mv drop last in det model

configs/det/det_mv3_db.yml

@@ -9,7 +9,6 @@ Global:
   eval_batch_step: 5000
   train_batch_size_per_card: 16
   test_batch_size_per_card: 16
-  drop_last: false
   image_shape: [3, 640, 640]
   reader_yml: ./configs/det/det_db_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/

configs/det/det_mv3_east.yml

@@ -9,7 +9,6 @@ Global:
   eval_batch_step: 5000
   train_batch_size_per_card: 16
   test_batch_size_per_card: 16
-  drop_last: false
   image_shape: [3, 512, 512]
   reader_yml: ./configs/det/det_east_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/

configs/det/det_r50_vd_db.yml

@@ -10,7 +10,6 @@ Global:
   train_batch_size_per_card: 8
   test_batch_size_per_card: 16
   image_shape: [3, 640, 640]
-  drop_last: false
   reader_yml: ./configs/det/det_db_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/ResNet50_vd_ssld_pretrained/
   save_res_path: ./output/det_db/predicts_db.txt

configs/det/det_r50_vd_east.yml

@@ -10,7 +10,6 @@ Global:
   train_batch_size_per_card: 8
   test_batch_size_per_card: 16
   image_shape: [3, 512, 512]
-  drop_last: false
   reader_yml: ./configs/det/det_east_icdar15_reader.yml
   pretrain_weights: ./pretrain_models/ResNet50_vd_ssld_pretrained/
   save_res_path: ./output/det_east/predicts_east.txt

configs/rec/rec_benchmark_reader.yml

 TrainReader:
   reader_function: ppocr.data.rec.dataset_traversal,LMDBReader
   num_workers: 8
-  lmdb_sets_dir: ./train_data/data_lmdb_release/training/
+  lmdb_sets_dir: ./train_data/data_lmdb_release/evaluation/
   
 EvalReader:
   reader_function: ppocr.data.rec.dataset_traversal,LMDBReader

configs/rec/rec_icdar15_train.yml

 Global:
   algorithm: CRNN
-  use_gpu: true
+  use_gpu: false
   epoch_num: 1000
   log_smooth_window: 20
   print_batch_step: 10
   save_model_dir: ./output/rec_CRNN
   save_epoch_step: 300
   eval_batch_step: 500
-  train_batch_size_per_card: 256
-  test_batch_size_per_card: 256
+  train_batch_size_per_card: 2
+  test_batch_size_per_card: 2
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en
   loss_type: ctc
   reader_yml: ./configs/rec/rec_icdar15_reader.yml
-  pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
+  pretrain_weights:
   checkpoints:
   save_inference_dir:
   infer_img:

configs/rec/rec_mv3_tps_bilstm_attn.yml

 Global:
   algorithm: RARE
-  use_gpu: true
+  use_gpu: false
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
   save_model_dir: output/rec_RARE
   save_epoch_step: 3
   eval_batch_step: 2000
-  train_batch_size_per_card: 256
-  test_batch_size_per_card: 256
+  train_batch_size_per_card: 2
+  test_batch_size_per_card: 2
   image_shape: [3, 32, 100]
   max_text_length: 25
   character_type: en

ppocr/data/det/dataset_traversal.py

@@ -32,7 +32,6 @@ class TrainReader(object):
         self.num_workers = params['num_workers']
         self.label_file_path = params['label_file_path']
         self.batch_size = params['train_batch_size_per_card']
-        self.drop_last = params['drop_last']
         assert 'process_function' in params,\
             "absence process_function in Reader"
         self.process = create_module(params['process_function'])(params)
@@ -62,9 +61,6 @@ class TrainReader(object):
                 if len(batch_outs) == self.batch_size:
                     yield batch_outs
                     batch_outs = []
-            if not self.drop_last:
-                if len(batch_outs) != 0:
-                    yield batch_outs
 
         return batch_iter_reader
 

ppocr/modeling/architectures/rec_model.py

@@ -94,9 +94,9 @@ class RecModel(object):
                     logger.info(
                         "WARNRNG!!!\n"
                         "TPS does not support variable shape in chinese!"
-                        "We set default shape=[3,32,320], it may affect the inference effect"
+                        "We set img_shape to be the same , it may affect the inference effect"
                     )
-                    image_shape[-1] = 320
+                    image_shape = deepcopy(self.image_shape)
             image = fluid.data(name='image', shape=image_shape, dtype='float32')
             labels = None
             loader = None