Delete loss

loss/loss.py

@@ -1,55 +0,0 @@
-import torch
-from torch import nn
-
-class Loss_VAE(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.mse = nn.MSELoss(reduction='sum')
-
-    def forward(self, recon_x, x, mu, log_var):
-        mse = self.mse(recon_x, x)
-        kld = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
-        loss = mse + kld
-        return loss
-    
-
-def DiceScore(
-    y_pred: torch.Tensor,
-    y: torch.Tensor,
-    include_background: bool = True,
-) -> torch.Tensor:
-    """Computes Dice score metric from full size Tensor and collects average.
-    Args:
-        y_pred: input data to compute, typical segmentation model output.
-            It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
-            should be binarized.
-        y: ground truth to compute mean dice metric. It must be one-hot format and first dim is batch.
-            The values should be binarized.
-        include_background: whether to skip Dice computation on the first channel of
-            the predicted output. Defaults to True.
-    Returns:
-        Dice scores per batch and per class, (shape [batch_size, num_classes]).
-    Raises:
-        ValueError: when `y_pred` and `y` have different shapes.
-    """
-
-    y = y.float()
-    y_pred = y_pred.float()
-
-    if y.shape != y_pred.shape:
-        raise ValueError("y_pred and y should have same shapes.")
-
-    # reducing only spatial dimensions (not batch nor channels)
-    n_len = len(y_pred.shape)
-    reduce_axis = list(range(2, n_len))
-    intersection = torch.sum(y * y_pred, dim=reduce_axis)
-
-    y_o = torch.sum(y, reduce_axis)
-    y_pred_o = torch.sum(y_pred, dim=reduce_axis)
-    denominator = y_o + y_pred_o
-
-    return torch.where(
-        denominator > 0,
-        (2.0 * intersection) / denominator,
-        torch.tensor(float("1"), device=y_o.device),
-    )