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| _base_ = [ | |
| '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py' | |
| ] | |
| plugin = True | |
| plugin_dir = 'projects/instance_segment_anything/' | |
| model = dict( | |
| type='DetWrapperInstanceSAM', | |
| det_wrapper_type='hdetr', | |
| det_wrapper_cfg=dict(aux_loss=False, | |
| backbone='swin_large', | |
| num_classes=91, | |
| cache_mode=False, | |
| dec_layers=6, | |
| dec_n_points=4, | |
| dilation=False, | |
| dim_feedforward=2048, | |
| drop_path_rate=0.5, | |
| dropout=0.0, | |
| enc_layers=6, | |
| enc_n_points=4, | |
| focal_alpha=0.25, | |
| frozen_weights=None, | |
| hidden_dim=256, | |
| k_one2many=6, | |
| lambda_one2many=1.0, | |
| look_forward_twice=True, | |
| masks=False, | |
| mixed_selection=True, | |
| nheads=8, | |
| num_feature_levels=4, | |
| num_queries_one2many=1500, | |
| num_queries_one2one=900, | |
| position_embedding='sine', | |
| position_embedding_scale=6.283185307179586, | |
| remove_difficult=False, | |
| topk=300, | |
| two_stage=True, | |
| use_checkpoint=False, | |
| use_fp16=False, | |
| use_wandb=False, | |
| with_box_refine=True), | |
| det_model_ckpt='ckpt/swin_l_hdetr.pth', | |
| num_classes=80, | |
| model_type='vit_h', | |
| sam_checkpoint='ckpt/sam_vit_h_4b8939.pth', | |
| use_sam_iou=True, | |
| ) | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| # test_pipeline, NOTE the Pad's size_divisor is different from the default | |
| # setting (size_divisor=32). While there is little effect on the performance | |
| # whether we use the default setting or use size_divisor=1. | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=1), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| data = dict( | |
| samples_per_gpu=1, | |
| workers_per_gpu=1, | |
| test=dict( | |
| type=dataset_type, | |
| ann_file=data_root + 'annotations/instances_val2017.json', | |
| img_prefix=data_root + 'val2017/', | |
| pipeline=test_pipeline)) | |