######################################################################
-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, dtype=torch.int64)
for n in progress_bar(range(nb)):
maze = create_maze(height, width, nb_walls)
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):
+def save_image(name, mazes, target_paths, predicted_paths=None, path_correct=None):
mazes, target_paths = mazes.cpu(), target_paths.cpu()
colors = torch.tensor(
.reshape(target_paths.size() + (-1,))
.permute(0, 3, 1, 2)
)
- img = torch.cat((mazes.unsqueeze(1), target_paths.unsqueeze(1)), 1)
+ imgs = torch.cat((mazes.unsqueeze(1), target_paths.unsqueeze(1)), 1)
if predicted_paths is not None:
predicted_paths = predicted_paths.cpu()
.reshape(predicted_paths.size() + (-1,))
.permute(0, 3, 1, 2)
)
- img = torch.cat((img, predicted_paths.unsqueeze(1)), 1)
-
- img = img.reshape((-1,) + img.size()[2:]).float() / 255.0
-
- torchvision.utils.save_image(img, name, padding=1, pad_value=0.85, nrow=6)
+ imgs = torch.cat((imgs, predicted_paths.unsqueeze(1)), 1)
+
+ # NxKxCxHxW
+ if path_correct is None:
+ path_correct = torch.zeros(imgs.size(0)) <= 1
+ path_correct = path_correct.cpu().long().view(-1, 1, 1, 1)
+ img = torch.tensor([224, 224, 224]).view(1, -1, 1, 1) * path_correct + torch.tensor(
+ [255, 0, 0]
+ ).view(1, -1, 1, 1) * (1 - path_correct)
+ img = img.expand(
+ -1, -1, imgs.size(3) + 2, 1 + imgs.size(1) * (1 + imgs.size(4))
+ ).clone()
+ for k in range(imgs.size(1)):
+ img[
+ :,
+ :,
+ 1 : 1 + imgs.size(3),
+ 1 + k * (1 + imgs.size(4)) : 1 + k * (1 + imgs.size(4)) + imgs.size(4),
+ ] = imgs[:, k]
+
+ img = img.float() / 255.0
+
+ torchvision.utils.save_image(img, name, nrow=4, padding=1, pad_value=224.0 / 256)
######################################################################
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)