[Fix] Fix some config errors

configs/_base_/recog_datasets/MJ_train.py

@@ -1,15 +1,14 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: Syn90k
-data_root = 'data/recog'
+data_root = 'data/rec'
 train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
 train_ann_file1 = 'Syn90k/label.json'
-file_client_args = dict(backend='disk')
 
-train = dict(
+MJ = dict(
     type='OCRDataset',
     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]
+train_list = [MJ]

configs/_base_/recog_datasets/ST_MJ_alphanumeric_train.py

@@ -2,12 +2,11 @@
 # Synthetic Datasets: SynthText, Syn90k
 # Both annotations are filtered so that
 # only alphanumeric terms are left
-data_root = 'data/recog'
+data_root = 'data/rec'
 train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
 train_ann_file1 = 'Syn90k/label.json'
-file_client_args = dict(backend='disk')
 
-train1 = dict(
+MJ = dict(
     type='OCRDataset',
     data_root=data_root,
     data_prefix=dict(img_path=train_img_prefix1),
@@ -17,9 +16,13 @@ train1 = dict(
 
 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['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]
+ST = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix2),
+    ann_file=train_ann_file2,
+    test_mode=False,
+    pipeline=None)
+
+train_list = [MJ, ST]

configs/_base_/recog_datasets/ST_MJ_train.py

@@ -1,13 +1,13 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: SynthText, Syn90k
 
-data_root = 'data/recog'
+data_root = 'data/rec'
 
 train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
 train_ann_file1 = 'Syn90k/label.json'
 file_client_args = dict(backend='disk')
 
-train1 = dict(
+MJ = dict(
     type='OCRDataset',
     data_root=data_root,
     data_prefix=dict(img_path=train_img_prefix1),
@@ -17,9 +17,13 @@ train1 = dict(
 
 train_img_prefix2 = 'SynthText/synthtext/SynthText_patch_horizontal'
 train_ann_file2 = 'SynthText/label.json'
-train2 = {key: value for key, value in train1.items()}
-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]
+ST = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix2),
+    ann_file=train_ann_file2,
+    test_mode=False,
+    pipeline=None)
+
+train_list = [MJ, ST]

configs/_base_/recog_datasets/ST_SA_MJ_real_train.py

@@ -1,7 +1,7 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: SynthText, SynthAdd, Syn90k
 # Real Dataset: IC11, IC13, IC15, COCO-Test, IIIT5k
-data_root = 'data/recog'
+data_root = 'data/rec'
 
 train_img_prefix1 = 'icdar_2011'
 train_img_prefix2 = 'icdar_2013'
@@ -9,19 +9,19 @@ 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_prefix7 = 'SynthText/synthtext/SynthText_patch_horizontal'
+train_img_prefix8 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
 
 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'
+train_ann_file6 = 'SynthText_Add/train_label.json',
+train_ann_file7 = 'SynthText/shuffle_label.json',
+train_ann_file8 = 'Syn90k/mnt/ramdisk/max/90kDICT32px/shuffle_label.json'
 
-train1 = dict(
+IC11 = dict(
     type='OCRDataset',
     data_root=data_root,
     data_prefix=dict(img_path=train_img_prefix1),
@@ -29,32 +29,60 @@ train1 = dict(
     test_mode=False,
     pipeline=None)
 
-train2 = {key: value for key, value in train1.items()}
-train2['data_prefix'] = dict(img_path=train_img_prefix2)
-train2['ann_file'] = train_ann_file2
+IC13 = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix2),
+    ann_file=train_ann_file2,
+    test_mode=False,
+    pipeline=None)
 
-train3 = {key: value for key, value in train1.items()}
-train3['img_prefix'] = dict(img_path=train_img_prefix3)
-train3['ann_file'] = train_ann_file3
+IC15 = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix3),
+    ann_file=train_ann_file3,
+    test_mode=False,
+    pipeline=None)
 
-train4 = {key: value for key, value in train1.items()}
-train4['img_prefix'] = dict(img_path=train_img_prefix4)
-train4['ann_file'] = train_ann_file4
+COCO = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix4),
+    ann_file=train_ann_file4,
+    test_mode=False,
+    pipeline=None)
 
-train5 = {key: value for key, value in train1.items()}
-train5['img_prefix'] = dict(img_path=train_img_prefix5)
-train5['ann_file'] = train_ann_file5
+IIIT5K = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix5),
+    ann_file=train_ann_file5,
+    test_mode=False,
+    pipeline=None)
 
-train6 = {key: value for key, value in train1.items()}
-train6['img_prefix'] = dict(img_path=train_img_prefix6)
-train6['ann_file'] = train_ann_file6
+STADD = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix6),
+    ann_file=train_ann_file6,
+    test_mode=False,
+    pipeline=None)
 
-train7 = {key: value for key, value in train1.items()}
-train7['img_prefix'] = dict(img_path=train_img_prefix7)
-train7['ann_file'] = train_ann_file7
+ST = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix7),
+    ann_file=train_ann_file7,
+    test_mode=False,
+    pipeline=None)
 
-train8 = {key: value for key, value in train1.items()}
-train8['img_prefix'] = dict(img_path=train_img_prefix8)
-train8['ann_file'] = train_ann_file8
+MJ = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix8),
+    ann_file=train_ann_file8,
+    test_mode=False,
+    pipeline=None)
 
-train_list = [train1, train2, train3, train4, train5, train6, train7, train8]
+train_list = [IC13, IC11, IC15, COCO, IIIT5K, STADD, ST, MJ]

configs/_base_/recog_datasets/ST_SA_MJ_train.py

@@ -1,17 +1,10 @@
 # Text Recognition Training set, including:
 # Synthetic Datasets: SynthText, Syn90k
-data_root = 'data/recog'
+data_root = 'data/rec'
+train_img_prefix1 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
+train_ann_file1 = 'Syn90k/label.json'
 
-train_img_prefix1 = 'SynthText_Add'
-train_img_prefix2 = 'SynthText/synthtext/' + \
-    'SynthText_patch_horizontal'
-train_img_prefix3 = 'Syn90k/mnt/ramdisk/max/90kDICT32px'
-
-train_ann_file1 = 'SynthText_Add/label.json',
-train_ann_file2 = 'SynthText/label.json',
-train_ann_file3 = 'Syn90k/label.json'
-
-train1 = dict(
+MJ = dict(
     type='OCRDataset',
     data_root=data_root,
     data_prefix=dict(img_path=train_img_prefix1),
@@ -19,12 +12,27 @@ train1 = dict(
     test_mode=False,
     pipeline=None)
 
-train2 = {key: value for key, value in train1.items()}
-train2['data_prefix'] = dict(img_path=train_img_prefix2)
-train2['ann_file'] = train_ann_file2
+train_img_prefix2 = 'SynthText/synthtext/' + \
+    'SynthText_patch_horizontal'
+train_ann_file2 = 'SynthText/label.json',
 
-train3 = {key: value for key, value in train1.items()}
-train3['img_prefix'] = dict(img_path=train_img_prefix3)
-train3['ann_file'] = train_ann_file3
+ST = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix2),
+    ann_file=train_ann_file2,
+    test_mode=False,
+    pipeline=None)
 
-train_list = [train1, train2, train3]
+train_img_prefix3 = 'SynthText_Add'
+train_ann_file3 = 'SynthText_Add/label.json'
+
+STADD = dict(
+    type='OCRDataset',
+    data_root=data_root,
+    data_prefix=dict(img_path=train_img_prefix3),
+    ann_file=train_ann_file3,
+    test_mode=False,
+    pipeline=None)
+
+train_list = [MJ, ST, STADD]

configs/_base_/recog_datasets/academic_test.py

@@ -2,23 +2,23 @@
 # Regular Datasets: IIIT5K, SVT, IC13
 # Irregular Datasets: IC15, SVTP, CT80
 
-test_root = 'data/recog'
+test_root = 'data/rec'
 
 test_img_prefix1 = 'IIIT5K/'
 test_img_prefix2 = 'svt/'
-test_img_prefix3 = 'icdar_2013/'
-test_img_prefix4 = 'icdar_2015/'
+test_img_prefix3 = 'icdar_2013/Challenge2_Test_Task3_Images/'
+test_img_prefix4 = 'icdar_2015/ch4_test_word_images_gt'
 test_img_prefix5 = 'svtp/'
 test_img_prefix6 = 'ct80/'
 
-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'
+test_ann_file1 = 'IIIT5K/test_label.json'
+test_ann_file2 = 'svt/test_label.json'
+test_ann_file3 = 'icdar_2013/test_label.json'
+test_ann_file4 = 'icdar_2015/test_label.json'
+test_ann_file5 = 'svtp/test_label.json'
+test_ann_file6 = 'ct80/test_label.json'
 
-test1 = dict(
+IIIT5K = dict(
     type='OCRDataset',
     data_root=test_root,
     data_prefix=dict(img_path=test_img_prefix1),
@@ -26,24 +26,44 @@ test1 = dict(
     test_mode=True,
     pipeline=None)
 
-test2 = {key: value for key, value in test1.items()}
-test2['data_prefix'] = dict(img_path=test_img_prefix2)
-test2['ann_file'] = test_ann_file2
+SVT = dict(
+    type='OCRDataset',
+    data_root=test_root,
+    data_prefix=dict(img_path=test_img_prefix2),
+    ann_file=test_ann_file2,
+    test_mode=True,
+    pipeline=None)
 
-test3 = {key: value for key, value in test1.items()}
-test3['data_prefix'] = dict(img_path=test_img_prefix3)
-test3['ann_file'] = test_ann_file3
+IC13 = dict(
+    type='OCRDataset',
+    data_root=test_root,
+    data_prefix=dict(img_path=test_img_prefix3),
+    ann_file=test_ann_file3,
+    test_mode=True,
+    pipeline=None)
 
-test4 = {key: value for key, value in test1.items()}
-test4['data_prefix'] = dict(img_path=test_img_prefix4)
-test4['ann_file'] = test_ann_file4
+IC15 = dict(
+    type='OCRDataset',
+    data_root=test_root,
+    data_prefix=dict(img_path=test_img_prefix4),
+    ann_file=test_ann_file4,
+    test_mode=True,
+    pipeline=None)
 
-test5 = {key: value for key, value in test1.items()}
-test5['data_prefix'] = dict(img_path=test_img_prefix5)
-test5['ann_file'] = test_ann_file5
+SVTP = dict(
+    type='OCRDataset',
+    data_root=test_root,
+    data_prefix=dict(img_path=test_img_prefix5),
+    ann_file=test_ann_file5,
+    test_mode=True,
+    pipeline=None)
 
-test6 = {key: value for key, value in test1.items()}
-test6['data_prefix'] = dict(img_path=test_img_prefix6)
-test6['ann_file'] = test_ann_file6
+CUTE80 = dict(
+    type='OCRDataset',
+    data_root=test_root,
+    data_prefix=dict(img_path=test_img_prefix6),
+    ann_file=test_ann_file6,
+    test_mode=True,
+    pipeline=None)
 
-test_list = [test1, test2, test3, test4, test5, test6]
+test_list = [IIIT5K, SVT, IC13, IC15, SVTP, CUTE80]

configs/_base_/recog_datasets/toy_data.py

@@ -1,4 +1,4 @@
-data_root = 'tests/data/recog_toy_dataset'
+data_root = 'tests/data/rec_toy_dataset'
 train_img_prefix = 'imgs/'
 train_anno_file = 'label.json'
 

configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py

@@ -1,5 +1,5 @@
 _base_ = [
-    'fcenet_r50_fpn.py',
+    'fcenet_r50dcnv2_fpn.py',
     '../../_base_/det_datasets/ctw1500.py',
     '../../_base_/default_runtime.py',
     '../../_base_/schedules/schedule_sgd_1500e.py',

configs/textrecog/master/master_r31_12e_ST_MJ_SA.py

@@ -44,7 +44,7 @@ test_pipeline = [
 
 train_dataloader = dict(
     batch_size=512,
-    num_workers=8,
+    num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(

configs/textrecog/nrtr/nrtr_modality_transform_academic.py

@@ -45,8 +45,8 @@ test_pipeline = [
 ]
 
 train_dataloader = dict(
-    batch_size=256,
-    num_workers=2,
+    batch_size=384,
+    num_workers=32,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
@@ -54,7 +54,7 @@ train_dataloader = dict(
 
 val_dataloader = dict(
     batch_size=128,
-    num_workers=2,
+    num_workers=4,
     persistent_workers=True,
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),

configs/textrecog/nrtr/nrtr_modality_transform_toy_dataset.py

@@ -40,16 +40,16 @@ test_pipeline = [
 ]
 
 train_dataloader = dict(
-    batch_size=256,
-    num_workers=2,
+    batch_size=8,
+    num_workers=4,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
         type='ConcatDataset', datasets=train_list, pipeline=test_pipeline))
 
 val_dataloader = dict(
-    batch_size=128,
-    num_workers=2,
+    batch_size=1,
+    num_workers=4,
     persistent_workers=True,
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),

configs/textrecog/nrtr/nrtr_r31.py

@@ -0,0 +1,30 @@
+dictionary = dict(
+    type='Dictionary',
+    dict_file='dicts/english_digits_symbols.txt',
+    with_padding=True,
+    with_unknown=True,
+    same_start_end=True,
+    with_start=True,
+    with_end=True)
+
+model = dict(
+    type='NRTR',
+    backbone=dict(
+        type='ResNet31OCR',
+        layers=[1, 2, 5, 3],
+        channels=[32, 64, 128, 256, 512, 512],
+        stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)),
+        last_stage_pool=True),
+    encoder=dict(type='NRTREncoder'),
+    decoder=dict(
+        type='NRTRDecoder',
+        module_loss=dict(
+            type='CEModuleLoss', ignore_first_char=True, flatten=True),
+        postprocessor=dict(type='AttentionPostprocessor'),
+        dictionary=dictionary,
+        max_seq_len=30,
+    ),
+    data_preprocessor=dict(
+        type='TextRecogDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375]))

configs/textrecog/nrtr/nrtr_r31_1by16_1by8_academic.py

@@ -1,5 +1,5 @@
 _base_ = [
-    '../../_base_/recog_datasets/ST_MJ_train.py',
+    'nrtr_r31.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'
@@ -13,34 +13,6 @@ 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',
-    with_padding=True,
-    with_unknown=True,
-    same_start_end=True,
-    with_start=True,
-    with_end=True)
-
-model = dict(
-    type='NRTR',
-    backbone=dict(
-        type='ResNet31OCR',
-        layers=[1, 2, 5, 3],
-        channels=[32, 64, 128, 256, 512, 512],
-        stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)),
-        last_stage_pool=True),
-    encoder=dict(type='NRTREncoder'),
-    decoder=dict(
-        type='NRTRDecoder',
-        module_loss=dict(
-            type='CEModuleLoss', ignore_first_char=True, flatten=True),
-        postprocessor=dict(type='AttentionPostprocessor')),
-    dictionary=dictionary,
-    max_seq_len=30,
-    preprocess_cfg=dict(
-        mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]))
-
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
     dict(type='LoadOCRAnnotations', with_text=True),
@@ -72,8 +44,8 @@ test_pipeline = [
 ]
 
 train_dataloader = dict(
-    batch_size=256,
-    num_workers=2,
+    batch_size=384,
+    num_workers=32,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
@@ -81,7 +53,7 @@ train_dataloader = dict(
 
 val_dataloader = dict(
     batch_size=128,
-    num_workers=2,
+    num_workers=4,
     persistent_workers=True,
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),

configs/textrecog/nrtr/nrtr_r31_1by8_1by4_academic.py

@@ -1,5 +1,5 @@
 _base_ = [
-    '../../_base_/recog_datasets/ST_MJ_train.py',
+    'nrtr_r31.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'
@@ -14,33 +14,7 @@ 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',
-    with_padding=True,
-    with_unknown=True,
-    same_start_end=True,
-    with_start=True,
-    with_end=True)
-
-model = dict(
-    type='NRTR',
-    backbone=dict(
-        type='ResNet31OCR',
-        layers=[1, 2, 5, 3],
-        channels=[32, 64, 128, 256, 512, 512],
-        stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)),
-        last_stage_pool=False),
-    encoder=dict(type='NRTREncoder'),
-    decoder=dict(
-        type='NRTRDecoder',
-        module_loss=dict(
-            type='CEModuleLoss', ignore_first_char=True, flatten=True),
-        postprocessor=dict(type='AttentionPostprocessor')),
-    dictionary=dictionary,
-    max_seq_len=30,
-    preprocess_cfg=dict(
-        mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]))
+model = dict(backbone=dict(last_stage_pool=False))
 
 train_pipeline = [
     dict(type='LoadImageFromFile', file_client_args=file_client_args),
@@ -73,8 +47,8 @@ test_pipeline = [
 ]
 
 train_dataloader = dict(
-    batch_size=256,
-    num_workers=2,
+    batch_size=384,
+    num_workers=32,
     persistent_workers=True,
     sampler=dict(type='DefaultSampler', shuffle=True),
     dataset=dict(
@@ -82,7 +56,7 @@ train_dataloader = dict(
 
 val_dataloader = dict(
     batch_size=128,
-    num_workers=2,
+    num_workers=4,
     persistent_workers=True,
     drop_last=False,
     sampler=dict(type='DefaultSampler', shuffle=False),

tests/datasets/test_recog_lmdb_dataset.py

@@ -28,7 +28,7 @@ class TestRecogLMDBDataset(TestCase):
 
         # test initialization
         dataset = RecogLMDBDataset(
-            ann_file='tests/data/recog_toy_dataset/label.lmdb',
+            ann_file='tests/data/rec_toy_dataset/label.lmdb',
             data_prefix=dict(img_path='imgs'),
             pipeline=[])
         dataset.full_init()
@@ -44,7 +44,7 @@ class TestRecogLMDBDataset(TestCase):
 
         # test initialization
         dataset = RecogLMDBDataset(
-            ann_file='tests/data/recog_toy_dataset/imgs.lmdb',
+            ann_file='tests/data/rec_toy_dataset/imgs.lmdb',
             data_prefix=dict(img_path='imgs'),
             pipeline=[])
         dataset.full_init()

tests/datasets/test_recog_text_dataset.py

@@ -10,7 +10,7 @@ class TestRecogTextDataset(TestCase):
 
         # test initialization
         dataset = RecogTextDataset(
-            ann_file='tests/data/recog_toy_dataset/old_label.txt',
+            ann_file='tests/data/rec_toy_dataset/old_label.txt',
             data_prefix=dict(img_path='imgs'),
             parser_cfg=dict(
                 type='LineStrParser',
@@ -30,7 +30,7 @@ class TestRecogTextDataset(TestCase):
 
     def test_jsonl_dataset(self):
         dataset = RecogTextDataset(
-            ann_file='tests/data/recog_toy_dataset/old_label.jsonl',
+            ann_file='tests/data/rec_toy_dataset/old_label.jsonl',
             data_prefix=dict(img_path='imgs'),
             parser_cfg=dict(type='LineJsonParser', keys=['filename', 'text']),
             pipeline=[])

tests/datasets/transforms/test_loading.py

@@ -142,12 +142,12 @@ class TestLoadImageFromLMDB(TestCase):
     def setUp(self):
         img_key = 'image-%09d' % 1
         self.results1 = {
-            'img_path': f'tests/data/recog_toy_dataset/imgs.lmdb/{img_key}'
+            'img_path': f'tests/data/rec_toy_dataset/imgs.lmdb/{img_key}'
         }
 
         img_key = 'image-%09d' % 100
         self.results2 = {
-            'img_path': f'tests/data/recog_toy_dataset/imgs.lmdb/{img_key}'
+            'img_path': f'tests/data/rec_toy_dataset/imgs.lmdb/{img_key}'
         }
 
     def test_transform(self):