[TODO] Updata det_datasets & recog_datasets

configs/_base_/det_datasets/ctw1500.py

@@ -1,18 +1,23 @@
-dataset_type = 'IcdarDataset'
-data_root = 'data/ctw1500'
+data_root = 'data/det/ctw1500'
 
-train = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_training.json',
-    img_prefix=f'{data_root}/imgs',
+train_anno_path = 'instances_training.json'
+test_anno_path = 'instances_test.json'
+
+train_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=train_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
     pipeline=None)
 
-test = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_test.json',
-    img_prefix=f'{data_root}/imgs',
+test_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=test_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    test_mode=True,
     pipeline=None)
 
-train_list = [train]
-
-test_list = [test]
+train_list = [train_dataset]
+test_list = [test_dataset]

configs/_base_/det_datasets/icdar2015.py

@@ -1,18 +1,23 @@
-dataset_type = 'IcdarDataset'
-data_root = 'data/icdar2015'
+data_root = 'data/det/icdar2015'
 
-train = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_training.json',
-    img_prefix=f'{data_root}/imgs',
+train_anno_path = 'instances_training.json'
+test_anno_path = 'instances_test.json'
+
+train_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=train_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
     pipeline=None)
 
-test = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_test.json',
-    img_prefix=f'{data_root}/imgs',
+test_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=test_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    test_mode=True,
     pipeline=None)
 
-train_list = [train]
-
-test_list = [test]
+train_list = [train_dataset]
+test_list = [test_dataset]

configs/_base_/det_datasets/icdar2017.py

@@ -1,18 +1,23 @@
-dataset_type = 'IcdarDataset'
-data_root = 'data/icdar2017'
+data_root = 'data/det/icdar2017'
 
-train = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_training.json',
-    img_prefix=f'{data_root}/imgs',
+train_anno_path = 'instances_training.json'
+test_anno_path = 'instances_test.json'
+
+train_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=train_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
     pipeline=None)
 
-test = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_val.json',
-    img_prefix=f'{data_root}/imgs',
+test_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=test_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    test_mode=True,
     pipeline=None)
 
-train_list = [train]
-
-test_list = [test]
+train_list = [train_dataset]
+test_list = [test_dataset]

configs/_base_/det_datasets/synthtext.py

@@ -1,18 +1,23 @@
-dataset_type = 'TextDetDataset'
-data_root = 'data/synthtext'
+data_root = 'data/det/synthtext'
 
-train = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_training.lmdb',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(
-            type='LineJsonParser',
-            keys=['file_name', 'height', 'width', 'annotations'])),
-    img_prefix=f'{data_root}/imgs',
+train_anno_path = 'instances_training.json'
+test_anno_path = 'instances_test.json'
+
+train_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=train_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
     pipeline=None)
 
-train_list = [train]
-test_list = [train]
+test_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=test_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    test_mode=True,
+    pipeline=None)
+
+train_list = [train_dataset]
+test_list = [test_dataset]

configs/_base_/det_datasets/toy_data.py

@@ -1,41 +1,23 @@
-root = 'tests/data/toy_dataset'
+data_root = 'tests/data/det_toy_dataset'
 
-# dataset with type='TextDetDataset'
-train1 = dict(
-    type='TextDetDataset',
-    img_prefix=f'{root}/imgs',
-    ann_file=f'{root}/instances_test.txt',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=4,
-        file_format='txt',
-        parser=dict(
-            type='LineJsonParser',
-            keys=['file_name', 'height', 'width', 'annotations'])),
-    pipeline=None,
-    test_mode=False)
+train_anno_path = 'instances_test.json'
+test_anno_path = 'instances_test.json'
 
-# dataset with type='IcdarDataset'
-train2 = dict(
-    type='IcdarDataset',
-    ann_file=f'{root}/instances_test.json',
-    img_prefix=f'{root}/imgs',
+train_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=train_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    filter_cfg=dict(filter_empty_gt=True, min_size=32),
     pipeline=None)
 
-test = dict(
-    type='TextDetDataset',
-    img_prefix=f'{root}/imgs',
-    ann_file=f'{root}/instances_test.txt',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineJsonParser',
-            keys=['file_name', 'height', 'width', 'annotations'])),
-    pipeline=None,
-    test_mode=True)
+test_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    ann_file=test_anno_path,
+    data_prefix=dict(img_path='imgs/'),
+    test_mode=True,
+    pipeline=None)
 
-train_list = [train1, train2]
-
-test_list = [test]
+train_list = [train_dataset]
+test_list = [test_dataset]

configs/_base_/recog_datasets/MJ_train.py

@@ -1,21 +1,15 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: Syn90k
-
-train_root = 'data/mixture/Syn90k'
-
-train_img_prefix = f'{train_root}/mnt/ramdisk/max/90kDICT32px'
-train_ann_file = f'{train_root}/label.lmdb'
+data_root = 'data/recog'
+train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
+train_ann_file1 = 'Syn90k/label.json'
+file_client_args = dict(backend='disk')
 
 train = dict(
     type='OCRDataset',
-    img_prefix=train_img_prefix,
-    ann_file=train_ann_file,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix1),
+    ann_file=train_ann_file1,
+    test_mode=False,
+    pipeline=None)
 train_list = [train]

configs/_base_/recog_datasets/ST_MJ_alphanumeric_train.py

@@ -2,30 +2,24 @@
 # Synthetic Datasets: SynthText, Syn90k
 # Both annotations are filtered so that
 # only alphanumeric terms are left
-
-train_root = 'data/mixture'
-
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
+data_root = 'data/recog'
+train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
+train_ann_file1 = 'Syn90k/label.json'
+file_client_args = dict(backend='disk')
 
 train1 = dict(
     type='OCRDataset',
-    img_prefix=train_img_prefix1,
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix1),
     ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/alphanumeric_label.lmdb'
+    test_mode=False,
+    pipeline=None)
 
+train_img_prefix2 = 'SynthText/synthtext/SynthText_patch_horizontal'
+train_ann_file2 = 'SynthText/alphanumeric_label.json'
 train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
+train2['data_root'] = data_root
+train2['data_prefix'] = dict(img_path=train_img_prefix2),
+train2['ann_file'] = dict(img_path=train_ann_file2),
 
 train_list = [train1, train2]

configs/_base_/recog_datasets/ST_MJ_train.py

@@ -1,29 +1,25 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: SynthText, Syn90k
 
-train_root = 'data/mixture'
+data_root = 'data/recog'
 
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
+train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
+train_ann_file1 = 'Syn90k/label.json'
+file_client_args = dict(backend='disk')
 
 train1 = dict(
     type='OCRDataset',
-    img_prefix=train_img_prefix1,
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix1),
     ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/label.lmdb'
+    test_mode=False,
+    pipeline=None)
 
+train_img_prefix2 = 'SynthText/synthtext/SynthText_patch_horizontal'
+train_ann_file2 = 'SynthText/label.json'
 train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
+train2['data_root'] = data_root
+train2['data_prefix'] = dict(img_path=train_img_prefix2),
+train2['ann_file'] = dict(img_path=train_ann_file2),
 
 train_list = [train1, train2]

configs/_base_/recog_datasets/ST_SA_MJ_real_train.py

@@ -1,81 +1,60 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: SynthText, SynthAdd, Syn90k
 # Real Dataset: IC11, IC13, IC15, COCO-Test, IIIT5k
+data_root = 'data/recog'
 
-train_prefix = 'data/mixture'
+train_img_prefix1 = 'icdar_2011'
+train_img_prefix2 = 'icdar_2013'
+train_img_prefix3 = 'icdar_2015'
+train_img_prefix4 = 'coco_text'
+train_img_prefix5 = 'IIIT5K'
+train_img_prefix6 = 'SynthText_Add'
+train_img_prefix7 = 'SynthText'
+train_img_prefix8 = 'Syn90k'
 
-train_img_prefix1 = f'{train_prefix}/icdar_2011'
-train_img_prefix2 = f'{train_prefix}/icdar_2013'
-train_img_prefix3 = f'{train_prefix}/icdar_2015'
-train_img_prefix4 = f'{train_prefix}/coco_text'
-train_img_prefix5 = f'{train_prefix}/IIIT5K'
-train_img_prefix6 = f'{train_prefix}/SynthText_Add'
-train_img_prefix7 = f'{train_prefix}/SynthText'
-train_img_prefix8 = f'{train_prefix}/Syn90k'
-
-train_ann_file1 = f'{train_prefix}/icdar_2011/train_label.txt',
-train_ann_file2 = f'{train_prefix}/icdar_2013/train_label.txt',
-train_ann_file3 = f'{train_prefix}/icdar_2015/train_label.txt',
-train_ann_file4 = f'{train_prefix}/coco_text/train_label.txt',
-train_ann_file5 = f'{train_prefix}/IIIT5K/train_label.txt',
-train_ann_file6 = f'{train_prefix}/SynthText_Add/label.txt',
-train_ann_file7 = f'{train_prefix}/SynthText/shuffle_labels.txt',
-train_ann_file8 = f'{train_prefix}/Syn90k/shuffle_labels.txt'
+train_ann_file1 = 'icdar_2011/train_label.json',
+train_ann_file2 = 'icdar_2013/train_label.json',
+train_ann_file3 = 'icdar_2015/train_label.json',
+train_ann_file4 = 'coco_text/train_label.json',
+train_ann_file5 = 'IIIT5K/train_label.json',
+train_ann_file6 = 'SynthText_Add/label.json',
+train_ann_file7 = 'SynthText/shuffle_labels.json',
+train_ann_file8 = 'Syn90k/shuffle_labels.json'
 
 train1 = dict(
     type='OCRDataset',
-    img_prefix=train_img_prefix1,
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix1),
     ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=20,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=False)
+    test_mode=False,
+    pipeline=None)
 
 train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
+train2['data_prefix'] = dict(img_path=train_img_prefix2)
 train2['ann_file'] = train_ann_file2
 
 train3 = {key: value for key, value in train1.items()}
-train3['img_prefix'] = train_img_prefix3
+train3['img_prefix'] = dict(img_path=train_img_prefix3)
 train3['ann_file'] = train_ann_file3
 
 train4 = {key: value for key, value in train1.items()}
-train4['img_prefix'] = train_img_prefix4
+train4['img_prefix'] = dict(img_path=train_img_prefix4)
 train4['ann_file'] = train_ann_file4
 
 train5 = {key: value for key, value in train1.items()}
-train5['img_prefix'] = train_img_prefix5
+train5['img_prefix'] = dict(img_path=train_img_prefix5)
 train5['ann_file'] = train_ann_file5
 
-train6 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix6,
-    ann_file=train_ann_file6,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=False)
+train6 = {key: value for key, value in train1.items()}
+train6['img_prefix'] = dict(img_path=train_img_prefix6)
+train6['ann_file'] = train_ann_file6
 
-train7 = {key: value for key, value in train6.items()}
-train7['img_prefix'] = train_img_prefix7
+train7 = {key: value for key, value in train1.items()}
+train7['img_prefix'] = dict(img_path=train_img_prefix7)
 train7['ann_file'] = train_ann_file7
 
-train8 = {key: value for key, value in train6.items()}
-train8['img_prefix'] = train_img_prefix8
+train8 = {key: value for key, value in train1.items()}
+train8['img_prefix'] = dict(img_path=train_img_prefix8)
 train8['ann_file'] = train_ann_file8
 
 train_list = [train1, train2, train3, train4, train5, train6, train7, train8]

configs/_base_/recog_datasets/ST_SA_MJ_train.py

@@ -1,48 +1,30 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: SynthText, Syn90k
+data_root = 'data/recog'
 
-train_root = 'data/mixture'
+train_img_prefix1 = 'SynthText_Add'
+train_img_prefix2 = 'SynthText/synthtext/' + \
+    'SynthText_patch_horizontal'
+train_img_prefix3 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
 
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
+train_ann_file1 = 'SynthText_Add/label.json',
+train_ann_file2 = 'SynthText/label.json',
+train_ann_file3 = 'Syn90k/label.json'
 
 train1 = dict(
     type='OCRDataset',
-    img_prefix=train_img_prefix1,
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix1),
     ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/label.lmdb'
-
-train_img_prefix3 = f'{train_root}/SynthText_Add'
-train_ann_file3 = f'{train_root}/SynthText_Add/label.txt'
+    test_mode=False,
+    pipeline=None)
 
 train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
+train2['data_prefix'] = dict(img_path=train_img_prefix2)
 train2['ann_file'] = train_ann_file2
 
-train3 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix3,
-    ann_file=train_ann_file3,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=False)
+train3 = {key: value for key, value in train1.items()}
+train3['img_prefix'] = dict(img_path=train_img_prefix3)
+train3['ann_file'] = train_ann_file3
 
 train_list = [train1, train2, train3]

configs/_base_/recog_datasets/ST_charbox_train.py

@@ -1,23 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: SynthText (with character level boxes)
-
-train_img_root = 'data/mixture'
-
-train_img_prefix = f'{train_img_root}/SynthText'
-
-train_ann_file = f'{train_img_root}/SynthText/instances_train.txt'
-
-train = dict(
-    type='OCRSegDataset',
-    img_prefix=train_img_prefix,
-    ann_file=train_ann_file,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineJsonParser', keys=['file_name', 'annotations', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_list = [train]

configs/_base_/recog_datasets/academic_test.py

@@ -2,56 +2,48 @@
 # Regular Datasets: IIIT5K, SVT, IC13
 # Irregular Datasets: IC15, SVTP, CT80
 
-test_root = 'data/mixture'
+test_root = 'data/recog'
 
-test_img_prefix1 = f'{test_root}/IIIT5K/'
-test_img_prefix2 = f'{test_root}/svt/'
-test_img_prefix3 = f'{test_root}/icdar_2013/'
-test_img_prefix4 = f'{test_root}/icdar_2015/'
-test_img_prefix5 = f'{test_root}/svtp/'
-test_img_prefix6 = f'{test_root}/ct80/'
+test_img_prefix1 = 'IIIT5K/'
+test_img_prefix2 = 'svt/'
+test_img_prefix3 = 'icdar_2013/'
+test_img_prefix4 = 'icdar_2015/'
+test_img_prefix5 = 'svtp/'
+test_img_prefix6 = 'ct80/'
 
-test_ann_file1 = f'{test_root}/IIIT5K/test_label.txt'
-test_ann_file2 = f'{test_root}/svt/test_label.txt'
-test_ann_file3 = f'{test_root}/icdar_2013/test_label_1015.txt'
-test_ann_file4 = f'{test_root}/icdar_2015/test_label.txt'
-test_ann_file5 = f'{test_root}/svtp/test_label.txt'
-test_ann_file6 = f'{test_root}/ct80/test_label.txt'
+test_ann_file1 = 'IIIT5K/test_label.josn'
+test_ann_file2 = 'svt/test_label.josn'
+test_ann_file3 = 'icdar_2013/test_label_1015.josn'
+test_ann_file4 = 'icdar_2015/test_label.josn'
+test_ann_file5 = 'svtp/test_label.josn'
+test_ann_file6 = 'ct80/test_label.josn'
 
 test1 = dict(
     type='OCRDataset',
-    img_prefix=test_img_prefix1,
+    data_root=test_root,
+    data_prefix=dict(img_path=test_img_prefix1),
     ann_file=test_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=True)
+    test_mode=True,
+    pipeline=None)
 
 test2 = {key: value for key, value in test1.items()}
-test2['img_prefix'] = test_img_prefix2
+test2['data_prefix'] = dict(img_path=test_img_prefix2)
 test2['ann_file'] = test_ann_file2
 
 test3 = {key: value for key, value in test1.items()}
-test3['img_prefix'] = test_img_prefix3
+test3['data_prefix'] = dict(img_path=test_img_prefix3)
 test3['ann_file'] = test_ann_file3
 
 test4 = {key: value for key, value in test1.items()}
-test4['img_prefix'] = test_img_prefix4
+test4['data_prefix'] = dict(img_path=test_img_prefix4)
 test4['ann_file'] = test_ann_file4
 
 test5 = {key: value for key, value in test1.items()}
-test5['img_prefix'] = test_img_prefix5
+test5['data_prefix'] = dict(img_path=test_img_prefix5)
 test5['ann_file'] = test_ann_file5
 
 test6 = {key: value for key, value in test1.items()}
-test6['img_prefix'] = test_img_prefix6
+test6['data_prefix'] = dict(img_path=test_img_prefix6)
 test6['ann_file'] = test_ann_file6
 
 test_list = [test1, test2, test3, test4, test5, test6]

configs/_base_/recog_datasets/seg_toy_data.py

@@ -1,34 +0,0 @@
-prefix = 'tests/data/ocr_char_ann_toy_dataset/'
-
-train = dict(
-    type='OCRSegDataset',
-    img_prefix=f'{prefix}/imgs',
-    ann_file=f'{prefix}/instances_train.txt',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=100,
-        file_format='txt',
-        parser=dict(
-            type='LineJsonParser', keys=['file_name', 'annotations', 'text'])),
-    pipeline=None,
-    test_mode=True)
-
-test = dict(
-    type='OCRDataset',
-    img_prefix=f'{prefix}/imgs',
-    ann_file=f'{prefix}/instances_test.txt',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=True)
-
-train_list = [train]
-
-test_list = [test]

configs/_base_/recog_datasets/toy_data.py

@@ -1,54 +1,24 @@
-dataset_type = 'OCRDataset'
+data_root = 'tests/data/recog_toy_dataset'
+train_img_prefix = 'imgs/'
+train_anno_file = 'label.json'
 
-root = 'tests/data/ocr_toy_dataset'
-img_prefix = f'{root}/imgs'
-train_anno_file1 = f'{root}/label.txt'
-
-train1 = dict(
-    type=dataset_type,
-    img_prefix=img_prefix,
-    ann_file=train_anno_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=100,
-        file_format='txt',
-        file_storage_backend='disk',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
+train_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix),
+    ann_file=train_anno_file,
     pipeline=None,
     test_mode=False)
 
-train_anno_file2 = f'{root}/label.lmdb'
-train2 = dict(
-    type=dataset_type,
-    img_prefix=img_prefix,
-    ann_file=train_anno_file2,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=100,
-        file_format='lmdb',
-        file_storage_backend='disk',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-test_anno_file1 = f'{root}/label.lmdb'
-test = dict(
-    type=dataset_type,
-    img_prefix=img_prefix,
-    ann_file=test_anno_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        file_storage_backend='disk',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
+test_anno_file = f'{data_root}/label.json'
+test_dataset = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix),
+    ann_file=train_anno_file,
     pipeline=None,
     test_mode=True)
 
-train_list = [train1, train2]
+train_list = [train_dataset]
 
-test_list = [test]
+test_list = [test_dataset]

configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py

@@ -1,13 +1,21 @@
 _base_ = [
     'dbnet_r18_fpnc.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1200e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), )
 
 train_pipeline_r18 = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -32,7 +40,10 @@ train_pipeline_r18 = [
 ]
 
 test_pipeline_1333_736 = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(1333, 736), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -40,37 +51,24 @@ test_pipeline_1333_736 = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/icdar2015'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline_r18)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline_1333_736)
-
 train_dataloader = dict(
     batch_size=16,
     num_workers=8,
     persistent_workers=False,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list,
+        pipeline=train_pipeline_r18))
+
 val_dataloader = dict(
     batch_size=16,
     num_workers=8,
     persistent_workers=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=test_list,
+        pipeline=test_pipeline_1333_736))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py

@@ -1,15 +1,23 @@
 _base_ = [
     'dbnet_r50dcnv2_fpnc.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1200e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), )
 
 load_from = 'checkpoints/textdet/dbnet/res50dcnv2_synthtext.pth'
 
 train_pipeline_r50dcnv2 = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_bbox=True,
@@ -34,7 +42,10 @@ train_pipeline_r50dcnv2 = [
 ]
 
 test_pipeline_4068_1024 = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(4068, 1024), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -42,37 +53,24 @@ test_pipeline_4068_1024 = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/icdar2015'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline_r50dcnv2)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline_4068_1024)
-
 train_dataloader = dict(
     batch_size=16,
     num_workers=8,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=train_list,
+        pipeline=train_pipeline_r50dcnv2))
 val_dataloader = dict(
     batch_size=16,
     num_workers=8,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=test_list,
+        pipeline=test_pipeline_4068_1024))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py

@@ -1,11 +1,21 @@
 _base_ = [
     'drrg_r50_fpn_unet.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1200e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), )
+
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_bbox=True,
@@ -55,7 +65,10 @@ train_pipeline = [
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(1024, 640), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -63,37 +76,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/ctw1500'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=4,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=1,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015.py

@@ -1,15 +1,23 @@
 _base_ = [
     'fcenet_r50_fpn.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1500e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(
     checkpoint=dict(type='CheckpointHook', interval=20),
     logger=dict(type='LoggerHook', interval=20))
 
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -55,7 +63,10 @@ train_pipeline = [
         meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(2260, 2260), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -63,37 +74,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/icdar2015'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=8,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py

@@ -1,15 +1,23 @@
 _base_ = [
     'fcenet_r50_fpn.py',
+    '../../_base_/det_datasets/ctw1500.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1500e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(
     checkpoint=dict(type='CheckpointHook', interval=20),
     logger=dict(type='LoggerHook', interval=20))
 
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -60,7 +68,10 @@ train_pipeline = [
         meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(1080, 736), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -68,37 +79,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/ctw'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=8,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py

@@ -1,15 +1,23 @@
 _base_ = [
     'ocr_mask_rcnn_r50_fpn_ohem_poly.py',
+    '../../_base_/det_datasets/ctw1500.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_160e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(
     checkpoint=dict(type='CheckpointHook', interval=20),
     logger=dict(type='LoggerHook', interval=20))
 
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -37,7 +45,10 @@ train_pipeline = [
                    'scale_factor', 'flip_direction'))
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='mmdet.Resize', scale=(1600, 1600), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -45,36 +56,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/ctw'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
 train_dataloader = dict(
     batch_size=8,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py

@@ -1,15 +1,23 @@
 _base_ = [
     'ocr_mask_rcnn_r50_fpn_ohem_poly.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_160e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(
     checkpoint=dict(type='CheckpointHook', interval=20),
     logger=dict(type='LoggerHook', interval=20))
 
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -37,7 +45,10 @@ train_pipeline = [
                    'scale_factor', 'flip_direction'))
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='mmdet.Resize', scale=(1920, 1920), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -45,37 +56,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/icdar2015'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=8,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py

@@ -1,15 +1,23 @@
 _base_ = [
     'ocr_mask_rcnn_r50_fpn_ohem_poly.py',
+    '../../_base_/det_datasets/icdar2017.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_160e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(
     checkpoint=dict(type='CheckpointHook', interval=20),
     logger=dict(type='LoggerHook', interval=20))
 
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -36,7 +44,10 @@ train_pipeline = [
                    'scale_factor', 'flip_direction'))
 ]
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='mmdet.Resize', scale=(1920, 1920), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -44,37 +55,22 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/icdar2017'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=8,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
+
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
+
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py

@@ -1,11 +1,21 @@
 _base_ = [
     'panet_r18_fpem_ffm.py',
     '../../_base_/default_runtime.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/schedules/schedule_adam_600e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), )
+
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -28,7 +38,10 @@ train_pipeline = [
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     # TODO Replace with mmcv.RescaleToShort when it's ready
     dict(
         type='RescaleToShortAspectJitter',
@@ -42,37 +55,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/det/icdar2015'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instance_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instance_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=8,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(

configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py

@@ -1,13 +1,25 @@
 _base_ = [
     'psenet_r50_fpnf.py',
+    '../../_base_/det_datasets/icdar2015.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_600e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
+default_hooks = dict(
+    checkpoint=dict(type='CheckpointHook', interval=20),
+    logger=dict(type='LoggerHook', interval=20))
+
 model = {{_base_.model_quad}}
 
 train_pipeline_icdar2015 = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_polygon=True,
@@ -29,7 +41,10 @@ train_pipeline_icdar2015 = [
 ]
 
 test_pipeline_icdar2015 = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(2240, 2240), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -37,37 +52,24 @@ test_pipeline_icdar2015 = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/icdar2015'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline_icdar2015)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline_icdar2015)
-
 train_dataloader = dict(
     batch_size=16,
     num_workers=8,
     persistent_workers=False,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=train_list,
+        pipeline=train_pipeline_icdar2015))
 val_dataloader = dict(
     batch_size=1,
     num_workers=4,
     persistent_workers=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset',
+        datasets=test_list,
+        pipeline=test_pipeline_icdar2015))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py

@@ -1,11 +1,23 @@
 _base_ = [
     'textsnake_r50_fpn_unet.py',
+    '../../_base_/det_datasets/ctw1500.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1200e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
+default_hooks = dict(
+    checkpoint=dict(type='CheckpointHook', interval=20),
+    logger=dict(type='LoggerHook', interval=20))
+
 train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(
         type='LoadOCRAnnotations',
         with_bbox=True,
@@ -46,7 +58,10 @@ train_pipeline = [
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args=file_client_args,
+        color_type='color_ignore_orientation'),
     dict(type='Resize', scale=(1333, 736), keep_ratio=True),
     dict(
         type='PackTextDetInputs',
@@ -54,37 +69,20 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/ctw1500'
-
-train_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_training.json',
-    data_prefix=dict(img_path='imgs/'),
-    filter_cfg=dict(filter_empty_gt=True, min_size=32),
-    pipeline=train_pipeline)
-
-test_dataset = dict(
-    type=dataset_type,
-    data_root=data_root,
-    ann_file='instances_test.json',
-    data_prefix=dict(img_path='imgs/'),
-    test_mode=True,
-    pipeline=test_pipeline)
-
 train_dataloader = dict(
     batch_size=4,
     num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 val_dataloader = dict(
     batch_size=1,
     num_workers=1,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
+    dataset=dict(
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='HmeanIOUMetric')

configs/textrecog/abinet/base.py

@@ -1,14 +1,15 @@
 _base_ = [
+    '../../_base_/recog_datasets/ST_MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_20e.py',
 ]
 
-default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
-
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -80,26 +81,13 @@ test_pipeline = [
                    'instances'))
 ]
 
-dataset_mj = dict(
-    type=dataset_type,
-    data_root=data_root,
-    data_prefix=dict(img_path='mnt/ramdisk/max/90kDICT32px/'),
-    ann_file='data/MJ/label.json',
-    pipeline=train_pipeline)
-dataset_st = dict(
-    type=dataset_type,
-    data_root=data_root,
-    data_prefix=dict(
-        img_path='SynthText/synthtext/SynthText_patch_horizontal/'),
-    ann_file='data/ST/alphanumeric_labels.json',
-    pipeline=train_pipeline)
-
 train_dataloader = dict(
     batch_size=192 * 4,
     num_workers=32,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(type='ConcatDataset', datasets=[dataset_mj, dataset_st]))
+    dataset=dict(
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=192,
@@ -108,12 +96,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path='testset/testset/IIIT5K/'),
-        ann_file='label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = dict(type='WordMetric', mode=['ignore_case_symbol'])

configs/textrecog/crnn/crnn_academic_dataset.py

@@ -2,15 +2,16 @@
 _base_ = [
     'crnn.py',
     '../../_base_/default_runtime.py',
+    '../../_base_/recog_datasets/MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/schedules/schedule_adadelta_5e.py',
 ]
 
-default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
-
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
 
 train_pipeline = [
     dict(
@@ -47,11 +48,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=1,
@@ -60,12 +57,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/master/master_r31_12e_ST_MJ_SA.py

@@ -1,15 +1,16 @@
 _base_ = [
     'master.py',
+    '../../_base_/recog_datasets/ST_SA_MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_12e.py',
 ]
 
-default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
-
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -47,11 +48,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=128,
@@ -60,12 +57,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=train_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/master/master_toy_dataset.py

@@ -1,15 +1,15 @@
 _base_ = [
     'master.py',
+    '../../_base_/recog_datasets/toy_data.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_12e.py',
 ]
 
-default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
-
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'test/data/recog_toy_dataset'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=50), )
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -47,11 +47,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=2,
@@ -60,12 +56,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/nrtr/nrtr_modality_transform_academic.py

@@ -1,5 +1,7 @@
 _base_ = [
-    'nrtr_modality_transform.py', '../../_base_/default_runtime.py',
+    'nrtr_modality_transform.py', '../../_base_/recog_datasets/ST_MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
+    '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_6e.py'
 ]
 
@@ -7,9 +9,10 @@ optimizer = dict(type='Adam', lr=3e-4)
 default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
 
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -47,11 +50,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=test_pipeline))
 
 val_dataloader = dict(
     batch_size=128,
@@ -60,12 +59,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/nrtr/nrtr_modality_transform_toy_dataset.py

@@ -3,12 +3,11 @@ _base_ = [
     '../../_base_/schedules/schedule_adam_step_6e.py'
 ]
 
-default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
-
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'test/data/recog_toy_dataset'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -46,11 +45,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=test_pipeline))
 
 val_dataloader = dict(
     batch_size=128,
@@ -59,12 +54,8 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
+
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/nrtr/nrtr_r31_1by16_1by8_academic.py

@@ -1,10 +1,17 @@
 _base_ = [
+    '../../_base_/recog_datasets/ST_MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_6e.py'
 ]
 
 optimizer = dict(type='Adam', lr=3e-4)
-default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
+
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 dictionary = dict(
     type='Dictionary',
@@ -34,11 +41,6 @@ model = dict(
     preprocess_cfg=dict(
         mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]))
 
-# dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
-file_client_args = dict(backend='disk')
-
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
     dict(type='LoadOCRAnnotations', with_text=True),
@@ -75,11 +77,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=128,
@@ -88,12 +86,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/nrtr/nrtr_r31_1by8_1by4_academic.py

@@ -1,4 +1,6 @@
 _base_ = [
+    '../../_base_/recog_datasets/ST_MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_6e.py'
 ]
@@ -6,6 +8,12 @@ _base_ = [
 optimizer = dict(type='Adam', lr=3e-4)
 default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
+
 dictionary = dict(
     type='Dictionary',
     dict_file='dicts/english_digits_symbols.txt',
@@ -34,11 +42,6 @@ model = dict(
     preprocess_cfg=dict(
         mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]))
 
-# dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
-file_client_args = dict(backend='disk')
-
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
     dict(type='LoadOCRAnnotations', with_text=True),
@@ -75,11 +78,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=128,
@@ -88,12 +87,8 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=train_pipeline))
+
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/robust_scanner/robustscanner_r31_academic.py

@@ -1,12 +1,15 @@
 _base_ = [
-    'robust_scanner.py', '../../_base_/default_runtime.py',
+    'robust_scanner.py', '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
+    '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_5e.py'
 ]
 
 # dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'data/recog'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -38,11 +41,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=''),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=1,
@@ -51,12 +50,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=''),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/sar/sar_r31_parallel_decoder_academic.py

@@ -1,12 +1,16 @@
 _base_ = [
     'sar.py',
+    '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_5e.py',
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -44,11 +48,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=1,
@@ -57,12 +57,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/sar/sar_r31_sequential_decoder_academic.py

@@ -1,12 +1,16 @@
 _base_ = [
     'sar.py',
+    '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_5e.py',
 ]
 
-dataset_type = 'OCRDataset'
-data_root = 'data/recog/'
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
 file_client_args = dict(backend='disk')
+default_hooks = dict(logger=dict(type='LoggerHook', interval=100))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -38,11 +42,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=1,
@@ -51,12 +51,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [

configs/textrecog/satrn/satrn_academic.py

@@ -1,15 +1,19 @@
 _base_ = [
-    'satrn.py'
+    'satrn.py',
+    '../../_base_/recog_datasets/ST_MJ_train.py',
+    '../../_base_/recog_datasets/academic_test.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_adam_step_5e.py',
 ]
 
+# dataset settings
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+file_client_args = dict(backend='disk')
 default_hooks = dict(logger=dict(type='LoggerHook', interval=50))
 
-# dataset settings
-dataset_type = 'OCRDataset'
-data_root = 'tests/data/ocr_toy_dataset'
-file_client_args = dict(backend='petrel')
+# optimizer
+optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=3e-4))
 
 model = dict(
     type='SATRN',
@@ -38,9 +42,6 @@ model = dict(
         max_seq_len=25,
         postprocessor=dict(type='AttentionPostprocessor')))
 
-# optimizer
-optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=3e-4))
-
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
     dict(type='LoadOCRAnnotations', with_text=True),
@@ -66,11 +67,7 @@ train_dataloader = dict(
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='train_label.json',
-        pipeline=train_pipeline))
+        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
 
 val_dataloader = dict(
     batch_size=64,
@@ -79,12 +76,7 @@ val_dataloader = dict(
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),
     dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        data_prefix=dict(img_path=None),
-        ann_file='test_label.json',
-        test_mode=True,
-        pipeline=test_pipeline))
+        type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
 test_dataloader = val_dataloader
 
 val_evaluator = [