######################################################################
-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: