######################################################################
-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
):
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
]
)
- 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: