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http://47.108.14.56:3000/c/b78fb9d0-d385-4dce-81dd-5173e1cf037a

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bishe 2025-02-23 16:44:28 +08:00
parent c0364b0170
commit 9966211e8b
4 changed files with 2 additions and 2 deletions
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1
checkpoints/ROMA_UNSB_001/loss_log.txt
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@ -4,3 +4,4 @@
================ Training Loss (Sun Feb 23 16:02:40 2025) ================ ================ Training Loss (Sun Feb 23 16:02:40 2025) ================
================ Training Loss (Sun Feb 23 16:05:19 2025) ================ ================ Training Loss (Sun Feb 23 16:05:19 2025) ================
================ Training Loss (Sun Feb 23 16:06:44 2025) ================ ================ Training Loss (Sun Feb 23 16:06:44 2025) ================
================ Training Loss (Sun Feb 23 16:09:38 2025) ================

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models/__pycache__/roma_unsb_model.cpython-39.pyc
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2
models/networks.py
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@ -984,7 +984,7 @@ class ResnetGenerator(nn.Module):
else: else:
# print("%d: skipping %s %d" % (layer_id, layer.__class__.__name__, feat.size(1))) # print("%d: skipping %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
pass pass
if layer_id == layers[-1] and encode_only: if layer_id == len(list(self.model)) - 1 and encode_only:
# print('encoder only return features') # print('encoder only return features')
return feats # return intermediate features alone; stop in the last layers return feats # return intermediate features alone; stop in the last layers

1
models/roma_unsb_model.py
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@ -405,7 +405,6 @@ class RomaUnsbModel(BaseModel):
self.mutil_real_A0_tokens = self.netPreViT(real_A0, self.atten_layers, get_tokens=True) self.mutil_real_A0_tokens = self.netPreViT(real_A0, self.atten_layers, get_tokens=True)
self.mutil_real_A1_tokens = self.netPreViT(real_A1, self.atten_layers, get_tokens=True) self.mutil_real_A1_tokens = self.netPreViT(real_A1, self.atten_layers, get_tokens=True)
print(self.mutil_real_A0_tokens)
self.mutil_real_A0_tokens = torch.cat(self.mutil_real_A0_tokens, dim=1).to(self.device) self.mutil_real_A0_tokens = torch.cat(self.mutil_real_A0_tokens, dim=1).to(self.device)
self.mutil_real_A1_tokens = torch.cat(self.mutil_real_A1_tokens, dim=1).to(self.device) self.mutil_real_A1_tokens = torch.cat(self.mutil_real_A1_tokens, dim=1).to(self.device)