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Update.
[culture.git]
/
main.py
diff --git
a/main.py
b/main.py
index
0144817
..
45bddb7
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-187,6
+187,8
@@
def masked_inplace_autoregression(
progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
+ # p = logits.softmax(1)
+ # entropy[:,s]= p.xlogy(p).sum(1) / math.log(2)
batches = zip(input.split(batch_size), ar_mask.split(batch_size))
if progress_bar_desc is not None:
tqdm.tqdm(
batches = zip(input.split(batch_size), ar_mask.split(batch_size))
if progress_bar_desc is not None:
tqdm.tqdm(
@@
-511,7
+513,7
@@
class TaskPicoCLVR(Task):
image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png")
torchvision.utils.save_image(
image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png")
torchvision.utils.save_image(
- img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=
1
.0
+ img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=
0
.0
)
log_string(f"wrote {image_name}")
)
log_string(f"wrote {image_name}")
@@
-622,15
+624,27
@@
class TaskMaze(Task):
def compute_error(self, model, split="train", nb_to_use=-1):
nb_total, nb_correct = 0, 0
count = torch.zeros(
def compute_error(self, model, split="train", nb_to_use=-1):
nb_total, nb_correct = 0, 0
count = torch.zeros(
- self.width * self.height, self.width * self.height, device=self.device
+ self.width * self.height,
+ self.width * self.height,
+ device=self.device,
+ dtype=torch.int64,
)
)
- for input in task.batches(split, nb_to_use):
+ for input in tqdm.tqdm(
+ task.batches(split, nb_to_use),
+ dynamic_ncols=True,
+ desc=f"test-mazes",
+ ):
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
result *= 1 - ar_mask
masked_inplace_autoregression(
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
result *= 1 - ar_mask
masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ progress_bar_desc=None,
+ device=self.device,
)
mazes, paths = self.seq2map(result)
path_correctness = maze.path_correctness(mazes, paths)
)
mazes, paths = self.seq2map(result)
path_correctness = maze.path_correctness(mazes, paths)
@@
-676,6
+690,8
@@
class TaskMaze(Task):
)
if count is not None:
)
if count is not None:
+ proportion_optimal = count.diagonal().sum().float() / count.sum()
+ log_string(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
with open(
os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
) as f:
with open(
os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
) as f:
@@
-703,6
+719,7
@@
class TaskMaze(Task):
target_paths=paths,
predicted_paths=predicted_paths,
path_correct=maze.path_correctness(mazes, predicted_paths),
target_paths=paths,
predicted_paths=predicted_paths,
path_correct=maze.path_correctness(mazes, predicted_paths),
+ path_optimal=maze.path_optimality(paths, predicted_paths),
)
log_string(f"wrote {filename}")
)
log_string(f"wrote {filename}")