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model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch',
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth',
prefix='backbone')),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=2,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, )))
dataset_type = 'CustomDataset'
classes = ['No', 'Yes']
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
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=(256, -1)),
dict(type='CenterCrop', crop_size=224),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
data = dict(
samples_per_gpu=16,
workers_per_gpu=4,
train=dict(
type='CustomDataset',
data_prefix='/work/home/acy25a367n/pornpics/pornpics-download-s',
ann_file=
'/work/home/acy25a367n/mmclassification/pornpics/outdoor/outdoor_train.csv',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['gt_label']),
dict(type='Collect', keys=['img', 'gt_label'])
]),
val=dict(
type='CustomDataset',
data_prefix='/work/home/acy25a367n/pornpics/pornpics-download-s',
ann_file=
'/work/home/acy25a367n/mmclassification/pornpics/outdoor/outdoor_valid.csv',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='Resize', size=(256, -1)),
dict(type='CenterCrop', crop_size=224),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]),
test=dict(
type='CustomDataset',
data_prefix='/work/home/acy25a367n/pornpics/pornpics-download-s',
ann_file=
'/work/home/acy25a367n/mmclassification/pornpics/outdoor/outdoor_valid.csv',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='Resize', size=(256, -1)),
dict(type='CenterCrop', crop_size=224),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]))
evaluation = dict(
interval=1, metric='accuracy', metric_options=dict(topk=(1, )))
optimizer = dict(
type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='CosineAnnealing',
min_lr=0,
warmup='linear',
warmup_iters=5,
warmup_ratio=0.01,
warmup_by_epoch=True)
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=1)
log_config = dict(interval=4, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth'
work_dir = 'work_dirs/resnet50_8xb32_outdoor'
gpu_ids = [0]
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