X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=beaver.py;h=517f29a5f1702ea7630600a44126633603d25125;hb=27bb2d1ab23422f26b05f88b4e0573deeb075cd2;hp=920a446f920e6cd2beb67ac0df96457bfac55225;hpb=61cd7a140e44ccb966bad941fa31e395e51e50e2;p=beaver.git

diff --git a/beaver.py b/beaver.py
index 920a446..517f29a 100755
--- a/beaver.py
+++ b/beaver.py
@@ -26,9 +26,7 @@ else:
 
 ######################################################################
 
-parser = argparse.ArgumentParser(
-    description="An implementation of GPT with cache to solve a toy geometric reasoning task."
-)
+parser = argparse.ArgumentParser(description="A maze shortest path solving with a GPT.")
 
 parser.add_argument("--log_filename", type=str, default="train.log")
 
@@ -75,11 +73,11 @@ parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 ##############################
 # maze options
 
-parser.add_argument("--world_height", type=int, default=13)
+parser.add_argument("--maze_height", type=int, default=13)
 
-parser.add_argument("--world_width", type=int, default=21)
+parser.add_argument("--maze_width", type=int, default=21)
 
-parser.add_argument("--world_nb_walls", type=int, default=15)
+parser.add_argument("--maze_nb_walls", type=int, default=15)
 
 ######################################################################
 
@@ -131,9 +129,8 @@ def masked_inplace_autoregression(model, batch_size, input, ar_mask):
     for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
         i = (ar_mask.sum(0) > 0).nonzero()
         if i.min() > 0:
-            model(
-                mygpt.BracketedSequence(input, 0, i.min())
-            )  # Needed to initialize the model's cache
+            # Needed to initialize the model's cache
+            model(mygpt.BracketedSequence(input, 0, i.min()))
         for s in range(i.min(), i.max() + 1):
             output = model(mygpt.BracketedSequence(input, s, 1)).x
             logits = output[:, s]
@@ -196,7 +193,6 @@ class TaskMaze(Task):
         )
         mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
         self.train_input = self.map2seq(mazes_train, paths_train)
-        self.nb_codes = self.train_input.max() + 1
 
         mazes_test, paths_test = maze.create_maze_data(
             nb_test_samples,
@@ -208,6 +204,8 @@ class TaskMaze(Task):
         mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
         self.test_input = self.map2seq(mazes_test, paths_test)
 
+        self.nb_codes = self.train_input.max() + 1
+
     def batches(self, split="train", nb_to_use=-1):
         assert split in {"train", "test"}
         input = self.train_input if split == "train" else self.test_input
@@ -227,6 +225,7 @@ class TaskMaze(Task):
             result = input.clone()
             ar_mask = result.new_zeros(result.size())
             ar_mask[:, self.height * self.width :] = 1
+            result *= 1 - ar_mask
             masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
             mazes, paths = self.seq2map(result)
             nb_correct += maze.path_correctness(mazes, paths).long().sum()
@@ -256,13 +255,19 @@ class TaskMaze(Task):
             input = self.test_input[:32]
             result = input.clone()
             ar_mask = result.new_zeros(result.size())
-
             ar_mask[:, self.height * self.width :] = 1
+            result *= 1 - ar_mask
             masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
 
             mazes, paths = self.seq2map(input)
             _, predicted_paths = self.seq2map(result)
-            maze.save_image(f"result_{n_epoch:04d}.png", mazes, paths, predicted_paths)
+            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),
+            )
 
             model.train(t)
 
@@ -276,9 +281,9 @@ task = TaskMaze(
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     batch_size=args.batch_size,
-    height=args.world_height,
-    width=args.world_width,
-    nb_walls=args.world_nb_walls,
+    height=args.maze_height,
+    width=args.maze_width,
+    nb_walls=args.maze_nb_walls,
     device=device,
 )
 
@@ -413,9 +418,6 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         for input in task.batches(split="test"):
             input = input.to(device)
 
-            # input, loss_masks, true_images = task.excise_last_image(input)
-            # input, loss_masks = task.add_true_image(input, true_images, loss_masks)
-
             output = model(mygpt.BracketedSequence(input)).x
             loss = F.cross_entropy(output.transpose(1, 2), input)
             acc_test_loss += loss.item() * input.size(0)