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work_dir = 'records/guoshoucai_auto_gen_ps_with_tianchi_psccnet_baseline_dct_balance_scale_0_05_1_0_15_epochs_cls_weight_1_5_more_negs_seed_4567' | |
dataset_type = 'MaskSegDatasetv2' | |
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
input_size = (512, 512) | |
train_pre_pipeline = [ | |
dict(type='LoadImageFromFile'), | |
dict( | |
type='LoadAnnotations', binary=True, train=True, img_label_binary=True) | |
] | |
train_post_pipeline = [ | |
dict(type='SimpleResize', size=(512, 512)), | |
dict(type='RandomFlip', prob=0.5), | |
dict( | |
type='Normalizev2', | |
mean=[0.485, 0.456, 0.406], | |
std=[0.229, 0.224, 0.225]), | |
dict(type='DefaultFormatBundle'), | |
dict(type='Collect', keys=['img', 'gt_semantic_seg', 'img_label']) | |
] | |
test_pipeline = [ | |
dict(type='LoadImageFromFile'), | |
dict(type='SimpleResize', size=(512, 512)), | |
dict( | |
type='Normalizev2', | |
mean=[0.485, 0.456, 0.406], | |
std=[0.229, 0.224, 0.225]), | |
dict(type='ImageToTensor', keys=['img']), | |
dict(type='Collect', keys=['img']) | |
] | |
data = dict( | |
samples_per_gpu=1, | |
workers_per_gpu=4, | |
train=dict( | |
type='MaskSegDatasetv2', | |
data_root='/mnt/disk1/data/image_forgery/text_forgery', | |
ann_path='guoshoucai_auto_gen_ps_with_tianchi_1.txt', | |
pipeline=[[{ | |
'type': 'LoadImageFromFile' | |
}, { | |
'type': 'LoadAnnotations', | |
'binary': True, | |
'train': True, | |
'img_label_binary': True | |
}], | |
[{ | |
'type': 'SimpleResize', | |
'size': (512, 512) | |
}, { | |
'type': 'RandomFlip', | |
'prob': 0.5 | |
}, { | |
'type': 'Normalizev2', | |
'mean': [0.485, 0.456, 0.406], | |
'std': [0.229, 0.224, 0.225] | |
}, { | |
'type': 'DefaultFormatBundle' | |
}, { | |
'type': 'Collect', | |
'keys': ['img', 'gt_semantic_seg', 'img_label'] | |
}]]), | |
val=[ | |
dict( | |
type='MaskSegDatasetv2', | |
data_root= | |
'/mnt/disk1/data/image_forgery/text_forgery/guoshoucai_auto_gen/test_forged_with_ps', | |
ann_path='test_1.txt', | |
test_mode=True, | |
pipeline=[ | |
dict(type='LoadImageFromFile'), | |
dict(type='SimpleResize', size=(512, 512)), | |
dict( | |
type='Normalizev2', | |
mean=[0.485, 0.456, 0.406], | |
std=[0.229, 0.224, 0.225]), | |
dict(type='ImageToTensor', keys=['img']), | |
dict(type='Collect', keys=['img']) | |
], | |
dataset_name='guoshoucai_text', | |
gt_seg_map_loader_cfg=dict(binary=True, img_label_binary=True)), | |
dict( | |
type='MaskSegDatasetv2', | |
data_root= | |
'/mnt/disk1/data/image_forgery/text_forgery/tianchi_text_forgory', | |
ann_path='val.txt', | |
test_mode=True, | |
pipeline=[ | |
dict(type='LoadImageFromFile'), | |
dict(type='SimpleResize', size=(512, 512)), | |
dict( | |
type='Normalizev2', | |
mean=[0.485, 0.456, 0.406], | |
std=[0.229, 0.224, 0.225]), | |
dict(type='ImageToTensor', keys=['img']), | |
dict(type='Collect', keys=['img']) | |
], | |
dataset_name='tianchi', | |
gt_seg_map_loader_cfg=dict(binary=True, img_label_binary=True)) | |
]) | |
norm_cfg = dict(type='SyncBN', requires_grad=True) | |
model = dict( | |
type='PSCCDetector', | |
base_model=dict( | |
type='PSCCNet', | |
crop_size=(512, 512), | |
pretrained= | |
'/home/yangwu/.cache/torch/checkpoints/hrnet_w18_small_v2.pth'), | |
train_cfg=dict( | |
seg_loss=dict(type='BCELoss', reduction='none'), | |
seg_loss_weights=(1.0, 1.0), | |
mask_loss_weights=(1.0, 1.0, 1.0, 1.0), | |
cls_loss=dict( | |
type='CrossEntropyLoss', | |
use_sigmoid=False, | |
class_weight=(1.0, 1.0)), | |
p_balance_scale=0.05, | |
n_balance_scale=1.0), | |
test_cfg=dict()) | |
optimizer = dict(type='Adam', lr=0.0001, weight_decay=1e-05) | |
optimizer_config = dict() | |
lr_config = dict(policy='CosineAnnealing', min_lr=1e-07, by_epoch=False) | |
runner = dict(type='IterBasedRunner', max_iters=121960) | |
checkpoint_config = dict(by_epoch=False, interval=4065, max_keep_ckpts=1) | |
evaluation = dict( | |
interval=4065, | |
metric='mFscore', | |
pre_eval=True, | |
mean=False, | |
thresh=0.5, | |
img_thresh=0.5) | |
log_config = dict( | |
interval=50, | |
hooks=[ | |
dict(type='TextLoggerHook', by_epoch=False), | |
dict(type='TensorboardLoggerHook') | |
]) | |
ext_test_dataset = ['CASIA1'] | |
dist_params = dict(backend='nccl') | |
log_level = 'INFO' | |
load_from = None | |
resume_from = None | |
workflow = [('train', 1)] | |
cudnn_benchmark = True | |
find_unused_parameters = False | |
auto_resume = False | |
gpu_ids = range(0, 4) | |