E5%83%8Focr)
+
+
+#### Q3.4.33: 如何多进程运行paddleocr？
+**A**：实例化多个paddleocr服务，然后将服务注册到注册中心，之后通过注册中心统一调度即可，关于注册中心，可以搜索eureka了解一下具体使用，其他的注册中心也行。
+
+#### Q3.4.34: 2.0训练出来的模型，能否在1.1版本上进行部署？
+**A**：这个是不建议的，2.0训练出来的模型建议使用dygraph分支里提供的部署代码。
diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index 59d1bc8c444e3a70bbea83f87afcbd2f5cf44191..c8fc280d80056395bbc841a973004b06844b1214 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -40,8 +40,8 @@ PaddleOCR基于动态图开源的文本识别算法列表：
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
-- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
-- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
+- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
+- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
@@ -53,5 +53,9 @@ PaddleOCR基于动态图开源的文本识别算法列表：
|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
+|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
+|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) |
+
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。
diff --git a/doc/doc_ch/angle_class.md b/doc/doc_ch/angle_class.md
index 1f5b4f6d3db2c67f4d4b36c7d21699bd6bba02a3..7e8e9b34f374624ea119d8daa2da6ba7c54e36bf 100644
--- a/doc/doc_ch/angle_class.md
+++ b/doc/doc_ch/angle_class.md
@@ -67,7 +67,7 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本。
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
```
-# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号，如果使用的paddle版本小于2.0rc1，请使用'--select_gpus'参数选择要使用的GPU
+# GPU训练 支持单卡，多卡训练，通过 '--gpus' 指定卡号。
# 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
```
diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md
index 8f0f69796a38ac16643e1168c3ecf73b92daa19a..a8dee65a220e3c66d8502181dd2a542cb01a29b5 100644
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -76,7 +76,7 @@ tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_model
# 单机单卡训练 mv3_db 模型
python3 tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
-# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID；如果使用的paddle版本小于2.0rc1，请使用'--select_gpus'参数选择要使用的GPU
+# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
-o Global.pretrain_weights=./pretrain_models/MobileNetV3_large_x0_5_pretrained/
```
diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md
index c4601e1526d29e0a8c62030a4b47d2b2cc193d5d..0daddd9bb02d41c139f1f16b1fcd81c03f43f6ac 100755
--- a/doc/doc_ch/inference.md
+++ b/doc/doc_ch/inference.md
@@ -22,8 +22,9 @@ inference 模型（`paddle.jit.save`保存的模型）
- [三、文本识别模型推理](#文本识别模型推理)
- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
- - [3. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- - [4. 多语言模型的推理](#多语言模型的推理)
+ - [3. 基于SRN损失的识别模型推理](#基于SRN损失的识别模型推理)
+ - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
+ - [5. 多语言模型的推理](#多语言模型的推理)
- [四、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理)
@@ -295,8 +296,20 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
+
+### 3. 基于SRN损失的识别模型推理
+基于SRN损失的识别模型，需要额外设置识别算法参数 --rec_algorithm="SRN"。
+同时需要保证预测shape与训练时一致，如： --rec_image_shape="1, 64, 256"
-### 3. 自定义文本识别字典的推理
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
+ --rec_model_dir="./inference/srn/" \
+ --rec_image_shape="1, 64, 256" \
+ --rec_char_type="en" \
+ --rec_algorithm="SRN"
+```
+
+### 4. 自定义文本识别字典的推理
如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，并且设置 `rec_char_type=ch`
```
@@ -304,7 +317,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
```
-### 4. 多语言模型的推理
+### 5. 多语言模型的推理
如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
diff --git a/doc/doc_ch/installation.md b/doc/doc_ch/installation.md
index 36565cd4197a9b8b8404f57b378aa49637cdc58b..fce151eb9fee567477c09eee211633f7377dddb3 100644
--- a/doc/doc_ch/installation.md
+++ b/doc/doc_ch/installation.md
@@ -2,7 +2,7 @@
经测试PaddleOCR可在glibc 2.23上运行，您也可以测试其他glibc版本或安装glic 2.23
PaddleOCR 工作环境
-- PaddlePaddle 1.8+ ，推荐使用 PaddlePaddle 2.0rc1
+- PaddlePaddle 2.0.0
- python3.7
- glibc 2.23
- cuDNN 7.6+ (GPU)
@@ -35,11 +35,11 @@ sudo docker container exec -it ppocr /bin/bash
pip3 install --upgrade pip
如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
-python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
如果您的机器是CPU，请运行以下命令安装
-python3 -m pip install paddlepaddle==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
```
diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md
index 80a8fb13f2d04961041211e83ef115842ff7054c..907cf24e1a31104096ab6c0cf0819457852d1490 100644
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@@ -88,6 +88,8 @@ train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单
如果你使用的是icdar2015的公开数据集，PaddleOCR 提供了一份用于训练 icdar2015 数据集的标签文件，通过以下方式下载：
+如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
+
```
# 训练集标签
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
@@ -142,7 +144,7 @@ PaddleOCR内置了一部分字典，可以按需使用。
目前的多语言模型仍处在demo阶段，会持续优化模型并补充语种，**非常欢迎您为我们提供其他语言的字典和字体**，
-如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict) 将语料文件提交至[corpus](../../ppocr/utils/corpus)，我们会在Repo中感谢您。
+如您愿意可将字典文件提交至 [dict](../../ppocr/utils/dict)，我们会在Repo中感谢您。
- 自定义字典
@@ -210,6 +212,9 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
| 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_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
+| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
+| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md
index 68bfd529972183208220b1c87227639d683fea62..77b9642e3b880547b1df6620d931689982db6d29 100755
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@@ -42,8 +42,8 @@ PaddleOCR open-source text recognition algorithms list:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
-- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
-- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
+- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
+- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
@@ -55,5 +55,8 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
+|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
+|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)
diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md
index d78bac9cef983fe1f68cde112158a0ab74e243bf..9b8073709598467c40c07492577b622721a91940 100644
--- a/doc/doc_en/angle_class_en.md
+++ b/doc/doc_en/angle_class_en.md
@@ -70,7 +70,7 @@ Start training:
```
# Set PYTHONPATH path
export PYTHONPATH=$PYTHONPATH:.
-# GPU training Support single card and multi-card training, specify the card number through --gpus. If your paddle version is less than 2.0rc1, please use '--selected_gpus'
+# GPU training Support single card and multi-card training, specify the card number through --gpus.
# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
```
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index 5c4a63e23ed4fe8f6d398bc68dec830bef6c09c9..3ee9092cc6a6f50b19f20df646c9cb1949d5d80f 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -76,7 +76,7 @@ You can also use `-o` to change the training parameters without modifying the ym
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
# multi-GPU training
-# Set the GPU ID used by the '--gpus' parameter; If your paddle version is less than 2.0rc1, please use '--selected_gpus'
+# Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md
index ccbb71847d5946e854b88817a162957af0e6ed00..c8ce1424f5451ca9ee22b9b49ac9b702be72826f 100755
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -25,6 +25,7 @@ Next, we first introduce how to convert a trained model into an inference model,
- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
+ - [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
- [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
- [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
@@ -304,8 +305,23 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```
+
+### 3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
+
+The recognition model based on SRN requires additional setting of the recognition algorithm parameter
+--rec_algorithm="SRN". At the same time, it is necessary to ensure that the predicted shape is consistent
+with the training, such as: --rec_image_shape="1, 64, 256"
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
+ --rec_model_dir="./inference/srn/" \
+ --rec_image_shape="1, 64, 256" \
+ --rec_char_type="en" \
+ --rec_algorithm="SRN"
+```
+
-### 3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
+### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
```
@@ -313,7 +329,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
```
-### 4. MULTILINGAUL MODEL INFERENCE
+### 5. MULTILINGAUL MODEL INFERENCE
If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
diff --git a/doc/doc_en/installation_en.md b/doc/doc_en/installation_en.md
index 7f1f0e83c94e4d4a18b99d620b4b192c47ffde7c..35c1881d12087e6509a68b504729d9ef20240e9c 100644
--- a/doc/doc_en/installation_en.md
+++ b/doc/doc_en/installation_en.md
@@ -3,7 +3,7 @@
After testing, paddleocr can run on glibc 2.23. You can also test other glibc versions or install glic 2.23 for the best compatibility.
PaddleOCR working environment:
-- PaddlePaddle 1.8+, Recommend PaddlePaddle 2.0rc1
+- PaddlePaddle 2.0.0
- python3.7
- glibc 2.23
@@ -38,10 +38,10 @@ sudo docker container exec -it ppocr /bin/bash
pip3 install --upgrade pip
# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
-python3 -m pip install paddlepaddle-gpu==2.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
# If you only have cpu on your machine, please run the following command to install
-python3 -m pip install paddlepaddle==2.0rc1 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
```
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md
index 3eb0cd237801aa62d1c741c177be7b73d9c08808..33033f8348fa4fb08d6e8998ff53cd62349c214e 100644
--- a/doc/doc_en/models_list_en.md
+++ b/doc/doc_en/models_list_en.md
@@ -93,7 +93,7 @@ python3 generate_multi_language_configs.py -l it \
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
| french_mobile_v2.0_rec |Lightweight model for French recognition|[rec_french_lite_train.yml](../../configs/rec/multi_language/rec_french_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_train.tar) |
-| german_mobile_v2.0_rec |Lightweight model for French recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
+| german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
| korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
| japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
| it_mobile_v2.0_rec |Lightweight model for Italian recognition|rec_it_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index 483df5d7ef58337b0b4e6a2453aefb6a2ca80f24..aeb9aa0d43e400c4d6e733b2c9f4a74559dccecb 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -135,7 +135,7 @@ PaddleOCR has built-in dictionaries, which can be used 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**,
-If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) or corpus file to [corpus](../../ppocr/utils/corpus) and we will thank you in the Repo.
+If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo.
To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
@@ -204,6 +204,10 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
| 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_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
+| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
+| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
+
For training Chinese data, it is recommended to use
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
diff --git a/doc/joinus.PNG b/doc/joinus.PNG
index 22258be049c275567953018f26f6d3949297e72e..9029762ef09cab88509c4b53dec37a0ae1a10bd9 100644
Binary files a/doc/joinus.PNG and b/doc/joinus.PNG differ
diff --git a/ppocr/data/__init__.py b/ppocr/data/__init__.py
index 4809886b7bf820f47a4f234a345ea1fac8fa5d49..7cb50d7a62aa3f24811e517768e0635ac7b7321a 100644
--- a/ppocr/data/__init__.py
+++ b/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']
@@ -54,7 +54,7 @@ signal.signal(signal.SIGTERM, term_mp)
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))
diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py
index 6ea4dd8ed6d0f58fbee3362e8eb82a0eda65e812..250ac75e7683df2353d9fad02ef42b9e133681d3 100644
--- a/ppocr/data/imaug/__init__.py
+++ b/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 *
diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py
index 14c1cc9c60989300c86e9965e68c4c663d2425d9..26ac4d818634f83ebbc160d593b73a5684776170 100644
--- a/ppocr/data/imaug/label_ops.py
+++ b/ppocr/data/imaug/label_ops.py
@@ -102,6 +102,8 @@ class BaseRecLabelEncode(object):
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)
@@ -197,16 +199,76 @@ class AttnLabelEncode(BaseRecLabelEncode):
super(AttnLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
+
+ def add_special_char(self, dict_character):
self.beg_str = "sos"
self.end_str = "eos"
+ dict_character = [self.beg_str] + dict_character + [self.end_str]
+ return dict_character
+
+ def __call__(self, data):
+ text = data['label']
+ text = self.encode(text)
+ if text is None:
+ return None
+ if len(text) >= self.max_text_len:
+ return None
+ data['length'] = np.array(len(text))
+ text = [0] + text + [len(self.character) - 1] + [0] * (self.max_text_len
+ - len(text) - 1)
+ 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
+
+
+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 = [self.beg_str, self.end_str] + dict_character
+ dict_character = dict_character + [self.beg_str, self.end_str]
return dict_character
- def __call__(self, text):
+ def __call__(self, data):
+ text = data['label']
text = self.encode(text)
- return 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":
diff --git a/ppocr/data/imaug/rec_img_aug.py b/ppocr/data/imaug/rec_img_aug.py
index 2ccb2d1d2b6780138098f08c78cce3be3e3b9ceb..28e6bd0bce768c45dbc334c15ace601fd6403f5d 100644
--- a/ppocr/data/imaug/rec_img_aug.py
+++ b/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
diff --git a/ppocr/data/lmdb_dataset.py b/ppocr/data/lmdb_dataset.py
index bd0630f6351d4e9e860f21b18f6503777a4d8679..e2d6dc9327bf3725d2fb6c32d18c0b71bd6ac408 100644
--- a/ppocr/data/lmdb_dataset.py
+++ b/ppocr/data/lmdb_dataset.py
@@ -20,9 +20,9 @@ import cv2
from .imaug import transform, create_operators
-class LMDBDateSet(Dataset):
+class LMDBDataSet(Dataset):
def __init__(self, config, mode, logger, seed=None):
- super(LMDBDateSet, self).__init__()
+ super(LMDBDataSet, self).__init__()
global_config = config['Global']
dataset_config = config[mode]['dataset']
diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py
index 4673d35cece0af221d41132a34c2932246567383..3881abf7741b8be78306bd070afb11df15606327 100755
--- a/ppocr/losses/__init__.py
+++ b/ppocr/losses/__init__.py
@@ -23,11 +23,16 @@ def build_loss(config):
# rec loss
from .rec_ctc_loss import CTCLoss
+ from .rec_att_loss import AttentionLoss
+ 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', 'AttentionLoss',
+ 'SRNLoss'
+ ]
config = copy.deepcopy(config)
module_name = config.pop('name')
diff --git a/ppocr/losses/rec_att_loss.py b/ppocr/losses/rec_att_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e2f67483c86a45f3aa1feb1e1fac1a5013bfb46
--- /dev/null
+++ b/ppocr/losses/rec_att_loss.py
@@ -0,0 +1,39 @@
+# copyright (c) 2021 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 AttentionLoss(nn.Layer):
+ def __init__(self, **kwargs):
+ super(AttentionLoss, self).__init__()
+ self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none')
+
+ def forward(self, predicts, batch):
+ targets = batch[1].astype("int64")
+ label_lengths = batch[2].astype('int64')
+ batch_size, num_steps, num_classes = predicts.shape[0], predicts.shape[
+ 1], predicts.shape[2]
+ assert len(targets.shape) == len(list(predicts.shape)) - 1, \
+ "The target's shape and inputs's shape is [N, d] and [N, num_steps]"
+
+ inputs = paddle.reshape(predicts, [-1, predicts.shape[-1]])
+ targets = paddle.reshape(targets, [-1])
+
+ return {'loss': paddle.sum(self.loss_func(inputs, targets))}
diff --git a/ppocr/losses/rec_srn_loss.py b/ppocr/losses/rec_srn_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d5b65ebaf1ee135d1fefe8d93ddc3f77985b132
--- /dev/null
+++ b/ppocr/losses/rec_srn_loss.py
@@ -0,0 +1,47 @@
+# 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}
diff --git a/ppocr/metrics/rec_metric.py b/ppocr/metrics/rec_metric.py
index a86fc8382f40b5b73edc7ec8e9d4dbe3e5822283..b3aa9f38f8378eee7104d7e3696b86bede0de903 100644
--- a/ppocr/metrics/rec_metric.py
+++ b/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}
diff --git a/ppocr/modeling/architectures/base_model.py b/ppocr/modeling/architectures/base_model.py
index ab44b53a2bf8214b63954aa867b67a3ed1e05fab..09b6e0346d998e3b90762e6163e8a34b48daff36 100644
--- a/ppocr/modeling/architectures/base_model.py
+++ b/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
diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py
index 43103e53d2413eb63c2831cfd54e91f357b3b496..03c15508a58313b234a72bb3ef47ac27dc3ebb7e 100755
--- a/ppocr/modeling/backbones/__init__.py
+++ b/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
diff --git a/ppocr/modeling/backbones/det_mobilenet_v3.py b/ppocr/modeling/backbones/det_mobilenet_v3.py
index ca3dc0e1630dd768dce8fa79cf2555a77547107a..bb451bbec9327e2624ab0d501a7adf4355dc3407 100755
--- a/ppocr/modeling/backbones/det_mobilenet_v3.py
+++ b/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 = []
@@ -138,7 +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))
@@ -192,10 +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
@@ -282,5 +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
diff --git a/ppocr/modeling/backbones/rec_mobilenet_v3.py b/ppocr/modeling/backbones/rec_mobilenet_v3.py
index bdf4a616d2cf03275ba311cff625a78d0140e442..1ff17159680372b00e6943e180e5fb638b39ec58 100644
--- a/ppocr/modeling/backbones/rec_mobilenet_v3.py
+++ b/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)
diff --git a/ppocr/modeling/backbones/rec_resnet_fpn.py b/ppocr/modeling/backbones/rec_resnet_fpn.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7e876a2bd52a0ea70479c2009a291e4e2f8ce1f
--- /dev/null
+++ b/ppocr/modeling/backbones/rec_resnet_fpn.py
@@ -0,0 +1,307 @@
+#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)
diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py
index 78074709056f4865098600a28e63b54e68eecf80..efe05718506e94a5ae8ad5ff47bcff26d44c1473 100755
--- a/ppocr/modeling/heads/__init__.py
+++ b/ppocr/modeling/heads/__init__.py
@@ -23,10 +23,15 @@ def build_head(config):
# rec head
from .rec_ctc_head import CTCHead
+ from .rec_att_head import AttentionHead
+ from .rec_srn_head import SRNHead
# cls head
from .cls_head import ClsHead
- support_dict = ['DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead']
+ support_dict = [
+ 'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
+ 'SRNHead'
+ ]
module_name = config.pop('name')
assert module_name in support_dict, Exception('head only support {}'.format(
diff --git a/ppocr/modeling/heads/rec_att_head.py b/ppocr/modeling/heads/rec_att_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7cfe1282141d4646bf3c410d4b0f9a3e94d28fb
--- /dev/null
+++ b/ppocr/modeling/heads/rec_att_head.py
@@ -0,0 +1,199 @@
+# copyright (c) 2021 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
+import paddle.nn as nn
+import paddle.nn.functional as F
+import numpy as np
+
+
+class AttentionHead(nn.Layer):
+ def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
+ super(AttentionHead, self).__init__()
+ self.input_size = in_channels
+ self.hidden_size = hidden_size
+ self.num_classes = out_channels
+
+ self.attention_cell = AttentionGRUCell(
+ in_channels, hidden_size, out_channels, use_gru=False)
+ self.generator = nn.Linear(hidden_size, out_channels)
+
+ def _char_to_onehot(self, input_char, onehot_dim):
+ input_ont_hot = F.one_hot(input_char, onehot_dim)
+ return input_ont_hot
+
+ def forward(self, inputs, targets=None, batch_max_length=25):
+ batch_size = inputs.shape[0]
+ num_steps = batch_max_length
+
+ hidden = paddle.zeros((batch_size, self.hidden_size))
+ output_hiddens = []
+
+ if targets is not None:
+ for i in range(num_steps):
+ char_onehots = self._char_to_onehot(
+ targets[:, i], onehot_dim=self.num_classes)
+ (outputs, hidden), alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+ output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
+ output = paddle.concat(output_hiddens, axis=1)
+ probs = self.generator(output)
+
+ else:
+ targets = paddle.zeros(shape=[batch_size], dtype="int32")
+ probs = None
+
+ for i in range(num_steps):
+ char_onehots = self._char_to_onehot(
+ targets, onehot_dim=self.num_classes)
+ (outputs, hidden), alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+ probs_step = self.generator(outputs)
+ if probs is None:
+ probs = paddle.unsqueeze(probs_step, axis=1)
+ else:
+ probs = paddle.concat(
+ [probs, paddle.unsqueeze(
+ probs_step, axis=1)], axis=1)
+ next_input = probs_step.argmax(axis=1)
+ targets = next_input
+
+ return probs
+
+
+class AttentionGRUCell(nn.Layer):
+ def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
+ super(AttentionGRUCell, self).__init__()
+ self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
+ self.h2h = nn.Linear(hidden_size, hidden_size)
+ self.score = nn.Linear(hidden_size, 1, bias_attr=False)
+
+ self.rnn = nn.GRUCell(
+ input_size=input_size + num_embeddings, hidden_size=hidden_size)
+
+ self.hidden_size = hidden_size
+
+ def forward(self, prev_hidden, batch_H, char_onehots):
+
+ batch_H_proj = self.i2h(batch_H)
+ prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1)
+
+ res = paddle.add(batch_H_proj, prev_hidden_proj)
+ res = paddle.tanh(res)
+ e = self.score(res)
+
+ alpha = F.softmax(e, axis=1)
+ alpha = paddle.transpose(alpha, [0, 2, 1])
+ context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
+ concat_context = paddle.concat([context, char_onehots], 1)
+
+ cur_hidden = self.rnn(concat_context, prev_hidden)
+
+ return cur_hidden, alpha
+
+
+class AttentionLSTM(nn.Layer):
+ def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
+ super(AttentionLSTM, self).__init__()
+ self.input_size = in_channels
+ self.hidden_size = hidden_size
+ self.num_classes = out_channels
+
+ self.attention_cell = AttentionLSTMCell(
+ in_channels, hidden_size, out_channels, use_gru=False)
+ self.generator = nn.Linear(hidden_size, out_channels)
+
+ def _char_to_onehot(self, input_char, onehot_dim):
+ input_ont_hot = F.one_hot(input_char, onehot_dim)
+ return input_ont_hot
+
+ def forward(self, inputs, targets=None, batch_max_length=25):
+ batch_size = inputs.shape[0]
+ num_steps = batch_max_length
+
+ hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros(
+ (batch_size, self.hidden_size)))
+ output_hiddens = []
+
+ if targets is not None:
+ for i in range(num_steps):
+ # one-hot vectors for a i-th char
+ char_onehots = self._char_to_onehot(
+ targets[:, i], onehot_dim=self.num_classes)
+ hidden, alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+
+ hidden = (hidden[1][0], hidden[1][1])
+ output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1))
+ output = paddle.concat(output_hiddens, axis=1)
+ probs = self.generator(output)
+
+ else:
+ targets = paddle.zeros(shape=[batch_size], dtype="int32")
+ probs = None
+
+ for i in range(num_steps):
+ char_onehots = self._char_to_onehot(
+ targets, onehot_dim=self.num_classes)
+ hidden, alpha = self.attention_cell(hidden, inputs,
+ char_onehots)
+ probs_step = self.generator(hidden[0])
+ hidden = (hidden[1][0], hidden[1][1])
+ if probs is None:
+ probs = paddle.unsqueeze(probs_step, axis=1)
+ else:
+ probs = paddle.concat(
+ [probs, paddle.unsqueeze(
+ probs_step, axis=1)], axis=1)
+
+ next_input = probs_step.argmax(axis=1)
+
+ targets = next_input
+
+ return probs
+
+
+class AttentionLSTMCell(nn.Layer):
+ def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
+ super(AttentionLSTMCell, self).__init__()
+ self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
+ self.h2h = nn.Linear(hidden_size, hidden_size)
+ self.score = nn.Linear(hidden_size, 1, bias_attr=False)
+ if not use_gru:
+ self.rnn = nn.LSTMCell(
+ input_size=input_size + num_embeddings, hidden_size=hidden_size)
+ else:
+ self.rnn = nn.GRUCell(
+ input_size=input_size + num_embeddings, hidden_size=hidden_size)
+
+ self.hidden_size = hidden_size
+
+ def forward(self, prev_hidden, batch_H, char_onehots):
+ batch_H_proj = self.i2h(batch_H)
+ prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1)
+ res = paddle.add(batch_H_proj, prev_hidden_proj)
+ res = paddle.tanh(res)
+ e = self.score(res)
+
+ alpha = F.softmax(e, axis=1)
+ alpha = paddle.transpose(alpha, [0, 2, 1])
+ context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
+ concat_context = paddle.concat([context, char_onehots], 1)
+ cur_hidden = self.rnn(concat_context, prev_hidden)
+
+ return cur_hidden, alpha
diff --git a/ppocr/modeling/heads/rec_srn_head.py b/ppocr/modeling/heads/rec_srn_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2c7fc028d28c79057708d4e6f306c417ba6306a
--- /dev/null
+++ b/ppocr/modeling/heads/rec_srn_head.py
@@ -0,0 +1,279 @@
+# 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 math
+import paddle
+from paddle import nn, ParamAttr
+from paddle.nn import functional as F
+import paddle.fluid as fluid
+import numpy as np
+from .self_attention import WrapEncoderForFeature
+from .self_attention import WrapEncoder
+from paddle.static import Program
+from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
+import paddle.fluid.framework as framework
+
+from collections import OrderedDict
+gradient_clip = 10
+
+
+class PVAM(nn.Layer):
+ def __init__(self, in_channels, char_num, max_text_length, num_heads,
+ num_encoder_tus, hidden_dims):
+ super(PVAM, self).__init__()
+ self.char_num = char_num
+ self.max_length = max_text_length
+ self.num_heads = num_heads
+ self.num_encoder_TUs = num_encoder_tus
+ self.hidden_dims = hidden_dims
+ # Transformer encoder
+ t = 256
+ c = 512
+ self.wrap_encoder_for_feature = WrapEncoderForFeature(
+ src_vocab_size=1,
+ max_length=t,
+ n_layer=self.num_encoder_TUs,
+ n_head=self.num_heads,
+ d_key=int(self.hidden_dims / self.num_heads),
+ d_value=int(self.hidden_dims / self.num_heads),
+ d_model=self.hidden_dims,
+ d_inner_hid=self.hidden_dims,
+ prepostprocess_dropout=0.1,
+ attention_dropout=0.1,
+ relu_dropout=0.1,
+ preprocess_cmd="n",
+ postprocess_cmd="da",
+ weight_sharing=True)
+
+ # PVAM
+ self.flatten0 = paddle.nn.Flatten(start_axis=0, stop_axis=1)
+ self.fc0 = paddle.nn.Linear(
+ in_features=in_channels,
+ out_features=in_channels, )
+ self.emb = paddle.nn.Embedding(
+ num_embeddings=self.max_length, embedding_dim=in_channels)
+ self.flatten1 = paddle.nn.Flatten(start_axis=0, stop_axis=2)
+ self.fc1 = paddle.nn.Linear(
+ in_features=in_channels, out_features=1, bias_attr=False)
+
+ def forward(self, inputs, encoder_word_pos, gsrm_word_pos):
+ b, c, h, w = inputs.shape
+ conv_features = paddle.reshape(inputs, shape=[-1, c, h * w])
+ conv_features = paddle.transpose(conv_features, perm=[0, 2, 1])
+ # transformer encoder
+ b, t, c = conv_features.shape
+
+ enc_inputs = [conv_features, encoder_word_pos, None]
+ word_features = self.wrap_encoder_for_feature(enc_inputs)
+
+ # pvam
+ b, t, c = word_features.shape
+ word_features = self.fc0(word_features)
+ word_features_ = paddle.reshape(word_features, [-1, 1, t, c])
+ word_features_ = paddle.tile(word_features_, [1, self.max_length, 1, 1])
+ word_pos_feature = self.emb(gsrm_word_pos)
+ word_pos_feature_ = paddle.reshape(word_pos_feature,
+ [-1, self.max_length, 1, c])
+ word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1])
+ y = word_pos_feature_ + word_features_
+ y = F.tanh(y)
+ attention_weight = self.fc1(y)
+ attention_weight = paddle.reshape(
+ attention_weight, shape=[-1, self.max_length, t])
+ attention_weight = F.softmax(attention_weight, axis=-1)
+ pvam_features = paddle.matmul(attention_weight,
+ word_features) #[b, max_length, c]
+ return pvam_features
+
+
+class GSRM(nn.Layer):
+ def __init__(self, in_channels, char_num, max_text_length, num_heads,
+ num_encoder_tus, num_decoder_tus, hidden_dims):
+ super(GSRM, self).__init__()
+ self.char_num = char_num
+ self.max_length = max_text_length
+ self.num_heads = num_heads
+ self.num_encoder_TUs = num_encoder_tus
+ self.num_decoder_TUs = num_decoder_tus
+ self.hidden_dims = hidden_dims
+
+ self.fc0 = paddle.nn.Linear(
+ in_features=in_channels, out_features=self.char_num)
+ self.wrap_encoder0 = WrapEncoder(
+ src_vocab_size=self.char_num + 1,
+ max_length=self.max_length,
+ n_layer=self.num_decoder_TUs,
+ n_head=self.num_heads,
+ d_key=int(self.hidden_dims / self.num_heads),
+ d_value=int(self.hidden_dims / self.num_heads),
+ d_model=self.hidden_dims,
+ d_inner_hid=self.hidden_dims,
+ prepostprocess_dropout=0.1,
+ attention_dropout=0.1,
+ relu_dropout=0.1,
+ preprocess_cmd="n",
+ postprocess_cmd="da",
+ weight_sharing=True)
+
+ self.wrap_encoder1 = WrapEncoder(
+ src_vocab_size=self.char_num + 1,
+ max_length=self.max_length,
+ n_layer=self.num_decoder_TUs,
+ n_head=self.num_heads,
+ d_key=int(self.hidden_dims / self.num_heads),
+ d_value=int(self.hidden_dims / self.num_heads),
+ d_model=self.hidden_dims,
+ d_inner_hid=self.hidden_dims,
+ prepostprocess_dropout=0.1,
+ attention_dropout=0.1,
+ relu_dropout=0.1,
+ preprocess_cmd="n",
+ postprocess_cmd="da",
+ weight_sharing=True)
+
+ self.mul = lambda x: paddle.matmul(x=x,
+ y=self.wrap_encoder0.prepare_decoder.emb0.weight,
+ transpose_y=True)
+
+ def forward(self, inputs, gsrm_word_pos, gsrm_slf_attn_bias1,
+ gsrm_slf_attn_bias2):
+ # ===== GSRM Visual-to-semantic embedding block =====
+ b, t, c = inputs.shape
+ pvam_features = paddle.reshape(inputs, [-1, c])
+ word_out = self.fc0(pvam_features)
+ word_ids = paddle.argmax(F.softmax(word_out), axis=1)
+ word_ids = paddle.reshape(x=word_ids, shape=[-1, t, 1])
+
+ #===== GSRM Semantic reasoning block =====
+ """
+ This module is achieved through bi-transformers,
+ ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
+ """
+ pad_idx = self.char_num
+
+ word1 = paddle.cast(word_ids, "float32")
+ word1 = F.pad(word1, [1, 0], value=1.0 * pad_idx, data_format="NLC")
+ word1 = paddle.cast(word1, "int64")
+ word1 = word1[:, :-1, :]
+ word2 = word_ids
+
+ enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1]
+ enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2]
+
+ gsrm_feature1 = self.wrap_encoder0(enc_inputs_1)
+ gsrm_feature2 = self.wrap_encoder1(enc_inputs_2)
+
+ gsrm_feature2 = F.pad(gsrm_feature2, [0, 1],
+ value=0.,
+ data_format="NLC")
+ gsrm_feature2 = gsrm_feature2[:, 1:, ]
+ gsrm_features = gsrm_feature1 + gsrm_feature2
+
+ gsrm_out = self.mul(gsrm_features)
+
+ b, t, c = gsrm_out.shape
+ gsrm_out = paddle.reshape(gsrm_out, [-1, c])
+
+ return gsrm_features, word_out, gsrm_out
+
+
+class VSFD(nn.Layer):
+ def __init__(self, in_channels=512, pvam_ch=512, char_num=38):
+ super(VSFD, self).__init__()
+ self.char_num = char_num
+ self.fc0 = paddle.nn.Linear(
+ in_features=in_channels * 2, out_features=pvam_ch)
+ self.fc1 = paddle.nn.Linear(
+ in_features=pvam_ch, out_features=self.char_num)
+
+ def forward(self, pvam_feature, gsrm_feature):
+ b, t, c1 = pvam_feature.shape
+ b, t, c2 = gsrm_feature.shape
+ combine_feature_ = paddle.concat([pvam_feature, gsrm_feature], axis=2)
+ img_comb_feature_ = paddle.reshape(
+ combine_feature_, shape=[-1, c1 + c2])
+ img_comb_feature_map = self.fc0(img_comb_feature_)
+ img_comb_feature_map = F.sigmoid(img_comb_feature_map)
+ img_comb_feature_map = paddle.reshape(
+ img_comb_feature_map, shape=[-1, t, c1])
+ combine_feature = img_comb_feature_map * pvam_feature + (
+ 1.0 - img_comb_feature_map) * gsrm_feature
+ img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1])
+
+ out = self.fc1(img_comb_feature)
+ return out
+
+
+class SRNHead(nn.Layer):
+ def __init__(self, in_channels, out_channels, max_text_length, num_heads,
+ num_encoder_TUs, num_decoder_TUs, hidden_dims, **kwargs):
+ super(SRNHead, self).__init__()
+ self.char_num = out_channels
+ self.max_length = max_text_length
+ self.num_heads = num_heads
+ self.num_encoder_TUs = num_encoder_TUs
+ self.num_decoder_TUs = num_decoder_TUs
+ self.hidden_dims = hidden_dims
+
+ self.pvam = PVAM(
+ in_channels=in_channels,
+ char_num=self.char_num,
+ max_text_length=self.max_length,
+ num_heads=self.num_heads,
+ num_encoder_tus=self.num_encoder_TUs,
+ hidden_dims=self.hidden_dims)
+
+ self.gsrm = GSRM(
+ in_channels=in_channels,
+ char_num=self.char_num,
+ max_text_length=self.max_length,
+ num_heads=self.num_heads,
+ num_encoder_tus=self.num_encoder_TUs,
+ num_decoder_tus=self.num_decoder_TUs,
+ hidden_dims=self.hidden_dims)
+ self.vsfd = VSFD(in_channels=in_channels, char_num=self.char_num)
+
+ self.gsrm.wrap_encoder1.prepare_decoder.emb0 = self.gsrm.wrap_encoder0.prepare_decoder.emb0
+
+ def forward(self, inputs, others):
+ encoder_word_pos = others[0]
+ gsrm_word_pos = others[1]
+ gsrm_slf_attn_bias1 = others[2]
+ gsrm_slf_attn_bias2 = others[3]
+
+ pvam_feature = self.pvam(inputs, encoder_word_pos, gsrm_word_pos)
+
+ gsrm_feature, word_out, gsrm_out = self.gsrm(
+ pvam_feature, gsrm_word_pos, gsrm_slf_attn_bias1,
+ gsrm_slf_attn_bias2)
+
+ final_out = self.vsfd(pvam_feature, gsrm_feature)
+ if not self.training:
+ final_out = F.softmax(final_out, axis=1)
+
+ _, decoded_out = paddle.topk(final_out, k=1)
+
+ predicts = OrderedDict([
+ ('predict', final_out),
+ ('pvam_feature', pvam_feature),
+ ('decoded_out', decoded_out),
+ ('word_out', word_out),
+ ('gsrm_out', gsrm_out),
+ ])
+
+ return predicts
diff --git a/ppocr/modeling/heads/self_attention.py b/ppocr/modeling/heads/self_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..51d5198f558dcb7e0351f04b3a884b71707104d4
--- /dev/null
+++ b/ppocr/modeling/heads/self_attention.py
@@ -0,0 +1,409 @@
+# 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 math
+
+import paddle
+from paddle import ParamAttr, nn
+from paddle import nn, ParamAttr
+from paddle.nn import functional as F
+import paddle.fluid as fluid
+import numpy as np
+gradient_clip = 10
+
+
+class WrapEncoderForFeature(nn.Layer):
+ def __init__(self,
+ src_vocab_size,
+ max_length,
+ n_layer,
+ n_head,
+ d_key,
+ d_value,
+ d_model,
+ d_inner_hid,
+ prepostprocess_dropout,
+ attention_dropout,
+ relu_dropout,
+ preprocess_cmd,
+ postprocess_cmd,
+ weight_sharing,
+ bos_idx=0):
+ super(WrapEncoderForFeature, self).__init__()
+
+ self.prepare_encoder = PrepareEncoder(
+ src_vocab_size,
+ d_model,
+ max_length,
+ prepostprocess_dropout,
+ bos_idx=bos_idx,
+ word_emb_param_name="src_word_emb_table")
+ self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
+ d_inner_hid, prepostprocess_dropout,
+ attention_dropout, relu_dropout, preprocess_cmd,
+ postprocess_cmd)
+
+ def forward(self, enc_inputs):
+ conv_features, src_pos, src_slf_attn_bias = enc_inputs
+ enc_input = self.prepare_encoder(conv_features, src_pos)
+ enc_output = self.encoder(enc_input, src_slf_attn_bias)
+ return enc_output
+
+
+class WrapEncoder(nn.Layer):
+ """
+ embedder + encoder
+ """
+
+ def __init__(self,
+ src_vocab_size,
+ max_length,
+ n_layer,
+ n_head,
+ d_key,
+ d_value,
+ d_model,
+ d_inner_hid,
+ prepostprocess_dropout,
+ attention_dropout,
+ relu_dropout,
+ preprocess_cmd,
+ postprocess_cmd,
+ weight_sharing,
+ bos_idx=0):
+ super(WrapEncoder, self).__init__()
+
+ self.prepare_decoder = PrepareDecoder(
+ src_vocab_size,
+ d_model,
+ max_length,
+ prepostprocess_dropout,
+ bos_idx=bos_idx)
+ self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
+ d_inner_hid, prepostprocess_dropout,
+ attention_dropout, relu_dropout, preprocess_cmd,
+ postprocess_cmd)
+
+ def forward(self, enc_inputs):
+ src_word, src_pos, src_slf_attn_bias = enc_inputs
+ enc_input = self.prepare_decoder(src_word, src_pos)
+ enc_output = self.encoder(enc_input, src_slf_attn_bias)
+ return enc_output
+
+
+class Encoder(nn.Layer):
+ """
+ encoder
+ """
+
+ def __init__(self,
+ n_layer,
+ n_head,
+ d_key,
+ d_value,
+ d_model,
+ d_inner_hid,
+ prepostprocess_dropout,
+ attention_dropout,
+ relu_dropout,
+ preprocess_cmd="n",
+ postprocess_cmd="da"):
+
+ super(Encoder, self).__init__()
+
+ self.encoder_layers = list()
+ for i in range(n_layer):
+ self.encoder_layers.append(
+ self.add_sublayer(
+ "layer_%d" % i,
+ EncoderLayer(n_head, d_key, d_value, d_model, d_inner_hid,
+ prepostprocess_dropout, attention_dropout,
+ relu_dropout, preprocess_cmd,
+ postprocess_cmd)))
+ self.processer = PrePostProcessLayer(preprocess_cmd, d_model,
+ prepostprocess_dropout)
+
+ def forward(self, enc_input, attn_bias):
+ for encoder_layer in self.encoder_layers:
+ enc_output = encoder_layer(enc_input, attn_bias)
+ enc_input = enc_output
+ enc_output = self.processer(enc_output)
+ return enc_output
+
+
+class EncoderLayer(nn.Layer):
+ """
+ EncoderLayer
+ """
+
+ def __init__(self,
+ n_head,
+ d_key,
+ d_value,
+ d_model,
+ d_inner_hid,
+ prepostprocess_dropout,
+ attention_dropout,
+ relu_dropout,
+ preprocess_cmd="n",
+ postprocess_cmd="da"):
+
+ super(EncoderLayer, self).__init__()
+ self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
+ prepostprocess_dropout)
+ self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
+ attention_dropout)
+ self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
+ prepostprocess_dropout)
+
+ self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
+ prepostprocess_dropout)
+ self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
+ self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
+ prepostprocess_dropout)
+
+ def forward(self, enc_input, attn_bias):
+ attn_output = self.self_attn(
+ self.preprocesser1(enc_input), None, None, attn_bias)
+ attn_output = self.postprocesser1(attn_output, enc_input)
+ ffn_output = self.ffn(self.preprocesser2(attn_output))
+ ffn_output = self.postprocesser2(ffn_output, attn_output)
+ return ffn_output
+
+
+class MultiHeadAttention(nn.Layer):
+ """
+ Multi-Head Attention
+ """
+
+ def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.):
+ super(MultiHeadAttention, self).__init__()
+ self.n_head = n_head
+ self.d_key = d_key
+ self.d_value = d_value
+ self.d_model = d_model
+ self.dropout_rate = dropout_rate
+ self.q_fc = paddle.nn.Linear(
+ in_features=d_model, out_features=d_key * n_head, bias_attr=False)
+ self.k_fc = paddle.nn.Linear(
+ in_features=d_model, out_features=d_key * n_head, bias_attr=False)
+ self.v_fc = paddle.nn.Linear(
+ in_features=d_model, out_features=d_value * n_head, bias_attr=False)
+ self.proj_fc = paddle.nn.Linear(
+ in_features=d_value * n_head, out_features=d_model, bias_attr=False)
+
+ def _prepare_qkv(self, queries, keys, values, cache=None):
+ if keys is None: # self-attention
+ keys, values = queries, queries
+ static_kv = False
+ else: # cross-attention
+ static_kv = True
+
+ q = self.q_fc(queries)
+ q = paddle.reshape(x=q, shape=[0, 0, self.n_head, self.d_key])
+ q = paddle.transpose(x=q, perm=[0, 2, 1, 3])
+
+ if cache is not None and static_kv and "static_k" in cache:
+ # for encoder-decoder attention in inference and has cached
+ k = cache["static_k"]
+ v = cache["static_v"]
+ else:
+ k = self.k_fc(keys)
+ v = self.v_fc(values)
+ k = paddle.reshape(x=k, shape=[0, 0, self.n_head, self.d_key])
+ k = paddle.transpose(x=k, perm=[0, 2, 1, 3])
+ v = paddle.reshape(x=v, shape=[0, 0, self.n_head, self.d_value])
+ v = paddle.transpose(x=v, perm=[0, 2, 1, 3])
+
+ if cache is not None:
+ if static_kv and not "static_k" in cache:
+ # for encoder-decoder attention in inference and has not cached
+ cache["static_k"], cache["static_v"] = k, v
+ elif not static_kv:
+ # for decoder self-attention in inference
+ cache_k, cache_v = cache["k"], cache["v"]
+ k = paddle.concat([cache_k, k], axis=2)
+ v = paddle.concat([cache_v, v], axis=2)
+ cache["k"], cache["v"] = k, v
+
+ return q, k, v
+
+ def forward(self, queries, keys, values, attn_bias, cache=None):
+ # compute q ,k ,v
+ keys = queries if keys is None else keys
+ values = keys if values is None else values
+ q, k, v = self._prepare_qkv(queries, keys, values, cache)
+
+ # scale dot product attention
+ product = paddle.matmul(x=q, y=k, transpose_y=True)
+ product = product * self.d_model**-0.5
+ if attn_bias is not None:
+ product += attn_bias
+ weights = F.softmax(product)
+ if self.dropout_rate:
+ weights = F.dropout(
+ weights, p=self.dropout_rate, mode="downscale_in_infer")
+ out = paddle.matmul(weights, v)
+
+ # combine heads
+ out = paddle.transpose(out, perm=[0, 2, 1, 3])
+ out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
+
+ # project to output
+ out = self.proj_fc(out)
+
+ return out
+
+
+class PrePostProcessLayer(nn.Layer):
+ """
+ PrePostProcessLayer
+ """
+
+ def __init__(self, process_cmd, d_model, dropout_rate):
+ super(PrePostProcessLayer, self).__init__()
+ self.process_cmd = process_cmd
+ self.functors = []
+ for cmd in self.process_cmd:
+ if cmd == "a": # add residual connection
+ self.functors.append(lambda x, y: x + y if y is not None else x)
+ elif cmd == "n": # add layer normalization
+ self.functors.append(
+ self.add_sublayer(
+ "layer_norm_%d" % len(
+ self.sublayers(include_sublayers=False)),
+ paddle.nn.LayerNorm(
+ normalized_shape=d_model,
+ weight_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Constant(1.)),
+ bias_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Constant(0.)))))
+ elif cmd == "d": # add dropout
+ self.functors.append(lambda x: F.dropout(
+ x, p=dropout_rate, mode="downscale_in_infer")
+ if dropout_rate else x)
+
+ def forward(self, x, residual=None):
+ for i, cmd in enumerate(self.process_cmd):
+ if cmd == "a":
+ x = self.functors[i](x, residual)
+ else:
+ x = self.functors[i](x)
+ return x
+
+
+class PrepareEncoder(nn.Layer):
+ def __init__(self,
+ src_vocab_size,
+ src_emb_dim,
+ src_max_len,
+ dropout_rate=0,
+ bos_idx=0,
+ word_emb_param_name=None,
+ pos_enc_param_name=None):
+ super(PrepareEncoder, self).__init__()
+ self.src_emb_dim = src_emb_dim
+ self.src_max_len = src_max_len
+ self.emb = paddle.nn.Embedding(
+ num_embeddings=self.src_max_len,
+ embedding_dim=self.src_emb_dim,
+ sparse=True)
+ self.dropout_rate = dropout_rate
+
+ def forward(self, src_word, src_pos):
+ src_word_emb = src_word
+ src_word_emb = fluid.layers.cast(src_word_emb, 'float32')
+ src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
+ src_pos = paddle.squeeze(src_pos, axis=-1)
+ src_pos_enc = self.emb(src_pos)
+ src_pos_enc.stop_gradient = True
+ enc_input = src_word_emb + src_pos_enc
+ if self.dropout_rate:
+ out = F.dropout(
+ x=enc_input, p=self.dropout_rate, mode="downscale_in_infer")
+ else:
+ out = enc_input
+ return out
+
+
+class PrepareDecoder(nn.Layer):
+ def __init__(self,
+ src_vocab_size,
+ src_emb_dim,
+ src_max_len,
+ dropout_rate=0,
+ bos_idx=0,
+ word_emb_param_name=None,
+ pos_enc_param_name=None):
+ super(PrepareDecoder, self).__init__()
+ self.src_emb_dim = src_emb_dim
+ """
+ self.emb0 = Embedding(num_embeddings=src_vocab_size,
+ embedding_dim=src_emb_dim)
+ """
+ self.emb0 = paddle.nn.Embedding(
+ num_embeddings=src_vocab_size,
+ embedding_dim=self.src_emb_dim,
+ padding_idx=bos_idx,
+ weight_attr=paddle.ParamAttr(
+ name=word_emb_param_name,
+ initializer=nn.initializer.Normal(0., src_emb_dim**-0.5)))
+ self.emb1 = paddle.nn.Embedding(
+ num_embeddings=src_max_len,
+ embedding_dim=self.src_emb_dim,
+ weight_attr=paddle.ParamAttr(name=pos_enc_param_name))
+ self.dropout_rate = dropout_rate
+
+ def forward(self, src_word, src_pos):
+ src_word = fluid.layers.cast(src_word, 'int64')
+ src_word = paddle.squeeze(src_word, axis=-1)
+ src_word_emb = self.emb0(src_word)
+ src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
+ src_pos = paddle.squeeze(src_pos, axis=-1)
+ src_pos_enc = self.emb1(src_pos)
+ src_pos_enc.stop_gradient = True
+ enc_input = src_word_emb + src_pos_enc
+ if self.dropout_rate:
+ out = F.dropout(
+ x=enc_input, p=self.dropout_rate, mode="downscale_in_infer")
+ else:
+ out = enc_input
+ return out
+
+
+class FFN(nn.Layer):
+ """
+ Feed-Forward Network
+ """
+
+ def __init__(self, d_inner_hid, d_model, dropout_rate):
+ super(FFN, self).__init__()
+ self.dropout_rate = dropout_rate
+ self.fc1 = paddle.nn.Linear(
+ in_features=d_model, out_features=d_inner_hid)
+ self.fc2 = paddle.nn.Linear(
+ in_features=d_inner_hid, out_features=d_model)
+
+ def forward(self, x):
+ hidden = self.fc1(x)
+ hidden = F.relu(hidden)
+ if self.dropout_rate:
+ hidden = F.dropout(
+ hidden, p=self.dropout_rate, mode="downscale_in_infer")
+ out = self.fc2(hidden)
+ return out
diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py
index c9b42e08391d0405a58a071460eb6f57bbb62c43..0156e438e9e24820943c9e48b04565710ea2fd4b 100644
--- a/ppocr/postprocess/__init__.py
+++ b/ppocr/postprocess/__init__.py
@@ -26,11 +26,12 @@ def build_post_process(config, global_config=None):
from .db_postprocess import DBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
- from .rec_postprocess import CTCLabelDecode, AttnLabelDecode
+ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
from .cls_postprocess import ClsPostProcess
support_dict = [
- 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess'
+ 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
+ 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
]
config = copy.deepcopy(config)
diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py
index 65ed467191caa7f2093859ff35a20a9ba6a9a08e..af243caa44e8390657b7a95e971aede0c0f90edd 100644
--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -33,6 +33,9 @@ class BaseRecLabelDecode(object):
assert character_type in support_character_type, "Only {} are supported now but get {}".format(
support_character_type, character_type)
+ self.beg_str = "sos"
+ self.end_str = "eos"
+
if character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
@@ -109,7 +112,6 @@ class CTCLabelDecode(BaseRecLabelDecode):
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
-
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
@@ -133,16 +135,143 @@ class AttnLabelDecode(BaseRecLabelDecode):
**kwargs):
super(AttnLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
- self.beg_str = "sos"
- self.end_str = "eos"
def add_special_char(self, dict_character):
- dict_character = [self.beg_str, self.end_str] + dict_character
+ self.beg_str = "sos"
+ self.end_str = "eos"
+ dict_character = dict_character
+ dict_character = [self.beg_str] + dict_character + [self.end_str]
return dict_character
- def __call__(self, text):
+ def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+ """ convert text-index into text-label. """
+ result_list = []
+ ignored_tokens = self.get_ignored_tokens()
+ [beg_idx, end_idx] = self.get_ignored_tokens()
+ batch_size = len(text_index)
+ for batch_idx in range(batch_size):
+ char_list = []
+ conf_list = []
+ for idx in range(len(text_index[batch_idx])):
+ if text_index[batch_idx][idx] in ignored_tokens:
+ continue
+ if int(text_index[batch_idx][idx]) == int(end_idx):
+ break
+ if is_remove_duplicate:
+ # only for predict
+ if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+ batch_idx][idx]:
+ continue
+ char_list.append(self.character[int(text_index[batch_idx][
+ idx])])
+ if text_prob is not None:
+ conf_list.append(text_prob[batch_idx][idx])
+ else:
+ conf_list.append(1)
+ text = ''.join(char_list)
+ result_list.append((text, np.mean(conf_list)))
+ return result_list
+
+ def __call__(self, preds, label=None, *args, **kwargs):
+ """
text = self.decode(text)
- return text
+ if label is None:
+ return text
+ else:
+ label = self.decode(label, is_remove_duplicate=False)
+ return text, label
+ """
+ if isinstance(preds, paddle.Tensor):
+ preds = preds.numpy()
+
+ preds_idx = preds.argmax(axis=2)
+ preds_prob = preds.max(axis=2)
+ text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+ if label is None:
+ return text
+ label = self.decode(label, is_remove_duplicate=False)
+ return text, label
+
+ 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
+
+
+class SRNLabelDecode(BaseRecLabelDecode):
+ """ Convert between text-label and text-index """
+
+ def __init__(self,
+ character_dict_path=None,
+ character_type='en',
+ use_space_char=False,
+ **kwargs):
+ super(SRNLabelDecode, self).__init__(character_dict_path,
+ character_type, use_space_char)
+
+ def __call__(self, preds, label=None, *args, **kwargs):
+ pred = preds['predict']
+ char_num = len(self.character_str) + 2
+ if isinstance(pred, paddle.Tensor):
+ pred = pred.numpy()
+ pred = np.reshape(pred, [-1, char_num])
+
+ preds_idx = np.argmax(pred, axis=1)
+ preds_prob = np.max(pred, axis=1)
+
+ preds_idx = np.reshape(preds_idx, [-1, 25])
+
+ preds_prob = np.reshape(preds_prob, [-1, 25])
+
+ text = self.decode(preds_idx, preds_prob)
+
+ if label is None:
+ text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
+ return text
+ label = self.decode(label)
+ return text, label
+
+ def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+ """ convert text-index into text-label. """
+ result_list = []
+ ignored_tokens = self.get_ignored_tokens()
+ batch_size = len(text_index)
+
+ for batch_idx in range(batch_size):
+ char_list = []
+ conf_list = []
+ for idx in range(len(text_index[batch_idx])):
+ if text_index[batch_idx][idx] in ignored_tokens:
+ continue
+ if is_remove_duplicate:
+ # only for predict
+ if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+ batch_idx][idx]:
+ continue
+ char_list.append(self.character[int(text_index[batch_idx][
+ idx])])
+ if text_prob is not None:
+ conf_list.append(text_prob[batch_idx][idx])
+ else:
+ conf_list.append(1)
+
+ text = ''.join(char_list)
+ result_list.append((text, np.mean(conf_list)))
+ return result_list
+
+ def add_special_char(self, dict_character):
+ dict_character = dict_character + [self.beg_str, self.end_str]
+ return dict_character
def get_ignored_tokens(self):
beg_idx = self.get_beg_end_flag_idx("beg")
diff --git a/tools/export_model.py b/tools/export_model.py
index a9b9e7dd5145e46eb4094da8e0c65e4678f0818a..1e9526e03d6b9001249d5891c37bee071c1f36a3 100755
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -31,6 +31,14 @@ from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser
+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()
+
+
def main():
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
@@ -52,23 +60,39 @@ def main():
save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
- infer_shape = [3, -1, -1]
- if config['Architecture']['model_type'] == "rec":
- infer_shape = [3, 32, -1] # for rec model, H must be 32
- if 'Transform' in config['Architecture'] and config['Architecture'][
- 'Transform'] is not None and config['Architecture'][
- 'Transform']['name'] == 'TPS':
- logger.info(
- 'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
- )
- infer_shape[-1] = 100
-
- model = to_static(
- model,
- input_spec=[
+ if config['Architecture']['algorithm'] == "SRN":
+ other_shape = [
paddle.static.InputSpec(
- shape=[None] + infer_shape, dtype='float32')
- ])
+ shape=[None, 1, 64, 256], dtype='float32'), [
+ paddle.static.InputSpec(
+ shape=[None, 256, 1],
+ dtype="int64"), paddle.static.InputSpec(
+ shape=[None, 25, 1],
+ dtype="int64"), paddle.static.InputSpec(
+ shape=[None, 8, 25, 25], dtype="int64"),
+ paddle.static.InputSpec(
+ shape=[None, 8, 25, 25], dtype="int64")
+ ]
+ ]
+ model = to_static(model, input_spec=other_shape)
+ else:
+ infer_shape = [3, -1, -1]
+ if config['Architecture']['model_type'] == "rec":
+ infer_shape = [3, 32, -1] # for rec model, H must be 32
+ if 'Transform' in config['Architecture'] and config['Architecture'][
+ 'Transform'] is not None and config['Architecture'][
+ 'Transform']['name'] == 'TPS':
+ logger.info(
+ 'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
+ )
+ infer_shape[-1] = 100
+ model = to_static(
+ model,
+ input_spec=[
+ paddle.static.InputSpec(
+ shape=[None] + infer_shape, dtype='float32')
+ ])
+
paddle.jit.save(model, save_path)
logger.info('inference model is saved to {}'.format(save_path))
diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index 974fdbb6c7f4d33bd39e818945be480d858c0d09..fd895e50719941877fd620cab929a20c7d88b8e5 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -25,6 +25,7 @@ import numpy as np
import math
import time
import traceback
+import paddle
import tools.infer.utility as utility
from ppocr.postprocess import build_post_process
@@ -46,6 +47,13 @@ class TextRecognizer(object):
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
+ if self.rec_algorithm == "SRN":
+ postprocess_params = {
+ 'name': 'SRNLabelDecode',
+ "character_type": args.rec_char_type,
+ "character_dict_path": args.rec_char_dict_path,
+ "use_space_char": args.use_space_char
+ }
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'rec', logger)
@@ -70,6 +78,78 @@ class TextRecognizer(object):
padding_im[:, :, 0:resized_w] = resized_image
return padding_im
+ def resize_norm_img_srn(self, 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(self, 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, 1, max_text_length, max_text_length])
+ gsrm_slf_attn_bias1 = np.tile(
+ gsrm_slf_attn_bias1,
+ [1, num_heads, 1, 1]).astype('float32') * [-1e9]
+
+ gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
+ [-1, 1, max_text_length, max_text_length])
+ gsrm_slf_attn_bias2 = np.tile(
+ gsrm_slf_attn_bias2,
+ [1, num_heads, 1, 1]).astype('float32') * [-1e9]
+
+ encoder_word_pos = encoder_word_pos[np.newaxis, :]
+ gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
+
+ return [
+ encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+ gsrm_slf_attn_bias2
+ ]
+
+ def process_image_srn(self, img, image_shape, num_heads, max_text_length):
+ norm_img = self.resize_norm_img_srn(img, image_shape)
+ norm_img = norm_img[np.newaxis, :]
+
+ [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
+ self.srn_other_inputs(image_shape, num_heads, max_text_length)
+
+ gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
+ gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
+ encoder_word_pos = encoder_word_pos.astype(np.int64)
+ gsrm_word_pos = gsrm_word_pos.astype(np.int64)
+
+ return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+ gsrm_slf_attn_bias2)
+
def __call__(self, img_list):
img_num = len(img_list)
# Calculate the aspect ratio of all text bars
@@ -93,21 +173,64 @@ class TextRecognizer(object):
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
- # norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
- norm_img = self.resize_norm_img(img_list[indices[ino]],
- max_wh_ratio)
- norm_img = norm_img[np.newaxis, :]
- norm_img_batch.append(norm_img)
+ if self.rec_algorithm != "SRN":
+ norm_img = self.resize_norm_img(img_list[indices[ino]],
+ max_wh_ratio)
+ norm_img = norm_img[np.newaxis, :]
+ norm_img_batch.append(norm_img)
+ else:
+ norm_img = self.process_image_srn(
+ img_list[indices[ino]], self.rec_image_shape, 8, 25)
+ encoder_word_pos_list = []
+ gsrm_word_pos_list = []
+ gsrm_slf_attn_bias1_list = []
+ gsrm_slf_attn_bias2_list = []
+ encoder_word_pos_list.append(norm_img[1])
+ gsrm_word_pos_list.append(norm_img[2])
+ gsrm_slf_attn_bias1_list.append(norm_img[3])
+ gsrm_slf_attn_bias2_list.append(norm_img[4])
+ norm_img_batch.append(norm_img[0])
norm_img_batch = np.concatenate(norm_img_batch)
norm_img_batch = norm_img_batch.copy()
- starttime = time.time()
- self.input_tensor.copy_from_cpu(norm_img_batch)
- self.predictor.run()
- outputs = []
- for output_tensor in self.output_tensors:
- output = output_tensor.copy_to_cpu()
- outputs.append(output)
- preds = outputs[0]
+
+ if self.rec_algorithm == "SRN":
+ starttime = time.time()
+ encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
+ gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
+ gsrm_slf_attn_bias1_list = np.concatenate(
+ gsrm_slf_attn_bias1_list)
+ gsrm_slf_attn_bias2_list = np.concatenate(
+ gsrm_slf_attn_bias2_list)
+
+ inputs = [
+ norm_img_batch,
+ encoder_word_pos_list,
+ gsrm_word_pos_list,
+ gsrm_slf_attn_bias1_list,
+ gsrm_slf_attn_bias2_list,
+ ]
+ input_names = self.predictor.get_input_names()
+ for i in range(len(input_names)):
+ input_tensor = self.predictor.get_input_handle(input_names[
+ i])
+ input_tensor.copy_from_cpu(inputs[i])
+ self.predictor.run()
+ outputs = []
+ for output_tensor in self.output_tensors:
+ output = output_tensor.copy_to_cpu()
+ outputs.append(output)
+ preds = {"predict": outputs[2]}
+ else:
+ starttime = time.time()
+ self.input_tensor.copy_from_cpu(norm_img_batch)
+ self.predictor.run()
+
+ outputs = []
+ for output_tensor in self.output_tensors:
+ output = output_tensor.copy_to_cpu()
+ outputs.append(output)
+ preds = outputs[0]
+
rec_result = self.postprocess_op(preds)
for rno in range(len(rec_result)):
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py
index 8c4f9214db9621fe4e0393ed3dac0e9a7ccedbf6..de7ee9d342063161f2e329c99d2428051c0ecf8c 100755
--- a/tools/infer/predict_system.py
+++ b/tools/infer/predict_system.py
@@ -184,4 +184,4 @@ def main(args):
if __name__ == "__main__":
- main(utility.parse_args())
\ No newline at end of file
+ main(utility.parse_args())
diff --git a/tools/infer_rec.py b/tools/infer_rec.py
index 7e4b081140c37ff1eb8c5e0085185b8961198a0b..075ec261e492cf21c668364ae6119fb4903f823b 100755
--- a/tools/infer_rec.py
+++ b/tools/infer_rec.py
@@ -62,7 +62,13 @@ def main():
elif op_name in ['RecResizeImg']:
op[op_name]['infer_mode'] = True
elif op_name == 'KeepKeys':
- op[op_name]['keep_keys'] = ['image']
+ if config['Architecture']['algorithm'] == "SRN":
+ op[op_name]['keep_keys'] = [
+ 'image', 'encoder_word_pos', 'gsrm_word_pos',
+ 'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
+ ]
+ else:
+ op[op_name]['keep_keys'] = ['image']
transforms.append(op)
global_config['infer_mode'] = True
ops = create_operators(transforms, global_config)
@@ -74,10 +80,25 @@ def main():
img = f.read()
data = {'image': img}
batch = transform(data, ops)
+ if config['Architecture']['algorithm'] == "SRN":
+ encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
+ gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
+ gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
+ gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
+
+ others = [
+ paddle.to_tensor(encoder_word_pos_list),
+ paddle.to_tensor(gsrm_word_pos_list),
+ paddle.to_tensor(gsrm_slf_attn_bias1_list),
+ paddle.to_tensor(gsrm_slf_attn_bias2_list)
+ ]
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
- preds = model(images)
+ if config['Architecture']['algorithm'] == "SRN":
+ preds = model(images, others)
+ else:
+ preds = model(images)
post_result = post_process_class(preds)
for rec_reuslt in post_result:
logger.info('\t result: {}'.format(rec_reuslt))
diff --git a/tools/program.py b/tools/program.py
index fb9e3802a0818b2ee92117d10bda6b70261abace..99a374326ceb71a21001f67534990c4f37effeac 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -174,6 +174,7 @@ def train(config,
best_model_dict = {main_indicator: 0}
best_model_dict.update(pre_best_model_dict)
train_stats = TrainingStats(log_smooth_window, ['lr'])
+ model_average = False
model.train()
if 'start_epoch' in best_model_dict:
@@ -194,7 +195,12 @@ def train(config,
break
lr = optimizer.get_lr()
images = batch[0]
- preds = model(images)
+ if config['Architecture']['algorithm'] == "SRN":
+ others = batch[-4:]
+ preds = model(images, others)
+ model_average = True
+ else:
+ preds = model(images)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
avg_loss.backward()
@@ -216,8 +222,8 @@ def train(config,
batch = [item.numpy() for item in batch]
post_result = post_process_class(preds, batch[1])
eval_class(post_result, batch)
- metirc = eval_class.get_metric()
- train_stats.update(metirc)
+ metric = eval_class.get_metric()
+ train_stats.update(metric)
if vdl_writer is not None and dist.get_rank() == 0:
for k, v in train_stats.get().items():
@@ -238,6 +244,13 @@ def train(config,
# eval
if global_step > start_eval_step and \
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
+ if model_average:
+ Model_Average = paddle.incubate.optimizer.ModelAverage(
+ 0.15,
+ parameters=model.parameters(),
+ min_average_window=10000,
+ max_average_window=15625)
+ Model_Average.apply()
cur_metric = eval(model, valid_dataloader, post_process_class,
eval_class)
cur_metric_str = 'cur metric, {}'.format(', '.join(
@@ -273,6 +286,7 @@ def train(config,
best_model_dict[main_indicator],
global_step)
global_step += 1
+ optimizer.clear_grad()
batch_start = time.time()
if dist.get_rank() == 0:
save_model(
@@ -313,7 +327,11 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
break
images = batch[0]
start = time.time()
- preds = model(images)
+ if "SRN" in str(model.head):
+ others = batch[-4:]
+ preds = model(images, others)
+ else:
+ preds = model(images)
batch = [item.numpy() for item in batch]
# Obtain usable results from post-processing methods
diff --git a/train.sh b/train.sh
index c511c51600cc2d939f0bc8c7f52a3f3c6ce52d58..8fe861a3d79d38929fc4a4f4464187f77d27ff2f 100644
--- a/train.sh
+++ b/train.sh
@@ -1,5 +1,2 @@
-# for paddle.__version__ >= 2.0rc1
+# recommended paddle.__version__ == 2.0.0
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml
-
-# for paddle.__version__ < 2.0rc1
-# python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml