Hugging Face's logo

Spaces:
chongzhou
/
EdgeTAM
Running on Zero

App Files Community
2
EdgeTAM
File size: 4,105 Bytes 
9bc4638
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
132
133
134
135
136
137
138
 
# @package _global_

# Model
model:
  _target_: sam2.modeling.sam2_base.SAM2Base
  image_encoder:
    _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
    scalp: 1
    trunk:
      _target_: sam2.modeling.backbones.timm.TimmBackbone
      name: repvit_m1.dist_in1k
      features:
      - layer0
      - layer1
      - layer2
      - layer3
    neck:
      _target_: sam2.modeling.backbones.image_encoder.FpnNeck
      position_encoding:
        _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
        num_pos_feats: 256
        normalize: true
        scale: null
        temperature: 10000
      d_model: 256
      backbone_channel_list: [384, 192, 96, 48]
      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
      fpn_interp_model: nearest

  memory_attention:
    _target_: sam2.modeling.memory_attention.MemoryAttention
    d_model: 256
    pos_enc_at_input: true
    layer:
      _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
      activation: relu
      dim_feedforward: 2048
      dropout: 0.1
      pos_enc_at_attn: false
      self_attention:
        _target_: sam2.modeling.sam.transformer.RoPEAttention
        rope_theta: 10000.0
        feat_sizes: [32, 32]
        embedding_dim: 256
        num_heads: 1
        downsample_rate: 1
        dropout: 0.1
      d_model: 256
      pos_enc_at_cross_attn_keys: true
      pos_enc_at_cross_attn_queries: false
      cross_attention:
        _target_: sam2.modeling.sam.transformer.RoPEAttentionv2
        rope_theta: 10000.0
        q_sizes: [64, 64]
        k_sizes: [16, 16]
        embedding_dim: 256
        num_heads: 1
        downsample_rate: 1
        dropout: 0.1
        kv_in_dim: 64
    num_layers: 2

  memory_encoder:
    _target_: sam2.modeling.memory_encoder.MemoryEncoder
    out_dim: 64
    position_encoding:
      _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
      num_pos_feats: 64
      normalize: true
      scale: null
      temperature: 10000
    mask_downsampler:
      _target_: sam2.modeling.memory_encoder.MaskDownSampler
      kernel_size: 3
      stride: 2
      padding: 1
    fuser:
      _target_: sam2.modeling.memory_encoder.Fuser
      layer:
        _target_: sam2.modeling.memory_encoder.CXBlock
        dim: 256
        kernel_size: 7
        padding: 3
        layer_scale_init_value: 1e-6
        use_dwconv: True  # depth-wise convs
      num_layers: 2

  spatial_perceiver:
    _target_: sam2.modeling.perceiver.PerceiverResampler
    depth: 2
    dim: 64
    dim_head: 64
    heads: 1
    ff_mult: 4
    hidden_dropout_p: 0.
    attention_dropout_p: 0.
    pos_enc_at_key_value: true # implicit pos
    concat_kv_latents: false
    num_latents: 256
    num_latents_2d: 256
    position_encoding:
        _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
        num_pos_feats: 64
        normalize: true
        scale: null
        temperature: 10000
    use_self_attn: true

  num_maskmem: 7
  image_size: 1024
  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
  sigmoid_scale_for_mem_enc: 20.0
  sigmoid_bias_for_mem_enc: -10.0
  use_mask_input_as_output_without_sam: true
  # Memory
  directly_add_no_mem_embed: true
  # use high-resolution feature map in the SAM mask decoder
  use_high_res_features_in_sam: true
  # output 3 masks on the first click on initial conditioning frames
  multimask_output_in_sam: true
  # SAM heads
  iou_prediction_use_sigmoid: True
  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
  use_obj_ptrs_in_encoder: true
  add_tpos_enc_to_obj_ptrs: false
  only_obj_ptrs_in_the_past_for_eval: true
  # object occlusion prediction
  pred_obj_scores: true
  pred_obj_scores_mlp: true
  fixed_no_obj_ptr: true
  # multimask tracking settings
  multimask_output_for_tracking: true
  use_multimask_token_for_obj_ptr: true
  multimask_min_pt_num: 0
  multimask_max_pt_num: 1
  use_mlp_for_obj_ptr_proj: true
  # Compilation flag
  compile_image_encoder: false