116 lines
3.1 KiB
Python
116 lines
3.1 KiB
Python
_base_ = '../_base_/default_runtime.py'
|
|
|
|
# dataset settings
|
|
dataset_type = 'ImageNet'
|
|
data_root = 'data/imagenet/'
|
|
data_preprocessor = dict(
|
|
type='TwoNormDataPreprocessor',
|
|
mean=[123.675, 116.28, 103.53],
|
|
std=[58.395, 57.12, 57.375],
|
|
second_mean=[-31.875, -31.875, -31.875],
|
|
second_std=[318.75, 318.75, 318.75],
|
|
to_rgb=True)
|
|
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(type='RandomFlip', prob=0.5),
|
|
dict(
|
|
type='RandomResizedCropAndInterpolationWithTwoPic',
|
|
size=224,
|
|
second_size=112,
|
|
interpolation='bicubic',
|
|
second_interpolation='lanczos',
|
|
scale=(0.08, 1.0)),
|
|
dict(
|
|
type='BEiTMaskGenerator',
|
|
input_size=(14, 14),
|
|
num_masking_patches=75,
|
|
max_num_patches=None,
|
|
min_num_patches=16),
|
|
dict(type='PackInputs')
|
|
]
|
|
|
|
train_dataloader = dict(
|
|
batch_size=256,
|
|
num_workers=8,
|
|
persistent_workers=True,
|
|
sampler=dict(type='DefaultSampler', shuffle=True),
|
|
collate_fn=dict(type='default_collate'),
|
|
dataset=dict(
|
|
type=dataset_type,
|
|
data_root=data_root,
|
|
ann_file='meta/train.txt',
|
|
data_prefix=dict(img_path='train/'),
|
|
pipeline=train_pipeline))
|
|
|
|
# model settings
|
|
model = dict(
|
|
type='CAE',
|
|
backbone=dict(
|
|
type='CAEPretrainViT',
|
|
arch='b',
|
|
patch_size=16,
|
|
layer_scale_init_value=0.1,
|
|
bias='qv_bias'),
|
|
neck=dict(
|
|
type='CAENeck',
|
|
embed_dims=768,
|
|
num_heads=12,
|
|
regressor_depth=4,
|
|
decoder_depth=4,
|
|
mlp_ratio=4,
|
|
layer_scale_init_value=0.1,
|
|
),
|
|
head=dict(type='CAEHead', loss=dict(type='CAELoss', lambd=2)),
|
|
target_generator=dict(
|
|
type='DALL-E',
|
|
init_cfg=dict(
|
|
type='Pretrained',
|
|
checkpoint= # noqa: E251
|
|
'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/dalle_encoder.pth', # noqa: E501
|
|
)),
|
|
base_momentum=0.0)
|
|
|
|
# optimizer wrapper
|
|
optim_wrapper = dict(
|
|
type='AmpOptimWrapper',
|
|
loss_scale='dynamic',
|
|
optimizer=dict(
|
|
type='AdamW', lr=1.5e-3, betas=(0.9, 0.999), weight_decay=0.05),
|
|
clip_grad=dict(max_norm=3.0),
|
|
paramwise_cfg=dict(
|
|
bias_decay_mult=0.0, norm_decay_mult=0.0, flat_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',
|
|
T_max=290,
|
|
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=10, 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)
|