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Commit dff2c686 authored by renzhc's avatar renzhc
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configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=9,
magnitude_std=0.5,
hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=0.3333333333333333,
fill_color=[103.53, 116.28, 123.675],
fill_std=[57.375, 57.12, 58.395]),
dict(type='PackInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs')
]
train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=128, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='VisionTransformer',
arch='large',
img_size=224,
patch_size=16,
drop_path_rate=0.2, # set to 0.2
out_type='avg_featmap',
final_norm=False,
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1024,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=[dict(type='TruncNormal', layer='Linear', std=2e-5)]),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
# optimizer wrapper
# learning rate and layer decay rate are set to 0.004 and 0.75 respectively
optim_wrapper = dict(
optimizer=dict(
type='AdamW', lr=4e-3, weight_decay=0.05, betas=(0.9, 0.999)),
constructor='LearningRateDecayOptimWrapperConstructor',
paramwise_cfg=dict(
layer_decay_rate=0.75,
custom_keys={
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=5,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=45,
by_epoch=True,
begin=5,
end=50,
eta_min=1e-6,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(by_epoch=True, max_epochs=50)
default_hooks = dict(
# save checkpoint per epoch.
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
randomness = dict(seed=0, diff_rank_seed=True)
configs/mae/benchmarks/vit-large-p16_8xb128-ds-coslr-50e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = ['./vit-large-p16_8xb128-coslr-50e_in1k.py']
# optimizer wrapper
optim_wrapper = dict(type='DeepSpeedOptimWrapper')
# training strategy
strategy = dict(
type='DeepSpeedStrategy',
fp16=dict(
enabled=True,
fp16_master_weights_and_grads=False,
loss_scale=0,
loss_scale_window=500,
hysteresis=2,
min_loss_scale=1,
initial_scale_power=15,
),
inputs_to_half=['inputs'],
zero_optimization=dict(
stage=1,
allgather_partitions=True,
reduce_scatter=True,
allgather_bucket_size=50000000,
reduce_bucket_size=50000000,
overlap_comm=True,
contiguous_gradients=True,
cpu_offload=False,
))
# runner which supports strategies
runner_type = 'FlexibleRunner'
configs/mae/benchmarks/vit-large-p16_8xb128-fsdp-coslr-50e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = ['./vit-large-p16_8xb128-coslr-50e_in1k.py']
strategy = dict(
type='FSDPStrategy',
model_wrapper=dict(
auto_wrap_policy=dict(
type='torch.distributed.fsdp.wrap.size_based_auto_wrap_policy',
min_num_params=1e7)))
optim_wrapper = dict(type='AmpOptimWrapper')
# runner which supports strategies
runner_type = 'FlexibleRunner'
configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../../_base_/datasets/imagenet_bs32_pil_resize.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
# dataset settings
train_dataloader = dict(batch_size=2048, drop_last=True)
val_dataloader = dict(drop_last=False)
test_dataloader = dict(drop_last=False)
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='VisionTransformer',
arch='large',
img_size=224,
patch_size=16,
frozen_stages=24,
out_type='cls_token',
final_norm=True,
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')),
neck=dict(type='ClsBatchNormNeck', input_features=1024),
head=dict(
type='VisionTransformerClsHead',
num_classes=1000,
in_channels=1024,
loss=dict(type='CrossEntropyLoss'),
init_cfg=[dict(type='TruncNormal', layer='Linear', std=0.01)]))
# optimizer
optim_wrapper = dict(
_delete_=True,
type='AmpOptimWrapper',
optimizer=dict(type='LARS', lr=6.4, weight_decay=0.0, momentum=0.9))
# 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=80,
by_epoch=True,
begin=10,
end=90,
eta_min=0.0,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(by_epoch=True, max_epochs=90)
default_hooks = dict(
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),
logger=dict(type='LoggerHook', interval=10))
randomness = dict(seed=0, diff_rank_seed=True)
configs/mae/mae_hivit-base-p16_8xb512-amp-coslr-1600e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_hivit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'norm': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=1560,
by_epoch=True,
begin=40,
end=1600,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
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)
# auto resume
resume = 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=4096)
configs/mae/mae_hivit-base-p16_8xb512-amp-coslr-400e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_hivit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'norm': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=360,
by_epoch=True,
begin=40,
end=400,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=400)
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)
# auto resume
resume = 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=4096)
configs/mae/mae_hivit-base-p16_8xb512-amp-coslr-800e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_hivit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'norm': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=760,
by_epoch=True,
begin=40,
end=800,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=800)
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)
# auto resume
resume = 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=4096)
configs/mae/mae_hivit-large-p16_8xb512-amp-coslr-1600e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_hivit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEHiViT', arch='large'),
neck=dict(type='MAEPretrainDecoder', embed_dim=768))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'norm': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=1560,
by_epoch=True,
begin=40,
end=1600,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
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)
# auto resume
resume = 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=4096)
configs/mae/mae_hivit-large-p16_8xb512-amp-coslr-400e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_hivit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEHiViT', arch='large'),
neck=dict(type='MAEPretrainDecoder', embed_dim=768))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'norm': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=360,
by_epoch=True,
begin=40,
end=400,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=400)
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)
# auto resume
resume = 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=4096)
configs/mae/mae_hivit-large-p16_8xb512-amp-coslr-800e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_hivit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEHiViT', arch='large'),
neck=dict(type='MAEPretrainDecoder', embed_dim=768))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'norm': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=760,
by_epoch=True,
begin=40,
end=800,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=800)
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)
# auto resume
resume = 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=4096)
configs/mae/mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=1560,
by_epoch=True,
begin=40,
end=1600,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-base-p16_8xb512-amp-coslr-300e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=260,
by_epoch=True,
begin=40,
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-base-p16_8xb512-amp-coslr-400e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=360,
by_epoch=True,
begin=40,
end=400,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=400)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-base-p16_8xb512-amp-coslr-800e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.000000001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=760,
by_epoch=True,
begin=40,
end=800,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=800)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-huge-p14_8xb512-amp-coslr-1600e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEViT', arch='h', patch_size=14),
neck=dict(
type='MAEPretrainDecoder',
embed_dim=1280,
patch_size=14,
num_patches=256),
head=dict(patch_size=14))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=1560,
by_epoch=True,
begin=40,
end=1600,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEViT', arch='l'),
neck=dict(type='MAEPretrainDecoder', embed_dim=1024))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=1560,
by_epoch=True,
begin=40,
end=1600,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-large-p16_8xb512-amp-coslr-300e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEViT', arch='l'),
neck=dict(type='MAEPretrainDecoder', embed_dim=1024))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=260,
by_epoch=True,
begin=40,
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-large-p16_8xb512-amp-coslr-400e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEViT', arch='l'),
neck=dict(type='MAEPretrainDecoder', embed_dim=1024))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=360,
by_epoch=True,
begin=40,
end=400,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=400)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/mae_vit-large-p16_8xb512-amp-coslr-800e_in1k.py 0 → 100644
View file @ dff2c686
_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
backbone=dict(type='MAEViT', arch='l'),
neck=dict(type='MAEPretrainDecoder', embed_dim=1024))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.000000001,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=760,
by_epoch=True,
begin=40,
end=800,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=800)
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)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)
configs/mae/metafile.yml 0 → 100644
View file @ dff2c686
Collections:
- Name: MAE
Metadata:
Training Data: ImageNet-1k
Training Techniques:
- AdamW
Training Resources: 8x A100-80G GPUs
Architecture:
- ViT
Paper:
Title: Masked Autoencoders Are Scalable Vision Learners
URL: https://arxiv.org/abs/2111.06377
README: configs/mae/README.md
Models:
- Name: mae_vit-base-p16_8xb512-amp-coslr-300e_in1k
Metadata:
Epochs: 300
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-300e_in1k/mae_vit-base-p16_8xb512-coslr-300e-fp16_in1k_20220829-c2cf66ba.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-300e_in1k.py
Downstream:
- vit-base-p16_mae-300e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-300e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-base-p16_8xb512-amp-coslr-400e_in1k
Metadata:
Epochs: 400
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-400e_in1k/mae_vit-base-p16_8xb512-coslr-400e-fp16_in1k_20220825-bc79e40b.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-400e_in1k.py
Downstream:
- vit-base-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-400e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-base-p16_8xb512-amp-coslr-800e_in1k
Metadata:
Epochs: 800
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-800e_in1k/mae_vit-base-p16_8xb512-coslr-800e-fp16_in1k_20220825-5d81fbc4.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-800e_in1k.py
Downstream:
- vit-base-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-800e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k
Metadata:
Epochs: 1600
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k_20220825-f7569ca2.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k.py
Downstream:
- vit-base-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-1600e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-large-p16_8xb512-amp-coslr-400e_in1k
Metadata:
Epochs: 400
Batch Size: 4096
FLOPs: 61603111936
Parameters: 329541888
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-400e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-400e_in1k_20220825-b11d0425.pth
Config: configs/mae/mae_vit-large-p16_8xb512-amp-coslr-400e_in1k.py
Downstream:
- vit-large-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
- vit-large-p16_mae-400e-pre_8xb128-coslr-50e_in1k
- Name: mae_vit-large-p16_8xb512-amp-coslr-800e_in1k
Metadata:
Epochs: 800
Batch Size: 4096
FLOPs: 61603111936
Parameters: 329541888
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-800e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-800e_in1k_20220825-df72726a.pth
Config: configs/mae/mae_vit-large-p16_8xb512-amp-coslr-800e_in1k.py
Downstream:
- vit-large-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
- vit-large-p16_mae-800e-pre_8xb128-coslr-50e_in1k
- Name: mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k
Metadata:
Epochs: 1600
Batch Size: 4096
FLOPs: 61603111936
Parameters: 329541888
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k_20220825-cc7e98c9.pth
Config: configs/mae/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py
Downstream:
- vit-large-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
- vit-large-p16_mae-1600e-pre_8xb128-coslr-50e_in1k
- Name: mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k
Metadata:
Epochs: 1600
Batch Size: 4096
FLOPs: 167400741120
Parameters: 657074508
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k_20220916-ff848775.pth
Config: configs/mae/mae_vit-huge-p14_8xb512-amp-coslr-1600e_in1k.py
Downstream:
- vit-huge-p14_mae-1600e-pre_8xb128-coslr-50e_in1k
- vit-huge-p14_mae-1600e-pre_32xb8-coslr-50e_in1k-448px
- Name: vit-base-p16_mae-300e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.1
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-400e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.3
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-800e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.3
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-1600e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.5
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20220825-cf70aa21.pth
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-300e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 60.8
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 62.5
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 65.1
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 67.1
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-large-p16_mae-400e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 61602103296
Parameters: 304324584
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.2
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py
- Name: vit-large-p16_mae-800e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 61602103296
Parameters: 304324584
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.4
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py
- Name: vit-large-p16_mae-1600e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 61602103296
Parameters: 304324584
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.7
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py
- Name: vit-large-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 61603112960
Parameters: 304326632
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 70.7
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-large-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 61603112960
Parameters: 304326632
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 73.7
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-large-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 61603112960
Parameters: 304326632
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 75.5
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-huge-p14_mae-1600e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 167399096320
Parameters: 632043240
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 86.9
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/vit-huge-p16_ft-8xb128-coslr-50e_in1k/vit-huge-p16_ft-8xb128-coslr-50e_in1k_20220916-0bfc9bfd.pth
Config: configs/mae/benchmarks/vit-huge-p14_8xb128-coslr-50e_in1k.py
- Name: vit-huge-p14_mae-1600e-pre_32xb8-coslr-50e_in1k-448px
Metadata:
Epochs: 50
Batch Size: 256
FLOPs: 732131983360
Parameters: 633026280
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 87.3
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448_20220916-95b6a0ce.pth
Config: configs/mae/benchmarks/vit-huge-p14_32xb8-coslr-50e_in1k-448px.py
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