85 lines
2.2 KiB
Python
85 lines
2.2 KiB
Python
_base_ = [
|
|
'../_base_/datasets/imagenet_bs256_itpn.py',
|
|
'../_base_/default_runtime.py',
|
|
]
|
|
|
|
model = dict(
|
|
type='iTPN',
|
|
backbone=dict(
|
|
type='iTPNHiViT',
|
|
arch='base',
|
|
drop_path_rate=0.0,
|
|
rpe=True,
|
|
layer_scale_init_value=0.1,
|
|
reconstruction_type='clip'),
|
|
neck=dict(
|
|
type='iTPNPretrainDecoder',
|
|
patch_size=16,
|
|
in_chans=3,
|
|
embed_dim=512,
|
|
mlp_ratio=4.,
|
|
reconstruction_type='clip',
|
|
# transformer pyramid
|
|
fpn_dim=256,
|
|
fpn_depth=2,
|
|
num_outs=3,
|
|
),
|
|
head=dict(
|
|
type='iTPNClipHead',
|
|
embed_dims=512,
|
|
num_embed=512,
|
|
loss=dict(type='CosineSimilarityLoss')),
|
|
target_generator=dict(
|
|
type='CLIPGenerator',
|
|
tokenizer_path= # noqa
|
|
'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/clip_vit_base_16.pth.tar' # noqa
|
|
),
|
|
)
|
|
|
|
# optimizer wrapper
|
|
optim_wrapper = dict(
|
|
type='AmpOptimWrapper',
|
|
loss_scale='dynamic',
|
|
# betas: (0.9, 0.98) for 300 epochs and (0.9, 0.999) for 1600 epochs.
|
|
optimizer=dict(
|
|
type='AdamW', lr=1.5e-3, betas=(0.9, 0.98), weight_decay=0.05),
|
|
clip_grad=dict(max_norm=3.0),
|
|
paramwise_cfg=dict(
|
|
custom_keys={
|
|
'.norm': dict(decay_mult=0.0),
|
|
'.pos_embed': dict(decay_mult=0.0),
|
|
'.gamma': dict(decay_mult=0.0),
|
|
}))
|
|
|
|
# learning rate scheduler
|
|
param_scheduler = [
|
|
dict(
|
|
type='LinearLR',
|
|
start_factor=1e-4,
|
|
by_epoch=True,
|
|
begin=0,
|
|
end=10,
|
|
convert_to_iter_based=True),
|
|
dict(
|
|
type='CosineAnnealingLR',
|
|
eta_min=1e-5,
|
|
by_epoch=True,
|
|
begin=10,
|
|
end=300,
|
|
convert_to_iter_based=True)
|
|
]
|
|
|
|
# runtime settings
|
|
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
|
|
default_hooks = dict(
|
|
# only keeps the latest 3 checkpoints
|
|
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
|
|
|
|
randomness = dict(seed=0, diff_rank_seed=True)
|
|
|
|
find_unused_parameters = True
|
|
|
|
# NOTE: `auto_scale_lr` is for automatically scaling LR
|
|
# based on the actual training batch size.
|
|
auto_scale_lr = dict(base_batch_size=2048)
|