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| Training probe on 9539 samples... |
| ep0: loss=0.03555 |
| ep20: loss=0.03525 |
| ep40: loss=0.03491 |
| ep60: loss=0.03451 |
| ep80: loss=0.03416 |
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| Baseline: mAP50-95=0.5521 IoU@75=0.5789 |
|
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| Traceback (most recent call last): |
| File "/home/user/goat/Scripts/eval_attention_probe.py", line 262, in <module> |
| main() |
| File "/home/user/goat/Scripts/eval_attention_probe.py", line 248, in main |
| b=apply_probe(model,probe,img,b) |
| File "/home/user/goat/Scripts/eval_attention_probe.py", line 170, in apply_probe |
| delta = probe(feat.float().unsqueeze(0).to(device)).cpu().numpy()[0] |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl |
| return self._call_impl(*args, **kwargs) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl |
| return forward_call(*args, **kwargs) |
| File "/home/user/goat/Scripts/eval_attention_probe.py", line 40, in forward |
| return self.net(feats) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl |
| return self._call_impl(*args, **kwargs) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl |
| return forward_call(*args, **kwargs) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/container.py", line 250, in forward |
| input = module(input) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl |
| return self._call_impl(*args, **kwargs) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl |
| return forward_call(*args, **kwargs) |
| File "/home/user/miniconda3/envs/dairygoat/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 125, in forward |
| return F.linear(input, self.weight, self.bias) |
| RuntimeError: Inference tensors cannot be saved for backward. To work around you can make a clone to get a normal tensor and use it in autograd. |
|
|
