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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.