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# Copyright (c) OpenMMLab. All rights reserved.
# This is a BETA new format config file, and the usage may change recently.
from mmcv.transforms import (LoadImageFromFile, RandomApply, RandomFlip,
RandomGrayscale)
from mmengine.dataset import DefaultSampler, default_collate
from mmpretrain.datasets import (ColorJitter, GaussianBlur, ImageNet,
MultiView, PackInputs, RandomResizedCrop)
from mmpretrain.models import SelfSupDataPreprocessor
# dataset settings
dataset_type = 'ImageNet'
data_root = 'data/imagenet/'
data_preprocessor = dict(
type=SelfSupDataPreprocessor,
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
view_pipeline = [
dict(type=RandomResizedCrop, scale=224, backend='pillow'),
dict(type=RandomFlip, prob=0.5),
dict(
type=RandomApply,
transforms=[
dict(
type=ColorJitter,
brightness=0.8,
contrast=0.8,
saturation=0.8,
hue=0.2)
],
prob=0.8),
dict(
type=RandomGrayscale,
prob=0.2,
keep_channels=True,
channel_weights=(0.114, 0.587, 0.2989)),
dict(
type=GaussianBlur,
magnitude_range=(0.1, 2.0),
magnitude_std='inf',
prob=0.5),
]
train_pipeline = [
dict(type=LoadImageFromFile),
dict(type=MultiView, num_views=2, transforms=[view_pipeline]),
dict(type=PackInputs)
]
train_dataloader = dict(
batch_size=32,
num_workers=4,
persistent_workers=True,
sampler=dict(type=DefaultSampler, shuffle=True),
collate_fn=dict(type=default_collate),
dataset=dict(
type=ImageNet,
data_root=data_root,
ann_file='meta/train.txt',
data_prefix=dict(img_path='train/'),
pipeline=train_pipeline))
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