fix conflicts

configs/rec/rec_resnet_stn_bilstm_att.yml

@@ -0,0 +1,106 @@
+Global:
+  use_gpu: True
+  epoch_num: 400
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/seed
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path: 
+  character_type: EN_symbol
+  max_text_length: 100
+  infer_mode: False
+  use_space_char: False
+  eval_filter: True
+  save_res_path: ./output/rec/predicts_seed.txt
+
+
+Optimizer:
+  name: Adadelta
+  weight_deacy: 0.0
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    decay_epochs: [4,5,8]
+    values: [1.0, 0.1, 0.01]
+  regularizer:
+    name: 'L2'
+    factor: 2.0e-05
+
+
+Architecture:
+  model_type: seed
+  algorithm: ASTER
+  Transform:
+    name: STN_ON
+    tps_inputsize: [32, 64]
+    tps_outputsize: [32, 100]
+    num_control_points: 20
+    tps_margins: [0.05,0.05]
+    stn_activation: none
+  Backbone:
+    name: ResNet_ASTER
+  Head:
+    name: AsterHead  # AttentionHead
+    sDim: 512
+    attDim: 512
+    max_len_labels: 100
+
+Loss:
+  name: AsterLoss
+
+PostProcess:
+  name: SEEDLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+  is_filter: True
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - Fasttext:
+          path: "./cc.en.300.bin"
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SEEDLabelEncode: # Class handling label
+      - SEEDResize:
+          image_shape: [3, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length', 'fast_label'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 6
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/evaluation/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - SEEDLabelEncode: # Class handling label
+      - SEEDResize:
+          image_shape: [3, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: True
+    batch_size_per_card: 256
+    num_workers: 4

ppocr/data/imaug/__init__.py

@@ -21,7 +21,7 @@
 from .make_shrink_map import MakeShrinkMap
 from .random_crop_data import EastRandomCropData, PSERandomCrop
 
-from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg
+from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, SEEDResize
 from .randaugment import RandAugment
 from .copy_paste import CopyPaste
 from .operators import *

ppocr/data/imaug/label_ops.py

@@ -106,6 +106,7 @@ def __init__(self,
         self.max_text_len = max_text_length
         self.beg_str = "sos"
         self.end_str = "eos"
+        self.unknown = "UNKNOWN"
         if character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
@@ -174,6 +175,7 @@ def __init__(self,
         super(NRTRLabelEncode,
               self).__init__(max_text_length, character_dict_path,
                              character_type, use_space_char)
+
     def __call__(self, data):
         text = data['label']
         text = self.encode(text)
@@ -185,10 +187,12 @@ def __call__(self, data):
         text = text + [0] * (self.max_text_len - len(text))
         data['label'] = np.array(text)
         return data
+
     def add_special_char(self, dict_character):
-        dict_character = ['blank','<unk>','<s>','</s>'] + dict_character
+        dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
         return dict_character
 
+
 class CTCLabelEncode(BaseRecLabelEncode):
     """ Convert between text-label and text-index """
 
@@ -337,6 +341,39 @@ def get_beg_end_flag_idx(self, beg_or_end):
         return idx
 
 
+class SEEDLabelEncode(BaseRecLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='ch',
+                 use_space_char=False,
+                 **kwargs):
+        super(SEEDLabelEncode,
+              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 = 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)) + 1  # conclue eos
+        text = text + [len(self.character) - 1] * (self.max_text_len - len(text)
+                                                   )
+        data['label'] = np.array(text)
+        return data
+
+
 class SRNLabelEncode(BaseRecLabelEncode):
     """ Convert between text-label and text-index """
 
@@ -416,7 +453,6 @@ def load_char_elem_dict(self, character_dict_path):
             substr = lines[0].decode('utf-8').strip("\r\n").split("\t")
             character_num = int(substr[0])
             elem_num = int(substr[1])
-
             for cno in range(1, 1 + character_num):
                 character = lines[cno].decode('utf-8').strip("\r\n")
                 list_character.append(character)
@@ -588,7 +624,7 @@ def __call__(self, data):
         data['length'] = np.array(len(text))
         target = [self.start_idx] + text + [self.end_idx]
         padded_text = [self.padding_idx for _ in range(self.max_text_len)]
-        
+
         padded_text[:len(target)] = target
         data['label'] = np.array(padded_text)
         return data

ppocr/data/imaug/operators.py

@@ -23,6 +23,7 @@
 import six
 import cv2
 import numpy as np
+import fasttext
 
 
 class DecodeImage(object):
@@ -83,12 +84,13 @@ def __call__(self, data):
         elif self.img_mode == 'RGB':
             assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
             img = img[:, :, ::-1]
-        img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         if self.channel_first:
             img = img.transpose((2, 0, 1))
         data['image'] = img
         return data
 
+
 class NormalizeImage(object):
     """ normalize image such as substract mean, divide std
     """
@@ -133,6 +135,17 @@ def __call__(self, data):
         return data
 
 
+class Fasttext(object):
+    def __init__(self, path="None", **kwargs):
+        self.fast_model = fasttext.load_model(path)
+
+    def __call__(self, data):
+        label = data['label']
+        fast_label = self.fast_model[label]
+        data['fast_label'] = fast_label
+        return data
+
+
 class KeepKeys(object):
     def __init__(self, keep_keys, **kwargs):
         self.keep_keys = keep_keys

ppocr/data/imaug/rec_img_aug.py

@@ -82,6 +82,18 @@ def __call__(self, data):
         return data
 
 
+class SEEDResize(object):
+    def __init__(self, image_shape, infer_mode=False, **kwargs):
+        self.image_shape = image_shape
+        self.infer_mode = infer_mode
+
+    def __call__(self, data):
+        img = data['image']
+        norm_img = resize_no_padding_img(img, self.image_shape)
+        data['image'] = norm_img
+        return data
+
+
 class SRNRecResizeImg(object):
     def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
         self.image_shape = image_shape
@@ -109,7 +121,8 @@ def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs):
 
     def __call__(self, data):
         img = data['image']
-        norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(img, self.image_shape, self.width_downsample_ratio)
+        norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(
+            img, self.image_shape, self.width_downsample_ratio)
         data['image'] = norm_img
         data['resized_shape'] = resize_shape
         data['pad_shape'] = pad_shape
@@ -175,6 +188,17 @@ def resize_norm_img(img, image_shape):
     return padding_im
 
 
+def resize_no_padding_img(img, image_shape):
+    imgC, imgH, imgW = image_shape
+    resized_image = cv2.resize(
+        img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
+    resized_image = resized_image.astype('float32')
+    resized_image = resized_image.transpose((2, 0, 1)) / 255
+    resized_image -= 0.5
+    resized_image /= 0.5
+    return resized_image
+
+
 def resize_norm_img_chinese(img, image_shape):
     imgC, imgH, imgW = image_shape
     # todo: change to 0 and modified image shape

ppocr/losses/__init__.py

@@ -42,10 +42,14 @@
 # table loss
 from .table_att_loss import TableAttentionLoss
 
+from .rec_aster_loss import AsterLoss
+
+
 def build_loss(config):
     support_dict = [
         'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss', 'SARLoss'
+        'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss',
+        'SARLoss', 'AsterLoss'
     ]
 
     config = copy.deepcopy(config)