Prototype MAML

lib/models/SharedUtils.py

@@ -6,29 +6,32 @@ import torch.nn as nn
 
 
 def additive_func(A, B):
-  assert A.dim() == B.dim() and A.size(0) == B.size(0), '{:} vs {:}'.format(A.size(), B.size())
-  C = min(A.size(1), B.size(1))
-  if A.size(1) == B.size(1):
-    return A + B
-  elif A.size(1) < B.size(1):
-    out = B.clone()
-    out[:,:C] += A
-    return out
-  else:
-    out = A.clone()
-    out[:,:C] += B
-    return out
+    assert A.dim() == B.dim() and A.size(0) == B.size(0), "{:} vs {:}".format(
+        A.size(), B.size()
+    )
+    C = min(A.size(1), B.size(1))
+    if A.size(1) == B.size(1):
+        return A + B
+    elif A.size(1) < B.size(1):
+        out = B.clone()
+        out[:, :C] += A
+        return out
+    else:
+        out = A.clone()
+        out[:, :C] += B
+        return out
 
 
 def change_key(key, value):
-  def func(m):
-    if hasattr(m, key):
-      setattr(m, key, value)
-  return func
+    def func(m):
+        if hasattr(m, key):
+            setattr(m, key, value)
+
+    return func
 
 
 def parse_channel_info(xstring):
-  blocks = xstring.split(' ')
-  blocks = [x.split('-') for x in blocks]
-  blocks = [[int(_) for _ in x] for x in blocks]
-  return blocks
+    blocks = xstring.split(" ")
+    blocks = [x.split("-") for x in blocks]
+    blocks = [[int(_) for _ in x] for x in blocks]
+    return blocks