class Grid(Task):
# Make a tensor from a list of strings
- def tensorize(self, descr):
+ def str2tensor(self, descr):
token_descr = [s.strip().split(" ") for s in descr]
l = max([len(s) for s in token_descr])
token_descr = [s + ["#"] * (l - len(s)) for s in token_descr]
return torch.tensor(id_descr, device=self.device)
# Make a list of strings from a tensor
- def detensorize(self, x):
+ def tensor2str(self, x):
return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
# trim all the tensors in the tuple z to remove as much token from
self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
self.t_nul = self.token2id["#"]
- self.t_true = self.token2id["<true>"]
- self.t_false = self.token2id["<false>"]
+ self.t_true = self.token2id["true"]
+ self.t_false = self.token2id["false"]
# Tokenize the train and test sets
- self.train_input = self.tensorize(self.train_descr)
- self.test_input = self.tensorize(self.test_descr)
+ self.train_input = self.str2tensor(self.train_descr)
+ self.test_input = self.str2tensor(self.test_descr)
def batches(self, split="train"):
assert split in {"train", "test"}
correct = self.test_input[:1000]
result = correct.clone()
ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
- result *= 1 - ar_mask
+ result *= 1 - ar_mask # paraaaaanoiaaaaaaa
+
+ logger(f"----------------------------------------------------------")
- for e in self.detensorize(result[:10]):
+ for e in self.tensor2str(result[:10]):
logger(f"test_before {e}")
masked_inplace_autoregression(
device=self.device,
)
- for e in self.detensorize(result[:10]):
- logger(f"test_after {e}")
+ logger(f"----------------------------------------------------------")
+
+ for e in self.tensor2str(result[:10]):
+ logger(f"test_after {e}")
+
+ logger(f"----------------------------------------------------------")
nb_total = ar_mask.sum().item()
nb_correct = ((correct == result).long() * ar_mask).sum().item()
- logger(f"test_performance {nb_total=} {nb_correct=}")
- logger(f"main_test_accuracy {nb_correct / nb_total}")
+ logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
+ logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
######################################################################