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# dataset settings
dataset_type = 'ImageNet'
img_norm_cfg = dict(
mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
to_rgb=True)
image_size = 448
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
size=image_size,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
# dict(
# type='RandAugment',
# policies={{_base_.rand_increasing_policies}},
# num_policies=2,
# total_level=10,
# magnitude_level=9,
# magnitude_std=0.5,
# hparams=dict(
# pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
# interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=img_norm_cfg['mean'][::-1],
fill_std=img_norm_cfg['std'][::-1]),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['gt_label']),
dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
size=(image_size, -1),
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=image_size),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
data = dict(
samples_per_gpu=64,
workers_per_gpu=8,
train=dict(
type=dataset_type,
data_prefix='data/imagenet/train',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
data_prefix='data/imagenet/val',
ann_file='data/imagenet/meta/val.txt',
pipeline=test_pipeline),
test=dict(
# replace `data/val` with `data/test` for standard test
type=dataset_type,
data_prefix='data/imagenet/val',
ann_file='data/imagenet/meta/val.txt',
pipeline=test_pipeline))
evaluation = dict(interval=10, metric='accuracy')
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