configs/deit_unept_ade20k.py [79:114]:
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    loss_decode=dict(
                     type='CrossEntropyLoss',
                     use_sigmoid=False,
                     loss_weight=1.0))
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='slide', num_classes=150, stride=(160,160), crop_size=(480, 480), num_queries=3600)

# optimizer
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.0001, betas=(0.9, 0.999), eps=1e-8,
                 paramwise_cfg = dict(custom_keys={'backbone': dict(lr_mult=0.1)}))

optimizer_config = dict()
# learning policy
lr_config = dict(policy='step', step=126000, by_epoch=False)
# runtime settings
# total_iters = 640000
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=10000)
evaluation = dict(interval=10000, metric='mIoU')


# yapf:disable
log_config = dict(
    interval=200,
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
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configs/res50_unept_ade20k.py [82:119]:
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    loss_decode=dict(
                     type='CrossEntropyLoss',
                     use_sigmoid=False,
                     loss_weight=1.0))
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='slide', num_classes=150, stride=(160,160), crop_size=(480, 480), num_queries=3600)
# test_cfg = dict(mode='whole', num_classes=150, num_queries=4096) 


# optimizer
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.0001, betas=(0.9, 0.999), eps=1e-8,
                 paramwise_cfg = dict(custom_keys={'backbone': dict(lr_mult=0.1)}))

optimizer_config = dict()
# learning policy
lr_config = dict(policy='step', step=126000, by_epoch=False)
# runtime settings
# total_iters = 640000
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=10000)
evaluation = dict(interval=10000, metric='mIoU')


# yapf:disable
log_config = dict(
    interval=200,
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
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