Comments (1)
Thanks for your attention to our job. It is an easy thing to visualize uncertainty graphs, which can be seen by only using the heat map of the matplotlib package, as follows:
def get_cmap(x: torch.Tensor, path: str):
import matplotlib.pyplot as plt
x_ = x.squeeze(0).cpu().numpy()
x_mean = x_.transpose(1,2,0).mean(2)
plt.imshow(x_mean,cmap='summer_r')
plt.savefig(path)
where x denotes the feature level of the uncertainty map. For example, you can obtain the final uncertainty map via nn.Sequential(*[nn.Conv2d(dim[0],dim[0],3,1,1), nn.ELU(),nn.Conv2d(dim[0],out_cahnnels,3,1,1)]), just like the line 839 in the UDR_S2Former.py.
from udr-s2former_deraining.
Related Issues (9)
- Cannot find UDR_S2Former.py file. HOT 1
- GPU and Time HOT 2
- Version of pytorch-lightning HOT 11
- Regarding the issue with testing HOT 7
- training on .pth files HOT 4
- the spelling of the results is wrong HOT 1
- Training loop HOT 2
- Pre-trained models HOT 1
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from udr-s2former_deraining.