Spaces:
Runtime error
Runtime error
File size: 4,948 Bytes
51f6859 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
_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='focalnet_dino',
det_wrapper_cfg=dict(num_classes=91,
param_dict_type='default',
ddetr_lr_param=False,
onecyclelr=False,
modelname='dino',
frozen_weights=None,
backbone='focalnet_L_384_22k_fl4',
focal_levels=4,
focal_windows=3,
use_checkpoint=False,
dilation=False,
position_embedding='sine',
pe_temperatureH=20,
pe_temperatureW=20,
return_interm_indices=[0, 1, 2, 3],
backbone_freeze_keywords=None,
enc_layers=6,
dec_layers=6,
unic_layers=0,
pre_norm=False,
dim_feedforward=2048,
hidden_dim=256,
dropout=0.0,
nheads=8,
num_queries=900,
query_dim=4,
num_patterns=0,
pdetr3_bbox_embed_diff_each_layer=False,
pdetr3_refHW=-1,
random_refpoints_xy=False,
fix_refpoints_hw=-1,
dabdetr_yolo_like_anchor_update=False,
dabdetr_deformable_encoder=False,
dabdetr_deformable_decoder=False,
use_deformable_box_attn=False,
box_attn_type='roi_align',
dec_layer_number=None,
num_feature_levels=5,
enc_n_points=4,
dec_n_points=4,
decoder_layer_noise=False,
dln_xy_noise=0.2,
dln_hw_noise=0.2,
add_channel_attention=False,
add_pos_value=False,
two_stage_type='standard',
two_stage_pat_embed=0,
two_stage_add_query_num=0,
two_stage_bbox_embed_share=False,
two_stage_class_embed_share=False,
two_stage_learn_wh=False,
two_stage_default_hw=0.05,
two_stage_keep_all_tokens=False,
num_select=300,
transformer_activation='relu',
batch_norm_type='FrozenBatchNorm2d',
masks=False,
aux_loss=True,
set_cost_class=2.0,
set_cost_bbox=5.0,
set_cost_giou=2.0,
no_interm_box_loss=False,
focal_alpha=0.25,
decoder_sa_type='sa', # ['sa', 'ca_label', 'ca_content']
matcher_type='HungarianMatcher', # or SimpleMinsumMatcher
decoder_module_seq=['sa', 'ca', 'ffn'],
nms_iou_threshold=-1,
dec_pred_bbox_embed_share=True,
dec_pred_class_embed_share=True,
use_dn=False,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
embed_init_tgt=True,
dn_labelbook_size=91,
match_unstable_error=True,
# for ema
use_ema=False,
ema_decay=0.9997,
ema_epoch=0,
use_detached_boxes_dec_out=False),
det_model_ckpt='ckpt/focalnet_l_dino.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))
|