X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=maze.py;h=8ac9fcef0495fbefc6c11f1652367fb44d916f40;hb=6045e9a7dd61f0dab60bd1c6ff71f6bd5c32778b;hp=f6a852e37241f176a65a7cd69c6c8cb934a3a1fc;hpb=f23843d33a4fa5a38f5034deab8f473793732ee3;p=picoclvr.git

diff --git a/maze.py b/maze.py
index f6a852e..8ac9fce 100755
--- a/maze.py
+++ b/maze.py
@@ -13,6 +13,8 @@ v_empty, v_wall, v_start, v_goal, v_path = 0, 1, 2, 3, 4
 
 
 def create_maze(h=11, w=17, nb_walls=8):
+    assert h % 2 == 1 and w % 2 == 1
+
     a, k = 0, 0
 
     while k < nb_walls:
@@ -219,8 +221,6 @@ def save_image(
     mazes,
     target_paths=None,
     predicted_paths=None,
-    score_paths=None,
-    score_truth=None,
     path_correct=None,
     path_optimal=None,
 ):
@@ -240,17 +240,6 @@ def save_image(
         colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
     )
 
-    if score_truth is not None:
-        score_truth = score_truth.cpu()
-        c_score_truth = score_truth.unsqueeze(1).expand(-1, 3, -1, -1)
-        c_score_truth = (
-            c_score_truth * colors[4].reshape(1, 3, 1, 1)
-            + (1 - c_score_truth) * colors[0].reshape(1, 3, 1, 1)
-        ).long()
-        c_mazes = (mazes.unsqueeze(1) != v_empty) * c_mazes + (
-            mazes.unsqueeze(1) == v_empty
-        ) * c_score_truth
-
     imgs = c_mazes.unsqueeze(1)
 
     if target_paths is not None:
@@ -273,18 +262,6 @@ def save_image(
         )
         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[0].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)
-
     img = torch.tensor([255, 255, 0]).view(1, -1, 1, 1)
 
     # NxKxCxHxW
@@ -305,6 +282,8 @@ def save_image(
         -1, -1, imgs.size(3) + 2, 1 + imgs.size(1) * (1 + imgs.size(4))
     ).clone()
 
+    print(f"{img.size()=} {imgs.size()=}")
+
     for k in range(imgs.size(1)):
         img[
             :,
@@ -322,9 +301,9 @@ def save_image(
 
 if __name__ == "__main__":
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    mazes, paths = create_maze_data(8)
+    mazes, paths, policies = create_maze_data(8)
     mazes, paths = mazes.to(device), paths.to(device)
-    save_image("test.png", mazes, paths, paths)
+    save_image("test.png", mazes=mazes, target_paths=paths, predicted_paths=paths)
     print(path_correctness(mazes, paths))
 
 ######################################################################