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Running
on
Zero
# @package _global_ | |
# Model | |
model: | |
_target_: sam2.modeling.sam2_base.SAM2Base | |
image_encoder: | |
_target_: sam2.modeling.backbones.image_encoder.ImageEncoder | |
scalp: 0 | |
trunk: | |
_target_: sam2.modeling.backbones.vitdet.ViT | |
patch_size: 16 | |
embed_dim: 384 | |
depth: 12 | |
num_heads: 6 | |
mlp_ratio: 4.0 | |
qkv_bias: true | |
drop_path_rate: 0.0 | |
use_rel_pos: false | |
window_size: 14 | |
window_block_indexes: [0, 1, 3, 4, 6, 7, 9, 10] | |
neck: | |
_target_: sam2.modeling.backbones.image_encoder.ViTDetNeck | |
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,] | |
neck_norm: LN | |
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.RoPEAttention | |
rope_theta: 10000.0 | |
feat_sizes: [32, 32] | |
rope_k_repeat: True | |
embedding_dim: 256 | |
num_heads: 1 | |
downsample_rate: 1 | |
dropout: 0.1 | |
kv_in_dim: 64 | |
num_layers: 4 | |
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 | |
num_maskmem: 7 | |
image_size: 1024 | |
# apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask | |
# SAM decoder | |
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 | |
use_high_res_features_in_sam: false | |
# 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: true | |