RuntimeError: quantile() input tensor is too large

[noivan0]

Traceback (most recent call last):
File "D:\code\git-DS\EfficientAD\efficientad.py", line 451, in
main()
File "D:\code\git-DS\EfficientAD\efficientad.py", line 268, in main
q_st_start, q_st_end, q_ae_start, q_ae_end = map_normalization(
File "C:\Users\2878045\AppData\Roaming\Python\Python39\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "D:\code\git-DS\EfficientAD\efficientad.py", line 374, in map_normalization
q_st_start = torch.quantile(maps_st, q=0.9)
RuntimeError: quantile() input tensor is too large

anyone solve this?

[FelixFu520]

Traceback (most recent call last): File "D:\code\git-DS\EfficientAD\efficientad.py", line 451, in main() File "D:\code\git-DS\EfficientAD\efficientad.py", line 268, in main q_st_start, q_st_end, q_ae_start, q_ae_end = map_normalization( File "C:\Users\2878045\AppData\Roaming\Python\Python39\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "D:\code\git-DS\EfficientAD\efficientad.py", line 374, in map_normalization q_st_start = torch.quantile(maps_st, q=0.9) RuntimeError: quantile() input tensor is too large

anyone solve this?

torch.quantile() limited process 16 million elements only，you could break maps_st into pieces, and process, like:
_q_st_start = []
for i in range(n):
_q_st_start,append(torch.quantile(maps_st[i: i+k], q=0.9))
q_st_start = torch.stack(_q_st_start).mean()