configs/detection/yolox/pai_yoloxs_8xb16_300e_coco.py [102:186]:
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        pipeline=[
            dict(type='LoadImageFromFile', to_float32=True),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        classes=CLASSES,
        filter_empty_gt=False,
        test_mode=True,
        iscrowd=True),
    pipeline=test_pipeline,
    dynamic_scale=None,
    label_padding=False)

data = dict(
    imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)

# additional hooks
interval = 10
custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        no_aug_epochs=15,
        skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
        priority=48),
    dict(
        type='SyncRandomSizeHook',
        ratio_range=random_size,
        img_scale=img_scale,
        interval=interval,
        priority=48),
    dict(
        type='SyncNormHook',
        num_last_epochs=15,
        interval=interval,
        priority=48)
]

# evaluation
eval_config = dict(
    interval=10,
    gpu_collect=False,
    visualization_config=dict(
        vis_num=10,
        score_thr=0.5,
    )  # show by TensorboardLoggerHookV2 and WandbLoggerHookV2
)
eval_pipelines = [
    dict(
        mode='test',
        data=data['val'],
        evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
    )
]

checkpoint_config = dict(interval=interval)

# optimizer
optimizer = dict(
    type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
optimizer_config = {}

# learning policy
lr_config = dict(
    policy='YOLOX',
    warmup='exp',
    by_epoch=False,
    warmup_by_epoch=True,
    warmup_ratio=1,
    warmup_iters=5,  # 5 epoch
    num_last_epochs=15,
    min_lr_ratio=0.05)

# exponetial model average
ema = dict(decay=0.9998)

# runtime settings
total_epochs = 300

# yapf:disable
log_config = dict(
    interval=100,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHookV2'),
        # dict(type='WandbLoggerHookV2'),
    ])
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configs/detection/yolox/yolox_s_8xb16_300e_coco.py [110:194]:
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        pipeline=[
            dict(type='LoadImageFromFile', to_float32=True),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        classes=CLASSES,
        filter_empty_gt=False,
        test_mode=True,
        iscrowd=True),
    pipeline=test_pipeline,
    dynamic_scale=None,
    label_padding=False)

data = dict(
    imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)

# additional hooks
interval = 10
custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        no_aug_epochs=15,
        skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
        priority=48),
    dict(
        type='SyncRandomSizeHook',
        ratio_range=random_size,
        img_scale=img_scale,
        interval=interval,
        priority=48),
    dict(
        type='SyncNormHook',
        num_last_epochs=15,
        interval=interval,
        priority=48)
]

# evaluation
eval_config = dict(
    interval=10,
    gpu_collect=False,
    visualization_config=dict(
        vis_num=10,
        score_thr=0.5,
    )  # show by TensorboardLoggerHookV2 and WandbLoggerHookV2
)
eval_pipelines = [
    dict(
        mode='test',
        data=data['val'],
        evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
    )
]

checkpoint_config = dict(interval=interval)

# optimizer
optimizer = dict(
    type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
optimizer_config = {}

# learning policy
lr_config = dict(
    policy='YOLOX',
    warmup='exp',
    by_epoch=False,
    warmup_by_epoch=True,
    warmup_ratio=1,
    warmup_iters=5,  # 5 epoch
    num_last_epochs=15,
    min_lr_ratio=0.05)

# exponetial model average
ema = dict(decay=0.9998)

# runtime settings
total_epochs = 300

# yapf:disable
log_config = dict(
    interval=100,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHookV2'),
        # dict(type='WandbLoggerHookV2'),
    ])
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