[255, 255, 255], # empty
[0, 0, 0], # wall
[0, 255, 0], # start
- [0, 0, 255], # goal
+ [127, 127, 255], # goal
[255, 0, 0], # path
]
)
mazes = mazes.cpu()
- mazes = colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
+ c_mazes = (
+ colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
+ )
- imgs = mazes.unsqueeze(1)
+ imgs = c_mazes.unsqueeze(1)
if target_paths is not None:
target_paths = target_paths.cpu()
- target_paths = (
+ c_target_paths = (
colors[target_paths.reshape(-1)]
.reshape(target_paths.size() + (-1,))
.permute(0, 3, 1, 2)
)
- imgs = torch.cat((imgs, target_paths.unsqueeze(1)), 1)
+ 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() * 255.0).long()
- score_paths = score_paths.unsqueeze(1).expand(-1, 3, -1, -1)
- imgs = torch.cat((imgs, score_paths.unsqueeze(1)), 1)
+ 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)
# NxKxCxHxW
if path_correct is None: