######################################################################
-def compute_distance(walls, i, j):
+def compute_distance(walls, goal_i, goal_j):
max_length = walls.numel()
dist = torch.full_like(walls, max_length)
- dist[i, j] = 0
+ dist[goal_i, goal_j] = 0
pred_dist = torch.empty_like(dist)
while True:
######################################################################
-def compute_policy(walls, i, j):
- distance = compute_distance(walls, i, j)
+def compute_policy(walls, goal_i, goal_j):
+ distance = compute_distance(walls, goal_i, goal_j)
distance = distance + walls.numel() * walls
value = distance.new_full((4,) + distance.size(), walls.numel())
- value[0, :, 1:] = distance[:, :-1]
- value[1, :, :-1] = distance[:, 1:]
- value[2, 1:, :] = distance[:-1, :]
- value[3, :-1, :] = distance[1:, :]
+ value[0, :, 1:] = distance[:, :-1] # <
+ value[1, :, :-1] = distance[:, 1:] # >
+ value[2, 1:, :] = distance[:-1, :] # ^
+ value[3, :-1, :] = distance[1:, :] # v
proba = (value.min(dim=0)[0][None] == value).float()
proba = proba / proba.sum(dim=0)[None]
return proba
+def stationary_densities(mazes, policies):
+ policies = policies * (mazes != v_goal)[:, None]
+ start = (mazes == v_start).nonzero(as_tuple=True)
+ probas = mazes.new_zeros(mazes.size(), dtype=torch.float32)
+ pred_probas = probas.clone()
+ probas[start] = 1.0
+
+ while not pred_probas.equal(probas):
+ pred_probas.copy_(probas)
+ probas.zero_()
+ probas[:, 1:, :] += pred_probas[:, :-1, :] * policies[:, 3, :-1, :]
+ probas[:, :-1, :] += pred_probas[:, 1:, :] * policies[:, 2, 1:, :]
+ probas[:, :, 1:] += pred_probas[:, :, :-1] * policies[:, 1, :, :-1]
+ probas[:, :, :-1] += pred_probas[:, :, 1:] * policies[:, 0, :, 1:]
+ probas[start] = 1.0
+
+ return probas
+
+
######################################################################
target_paths=None,
predicted_paths=None,
score_paths=None,
+ score_truth=None,
path_correct=None,
):
colors = torch.tensor(
[255, 255, 255], # empty
[0, 0, 0], # wall
[0, 255, 0], # start
- [0, 0, 255], # goal
+ [127, 127, 255], # goal
[255, 0, 0], # path
]
)
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:
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)
+ + (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