mvitv2-base_8xb256_in1k.py

_base_ = [
    '../_base_/models/mvit/mvitv2-base.py',
    '../_base_/datasets/imagenet_bs64_swin_224.py',
    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
    '../_base_/default_runtime.py'
]

# dataset settings
train_dataloader = dict(batch_size=256)
val_dataloader = dict(batch_size=256)
test_dataloader = dict(batch_size=256)

# schedule settings
optim_wrapper = dict(
    optimizer=dict(lr=2.5e-4),
    paramwise_cfg=dict(
        norm_decay_mult=0.0,
        bias_decay_mult=0.0,
        custom_keys={
            '.pos_embed': dict(decay_mult=0.0),
            '.rel_pos_h': dict(decay_mult=0.0),
            '.rel_pos_w': dict(decay_mult=0.0)
        }),
    clip_grad=dict(max_norm=1.0),
)

# learning policy
param_scheduler = [
    # warm up learning rate scheduler
    dict(
        type='LinearLR',
        start_factor=1e-3,
        by_epoch=True,
        end=70,
        # update by iter
        convert_to_iter_based=True),
    # main learning rate scheduler
    dict(type='CosineAnnealingLR', eta_min=1e-5, by_epoch=True, begin=70)
]

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)