Update.

diff --git a/mine_mnist.py b/mine_mnist.py
index 82f6530..6f65136 100755 (executable)
--- a/mine_mnist.py
+++ b/mine_mnist.py
@@ -12,27 +12,30 @@ from torch.nn import functional as F
 
 ######################################################################
 
-# Returns a pair of tensors (a, b, c), where a and b are Nx1x28x28
-# tensors containing images, with a[i] and b[i] of same class for any
-# i, and c is a 1d long tensor with the count of pairs per class used.
+# Returns a pair of tensors (a, b, c), where a and b are tensors
+# containing each half of the samples, with a[i] and b[i] of same
+# class for any i, and c is a 1d long tensor with the count of pairs
+# per class used.
 
 def create_pair_set(used_classes, input, target):
-    u = []
+    ua, ub = [], []
 
     for i in used_classes:
         used_indices = torch.arange(input.size(0), device = target.device)\
                             .masked_select(target == i.item())
         x = input[used_indices]
         x = x[torch.randperm(x.size(0))]
-        # Careful with odd numbers of samples in a class
-        x = x[0:2 * (x.size(0) // 2)].reshape(-1, 2, 28, 28)
-        u.append(x)
+        ua.append(x.narrow(0, 0, x.size(0)//2))
+        ub.append(x.narrow(0, x.size(0)//2, x.size(0)//2))
 
-    x = torch.cat(u, 0)
-    x = x[torch.randperm(x.size(0))]
-    c = torch.tensor([x.size(0) for x in u])
+    a = torch.cat(ua, 0)
+    b = torch.cat(ub, 0)
+    perm = torch.randperm(a.size(0))
+    a = a[perm].contiguous()
+    b = b[perm].contiguous()
+    c = torch.tensor([x.size(0) for x in ua])
 
-    return x.narrow(1, 0, 1).contiguous(), x.narrow(1, 1, 1).contiguous(), c
+    return a, b, c
 
 ######################################################################