I am trying to implement soft-mIoU loss for semantic segmentation as per the following equation. but loss is very low and I am not able to find the wrong step in the implementation.

```
def to_one_hot(tensor,nClasses):
n,h,w = tensor.size()
one_hot = torch.zeros(n,nClasses,h,w).scatter_(1,tensor.view(n,1,h,w),1)
return one_hot
class mIoULoss(nn.Module):
def __init__(self, weight=None, size_average=True, n_classes=2):
super(mIoULoss, self).__init__()
self.classes = n_classes
def forward(self, inputs, target_oneHot):
# inputs => N x Classes x H x W
# target_oneHot => N x Classes x H x W
N = inputs.size()[0]
# predicted probabilities for each pixel along channel
inputs = F.softmax(inputs,dim=1)
# Numerator Product
inter = inputs * target_oneHot
## Sum over all pixels N x C x H x W => N x C
inter = inter.view(N,self.classes,-1).sum(2)
#Denominator
union= inputs + target_oneHot - (inputs*target_oneHot)
## Sum over all pixels N x C x H x W => N x C
union = union.view(N,self.classes,-1).sum(2)
loss = inter/union
## Return average loss over classes and batch
return loss.mean()
```

Can any one help me?

Thanks