Open WipJesusFI opened 11 months ago
Hi,
I am trying to use WHD in order to optimize a semantic segmentation model. I have written this code:
import torch
from losses import *
whd = WeightedHausdorffDistance(resized_height = 192, resized_width = 192) prob_map = torch.rand(1, 192, 192, requires_grad = True) prob_map.requires_grad = True gt = [torch.randint(0, 2, (192, 192))] orig_sizes = np.array([[192, 192]]) whd(prob_map, gt, orig_sizes)
The code gives me the next error:
AttributeError: 'WeightedHausdorffDistance' object has no attribute '_backward_hooks'
¿Can someone help me?
Thanks in advance
You can add super(WeightedHausdorffDistance, self).__init__() instead of super(nn.Module, self).__init__()
super(WeightedHausdorffDistance, self).__init__()
super(nn.Module, self).__init__()
Hi,
I am trying to use WHD in order to optimize a semantic segmentation model. I have written this code:
import torch
from losses import *
whd = WeightedHausdorffDistance(resized_height = 192, resized_width = 192) prob_map = torch.rand(1, 192, 192, requires_grad = True) prob_map.requires_grad = True gt = [torch.randint(0, 2, (192, 192))] orig_sizes = np.array([[192, 192]]) whd(prob_map, gt, orig_sizes)
The code gives me the next error:
AttributeError: 'WeightedHausdorffDistance' object has no attribute '_backward_hooks'
¿Can someone help me?
Thanks in advance