-
Notifications
You must be signed in to change notification settings - Fork 286
/
imagenet.yaml
36 lines (36 loc) · 1.22 KB
/
imagenet.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
_name_: imagenet
__l_max: ${eval:${.image_size}**2 // ${model.patch_size}**2}
data_dirname: "imagenet" # path to splits
cache_dir: None
image_size: 224
val_split: 0.
shuffle: True # for train
num_aug_repeats: 3 # Repeat Aug - only works when num_gpus > 1
num_gpus: ${trainer.devices}
loader_fft: false
train_transforms:
_target_: timm.data.create_transform
input_size: ${dataset.image_size}
is_training: True
auto_augment: rand-m9-mstd0.5-inc1 # Use AutoAugment policy
interpolation: random
re_prob: 0.25 # Random erase prob1
re_mode: pixel # Random erase mode
val_transforms: # Taken from model definition in t2t_vit.py
_target_: timm.data.create_transform
input_size: ${dataset.image_size}
interpolation: bicubic
crop_pct: 0.9
test_transforms:
_target_: timm.data.create_transform
input_size: ${dataset.image_size}
interpolation: bicubic
crop_pct: 0.9
mixup:
# _target_: src.dataloaders.timm_mixup.TimmMixup
_target_: src.dataloaders.utils.timm_mixup.TimmMixup
mixup_alpha: 0.8
cutmix_alpha: 1.0
# if using timm soft cross entropy, pass label_smoothing here
# if using pytorch soft cross entropy instead, would need to remove label_smoothing here, since PT handles it itself
# label_smoothing: 0.1