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

ppocr/data/imaug/make_shrink_map.py

@@ -44,21 +44,34 @@ class MakeShrinkMap(object):
                 ignore_tags[i] = True
             else:
                 polygon_shape = Polygon(polygon)
-                distance = polygon_shape.area * (
-                    1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length
-                subject = [tuple(l) for l in text_polys[i]]
+                subject = [tuple(l) for l in polygon]
                 padding = pyclipper.PyclipperOffset()
                 padding.AddPath(subject, pyclipper.JT_ROUND,
                                 pyclipper.ET_CLOSEDPOLYGON)
-                shrinked = padding.Execute(-distance)
+                shrinked = []
+
+                # Increase the shrink ratio every time we get multiple polygon returned back
+                possible_ratios = np.arange(self.shrink_ratio, 1,
+                                            self.shrink_ratio)
+                np.append(possible_ratios, 1)
+                # print(possible_ratios)
+                for ratio in possible_ratios:
+                    # print(f"Change shrink ratio to {ratio}")
+                    distance = polygon_shape.area * (
+                        1 - np.power(ratio, 2)) / polygon_shape.length
+                    shrinked = padding.Execute(-distance)
+                    if len(shrinked) == 1:
+                        break
+
                 if shrinked == []:
                     cv2.fillPoly(mask,
                                  polygon.astype(np.int32)[np.newaxis, :, :], 0)
                     ignore_tags[i] = True
                     continue
-                shrinked = np.array(shrinked[0]).reshape(-1, 2)
-                cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1)
-                # cv2.fillPoly(gt[0], [shrinked.astype(np.int32)], 1)
+
+                for each_shirnk in shrinked:
+                    shirnk = np.array(each_shirnk).reshape(-1, 2)
+                    cv2.fillPoly(gt, [shirnk.astype(np.int32)], 1)
 
         data['shrink_map'] = gt
         data['shrink_mask'] = mask
@@ -84,11 +97,12 @@ class MakeShrinkMap(object):
         return polygons, ignore_tags
 
     def polygon_area(self, polygon):
-        # return cv2.contourArea(polygon.astype(np.float32))
-        edge = 0
-        for i in range(polygon.shape[0]):
-            next_index = (i + 1) % polygon.shape[0]
-            edge += (polygon[next_index, 0] - polygon[i, 0]) * (
-                polygon[next_index, 1] - polygon[i, 1])
-
-        return edge / 2.
+        """
+        compute polygon area
+        """
+        area = 0
+        q = polygon[-1]
+        for p in polygon:
+            area += p[0] * q[1] - p[1] * q[0]
+            q = p
+        return area / 2.0

ppocr/data/imaug/operators.py

@@ -185,8 +185,8 @@ class DetResizeForTest(object):
         resize_h = int(h * ratio)
         resize_w = int(w * ratio)
 
-        resize_h = int(round(resize_h / 32) * 32)
-        resize_w = int(round(resize_w / 32) * 32)
+        resize_h = max(int(round(resize_h / 32) * 32), 32)
+        resize_w = max(int(round(resize_w / 32) * 32), 32)
 
         try:
             if int(resize_w) <= 0 or int(resize_h) <= 0:

ppocr/losses/__init__.py

@@ -23,13 +23,15 @@ 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', 'SRNLoss'
+        'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
+        'SRNLoss'
     ]
 
     config = copy.deepcopy(config)

ppocr/losses/det_basic_loss.py

@@ -200,6 +200,6 @@ def ohem_batch(scores, gt_texts, training_masks, ohem_ratio):
                 i, :, :], ohem_ratio))
 
     selected_masks = np.concatenate(selected_masks, 0)
-    selected_masks = paddle.to_variable(selected_masks)
+    selected_masks = paddle.to_tensor(selected_masks)
 
     return selected_masks

ppocr/losses/rec_att_loss.py

+# 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))}

ppocr/metrics/rec_metric.py

@@ -29,7 +29,7 @@ class RecMetric(object):
             pred = pred.replace(" ", "")
             target = target.replace(" ", "")
             norm_edit_dis += Levenshtein.distance(pred, target) / max(
-                len(pred), len(target))
+                len(pred), len(target), 1)
             if pred == target:
                 correct_num += 1
             all_num += 1

ppocr/modeling/heads/__init__.py

@@ -23,12 +23,14 @@ 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', 'SRNHead'
+        'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
+        'SRNHead'
     ]
 
     module_name = config.pop('name')

ppocr/modeling/heads/rec_att_head.py

+# 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
+            char_onehots = None
+            outputs = None
+            alpha = 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

ppocr/modeling/heads/rec_srn_head.py

@@ -246,7 +246,7 @@ class SRNHead(nn.Layer):
             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)
+        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
 

ppocr/postprocess/rec_postprocess.py

@@ -135,16 +135,62 @@ 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")

requirements.txt

 shapely
-imgaug
+scikit-image==0.17.2
+imgaug==0.4.0
 pyclipper
 lmdb
 opencv-python==4.2.0.32

tools/eval.py

@@ -47,6 +47,7 @@ def main():
         config['Architecture']["Head"]['out_channels'] = len(
             getattr(post_process_class, 'character'))
     model = build_model(config['Architecture'])
+    use_srn = config['Architecture']['algorithm'] == "SRN"
 
     best_model_dict = init_model(config, model, logger)
     if len(best_model_dict):
@@ -59,7 +60,7 @@ def main():
 
     # start eval
     metirc = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class)
+                          eval_class, use_srn)
     logger.info('metric eval ***************')
     for k, v in metirc.items():
         logger.info('{}:{}'.format(k, v))

tools/infer/predict_det.py

@@ -64,7 +64,7 @@ class TextDetector(object):
             postprocess_params["box_thresh"] = args.det_db_box_thresh
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
-            postprocess_params["use_dilation"] = True
+            postprocess_params["use_dilation"] = args.use_dilation
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh

tools/infer/predict_rec.py

@@ -54,6 +54,13 @@ class TextRecognizer(object):
                 "character_dict_path": args.rec_char_dict_path,
                 "use_space_char": args.use_space_char
             }
+        elif self.rec_algorithm == "RARE":
+            postprocess_params = {
+                'name': 'AttnLabelDecode',
+                "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)
@@ -241,9 +248,11 @@ class TextRecognizer(object):
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_recognizer = TextRecognizer(args)
+    total_run_time = 0.0
+    total_images_num = 0
     valid_image_file_list = []
     img_list = []
-    for image_file in image_file_list:
+    for idx, image_file in enumerate(image_file_list):
         img, flag = check_and_read_gif(image_file)
         if not flag:
             img = cv2.imread(image_file)
@@ -252,22 +261,29 @@ def main(args):
             continue
         valid_image_file_list.append(image_file)
         img_list.append(img)
-    try:
-        rec_res, predict_time = text_recognizer(img_list)
-    except:
-        logger.info(traceback.format_exc())
-        logger.info(
-            "ERROR!!!! \n"
-            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
-            "If your model has tps module:  "
-            "TPS does not support variable shape.\n"
-            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
-        exit()
-    for ino in range(len(img_list)):
-        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
-                                               rec_res[ino]))
+        if len(img_list) >= args.rec_batch_num or idx == len(
+                image_file_list) - 1:
+            try:
+                rec_res, predict_time = text_recognizer(img_list)
+                total_run_time += predict_time
+            except:
+                logger.info(traceback.format_exc())
+                logger.info(
+                    "ERROR!!!! \n"
+                    "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
+                    "If your model has tps module:  "
+                    "TPS does not support variable shape.\n"
+                    "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' "
+                )
+                exit()
+            for ino in range(len(img_list)):
+                logger.info("Predicts of {}:{}".format(valid_image_file_list[
+                    ino], rec_res[ino]))
+            total_images_num += len(valid_image_file_list)
+            valid_image_file_list = []
+            img_list = []
     logger.info("Total predict time for {} images, cost: {:.3f}".format(
-        len(img_list), predict_time))
+        total_images_num, total_run_time))
 
 
 if __name__ == "__main__":

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())

tools/infer/utility.py

@@ -47,6 +47,7 @@ def parse_args():
     parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
     parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
     parser.add_argument("--max_batch_size", type=int, default=10)
+    parser.add_argument("--use_dilation", type=bool, default=False)
     # EAST parmas
     parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
     parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
@@ -123,6 +124,8 @@ def create_predictor(args, mode, logger):
             # cache 10 different shapes for mkldnn to avoid memory leak
             config.set_mkldnn_cache_capacity(10)
             config.enable_mkldnn()
+            #  TODO LDOUBLEV: fix mkldnn bug when bach_size  > 1
+            #config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'})
             args.rec_batch_num = 1
 
     # config.enable_memory_optim()

tools/program.py

@@ -163,6 +163,11 @@ def train(config,
     if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
         start_eval_step = eval_batch_step[0]
         eval_batch_step = eval_batch_step[1]
+        if len(valid_dataloader) == 0:
+            logger.info(
+                'No Images in eval dataset, evaluation during training will be disabled'
+            )
+            start_eval_step = 1e111
         logger.info(
             "During the training process, after the {}th iteration, an evaluation is run every {} iterations".
             format(start_eval_step, eval_batch_step))
@@ -177,6 +182,8 @@ def train(config,
     model_average = False
     model.train()
 
+    use_srn = config['Architecture']['algorithm'] == "SRN"
+
     if 'start_epoch' in best_model_dict:
         start_epoch = best_model_dict['start_epoch']
     else:
@@ -195,7 +202,7 @@ def train(config,
                 break
             lr = optimizer.get_lr()
             images = batch[0]
-            if config['Architecture']['algorithm'] == "SRN":
+            if use_srn:
                 others = batch[-4:]
                 preds = model(images, others)
                 model_average = True
@@ -222,8 +229,8 @@ 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():
@@ -251,8 +258,12 @@ def train(config,
                         min_average_window=10000,
                         max_average_window=15625)
                     Model_Average.apply()
-                cur_metirc = eval(model, valid_dataloader, post_process_class,
-                                  eval_class)
+                cur_metric = eval(
+                    model,
+                    valid_dataloader,
+                    post_process_class,
+                    eval_class,
+                    use_srn=use_srn)
                 cur_metric_str = 'cur metric, {}'.format(', '.join(
                     ['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
                 logger.info(cur_metric_str)
@@ -316,7 +327,8 @@ def train(config,
     return
 
 
-def eval(model, valid_dataloader, post_process_class, eval_class):
+def eval(model, valid_dataloader, post_process_class, eval_class,
+         use_srn=False):
     model.eval()
     with paddle.no_grad():
         total_frame = 0.0
@@ -326,9 +338,13 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
             if idx >= len(valid_dataloader):
                 break
             images = batch[0]
-            others = batch[-4:]
             start = time.time()
-            preds = model(images, others)
+
+            if use_srn:
+                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
@@ -378,6 +394,7 @@ def preprocess(is_train=False):
     logger = get_logger(name='root', log_file=log_file)
     if config['Global']['use_visualdl']:
         from visualdl import LogWriter
+        save_model_dir = config['Global']['save_model_dir']
         vdl_writer_path = '{}/vdl/'.format(save_model_dir)
         os.makedirs(vdl_writer_path, exist_ok=True)
         vdl_writer = LogWriter(logdir=vdl_writer_path)

tools/train.py

@@ -50,6 +50,12 @@ def main(config, device, logger, vdl_writer):
 
     # build dataloader
     train_dataloader = build_dataloader(config, 'Train', device, logger)
+    if len(train_dataloader) == 0:
+        logger.error(
+            'No Images in train dataset, please check annotation file and path in the configuration file'
+        )
+        return
+
     if config['Eval']:
         valid_dataloader = build_dataloader(config, 'Eval', device, logger)
     else:

train.sh

 # 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
+python3 -m paddle.distributed.launch --log_dir=./debug/ --gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml