when i run the second part: experiments_and_evaluation_utk_latest.ipynb
it has the error like:
Number of trainable parameters : 706,278
Number of non-trainable parameters : 0
RuntimeError Traceback (most recent call last)
in ()
4 resume_model=None, resume_optimizer=None, backup_step=5, backup_path=backup_path,
5 n_epochs_freeze=0, lr_after_freeze=None,
----> 6 loss_weights=[1/10, 1/0.16, 1/0.44])
7 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in convert(t)
382
383 def convert(t):
--> 384 return t.to(device, dtype if t.is_floating_point() else None, non_blocking)
385
386 return self._apply(convert)
RuntimeError: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 14.73 GiB total capacity; 12.95 GiB already allocated; 4.94 MiB free; 91.58 MiB cached)
does it has any thing to do with the batch_size and i can't find the way to change it . does any body can tell me how to fix it.
when i run the second part: experiments_and_evaluation_utk_latest.ipynb
it has the error like:
Number of trainable parameters : 706,278
Number of non-trainable parameters : 0
RuntimeError Traceback (most recent call last)
in ()
4 resume_model=None, resume_optimizer=None, backup_step=5, backup_path=backup_path,
5 n_epochs_freeze=0, lr_after_freeze=None,
----> 6 loss_weights=[1/10, 1/0.16, 1/0.44])
7 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in convert(t)
382
383 def convert(t):
--> 384 return t.to(device, dtype if t.is_floating_point() else None, non_blocking)
385
386 return self._apply(convert)
RuntimeError: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 14.73 GiB total capacity; 12.95 GiB already allocated; 4.94 MiB free; 91.58 MiB cached)
does it has any thing to do with the batch_size and i can't find the way to change it . does any body can tell me how to fix it.