in sam2/modeling/sam2_base.py [0:0]
def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):
"""
Directly turn binary `mask_inputs` into a output mask logits without using SAM.
(same input and output shapes as in _forward_sam_heads above).
"""
# Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).
out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05
mask_inputs_float = mask_inputs.float()
high_res_masks = mask_inputs_float * out_scale + out_bias
low_res_masks = F.interpolate(
high_res_masks,
size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4),
align_corners=False,
mode="bilinear",
antialias=True, # use antialias for downsampling
)
# a dummy IoU prediction of all 1's under mask input
ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()
if not self.use_obj_ptrs_in_encoder:
# all zeros as a dummy object pointer (of shape [B, C])
obj_ptr = torch.zeros(
mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device
)
else:
# produce an object pointer using the SAM decoder from the mask input
_, _, _, _, _, obj_ptr, _ = self._forward_sam_heads(
backbone_features=backbone_features,
mask_inputs=self.mask_downsample(mask_inputs_float),
high_res_features=high_res_features,
)
# In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;
# Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying
# on the object_scores from the SAM decoder.
is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)
is_obj_appearing = is_obj_appearing[..., None]
lambda_is_obj_appearing = is_obj_appearing.float()
object_score_logits = out_scale * lambda_is_obj_appearing + out_bias
if self.pred_obj_scores:
if self.fixed_no_obj_ptr:
obj_ptr = lambda_is_obj_appearing * obj_ptr
obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
return (
low_res_masks,
high_res_masks,
ious,
low_res_masks,
high_res_masks,
obj_ptr,
object_score_logits,
)