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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)
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