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# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='AutoAugment',
policies='imagenet',
hparams=dict(pad_val=[round(x) for x in bgr_mean])),
dict(
type='RandomErasing',
erase_prob=0.2,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=bgr_mean,
fill_std=bgr_std),
dict(type='PackClsInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackClsInputs'),
]
train_dataloader = dict(
batch_size=128,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
ann_file='meta/train.txt',
data_prefix='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=128,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
ann_file='meta/val.txt',
data_prefix='val',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, 5))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
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