segnext.base.512x512.celebamaskhq.160k.py

_base_ = [
    '../../_base_/models/mscan.py',
    '../../_base_/datasets/celebamaskhq.py',
    '../../_base_/default_runtime.py',
    '../../_base_/schedules/schedule_160k_adamw.py'
]
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
find_unused_parameters = True
model = dict(
    type='EncoderDecoder',
    backbone=dict(
        embed_dims=[64, 128, 320, 512],
        depths=[3, 3, 12, 3],
        init_cfg=dict(type='Pretrained', checkpoint='pretrained/mscan_b.pth'),
        drop_path_rate=0.1),
    decode_head=dict(
        type='LightHamHead',
        in_channels=[128, 320, 512],
        in_index=[1, 2, 3],
        channels=512,
        ham_channels=512,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=ham_norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))

data = dict(samples_per_gpu=6)
evaluation = dict(interval=10000, metric='mIoU', save_best="mIoU")
checkpoint_config = dict(by_epoch=False, interval=10000, max_keep_ckpts=5)
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
                 paramwise_cfg=dict(custom_keys={'pos_block': dict(decay_mult=0.),
                                                 'norm': dict(decay_mult=0.),
                                                 'head': dict(lr_mult=10.)
                                                 }))

lr_config = dict(_delete_=True, policy='poly',
                 warmup='linear',
                 warmup_iters=1500,
                 warmup_ratio=1e-6,
                 power=1.0, min_lr=0.0, by_epoch=False)