)
result *= 1 - ar_mask
- # snake.solver(result,ar_mask)
-
masked_inplace_autoregression(
model,
self.batch_size,
nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
-
return nb_total, nb_correct
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
-
- # logger(
- # f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- # )
-
test_nb_total, test_nb_correct = compute_nb_correct(
self.test_input[:1000], self.test_prior_visits[:1000]
)
values_input = expr.extract_results([self.seq2str(s) for s in input])
values_result = expr.extract_results([self.seq2str(s) for s in result])
- for i, r in zip(values_input, values_result):
- for n, vi in i.items():
- vr = r.get(n)
- if vr is None or vr < 0:
- nb_missed += 1
- else:
- d = abs(vr - vi)
- if d >= nb_delta.size(0):
+ filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
+
+ with open(filename, "w") as f:
+ for i, r in zip(values_input, values_result):
+ for n, vi in i.items():
+ vr = r.get(n)
+ f.write(f"{vi} {-1 if vr is None else vr}\n")
+
+ if vr is None or vr < 0:
nb_missed += 1
else:
- nb_delta[d] += 1
+ d = abs(vr - vi)
+ if d >= nb_delta.size(0):
+ nb_missed += 1
+ else:
+ nb_delta[d] += 1
######################################################################
##############################################################
+######################################################################
+import world
+
+
+class World(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.device = device
+
+ (
+ self.train_input,
+ self.train_actions,
+ self.test_input,
+ self.test_actions,
+ self.frame2seq,
+ self.seq2frame,
+ ) = world.create_data_and_processors(
+ nb_train_samples,
+ nb_test_samples,
+ mode="first_last",
+ nb_steps=30,
+ nb_epochs=2,
+ )
+
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+ def batches(self, split="train", nb_to_use=-1, desc=None):
+ assert split in {"train", "test"}
+ input = self.train_input if split == "train" else self.test_input
+ if nb_to_use > 0:
+ input = input[:nb_to_use]
+ if desc is None:
+ desc = f"epoch-{split}"
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ pass
+
+
######################################################################