Update

[beaver.git] / beaver.py

diff --git a/beaver.py b/beaver.py
index c29dea5..c3b7e09 100755 (executable)
--- a/beaver.py
+++ b/beaver.py
@@ -169,28 +169,32 @@ def compute_perplexity(model, split="train"):
 ######################################################################
 
 
-def nb_rank_error(output, targets):
-    output = output.reshape(-1, output.size(-1))
-    targets = targets.reshape(-1, targets.size(-1))
-    i = outputs.argmax(1)
-    # out=input.gather out[i][j]=input[i][index[i][j]]
-    # u[k]=targets[k][i[k]]
-    return output[targets.argmax(1)]
-
-
 def one_shot(gpt, task):
     t = gpt.training
     gpt.eval()
-    model = nn.Linear(args.dim_model, 4).to(device)
+    model = nn.Sequential(
+        nn.Linear(args.dim_model, args.dim_model),
+        nn.ReLU(),
+        nn.Linear(args.dim_model, args.dim_model),
+        nn.ReLU(),
+        nn.Linear(args.dim_model, 4),
+    ).to(device)
 
     for n_epoch in range(args.nb_epochs):
-        optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+        learning_rate = learning_rate_schedule[n_epoch]
+        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"):
             output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
             output = model(output_gpt)
-            loss = -(output.log_softmax(-1) * targets).sum(-1).mean()
+            targets = targets * (input.unsqueeze(-1) == maze.v_empty)
+            output = output * (input.unsqueeze(-1) == maze.v_empty)
+            loss = (
+                -(output.log_softmax(-1) * targets).sum()
+                / (input == maze.v_empty).sum()
+                + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
+            )
             acc_train_loss += loss.item() * input.size(0)
             nb_train_samples += input.size(0)
 
@@ -202,13 +206,36 @@ def one_shot(gpt, task):
         for input, targets in task.policy_batches(split="test"):
             output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
             output = model(output_gpt)
-            loss = -(output.log_softmax(-1) * targets).sum(-1).mean()
+            targets = targets * (input.unsqueeze(-1) == maze.v_empty)
+            output = output * (input.unsqueeze(-1) == maze.v_empty)
+            loss = (
+                -(output.log_softmax(-1) * targets).sum()
+                / (input == maze.v_empty).sum()
+                + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
+            )
             acc_test_loss += loss.item() * input.size(0)
             nb_test_samples += input.size(0)
 
-        print(
-            f"{n_epoch=} {acc_train_loss/nb_train_samples=} {acc_test_loss/nb_test_samples=}"
+        log_string(
+            f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
+        )
+
+        # -------------------
+        input = task.test_input[:32, : task.height * task.width]
+        targets = task.test_policies[:32]
+        output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+        output = model(output_gpt)
+        losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
+        losses = losses * (input == maze.v_empty)
+        losses = losses / losses.max()
+        losses = losses.reshape(-1, args.maze_height, args.maze_width)
+        input = input.reshape(-1, args.maze_height, args.maze_width)
+        maze.save_image(
+            os.path.join(args.result_dir, f"oneshot_{n_epoch:04d}.png"),
+            mazes=input,
+            score_paths=losses,
         )
+        # -------------------
 
     gpt.train(t)
 
@@ -352,10 +379,10 @@ class TaskMaze(Task):
             _, predicted_paths = self.seq2map(result)
             maze.save_image(
                 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
-                mazes,
-                paths,
-                predicted_paths,
-                maze.path_correctness(mazes, predicted_paths),
+                mazes=mazes,
+                target_paths=paths,
+                predicted_paths=predicted_paths,
+                path_correct=maze.path_correctness(mazes, predicted_paths),
             )
 
             model.train(t)