X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=maze.py;h=754cdeab2728b756bae6c17d017f1c95e680214d;hb=ffc0a92cddefa9fda9f25468c25ae1365b52be47;hp=e377d2f0e6b2ab18684c93b6d85f7c6093cc67ea;hpb=483494965a97df9a09cd572246d1f3a4dc7248de;p=beaver.git

diff --git a/maze.py b/maze.py
index e377d2f..754cdea 100755
--- a/maze.py
+++ b/maze.py
@@ -113,18 +113,16 @@ def compute_policy(walls, i, j):
 ######################################################################
 
 
-def mark_path(walls, i, j, goal_i, goal_j):
-    policy = compute_policy(walls, goal_i, goal_j)
+def mark_path(walls, i, j, goal_i, goal_j, policy):
     action = torch.distributions.categorical.Categorical(
         policy.permute(1, 2, 0)
     ).sample()
-    walls[i, j] = 4
     n, nmax = 0, walls.numel()
     while i != goal_i or j != goal_j:
         di, dj = [(0, -1), (0, 1), (-1, 0), (1, 0)][action[i, j]]
         i, j = i + di, j + dj
         assert walls[i, j] == 0
-        walls[i, j] = 4
+        walls[i, j] = v_path
         n += 1
         assert n < nmax
 
@@ -160,34 +158,43 @@ def create_maze_data(
 ):
     mazes = torch.empty(nb, height, width, dtype=torch.int64)
     paths = torch.empty(nb, height, width, dtype=torch.int64)
+    policies = torch.empty(nb, 4, height, width)
 
     for n in progress_bar(range(nb)):
         maze = create_maze(height, width, nb_walls)
-        i = (1 - maze).nonzero()
+        i = (maze == v_empty).nonzero()
         while True:
             start, goal = i[torch.randperm(i.size(0))[:2]]
             if (start - goal).abs().sum() >= dist_min:
                 break
+        start_i, start_j, goal_i, goal_j = start[0], start[1], goal[0], goal[1]
 
+        policy = compute_policy(maze, goal_i, goal_j)
         path = maze.clone()
-        mark_path(path, start[0], start[1], goal[0], goal[1])
-        maze[start[0], start[1]] = v_start
-        maze[goal[0], goal[1]] = v_goal
-        path[start[0], start[1]] = v_start
-        path[goal[0], goal[1]] = v_goal
+        mark_path(path, start_i, start_j, goal_i, goal_j, policy)
+        maze[start_i, start_j] = v_start
+        maze[goal_i, goal_j] = v_goal
+        path[start_i, start_j] = v_start
+        path[goal_i, goal_j] = v_goal
 
         mazes[n] = maze
         paths[n] = path
+        policies[n] = policy
 
-    return mazes, paths
+    return mazes, paths, policies
 
 
 ######################################################################
 
 
-def save_image(name, mazes, target_paths, predicted_paths=None, path_correct=None):
-    mazes, target_paths = mazes.cpu(), target_paths.cpu()
-
+def save_image(
+    name,
+    mazes,
+    target_paths=None,
+    predicted_paths=None,
+    score_paths=None,
+    path_correct=None,
+):
     colors = torch.tensor(
         [
             [255, 255, 255],  # empty
@@ -198,22 +205,45 @@ def save_image(name, mazes, target_paths, predicted_paths=None, path_correct=Non
         ]
     )
 
-    mazes = colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
-    target_paths = (
-        colors[target_paths.reshape(-1)]
-        .reshape(target_paths.size() + (-1,))
-        .permute(0, 3, 1, 2)
+    mazes = mazes.cpu()
+
+    c_mazes = (
+        colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
     )
-    imgs = torch.cat((mazes.unsqueeze(1), target_paths.unsqueeze(1)), 1)
+
+    imgs = c_mazes.unsqueeze(1)
+
+    if target_paths is not None:
+        target_paths = target_paths.cpu()
+
+        c_target_paths = (
+            colors[target_paths.reshape(-1)]
+            .reshape(target_paths.size() + (-1,))
+            .permute(0, 3, 1, 2)
+        )
+
+        imgs = torch.cat((imgs, c_target_paths.unsqueeze(1)), 1)
 
     if predicted_paths is not None:
         predicted_paths = predicted_paths.cpu()
-        predicted_paths = (
+        c_predicted_paths = (
             colors[predicted_paths.reshape(-1)]
             .reshape(predicted_paths.size() + (-1,))
             .permute(0, 3, 1, 2)
         )
-        imgs = torch.cat((imgs, predicted_paths.unsqueeze(1)), 1)
+        imgs = torch.cat((imgs, c_predicted_paths.unsqueeze(1)), 1)
+
+    if score_paths is not None:
+        score_paths = score_paths.cpu()
+        c_score_paths = score_paths.unsqueeze(1).expand(-1, 3, -1, -1)
+        c_score_paths = (
+            c_score_paths * colors[4].reshape(1, 3, 1, 1)
+            + (1 - c_score_paths) * colors[3].reshape(1, 3, 1, 1)
+        ).long()
+        c_score_paths = c_score_paths * (mazes.unsqueeze(1) == v_empty) + c_mazes * (
+            mazes.unsqueeze(1) != v_empty
+        )
+        imgs = torch.cat((imgs, c_score_paths.unsqueeze(1)), 1)
 
     # NxKxCxHxW
     if path_correct is None:
@@ -241,7 +271,6 @@ def save_image(name, mazes, target_paths, predicted_paths=None, path_correct=Non
 ######################################################################
 
 if __name__ == "__main__":
-
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     mazes, paths = create_maze_data(8)
     mazes, paths = mazes.to(device), paths.to(device)