57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
# accuracy_top-1 : 81.52 accuracy_top-5 : 95.73
|
|
_base_ = [
|
|
'../_base_/models/tnt_s_patch16_224.py',
|
|
'../_base_/datasets/imagenet_bs32_pil_resize.py',
|
|
'../_base_/default_runtime.py'
|
|
]
|
|
|
|
# dataset settings
|
|
data_preprocessor = dict(
|
|
mean=[127.5, 127.5, 127.5],
|
|
std=[127.5, 127.5, 127.5],
|
|
# convert image from BGR to RGB
|
|
to_rgb=True,
|
|
)
|
|
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='ResizeEdge',
|
|
scale=248,
|
|
edge='short',
|
|
backend='pillow',
|
|
interpolation='bicubic'),
|
|
dict(type='CenterCrop', crop_size=224),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
train_dataloader = dict(batch_size=64)
|
|
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
|
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
|
|
|
# schedule settings
|
|
optim_wrapper = dict(optimizer=dict(type='AdamW', lr=1e-3, weight_decay=0.05))
|
|
|
|
param_scheduler = [
|
|
# warm up learning rate scheduler
|
|
dict(
|
|
type='LinearLR',
|
|
start_factor=1e-3,
|
|
by_epoch=True,
|
|
begin=0,
|
|
end=5,
|
|
# update by iter
|
|
convert_to_iter_based=True),
|
|
# main learning rate scheduler
|
|
dict(type='CosineAnnealingLR', T_max=295, by_epoch=True, begin=5, end=300)
|
|
]
|
|
|
|
train_cfg = dict(by_epoch=True, max_epochs=300, val_interval=1)
|
|
val_cfg = dict()
|
|
test_cfg = dict()
|
|
|
|
# NOTE: `auto_scale_lr` is for automatically scaling LR
|
|
# based on the actual training batch size.
|
|
# base_batch_size = (16 GPUs) x (64 samples per GPU)
|
|
auto_scale_lr = dict(base_batch_size=1024)
|