Update

diff --git a/beaver.py b/beaver.py
index e7decd1..33d174d 100755 (executable)
--- a/beaver.py
+++ b/beaver.py
@@ -188,16 +188,19 @@ def one_shot(gpt, task):
         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 
         acc_train_loss, nb_train_samples = 0, 0
-        for input, targets in task.policy_batches(split="train"):
+        for input, policies in task.policy_batches(split="train"):
+            ####
+            # print(f'{input.size()=} {policies.size()=}')
+            # s = maze.stationary_densities(
+            # exit(0)
+            ####
+            mask = input.unsqueeze(-1) == maze.v_empty
             output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
             output = model(output_gpt)
-            targets = targets * (input.unsqueeze(-1) == maze.v_empty)
-            output = output * (input.unsqueeze(-1) == maze.v_empty)
+            targets = policies.permute(0, 2, 1) * mask
+            output = output * mask
             # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
-            loss = (
-                -(output.log_softmax(-1) * targets).sum()
-                / (input == maze.v_empty).sum()
-            )
+            loss = -(output.log_softmax(-1) * targets).sum() / mask.sum()
             acc_train_loss += loss.item() * input.size(0)
             nb_train_samples += input.size(0)
 
@@ -206,16 +209,14 @@ def one_shot(gpt, task):
             optimizer.step()
 
         acc_test_loss, nb_test_samples = 0, 0
-        for input, targets in task.policy_batches(split="test"):
+        for input, policies in task.policy_batches(split="test"):
+            mask = input.unsqueeze(-1) == maze.v_empty
             output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
             output = model(output_gpt)
-            targets = targets * (input.unsqueeze(-1) == maze.v_empty)
-            output = output * (input.unsqueeze(-1) == maze.v_empty)
+            targets = policies.permute(0, 2, 1) * mask
+            output = output * mask
             # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
-            loss = (
-                -(output.log_softmax(-1) * targets).sum()
-                / (input == maze.v_empty).sum()
-            )
+            loss = -(output.log_softmax(-1) * targets).sum() / mask.sum()
             acc_test_loss += loss.item() * input.size(0)
             nb_test_samples += input.size(0)
 
@@ -225,11 +226,11 @@ def one_shot(gpt, task):
 
         # -------------------
         input = task.test_input[:32, : task.height * task.width]
-        targets = task.test_policies[:32]
+        targets = task.test_policies[:32].permute(0, 2, 1)
         output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
         output = model(output_gpt)
         # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
-        # losses = losses * (input == maze.v_empty)
+        # losses = losses * mask
         # losses = losses / losses.max()
         # losses = (output.softmax(-1) - targets).abs().max(-1).values
         # losses = (losses >= 0.05).float()
@@ -300,7 +301,7 @@ class TaskMaze(Task):
             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
         )
         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
-        self.train_policies = train_policies.flatten(-2).permute(0, 2, 1).to(device)
+        self.train_policies = train_policies.flatten(-2).to(device)
 
         test_mazes, test_paths, test_policies = maze.create_maze_data(
             nb_test_samples,
@@ -310,7 +311,7 @@ class TaskMaze(Task):
             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
         )
         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
-        self.test_policies = test_policies.flatten(-2).permute(0, 2, 1).to(device)
+        self.test_policies = test_policies.flatten(-2).to(device)
 
         self.nb_codes = self.train_input.max() + 1
 
@@ -327,16 +328,16 @@ class TaskMaze(Task):
     def policy_batches(self, split="train", nb_to_use=-1):
         assert split in {"train", "test"}
         input = self.train_input if split == "train" else self.test_input
-        targets = self.train_policies if split == "train" else self.test_policies
+        policies = self.train_policies if split == "train" else self.test_policies
         input = input[:, : self.height * self.width]
-        targets = targets * (input != maze.v_wall)[:, :, None]
+        policies = policies * (input != maze.v_wall)[:, None]
 
         if nb_to_use > 0:
             input = input[:nb_to_use]
-            targets = targets[:nb_to_use]
+            policies = policies[:nb_to_use]
 
         for batch in tqdm.tqdm(
-            zip(input.split(self.batch_size), targets.split(self.batch_size)),
+            zip(input.split(self.batch_size), policies.split(self.batch_size)),
             dynamic_ncols=True,
             desc=f"epoch-{split}",
         ):

diff --git a/maze.py b/maze.py
index 36eef25..d09e860 100755 (executable)
--- a/maze.py
+++ b/maze.py
@@ -110,19 +110,22 @@ def compute_policy(walls, goal_i, goal_j):
     return proba
 
 
-def stationary_density(policy, start_i, start_j):
-    probas = policy.new_zeros(policy.size()[:-1])
+def stationary_densities(mazes, policies):
+    start = (mazes == v_start).nonzero(as_tuple=True)
+    probas = mazes.new_zeros(mazes.size())
     pred_probas = probas.clone()
-    probas[start_i, start_j] = 1.0
+    probas[start] = 1.0
 
     while not pred_probas.equal(probas):
         pred_probas.copy_(probas)
         probas.zero_()
-        probas[1:, :] = pred_probas[:-1, :] * policy[0, :-1, :]
-        probas[:-1, :] = pred_probas[1:, :] * policy[1, 1:, :]
-        probas[:, 1:] = pred_probas[:, :-1] * policy[2, :, :-1]
-        probas[:, :-1] = pred_probas[:, 1:] * policy[3, :, 1:]
-        probas[start_i, start_j] = 1.0
+        probas[:, 1:, :] = pred_probas[:, :-1, :] * policies[:, 0, :-1, :]
+        probas[:, :-1, :] = pred_probas[:, 1:, :] * policies[:, 1, 1:, :]
+        probas[:, :, 1:] = pred_probas[:, :, :-1] * policies[:, 2, :, :-1]
+        probas[:, :, :-1] = pred_probas[:, :, 1:] * policies[:, 3, :, 1:]
+        probas[start] = 1.0
+
+    return probas
 
 
 ######################################################################