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imagenet_128.py
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imagenet_128.py
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# dataset settings
dataset_type = 'mmcls.ImageNet'
# different from mmcls, we adopt the setting used in BigGAN.
# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.
# Importantly, the `to_rgb` is set to `False` to remain image orders as BGR.
img_norm_cfg = dict(
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomCropLongEdge', keys=['img']),
dict(type='Resize', size=(128, 128), backend='pillow'),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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='CenterCropLongEdge', keys=['img']),
dict(type='Resize', size=(128, 128), backend='pillow'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
data = dict(
samples_per_gpu=None,
workers_per_gpu=2,
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))