TypeError: __init__() got an unexpected keyword argument 'data_filter'

[Y-pandaman]

python tools/train.py configs/resnet_ctc_xg.py 2022-06-28 16:28:49,958 - INFO - Use CPU 2022-06-28 16:28:49,958 - INFO - Set cudnn deterministic False 2022-06-28 16:28:49,959 - INFO - Set cudnn benchmark True 2022-06-28 16:28:49,959 - INFO - Set seed 1111 2022-06-28 16:28:49,964 - INFO - Build model 2022-06-28 16:28:50,349 - INFO - GResNet init weights 2022-06-28 16:28:50,710 - INFO - CTCHead init weights Traceback (most recent call last): File "tools/train.py", line 42, in main() File "tools/train.py", line 37, in main runner = TrainRunner(train_cfg, deploy_cfg, common_cfg) File "tools/../vedastr/runners/train_runner.py", line 19, in init train_cfg['data']['train']) File "tools/../vedastr/runners/base.py", line 84, in _build_dataloader dataset = build_datasets(cfg['dataset'], dict(transform=transform)) File "tools/../vedastr/datasets/builder.py", line 16, in build_datasets datasets = build_from_cfg(cfg, DATASETS, default_args) File "tools/../vedastr/utils/common.py", line 14, in build_from_cfg return obj_from_dict_registry(cfg, parent, default_args) File "tools/../vedastr/utils/common.py", line 79, in obj_from_dict_registry return obj_cls(**args) File "tools/../vedastr/datasets/concat_dataset.py", line 12, in init datasets = build_datasets(datasets, default_args=kwargs) File "tools/../vedastr/datasets/builder.py", line 13, in build_datasets ds = build_from_cfg(icfg, DATASETS, default_args) File "tools/../vedastr/utils/common.py", line 14, in build_from_cfg return obj_from_dict_registry(cfg, parent, default_args) File "tools/../vedastr/utils/common.py", line 79, in obj_from_dict_registry return obj_cls(**args) File "tools/../vedastr/datasets/concat_dataset.py", line 12, in init datasets = build_datasets(datasets, default_args=kwargs) File "tools/../vedastr/datasets/builder.py", line 13, in build_datasets ds = build_from_cfg(icfg, DATASETS, default_args) File "tools/../vedastr/utils/common.py", line 14, in build_from_cfg return obj_from_dict_registry(cfg, parent, default_args) File "tools/../vedastr/utils/common.py", line 79, in obj_from_dict_registry return obj_cls(**args) TypeError: init() got an unexpected keyword argument 'data_filter'

###############################################################################

1. deploy

size = (32, 100) mean, std = 0.5, 0.5

sensitive = False

character = 'abcdefghijklmnopqrstuvwxyz0123456789'

character = '0123456789abcdefghijklmnopq'
'rstuvwxyzABCDEFGHIJKLMNOPQRS'
'TUVWXYZ-:' # need character test_sensitive = False test_character = '0123456789abcdefghijklmnopqrstuvwxyz' batch_max_length = 25

norm_cfg = dict(type='BN') num_class = len(character) + 1

deploy = dict( gpu_id='0', transform=[ dict(type='Sensitive', sensitive=sensitive, need_character=character), dict(type='ToGray'), dict(type='Resize', size=size), dict(type='Normalize', mean=mean, std=std), dict(type='ToTensor'), ], converter=dict( type='CTCConverter', character=character, batch_max_length=batch_max_length, ), model=dict( type='GModel', need_text=False, body=dict( type='GBody', pipelines=[ dict( type='FeatureExtractorComponent', from_layer='input', to_layer='cnn_feat', arch=dict( encoder=dict( backbone=dict( type='GResNet', layers=[ ('conv', dict(type='ConvModule', in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)), ('conv', dict(type='ConvModule', in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)), ('pool', dict(type='MaxPool2d', kernel_size=2, stride=2, padding=0)), ('block', dict(block_name='BasicBlock', planes=128, blocks=1, stride=1)), ('conv', dict(type='ConvModule', in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)), ('pool', dict(type='MaxPool2d', kernel_size=2, stride=2, padding=0)), ('block', dict(block_name='BasicBlock', planes=256, blocks=2, stride=1)), ('conv', dict(type='ConvModule', in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)), ('pool', dict(type='MaxPool2d', kernel_size=2, stride=(2, 1), padding=(0, 1))), ('block', dict(block_name='BasicBlock', planes=512, blocks=5, stride=1)), ('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, norm_cfg=norm_cfg)), ('block', dict(block_name='BasicBlock', planes=512, blocks=3, stride=1)), ('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=2, stride=(2, 1), padding=(0, 1), norm_cfg=norm_cfg)), ('conv', dict(type='ConvModule', in_channels=512, out_channels=512, kernel_size=2, stride=1, padding=0, norm_cfg=norm_cfg)), ], ), ), collect=dict(type='CollectBlock', from_layer='c4'), ), ), ], ), head=dict( type='CTCHead', from_layer='cnn_feat', num_class=num_class, in_channels=512, pool=dict( type='AdaptiveAvgPool2d', output_size=(1, None), ), ), ), postprocess=dict( sensitive=test_sensitive, character=test_character, ), )

###############################################################################

2.common

common = dict( seed=1111, logger=dict( handlers=( dict(type='StreamHandler', level='INFO'), dict(type='FileHandler', level='INFO'), ), ), cudnn_deterministic=False, cudnn_benchmark=True, metric=dict(type='Accuracy'), ) ###############################################################################

dataset_params = dict( batch_max_length=batch_max_length, data_filter=True, character=character, )

test_dataset_params = dict( batch_max_length=batch_max_length, data_filter=False, character=test_character, )

data_root = '/public/datasets_neo/deep-text-recognition-benchmark/' data_root_xg = '/public/experiments/ypt/xg/'

###############################################################################

3. test

batch_size = 192

data

test_root = data_root + 'evaluation/'

test_folder_names = ['CUTE80']

test_folder_names = ['CUTE80', 'IC03_867', 'IC13_1015', 'IC15_2077', 'IIIT5k_3000', 'SVT', 'SVTP'] test_dataset = [dict(type='LmdbDataset', root=test_root + f_name, **test_dataset_params) for f_name in test_folder_names]

test = dict( data=dict( dataloader=dict( type='DataLoader', batch_size=batch_size, num_workers=4, shuffle=False, ), dataset=test_dataset, transform=[ dict(type='Sensitive', sensitive=test_sensitive, need_character=test_character), dict(type='ToGray'), dict(type='Resize', size=size), dict(type='Normalize', mean=mean, std=std), dict(type='ToTensor'), ], ), postprocess_cfg=dict( sensitive=test_sensitive, character=test_character, ), )

###############################################################################

4. train

root_workdir = 'workdir'

train data

train_root = data_root + 'training/' train_root_xg = data_root_xg + 'training/'

MJ dataset

train_root_mj = train_root + 'MJ/' mj_folder_names = ['MJ_test', 'MJ_valid', 'MJ_train']

ST dataset

train_root_st = train_root + 'SynthText/'

TODG dataset

train_root_todg = train_root + 'todg/'

TODG-plain dataset

train_root_todg_plain = train_root + 'todg_plain/'

xg dataset

train_root_xg = train_root + 'todg_xg/'

train_dataset_mj = [dict(type='LmdbDataset', root=train_root_mj + folder_name) for folder_name in mj_folder_names] train_dataset_st = [dict(type='LmdbDataset', root=train_root_st)] train_dataset_todg = [dict(type='TxtDataset', root=train_root_todg)] train_dataset_todg_plain = [dict(type='TxtDataset', root=train_root_todg_plain)] train_dataset_xg = [dict(type='TxtDataset', root=train_root_xg)]

valid

valid_root = data_root + 'validation/' valid_dataset = dict(type='LmdbDataset', root=valid_root, **test_dataset_params)

train transforms

train_transforms = [ dict(type='Sensitive', sensitive=sensitive, need_character=character), dict(type='ToGray'), dict(type='Resize', size=size), dict(type='Normalize', mean=mean, std=std), dict(type='ToTensor'), ]

max_iterations = 300000 milestones = [150000, 250000]

train = dict( data=dict( train=dict( dataloader=dict( type='DataLoader', batch_size=batch_size, num_workers=8, ), sampler=dict( type='BalanceSampler', batch_size=batch_size, shuffle=True, oversample=True, ), dataset=dict( type='ConcatDatasets', datasets=[ # dict( # type='ConcatDatasets', # datasets=train_dataset_mj, # ), # dict( # type='ConcatDatasets', # datasets=train_dataset_st, # ), dict( type='ConcatDatasets', datasets=train_dataset_todg, ), dict( type='ConcatDatasets', datasets=train_dataset_todg_plain, ), dict( type='ConcatDatasets', datasets=train_dataset_xg, ) ], batch_ratio=[0.5, 0.5], **dataset_params, ), transform=train_transforms, ), val=dict( dataloader=dict( type='DataLoader', batch_size=batch_size, num_workers=8, shuffle=False, ), dataset=valid_dataset, transform=deploy['transform'], ), ), optimizer=dict(type='Adadelta', lr=1.0, rho=0.95, eps=1e-8), criterion=dict(type='CTCLoss', zero_infinity=True), lr_scheduler=dict(type='StepLR', iter_based=True, milestones=milestones, ), max_iterations=max_iterations, log_interval=10, trainval_ratio=2000, snapshot_interval=20000, save_best=True, resume=None, )