Merge pull request #1601 from AndreaCossu/master

LaMAMLv2 buffer now uses subset instead of indexing

avalanche/training/supervised/lamaml_v2.py

@@ -99,7 +99,6 @@ def __init__(
             buffer_mb_size=buffer_mb_size,
             device=device,
         )
-
         self.model.apply(init_kaiming_normal)
 
     def _before_training_exp(self, **kwargs):
@@ -305,16 +304,15 @@ def __len__(self):
 
     def get_buffer_batch(self):
         rnd_ind = torch.randperm(len(self))[: self.buffer_mb_size]
-        buff_x = torch.cat(
-            [self.storage_policy.buffer[i][0].unsqueeze(0) for i in rnd_ind]
-        ).to(self.device)
-        buff_y = torch.LongTensor(
-            [self.storage_policy.buffer[i][1] for i in rnd_ind]
-        ).to(self.device)
-        buff_t = torch.LongTensor(
-            [self.storage_policy.buffer[i][2] for i in rnd_ind]
-        ).to(self.device)
-
+        buff = self.storage_policy.buffer.subset(rnd_ind)
+        buff_x, buff_y, buff_t = [], [], []
+        for bx, by, bt in buff:
+            buff_x.append(bx)
+            buff_y.append(by)
+            buff_t.append(bt)
+        buff_x = torch.stack(buff_x, dim=0).to(self.device)
+        buff_y = torch.tensor(buff_y).to(self.device).long()
+        buff_t = torch.tensor(buff_t).to(self.device).long()
         return buff_x, buff_y, buff_t