models/gpvit/configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py [20:113]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - drop_path_rate=0.1, out_indices=(11,), final_norm=False, init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), convert_syncbn=True), neck=dict( type='FPN', in_channels=[embed_dims, embed_dims, embed_dims], out_channels=256, add_extra_convs='on_output', num_outs=5, norm_cfg=dict(type='SyncBN', requires_grad=True)), bbox_head=dict( type='RetinaHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', octave_base_scale=4, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), # model training and testing settings train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) # optimizer img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) # augmentation strategy originates from DETR / Sparse RCNN train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline)) optimizer = dict( _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, paramwise_cfg=dict( custom_keys={ 'level_embed': dict(decay_mult=0.), 'pos_embed': dict(decay_mult=0.), 'norm': dict(decay_mult=0.), 'bias': dict(decay_mult=0.), '.absolute_pos_embed': dict(decay_mult=0.0), '.relative_position_bias_table': dict(decay_mult=0.0), '.pos_embed': dict(decay_mult=0.0), '.group_token': dict(decay_mult=0.0), '.dw_norm': dict(decay_mult=0.0) })) optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) fp16 = dict(loss_scale=dict(init_scale=512)) checkpoint_config = dict( interval=1, max_keep_ckpts=3, save_last=True, ) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - models/gpvit/configs/gpvit/retinanet/gpvit_l2_retinanet_1x.py [21:114]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - drop_path_rate=0.1, out_indices=(11,), final_norm=False, init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), convert_syncbn=True), neck=dict( type='FPN', in_channels=[embed_dims, embed_dims, embed_dims], out_channels=256, add_extra_convs='on_output', num_outs=5, norm_cfg=dict(type='SyncBN', requires_grad=True)), bbox_head=dict( type='RetinaHead', num_classes=80, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', octave_base_scale=4, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), # model training and testing settings train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)) # optimizer img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) # augmentation strategy originates from DETR / Sparse RCNN train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict(pipeline=train_pipeline)) optimizer = dict( _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, paramwise_cfg=dict( custom_keys={ 'level_embed': dict(decay_mult=0.), 'pos_embed': dict(decay_mult=0.), 'norm': dict(decay_mult=0.), 'bias': dict(decay_mult=0.), '.absolute_pos_embed': dict(decay_mult=0.0), '.relative_position_bias_table': dict(decay_mult=0.0), '.pos_embed': dict(decay_mult=0.0), '.group_token': dict(decay_mult=0.0), '.dw_norm': dict(decay_mult=0.0) })) optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) fp16 = dict(loss_scale=dict(init_scale=512)) checkpoint_config = dict( interval=1, max_keep_ckpts=3, save_last=True, ) - 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