mazes,
target_paths=None,
predicted_paths=None,
- score_paths=None,
- score_truth=None,
path_correct=None,
path_optimal=None,
):
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:
)
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
-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[
:,
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))
######################################################################