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Update
[beaver.git]
/
beaver.py
diff --git
a/beaver.py
b/beaver.py
index
6ed9dd2
..
9f8bc41
100755
(executable)
--- a/
beaver.py
+++ b/
beaver.py
@@
-238,6
+238,11
@@
def oneshot(model, learning_rate_scheduler, task):
model.eval()
mazes = task.test_input[:32].clone()
mazes[:, task.height * task.width :] = 0
model.eval()
mazes = task.test_input[:32].clone()
mazes[:, task.height * task.width :] = 0
+ policies = task.test_policies[:32]
+ targets = maze.stationary_densities(
+ mazes[:, : task.height * task.width].view(-1, task.height, task.width),
+ policies.view(-1, 4, task.height, task.width),
+ ).flatten(-2)
output = eval_mygpt(model, mazes, prompt_len=task.height * task.width)
output = F.softmax(output, dim=2)
print(f"{output.size()=}")
output = eval_mygpt(model, mazes, prompt_len=task.height * task.width)
output = F.softmax(output, dim=2)
print(f"{output.size()=}")
@@
-245,13
+250,17
@@
def oneshot(model, learning_rate_scheduler, task):
-1, task.height, task.width
)
mazes = mazes[:, : task.height * task.width].reshape(-1, task.height, task.width)
-1, task.height, task.width
)
mazes = mazes[:, : task.height * task.width].reshape(-1, task.height, task.width)
- # targets = targets.reshape(-1, task.height, task.width)
+ targets = targets.reshape(-1, task.height, task.width)
+ paths = task.test_input[:32, task.height * task.width :].reshape(
+ -1, task.height, task.width
+ )
filename = f"oneshot.png"
maze.save_image(
os.path.join(args.result_dir, filename),
mazes=mazes,
filename = f"oneshot.png"
maze.save_image(
os.path.join(args.result_dir, filename),
mazes=mazes,
+ # target_paths=paths,
score_paths=proba_path,
score_paths=proba_path,
-
#
score_truth=targets,
+ score_truth=targets,
)
log_string(f"wrote {filename}")
)
log_string(f"wrote {filename}")