X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=b774fce4aba98521e6b7fc3069f4cd4d3c063c6b;hb=38d3035f027881bb2baffdaffc8cd666d3df5dba;hp=319e94b1856f49f322d593bc902bebfc09a6a2d3;hpb=b5fd9b344c8c782460941c604b6e637d7549fe7d;p=picoclvr.git

diff --git a/main.py b/main.py
index 319e94b..b774fce 100755
--- a/main.py
+++ b/main.py
@@ -32,7 +32,10 @@ parser = argparse.ArgumentParser(
 )
 
 parser.add_argument(
-    "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack, expr"
+    "--task",
+    type=str,
+    default="picoclvr",
+    help="picoclvr, mnist, maze, snake, stack, expr",
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
@@ -223,7 +226,6 @@ def masked_inplace_autoregression(
     progress_bar_desc="autoregression",
     device=torch.device("cpu"),
 ):
-
     batches = zip(input.split(batch_size), ar_mask.split(batch_size))
 
     if progress_bar_desc is not None:
@@ -1018,11 +1020,32 @@ class TaskExpr(Task):
 
         train_sequences = expr.generate_sequences(nb_train_samples)
         test_sequences = expr.generate_sequences(nb_test_samples)
-        self.char2id = dict([ (c,n) for n,c in enumerate(set("".join(train_sequences + test_sequences))) ])
-        self.id2char = dict([ (n,c) for n,c in self.char2id.items() ])
+        self.char2id = dict(
+            [
+                (c, n)
+                for n, c in enumerate(set("".join(train_sequences + test_sequences)))
+            ]
+        )
+        self.id2char = dict([(n, c) for n, c in self.char2id.items()])
         len_max = max([len(x) for x in train_sequences + test_sequences])
-        self.train_input = torch.cat([torch.tensor([char2id(c) for c in s + " "*(len_max-len(s))] for s in train_sequences)], 0)
-        self.test_input = torch.cat([torch.tensor([char2id(c) for c in s + " "*(len_max-len(s))] for s in test_sequences)], 0)
+        self.train_input = torch.cat(
+            [
+                torch.tensor(
+                    [char2id(c) for c in s + " " * (len_max - len(s))]
+                    for s in train_sequences
+                )
+            ],
+            0,
+        )
+        self.test_input = torch.cat(
+            [
+                torch.tensor(
+                    [char2id(c) for c in s + " " * (len_max - len(s))]
+                    for s in test_sequences
+                )
+            ],
+            0,
+        )
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
     def batches(self, split="train", nb_to_use=-1, desc=None):