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l-448-ce-rounda3-0.5.py
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l-448-ce-rounda3-0.5.py
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dataset_type = 'ImageNet'
data_preprocessor = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
bgr_mean = [103.53, 116.28, 123.675]
bgr_std = [57.375, 57.12, 58.395]
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args=dict(
backend='memcached',
server_list_cfg='/mnt/lustre/share/pymc/pcs_server_list.conf',
client_cfg='/mnt/lustre/share/pymc/mc.conf',
sys_path='/mnt/lustre/share/pymc')),
dict(
type='RandomResizedCrop',
scale=448,
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='PackClsInputs')
]
test_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args=dict(
backend='petrel',
path_mapping=dict({
'./data/WebiNat5000':
's3://openmmlab/datasets/classification/WebiNat5000/',
'data/WebiNat5000':
's3://openmmlab/datasets/classification/WebiNat5000/'
}))),
dict(
type='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackClsInputs')
]
train_dataloader = dict(
pin_memory=True,
persistent_workers=True,
collate_fn=dict(type='default_collate'),
batch_size=16,
num_workers=16,
dataset=dict(
type='ImageNet',
data_root='data/WebiNat5000/',
ann_file=
'/mnt/petrelfs/share_data/yuzhaohui/ACCV_workshop/round3-train-thr5.txt',
data_prefix='train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=448,
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='PackClsInputs')
],
_scope_='mmcls'),
sampler=dict(type='DefaultSampler', shuffle=True, _scope_='mmcls'))
val_dataloader = dict(
pin_memory=True,
persistent_workers=True,
collate_fn=dict(type='default_collate'),
batch_size=128,
num_workers=5,
dataset=dict(
type='ImageNet',
data_root='data/WebiNat5000/',
ann_file='/mnt/cache/liuyuan/research/draw/webinat/meta/val.txt',
data_prefix='train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=512,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=448),
dict(type='PackClsInputs')
],
_scope_='mmcls'),
sampler=dict(type='DefaultSampler', shuffle=False, _scope_='mmcls'))
val_evaluator = dict(type='Accuracy', topk=(1, 5), _scope_='mmcls')
test_dataloader = dict(
pin_memory=True,
persistent_workers=True,
collate_fn=dict(type='default_collate'),
batch_size=128,
num_workers=5,
dataset=dict(
type='ImageNet',
data_root='data/WebiNat5000/',
ann_file='/mnt/cache/liuyuan/research/draw/webinat/meta/val.txt',
data_prefix='train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=512,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=448),
dict(type='PackClsInputs')
],
_scope_='mmcls'),
sampler=dict(type='DefaultSampler', shuffle=False, _scope_='mmcls'))
test_evaluator = dict(type='Accuracy', topk=(1, 5), _scope_='mmcls')
optim_wrapper = dict(
optimizer=dict(
type='AdamW',
lr=0.002,
weight_decay=0.05,
eps=1e-08,
betas=(0.9, 0.999),
_scope_='mmcls',
model_type='vit',
layer_decay_rate=0.75),
paramwise_cfg=dict(
norm_decay_mult=0.0,
bias_decay_mult=0.0,
custom_keys=dict({
'.absolute_pos_embed': dict(decay_mult=0.0),
'.relative_position_bias_table': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0)
})),
constructor='mmselfsup.LearningRateDecayOptimWrapperConstructor')
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=5,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=25,
by_epoch=True,
begin=5,
end=30,
eta_min=1e-06,
convert_to_iter_based=True)
]
train_cfg = dict(by_epoch=True, max_epochs=30, val_interval=1)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(base_batch_size=1024)
default_scope = 'mmcls'
default_hooks = dict(
timer=dict(type='IterTimerHook', _scope_='mmcls'),
logger=dict(type='LoggerHook', interval=100, _scope_='mmcls'),
param_scheduler=dict(type='ParamSchedulerHook', _scope_='mmcls'),
checkpoint=dict(
type='CheckpointHook', interval=1, _scope_='mmcls', max_keep_ckpts=1),
sampler_seed=dict(type='DistSamplerSeedHook', _scope_='mmcls'),
visualization=dict(
type='VisualizationHook', enable=False, _scope_='mmcls'))
env_cfg = dict(
cudnn_benchmark=True,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend', _scope_='mmcls')]
visualizer = dict(
type='ClsVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
_scope_='mmcls')
log_level = 'INFO'
load_from = None
resume = False
model = dict(
type='ImageClassifier',
backbone=dict(
type='VisionTransformer',
arch='large',
img_size=448,
patch_size=16,
drop_path_rate=0.1,
avg_token=True,
output_cls_token=False,
final_norm=False,
init_cfg=dict(
type='Pretrained',
checkpoint=
'work_dirs/selfsup/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k/epoch_1600.pth',
prefix='backbone.')),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=5000,
in_channels=1024,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=[dict(type='TruncNormal', layer='Linear', std=2e-05)]),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8, num_classes=5000),
dict(type='CutMix', alpha=1.0, num_classes=5000)
]))
file_client_args = dict(
backend='memcached',
path_mapping=dict({
'./data/WebiNat5000':
's3://openmmlab/datasets/classification/WebiNat5000/',
'data/WebiNat5000':
's3://openmmlab/datasets/classification/WebiNat5000/'
}),
server_list_cfg='/mnt/lustre/share/pymc/pcs_server_list.conf',
client_cfg='/mnt/lustre/share/pymc/mc.conf',
sys_path='/mnt/lustre/share/pymc')
data_root = 'data/WebiNat5000/'
train_ann_file = '/mnt/cache/liuyuan/research/accv/filter_data/all_new.txt'
val_ann_file = '/mnt/cache/liuyuan/research/draw/webinat/meta/val.txt'
custom_imports = dict(imports='mmselfsup.engine', allow_failed_imports=False)
randomness = dict(seed=0)
val_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args=dict(
backend='memcached',
server_list_cfg='/mnt/lustre/share/pymc/pcs_server_list.conf',
client_cfg='/mnt/lustre/share/pymc/mc.conf',
sys_path='/mnt/lustre/share/pymc')),
dict(
type='ResizeEdge',
scale=512,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=448),
dict(type='PackClsInputs')
]
launcher = 'slurm'
work_dir = './work_dirs/vit-large-p16_ft-8xb128-coslr-30e_in1k-448'