mmdetection/configs/barcode_detection/bd_yolox_8xb2e50.py

#yolo tta
default_scope = 'mmdet'
_base_=['./yolox_tta.py']

#yolox_s_8xb8-300e_coco
model = dict(
    type='YOLOX',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        pad_size_divisor=32,
    #     batch_augments=[
    #     dict(
    #         type='BatchSyncRandomResize',
    #         random_size_range=(320, 640),
    #         size_divisor=32,
    #         interval=10)
    # ]
    ),
    backbone=dict(
        type='CSPDarknet',
        deepen_factor=0.33,
        widen_factor=0.375,
        out_indices=(2, 3, 4),
        use_depthwise=False,
        dcn=False,
        spp_kernal_sizes=(5, 9, 13),
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish'),
    ),
    neck=dict(
        type='YOLOXPAFPN',
        in_channels=[96, 192, 384], 
        out_channels=96,
        num_csp_blocks=1,
        tr=False,
        use_depthwise=False,
        upsample_cfg=dict(scale_factor=2, mode='nearest'),
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish')),
    bbox_head=dict(
        type='YOLOXHead',
        num_classes=1,
        in_channels=96,
        feat_channels=96,
        stacked_convs=2,
        strides=(8, 16, 32),
        use_depthwise=False,
        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
        act_cfg=dict(type='Swish'),
        # dcn_on_last_conv=True,
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=1.0),
        loss_bbox=dict(
    #use giouloss intead of Iou to get better performance on bbox loss
            type='SIoULoss',
            eps=1e-16,
            reduction='sum',
            loss_weight=5.0),
        loss_obj=dict(
            type='CrossEntropyLoss',
            use_sigmoid=True,
            reduction='sum',
            loss_weight=1.0),
        loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
    #get top 5 instead of top 10
    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2,candidate_topk=10)),
    # In order to align the source code, the threshold of the val phase is
    # 0.01, and the threshold of the test phase is 0.001.
    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.5)))

img_scale = (640, 640)  # width, height

dataset_type = 'CocoDataset'
data_root = '../../../media/tricolops/T7/coco_format/'
base_batch_size=16
metainfo = {
    'classes': ('barcode',),
    'palette': [
        (220, 20, 60),
    ]
}
backend_args=None

train_pipeline = [
    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
    dict(
        type='RandomAffine',
        scaling_ratio_range=(0.1, 2),
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
    dict(
        type='MixUp',
        img_scale=img_scale,
        ratio_range=(0.8, 1.6),
        pad_val=114.0),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    # According to the official implementation, multi-scale
    # training is not considered here but in the
    # 'mmdet/models/detectors/yolox.py'.
    # Resize and Pad are for the last 15 epochs when Mosaic,
    # RandomAffine, and MixUp are closed by YOLOXModeSwitchHook.
    dict(type='Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        # If the image is three-channel, the pad value needs
        # to be set separately for each channel.
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
    dict(type='PackDetInputs')
]
train_dataset = dict(
    # use MultiImageMixDataset wrapper to support mosaic and mixup
    type='MultiImageMixDataset',
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='Train/Train.json',
        metainfo=metainfo,
        data_prefix=dict(img='Train/'),
        pipeline=[
            dict(type='LoadImageFromFile', backend_args=backend_args),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        backend_args=backend_args),
    pipeline=train_pipeline)
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=backend_args),
    dict(type='Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=base_batch_size,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=train_dataset)
val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='Val/Val.json',
        data_prefix=dict(img='Val/'),
        metainfo=metainfo,
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args))    
test_dataloader = val_dataloader
val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'Val/Val.json',
    metric='bbox',)
test_evaluator = val_evaluator

#schedule 1x
# training schedule for 1x
max_epochs=500
num_last_epochs = 50
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=20)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
base_lr=0.0025
param_scheduler = [
    dict(
        # use quadratic formula to warm up 5 epochs
        # and lr is updated by iteration
        # TODO: fix default scope in get function
        type='mmdet.QuadraticWarmupLR',
        by_epoch=True,
        begin=0,
        end=5,
        convert_to_iter_based=True),
    dict(
        # use cosine lr from 5 to 285 epoch
        type='CosineAnnealingLR',
        eta_min=base_lr * 0.05,
        begin=5,
        T_max=max_epochs - num_last_epochs,
        end=max_epochs - num_last_epochs,
        by_epoch=True,
        convert_to_iter_based=True),
    dict(
        # use fixed lr during last 15 epochs
        type='ConstantLR',
        by_epoch=True,
        factor=1,
        begin=max_epochs - num_last_epochs,
        end=max_epochs,
    )
]

# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=base_lr, momentum=0.9, weight_decay=0.0005, nesterov=True),
    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=base_batch_size)

#default_runtime


default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))

env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'),
)

vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)

log_level = 'INFO'
load_from = None
resume = False
custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        num_last_epochs=num_last_epochs,
        priority=48),
    dict(type='SyncNormHook', priority=48),
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        update_buffers=True,
        priority=49)
]
# load_from = 'epoch_1000_yolox.pth'