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loss.py
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loss.py
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import torch.nn as nn
import torch
import matplotlib.pyplot as plt
from torch.nn.functional import mse_loss
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
self.smooth = 1.0
def forward(self, y_pred, y_true):
assert y_pred.size() == y_true.size()
dscs = torch.zeros(y_pred.shape[1])
for i in range(y_pred.shape[1]):
y_pred_ch = y_pred[:, i].contiguous().view(-1)
y_true_ch = y_true[:, i].contiguous().view(-1)
intersection = (y_pred_ch * y_true_ch).sum()
dscs[i] = (2. * intersection + self.smooth) / (
y_pred_ch.sum() + y_true_ch.sum() + self.smooth
)
print(torch.mean(dscs))
return 1. - torch.mean(dscs)