Update.

diff --git a/main.py b/main.py
index bcba9ee..83227bb 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -160,8 +160,8 @@ class TaskPicoCLVR(Task):
     def tensorize(self, descr):
         token_descr = [ s.strip().split(' ') for s in descr ]
         l = max([ len(s) for s in token_descr ])
-        token_descr = [ [ '<nul>' ] * (l - len(s)) + s for s in token_descr ]
-        #token_descr = [ s + [ '<nul>' ] * (l - len(s)) for s in token_descr ]
+        #token_descr = [ [ '<nul>' ] * (l - len(s)) + s for s in token_descr ]
+        token_descr = [ s + [ '<nul>' ] * (l - len(s)) for s in token_descr ]
         id_descr = [ [ self.token2id[u] for u in s ] for s in token_descr ]
         return torch.tensor(id_descr, device = self.device)
 

diff --git a/mygpt.py b/mygpt.py
index d6879dc..9da2e68 100755 (executable)
--- a/mygpt.py
+++ b/mygpt.py
@@ -14,7 +14,7 @@ from torch.nn import functional as F
 
 ##############################
 
-class Residual(nn.Module):
+class WithResidual(nn.Module):
     def __init__(self, *f):
         super().__init__()
         self.f = f[0] if len(f) == 1 else nn.Sequential(*f)
@@ -103,7 +103,7 @@ class MyGPT(nn.Module):
 
         for _ in range(nb_blocks):
             trunk_blocks += [
-                Residual(
+                WithResidual(
                     nn.LayerNorm((dim_model,)),
                     QKVAttention(
                         dim_in = dim_model,
@@ -113,7 +113,7 @@ class MyGPT(nn.Module):
                         causal = True, attention_dropout = dropout
                     ),
                 ),
-                Residual(
+                WithResidual(
                     nn.LayerNorm((dim_model,)),
                     nn.Linear(in_features = dim_model, out_features = dim_hidden),
                     nn.ReLU(),

diff --git a/picoclvr.py b/picoclvr.py
index f097eb0..8201f5d 100755 (executable)
--- a/picoclvr.py
+++ b/picoclvr.py
@@ -172,7 +172,7 @@ def descr2properties(descr, height, width):
                 return []
             seen[x] = (color_id[x], k // width, k % width)
 
-    square_infos = zip(*seen.values())
+    square_infos = tuple(zip(*seen.values()))
     square_c = torch.tensor(square_infos[0])
     square_i = torch.tensor(square_infos[1])
     square_j = torch.tensor(square_infos[2])