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Ultralytics Code Refactor https://ultralytics.com/actions (#16047)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ultralytics Assistant 2024-09-06 03:54:35 +08:00 • committed by GitHub
parent 95d54828bb
commit ac2c2be8f3
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12 changed files with 45 additions and 62 deletions
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9
ultralytics/models/sam/modules/decoders.py
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@ -435,9 +435,9 @@ class SAM2MaskDecoder(nn.Module):
upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)
hyper_in_list: List[torch.Tensor] = []
for i in range(self.num_mask_tokens):
hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
hyper_in_list: List[torch.Tensor] = [
self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)
]
hyper_in = torch.stack(hyper_in_list, dim=1)
b, c, h, w = upscaled_embedding.shape
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
@ -459,8 +459,7 @@ class SAM2MaskDecoder(nn.Module):
stability_delta = self.dynamic_multimask_stability_delta
area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
return stability_scores
return torch.where(area_u > 0, area_i / area_u, 1.0)
def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
"""