added deepsad base code

Deep-SAD-PyTorch/src/utils/misc.py

@@ -0,0 +1,46 @@
+import torch
+
+from torch.autograd import Variable
+
+
+# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
+def enumerate_discrete(x, y_dim):
+    """
+    Generates a 'torch.Tensor' of size batch_size x n_labels of the given label.
+
+    :param x: tensor with batch size to mimic
+    :param y_dim: number of total labels
+    :return variable
+    """
+
+    def batch(batch_size, label):
+        labels = (torch.ones(batch_size, 1) * label).type(torch.LongTensor)
+        y = torch.zeros((batch_size, y_dim))
+        y.scatter_(1, labels, 1)
+        return y.type(torch.LongTensor)
+
+    batch_size = x.size(0)
+    generated = torch.cat([batch(batch_size, i) for i in range(y_dim)])
+
+    if x.is_cuda:
+        generated = generated.to(x.device)
+
+    return Variable(generated.float())
+
+
+def log_sum_exp(tensor, dim=-1, sum_op=torch.sum):
+    """
+    Uses the LogSumExp (LSE) as an approximation for the sum in a log-domain.
+
+    :param tensor: Tensor to compute LSE over
+    :param dim: dimension to perform operation over
+    :param sum_op: reductive operation to be applied, e.g. torch.sum or torch.mean
+    :return: LSE
+    """
+    max, _ = torch.max(tensor, dim=dim, keepdim=True)
+    return torch.log(sum_op(torch.exp(tensor - max), dim=dim, keepdim=True) + 1e-8) + max
+
+
+def binary_cross_entropy(x, y):
+    eps = 1e-8
+    return -torch.sum(y * torch.log(x + eps) + (1 - y) * torch.log(1 - x + eps), dim=-1)