Update

diff --git a/beaver.py b/beaver.py
index f5f092b..86008f6 100755 (executable)
--- a/beaver.py
+++ b/beaver.py
@@ -143,10 +143,10 @@ def generation_order(x, fixed_len):
     return order
 
 
-def reorder(x, order, back=False):  # x is NxTxD1x...xDk, order is NxT'
+def reorder(x, order, reverse=False):  # x is NxTxD1x...xDk, order is NxT'
     u = x.reshape(x.size()[:2] + (-1,))
     order = order.unsqueeze(-1).expand(-1, -1, u.size(-1))
-    if back:
+    if reverse:
         v = u.new(u.size())
         v.scatter_(1, order, u)
     else:
@@ -199,7 +199,7 @@ def compute_perplexity(model, task, fixed_len, split="train"):
             input = input.to(device)
             x, order = shuffle(input, fixed_len)
             x = model(mygpt.BracketedSequence(x), order=order).x
-            output = reorder(x, order, back=True)
+            output = reorder(x, order, reverse=True)
             loss = F.cross_entropy(output.transpose(1, 2), input)
             acc_loss += loss.item() * input.size(0)
             nb_samples += input.size(0)
@@ -265,7 +265,7 @@ def oneshot(gpt, task):
         for mazes, policies in task.policy_batches(split="train"):
             x, order = shuffle(mazes, task.height * task.width)
             x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
-            output_gpt = reorder(x, order, back=True)
+            output_gpt = reorder(x, order, reverse=True)
             output = model(output_gpt)
 
             loss = compute_loss(mazes, output, policies, task.height, task.width)
@@ -280,7 +280,7 @@ def oneshot(gpt, task):
         for mazes, policies in task.policy_batches(split="test"):
             x, order = shuffle(mazes, task.height * task.width)
             x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
-            output_gpt = reorder(x, order, back=True)
+            output_gpt = reorder(x, order, reverse=True)
             output = model(output_gpt)
             loss = compute_loss(mazes, output, policies, task.height, task.width)
             acc_test_loss += loss.item() * mazes.size(0)
@@ -295,7 +295,7 @@ def oneshot(gpt, task):
         policies = task.test_policies[:32]
         x, order = shuffle(mazes, task.height * task.width)
         x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
-        output_gpt = reorder(x, order, back=True)
+        output_gpt = reorder(x, order, reverse=True)
         output = model(output_gpt)
         if args.oneshot_output == "policy":
             targets = policies.permute(0, 2, 1)
@@ -440,7 +440,7 @@ class TaskMaze(Task):
             masked_inplace_autoregression(
                 model, self.batch_size, x, ar_mask, order=order
             )
-            result = reorder(x, order, back=True)
+            result = reorder(x, order, reverse=True)
             mazes, paths = self.seq2map(result)
             nb_correct += maze.path_correctness(mazes, paths).long().sum()
             nb_total += mazes.size(0)
@@ -475,7 +475,7 @@ class TaskMaze(Task):
             masked_inplace_autoregression(
                 model, self.batch_size, x, ar_mask, order=order
             )
-            result = reorder(x, order, back=True)
+            result = reorder(x, order, reverse=True)
 
             mazes, paths = self.seq2map(input)
             _, predicted_paths = self.seq2map(result)
@@ -630,7 +630,7 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs):
         input = input.to(device)
         x, order = shuffle(input, task.height * task.width)
         x = model(mygpt.BracketedSequence(x), order=order).x
-        output = reorder(x, order, back=True)
+        output = reorder(x, order, reverse=True)
         loss = F.cross_entropy(output.transpose(1, 2), input)
         acc_train_loss += loss.item() * input.size(0)
         nb_train_samples += input.size(0)