polish seed code

configs/rec/rec_resnet_stn_bilstm_att.yml

@@ -19,7 +19,6 @@ Global:
   max_text_length: 100
   infer_mode: False
   use_space_char: False
-  eval_filter: True
   save_res_path: ./output/rec/predicts_seed.txt
 
 
@@ -37,8 +36,8 @@ Optimizer:
 
 
 Architecture:
-  model_type: seed
-  algorithm: ASTER
+  model_type: rec
+  algorithm: seed
   Transform:
     name: STN_ON
     tps_inputsize: [32, 64]
@@ -76,8 +75,10 @@ Train:
           img_mode: BGR
           channel_first: False
       - SEEDLabelEncode: # Class handling label
-      - SEEDResize:
+      - RecResizeImg:
+          character_type: en
           image_shape: [3, 64, 256]
+          padding: False
       - KeepKeys:
           keep_keys: ['image', 'label', 'length', 'fast_label'] # dataloader will return list in this order
   loader:
@@ -95,8 +96,10 @@ Eval:
           img_mode: BGR
           channel_first: False
       - SEEDLabelEncode: # Class handling label
-      - SEEDResize:
+      - RecResizeImg:
+          character_type: en
           image_shape: [3, 64, 256]
+          padding: False
       - KeepKeys:
           keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
   loader:

ppocr/data/imaug/label_ops.py

@@ -106,7 +106,6 @@ class BaseRecLabelEncode(object):
         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)
@@ -357,7 +356,6 @@ class SEEDLabelEncode(BaseRecLabelEncode):
                              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

ppocr/data/imaug/rec_img_aug.py

@@ -88,29 +88,19 @@ class RecResizeImg(object):
                  image_shape,
                  infer_mode=False,
                  character_type='ch',
+                 padding=True,
                  **kwargs):
         self.image_shape = image_shape
         self.infer_mode = infer_mode
         self.character_type = character_type
+        self.padding = padding
 
     def __call__(self, data):
         img = data['image']
         if self.infer_mode and self.character_type == "ch":
             norm_img = resize_norm_img_chinese(img, self.image_shape)
         else:
-            norm_img = resize_norm_img(img, self.image_shape)
-        data['image'] = norm_img
-        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)
+            norm_img = resize_norm_img(img, self.image_shape, self.padding)
         data['image'] = norm_img
         return data
 
@@ -186,16 +176,21 @@ def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
     return padding_im, resize_shape, pad_shape, valid_ratio
 
 
-def resize_norm_img(img, image_shape):
+def resize_norm_img(img, image_shape, padding=True):
     imgC, imgH, imgW = image_shape
     h = img.shape[0]
     w = img.shape[1]
-    ratio = w / float(h)
-    if math.ceil(imgH * ratio) > imgW:
+    if not padding:
+        resized_image = cv2.resize(
+            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
         resized_w = imgW
     else:
-        resized_w = int(math.ceil(imgH * ratio))
-    resized_image = cv2.resize(img, (resized_w, imgH))
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
     resized_image = resized_image.astype('float32')
     if image_shape[0] == 1:
         resized_image = resized_image / 255
@@ -209,17 +204,6 @@ 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/modeling/transforms/__init__.py

@@ -17,7 +17,7 @@ __all__ = ['build_transform']
 
 def build_transform(config):
     from .tps import TPS
-    from .tps import STN_ON
+    from .stn import STN_ON
 
     support_dict = ['TPS', 'STN_ON']
 

ppocr/modeling/transforms/stn.py

@@ -22,6 +22,8 @@ from paddle import nn, ParamAttr
 from paddle.nn import functional as F
 import numpy as np
 
+from .tps_spatial_transformer import TPSSpatialTransformer
+
 
 def conv3x3_block(in_channels, out_channels, stride=1):
     n = 3 * 3 * out_channels
@@ -106,3 +108,25 @@ class STN(nn.Layer):
             x = F.sigmoid(x)
         x = paddle.reshape(x, shape=[-1, self.num_ctrlpoints, 2])
         return img_feat, x
+
+
+class STN_ON(nn.Layer):
+    def __init__(self, in_channels, tps_inputsize, tps_outputsize,
+                 num_control_points, tps_margins, stn_activation):
+        super(STN_ON, self).__init__()
+        self.tps = TPSSpatialTransformer(
+            output_image_size=tuple(tps_outputsize),
+            num_control_points=num_control_points,
+            margins=tuple(tps_margins))
+        self.stn_head = STN(in_channels=in_channels,
+                            num_ctrlpoints=num_control_points,
+                            activation=stn_activation)
+        self.tps_inputsize = tps_inputsize
+        self.out_channels = in_channels
+
+    def forward(self, image):
+        stn_input = paddle.nn.functional.interpolate(
+            image, self.tps_inputsize, mode="bilinear", align_corners=True)
+        stn_img_feat, ctrl_points = self.stn_head(stn_input)
+        x, _ = self.tps(image, ctrl_points)
+        return x

ppocr/modeling/transforms/tps.py

@@ -22,9 +22,6 @@ from paddle import nn, ParamAttr
 from paddle.nn import functional as F
 import numpy as np
 
-from .tps_spatial_transformer import TPSSpatialTransformer
-from .stn import STN
-
 
 class ConvBNLayer(nn.Layer):
     def __init__(self,
@@ -305,25 +302,3 @@ class TPS(nn.Layer):
             [-1, image.shape[2], image.shape[3], 2])
         batch_I_r = F.grid_sample(x=image, grid=batch_P_prime)
         return batch_I_r
-
-
-class STN_ON(nn.Layer):
-    def __init__(self, in_channels, tps_inputsize, tps_outputsize,
-                 num_control_points, tps_margins, stn_activation):
-        super(STN_ON, self).__init__()
-        self.tps = TPSSpatialTransformer(
-            output_image_size=tuple(tps_outputsize),
-            num_control_points=num_control_points,
-            margins=tuple(tps_margins))
-        self.stn_head = STN(in_channels=in_channels,
-                            num_ctrlpoints=num_control_points,
-                            activation=stn_activation)
-        self.tps_inputsize = tps_inputsize
-        self.out_channels = in_channels
-
-    def forward(self, image):
-        stn_input = paddle.nn.functional.interpolate(
-            image, self.tps_inputsize, mode="bilinear", align_corners=True)
-        stn_img_feat, ctrl_points = self.stn_head(stn_input)
-        x, _ = self.tps(image, ctrl_points)
-        return x

ppocr/postprocess/rec_postprocess.py

@@ -322,7 +322,6 @@ class SEEDLabelDecode(BaseRecLabelDecode):
     def add_special_char(self, dict_character):
         self.beg_str = "sos"
         self.end_str = "eos"
-        dict_character = dict_character
         dict_character = dict_character + [self.end_str]
         return dict_character
 

requirements.txt

@@ -11,4 +11,5 @@ opencv-contrib-python==4.4.0.46
 cython
 lxml
 premailer
-openpyxl
+openpyxl
\ No newline at end of file
+fasttext==0.9.1
\ No newline at end of file

tools/program.py

@@ -186,9 +186,8 @@ def train(config,
     model.train()
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
-    use_nrtr = config['Architecture']['algorithm'] == "NRTR"
-    use_sar = config['Architecture']['algorithm'] == 'SAR'
-    use_seed = config['Architecture']['algorithm'] == 'SEED'
+    extra_input = config['Architecture'][
+        'algorithm'] in ["SRN", "NRTR", "SAR", "SEED"]
     try:
         model_type = config['Architecture']['model_type']
     except:
@@ -217,7 +216,7 @@ def train(config,
             images = batch[0]
             if use_srn:
                 model_average = True
-            if use_srn or model_type == 'table' or use_nrtr or use_sar or use_seed:
+            if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
             else:
                 preds = model(images)
@@ -281,8 +280,7 @@ def train(config,
                     post_process_class,
                     eval_class,
                     model_type,
-                    use_srn=use_srn,
-                    use_sar=use_sar)
+                    extra_input=extra_input)
                 cur_metric_str = 'cur metric, {}'.format(', '.join(
                     ['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
                 logger.info(cur_metric_str)
@@ -354,8 +352,7 @@ def eval(model,
          post_process_class,
          eval_class,
          model_type=None,
-         use_srn=False,
-         use_sar=False):
+         extra_input=False):
     model.eval()
     with paddle.no_grad():
         total_frame = 0.0
@@ -368,7 +365,7 @@ def eval(model,
                 break
             images = batch[0]
             start = time.time()
-            if use_srn or model_type == 'table' or use_sar:
+            if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
             else:
                 preds = model(images)