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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) | |