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Running
on
Zero
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300, val_interval=10) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| param_scheduler = [ | |
| dict( | |
| type='mmdet.QuadraticWarmupLR', | |
| by_epoch=True, | |
| begin=0, | |
| end=5, | |
| convert_to_iter_based=True), | |
| dict( | |
| type='CosineAnnealingLR', | |
| eta_min=0.0005, | |
| begin=5, | |
| T_max=285, | |
| end=285, | |
| by_epoch=True, | |
| convert_to_iter_based=True), | |
| dict(type='ConstantLR', by_epoch=True, factor=1, begin=285, end=300) | |
| ] | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict( | |
| type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True), | |
| paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0)) | |
| auto_scale_lr = dict(enable=False, base_batch_size=64) | |
| default_scope = 'mmdet' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=50), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3), | |
| 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=[dict(type='LocalVisBackend')], | |
| name='visualizer') | |
| log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) | |
| log_level = 'INFO' | |
| load_from = 'https://download.openmmlab.com/mmdetection/' \ | |
| 'v2.0/yolox/yolox_s_8x8_300e_coco/' \ | |
| 'yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth' | |
| resume = False | |
| img_scale = (640, 640) | |
| model = dict( | |
| type='YOLOX', | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| pad_size_divisor=32, | |
| batch_augments=[ | |
| dict( | |
| type='BatchSyncRandomResize', | |
| random_size_range=(480, 800), | |
| size_divisor=32, | |
| interval=10) | |
| ]), | |
| backbone=dict( | |
| type='CSPDarknet', | |
| deepen_factor=0.33, | |
| widen_factor=0.5, | |
| out_indices=(2, 3, 4), | |
| use_depthwise=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=[128, 256, 512], | |
| out_channels=128, | |
| num_csp_blocks=1, | |
| 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=128, | |
| feat_channels=128, | |
| 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'), | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', | |
| use_sigmoid=True, | |
| reduction='sum', | |
| loss_weight=1.0), | |
| loss_bbox=dict( | |
| type='IoULoss', | |
| mode='square', | |
| 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)), | |
| train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)), | |
| test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65))) | |
| data_root = 'data/coco/' | |
| dataset_type = 'CocoDataset' | |
| backend_args = dict(backend='local') | |
| train_pipeline = [ | |
| dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), | |
| dict( | |
| type='RandomAffine', scaling_ratio_range=(0.1, 2), | |
| border=(-320, -320)), | |
| dict( | |
| type='MixUp', | |
| img_scale=(640, 640), | |
| ratio_range=(0.8, 1.6), | |
| pad_val=114.0), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
| dict( | |
| type='Pad', | |
| pad_to_square=True, | |
| 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( | |
| type='MultiImageMixDataset', | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/instances_train2017.json', | |
| data_prefix=dict(img='train2017/'), | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=dict(backend='local')), | |
| dict(type='LoadAnnotations', with_bbox=True) | |
| ], | |
| filter_cfg=dict(filter_empty_gt=False, min_size=32)), | |
| pipeline=[ | |
| dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), | |
| dict( | |
| type='RandomAffine', | |
| scaling_ratio_range=(0.1, 2), | |
| border=(-320, -320)), | |
| dict( | |
| type='MixUp', | |
| img_scale=(640, 640), | |
| ratio_range=(0.8, 1.6), | |
| pad_val=114.0), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
| dict( | |
| type='Pad', | |
| pad_to_square=True, | |
| 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') | |
| ]) | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=dict(backend='local')), | |
| dict(type='Resize', scale=(640, 640), 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=8, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=dict( | |
| type='MultiImageMixDataset', | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/coco_face_train.json', | |
| data_prefix=dict(img='train2017/'), | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| backend_args=dict(backend='local')), | |
| dict(type='LoadAnnotations', with_bbox=True) | |
| ], | |
| filter_cfg=dict(filter_empty_gt=False, min_size=32), | |
| metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))), | |
| pipeline=[ | |
| dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), | |
| dict( | |
| type='RandomAffine', | |
| scaling_ratio_range=(0.1, 2), | |
| border=(-320, -320)), | |
| dict( | |
| type='MixUp', | |
| img_scale=(640, 640), | |
| ratio_range=(0.8, 1.6), | |
| pad_val=114.0), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
| dict( | |
| type='Pad', | |
| pad_to_square=True, | |
| 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') | |
| ])) | |
| val_dataloader = dict( | |
| batch_size=8, | |
| num_workers=4, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/coco_face_val.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=dict(backend='local')), | |
| dict(type='Resize', scale=(640, 640), 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')) | |
| ], | |
| metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60)))) | |
| test_dataloader = dict( | |
| batch_size=8, | |
| num_workers=4, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/coco_face_val.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=dict(backend='local')), | |
| dict(type='Resize', scale=(640, 640), 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')) | |
| ], | |
| metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60)))) | |
| val_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file='data/coco/annotations/coco_face_val.json', | |
| metric='bbox') | |
| test_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file='data/coco/annotations/instances_val2017.json', | |
| metric='bbox') | |
| max_epochs = 300 | |
| num_last_epochs = 15 | |
| interval = 10 | |
| base_lr = 0.01 | |
| custom_hooks = [ | |
| dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48), | |
| dict(type='SyncNormHook', priority=48), | |
| dict( | |
| type='EMAHook', | |
| ema_type='ExpMomentumEMA', | |
| momentum=0.0001, | |
| strict_load=False, | |
| update_buffers=True, | |
| priority=49) | |
| ] | |
| metainfo = dict(CLASSES=('person', ), PALETTE=(220, 20, 60)) | |
| launcher = 'pytorch' | |