added deepsad base code

Deep-SAD-PyTorch/src/networks/layers/standard.py

@@ -0,0 +1,52 @@
+import torch
+
+from torch.nn import Module
+from torch.nn import init
+from torch.nn.parameter import Parameter
+
+
+# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
+class Standardize(Module):
+    """
+    Applies (element-wise) standardization with trainable translation parameter μ and scale parameter σ, i.e. computes
+    (x - μ) / σ where '/' is applied element-wise.
+
+    Args:
+        in_features: size of each input sample
+        out_features: size of each output sample
+        bias: If set to False, the layer will not learn a translation parameter μ.
+            Default: ``True``
+
+    Attributes:
+        mu: the learnable translation parameter μ.
+        std: the learnable scale parameter σ.
+    """
+    __constants__ = ['mu']
+
+    def __init__(self, in_features, bias=True, eps=1e-6):
+        super(Standardize, self).__init__()
+        self.in_features = in_features
+        self.out_features = in_features
+        self.eps = eps
+        self.std = Parameter(torch.Tensor(in_features))
+        if bias:
+            self.mu = Parameter(torch.Tensor(in_features))
+        else:
+            self.register_parameter('mu', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        init.constant_(self.std, 1)
+        if self.mu is not None:
+            init.constant_(self.mu, 0)
+
+    def forward(self, x):
+        if self.mu is not None:
+            x -= self.mu
+        x = torch.div(x, self.std + self.eps)
+        return x
+
+    def extra_repr(self):
+        return 'in_features={}, out_features={}, bias={}'.format(
+            self.in_features, self.out_features, self.mu is not None
+        )