Basic cleanup

map2map/models/adversary.py

@@ -5,14 +5,14 @@ def adv_model_wrapper(module):
     """Wrap an adversary model to also take lists of Tensors as input,
     to be concatenated along the batch dimension
     """
-    class new_module(module):
+    class _new_module(module):
         def forward(self, x):
             if not isinstance(x, torch.Tensor):
                 x = torch.cat(x, dim=0)
 
             return super().forward(x)
 
-    return new_module
+    return _new_module
 
 
 def adv_criterion_wrapper(module):
@@ -22,7 +22,7 @@ def adv_criterion_wrapper(module):
     * expand target shape as that of input
     * return a list of losses, one for each pair of input and target Tensors
     """
-    class new_module(module):
+    class _new_module(module):
         def forward(self, input, target):
             assert isinstance(input, torch.Tensor)
 
@@ -50,4 +50,4 @@ def adv_criterion_wrapper(module):
 
             return torch.split(input, size, dim=0)
 
-    return new_module
+    return _new_module

map2map/models/power.py

@@ -1,7 +1,7 @@
 import torch
 
 
-def power(x):
+def power(x: torch.Tensor):
     """Compute power spectra of input fields
 
     Each field should have batch and channel dimensions followed by spatial

map2map/train.py

@@ -416,7 +416,7 @@ def dist_init(rank, args):
         os.remove(dist_file)
 
 
-def init_weights(m):
+def init_weights(m, args):
     if isinstance(m, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d,
         nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d)):
         m.weight.data.normal_(0.0, args.init_weight_std)
@@ -429,13 +429,20 @@ def init_weights(m):
             m.bias.data.fill_(0)
 
 
-def set_requires_grad(module, requires_grad=False):
+def set_requires_grad(module: torch.nn.Module, requires_grad : bool =False):
     for param in module.parameters():
         param.requires_grad = requires_grad
 
 
-def get_grads(model):
+def get_grads(model: torch.nn.Module):
-    """gradients of the weights of the first and the last layer
+    """
+    Calculate the gradients of the weights of the first and the last layer.
+
+    Args:
+        model: The model for which to calculate the gradients.
+
+    Returns:
+        A list containing the norms of the gradients of the first and the last layer weights.
     """
     grads = list(p.grad for n, p in model.named_parameters()
                  if '.weight' in n)