Merge remote-tracking branch 'origin/lite' into lite

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/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)
@@ -51,14 +59,40 @@ def main():
     model.eval()
 
     save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
-    infer_shape = [3, 32, 100] if config['Architecture'][
-        'model_type'] != "det" else [3, 640, 640]
-    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))
 

tools/infer/predict_cls.py

@@ -98,10 +98,10 @@ class TextClassifier(object):
             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()
             prob_out = self.output_tensors[0].copy_to_cpu()
+            self.predictor.try_shrink_memory()
             cls_result = self.postprocess_op(prob_out)
             elapse += time.time() - starttime
             for rno in range(len(cls_result)):

tools/infer/predict_det.py

@@ -39,10 +39,7 @@ class TextDetector(object):
         self.args = args
         self.det_algorithm = args.det_algorithm
         pre_process_list = [{
-            'DetResizeForTest': {
-                'limit_side_len': args.det_limit_side_len,
-                'limit_type': args.det_limit_type
-            }
+            'DetResizeForTest': None
         }, {
             'NormalizeImage': {
                 'std': [0.229, 0.224, 0.225],
@@ -64,7 +61,7 @@ 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
@@ -183,7 +180,7 @@ class TextDetector(object):
             preds['maps'] = outputs[0]
         else:
             raise NotImplementedError
-
+        self.predictor.try_shrink_memory()
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
         if self.det_algorithm == "SAST" and self.det_sast_polygon:

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 +47,20 @@ 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
+            }
+        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)
@@ -70,6 +85,78 @@ 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 +180,64 @@ 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]
+            self.predictor.try_shrink_memory()
             rec_result = self.postprocess_op(preds)
             for rno in range(len(rec_result)):
                 rec_res[indices[beg_img_no + rno]] = rec_result[rno]
@@ -118,9 +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)
@@ -129,22 +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())
+    main(utility.parse_args())
\ No newline at end of file

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)
@@ -70,7 +71,7 @@ def parse_args():
         default="./ppocr/utils/ppocr_keys_v1.txt")
     parser.add_argument("--use_space_char", type=str2bool, default=True)
     parser.add_argument(
-        "--vis_font_path", type=str, default="./doc/simfang.ttf")
+        "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
     parser.add_argument("--drop_score", type=float, default=0.5)
 
     # params for text classifier
@@ -123,9 +124,12 @@ 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()
+    # enable memory optim
+    config.enable_memory_optim()
     config.disable_glog_info()
 
     config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")

tools/infer_det.py

@@ -97,7 +97,7 @@ def main():
             preds = model(images)
             post_result = post_process_class(preds, shape_list)
             boxes = post_result[0]['points']
-            # write resule
+            # write result
             dt_boxes_json = []
             for box in boxes:
                 tmp_json = {"transcription": ""}

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))

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))
@@ -174,16 +179,19 @@ 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()
 
+    use_srn = config['Architecture']['algorithm'] == "SRN"
+
     if 'start_epoch' in best_model_dict:
         start_epoch = best_model_dict['start_epoch']
     else:
         start_epoch = 1
 
     for epoch in range(start_epoch, epoch_num + 1):
-        if epoch > 0:
-            train_dataloader = build_dataloader(config, 'Train', device, logger)
+        train_dataloader = build_dataloader(
+            config, 'Train', device, logger, seed=epoch)
         train_batch_cost = 0.0
         train_reader_cost = 0.0
         batch_sum = 0
@@ -194,7 +202,12 @@ def train(config,
                 break
             lr = optimizer.get_lr()
             images = batch[0]
-            preds = model(images)
+            if use_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()
@@ -212,12 +225,12 @@ def train(config,
             stats['lr'] = lr
             train_stats.update(stats)
 
-            if cal_metric_during_train:  # onlt rec and cls need
+            if cal_metric_during_train:  # only rec and cls need
                 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():
@@ -238,21 +251,32 @@ 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:
-                cur_metirc = eval(model, valid_dataloader, post_process_class,
-                                  eval_class)
-                cur_metirc_str = 'cur metirc, {}'.format(', '.join(
-                    ['{}: {}'.format(k, v) for k, v in cur_metirc.items()]))
-                logger.info(cur_metirc_str)
+                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,
+                    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)
 
                 # logger metric
                 if vdl_writer is not None:
-                    for k, v in cur_metirc.items():
+                    for k, v in cur_metric.items():
                         if isinstance(v, (float, int)):
                             vdl_writer.add_scalar('EVAL/{}'.format(k),
-                                                  cur_metirc[k], global_step)
-                if cur_metirc[main_indicator] >= best_model_dict[
+                                                  cur_metric[k], global_step)
+                if cur_metric[main_indicator] >= best_model_dict[
                         main_indicator]:
-                    best_model_dict.update(cur_metirc)
+                    best_model_dict.update(cur_metric)
                     best_model_dict['best_epoch'] = epoch
                     save_model(
                         model,
@@ -263,7 +287,7 @@ def train(config,
                         prefix='best_accuracy',
                         best_model_dict=best_model_dict,
                         epoch=epoch)
-                best_str = 'best metirc, {}'.format(', '.join([
+                best_str = 'best metric, {}'.format(', '.join([
                     '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                 ]))
                 logger.info(best_str)
@@ -273,6 +297,7 @@ 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(
@@ -294,7 +319,7 @@ def train(config,
                 prefix='iter_epoch_{}'.format(epoch),
                 best_model_dict=best_model_dict,
                 epoch=epoch)
-    best_str = 'best metirc, {}'.format(', '.join(
+    best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
     logger.info(best_str)
     if dist.get_rank() == 0 and vdl_writer is not None:
@@ -302,7 +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
@@ -313,7 +339,12 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
                 break
             images = batch[0]
             start = time.time()
-            preds = model(images)
+
+            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
@@ -323,13 +354,13 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
             eval_class(post_result, batch)
             pbar.update(1)
             total_frame += len(images)
-        # Get final metirc，eg. acc or hmean
-        metirc = eval_class.get_metric()
+        # Get final metric，eg. acc or hmean
+        metric = eval_class.get_metric()
 
     pbar.close()
     model.train()
-    metirc['fps'] = total_frame / total_time
-    return metirc
+    metric['fps'] = total_frame / total_time
+    return metric
 
 
 def preprocess(is_train=False):
@@ -363,6 +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:
@@ -83,8 +89,10 @@ def main(config, device, logger, vdl_writer):
     # load pretrain model
     pre_best_model_dict = init_model(config, model, logger, optimizer)
 
-    logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
-                format(len(train_dataloader), len(valid_dataloader)))
+    logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
+    if valid_dataloader is not None:
+        logger.info('valid dataloader has {} iters'.format(
+            len(valid_dataloader)))
     # start train
     program.train(config, train_dataloader, valid_dataloader, device, model,
                   loss_class, optimizer, lr_scheduler, post_process_class,

train.sh

-# for paddle.__version__ >= 2.0rc1
-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
+# recommended paddle.__version__ == 2.0.0
+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