Source code for imagenet_128

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.dataset import DefaultSampler

from mmagic.datasets.transforms import (CenterCropLongEdge, Flip,
                                        LoadImageFromFile, PackInputs,
                                        RandomCropLongEdge, Resize)

# dataset settings
[docs]dataset_type = 'ImageNet'

# different from mmcls, we adopt the setting used in BigGAN.
# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.
[docs]train_pipeline = [
    dict(type=LoadImageFromFile, key='gt'),
    dict(type=RandomCropLongEdge, keys='gt'),
    dict(type=Resize, scale=(128, 128), keys='gt', backend='pillow'),
    dict(type=Flip, keys='gt', flip_ratio=0.5, direction='horizontal'),
    dict(type=PackInputs)
]

[docs]test_pipeline = [
    dict(type=LoadImageFromFile, key='gt'),
    dict(type=CenterCropLongEdge, keys='gt'),
    dict(type=Resize, scale=(128, 128), keys='gt', backend='pillow'),
    dict(type=PackInputs)
]

[docs]train_dataloader = dict(
    batch_size=None,
    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),
    persistent_workers=True)

[docs]val_dataloader = dict(
    batch_size=64,
    num_workers=5,
    dataset=dict(
        type=dataset_type,
        data_root='./data/imagenet/',
        ann_file='meta/train.txt',
        data_prefix='train',
        pipeline=test_pipeline),
    sampler=dict(type=DefaultSampler, shuffle=False),
    persistent_workers=True)

[docs]test_dataloader = val_dataloader