result = self.tensorize(primer)
fill = result.new_full(
- result.size()[:-1] + (self.height * self.width,), self.t_nul
+ result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
)
result = torch.cat((result, fill), 1)
ar_mask = (result == self.t_nul).long()
class Expr(Task):
+ def tensorize(self, sequences):
+ len_max = max([len(x) for x in sequences])
+ return torch.cat(
+ [
+ torch.tensor(
+ [
+ [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+ for s in sequences
+ ]
+ )
+ ],
+ 0,
+ ).to(self.device)
+
def __init__(
self,
nb_train_samples,
nb_variables=nb_variables,
length=sequence_length,
)
- self.char2id = dict(
- [
- (c, n)
- for n, c in enumerate(
- set("#" + "".join(train_sequences + test_sequences))
- )
- ]
- )
+
+ symbols = list(set("#" + "".join(train_sequences + test_sequences)))
+ symbols.sort()
+
+ self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
self.id2char = dict([(n, c) for c, n in self.char2id.items()])
self.filler, self.space = self.char2id["#"], self.char2id[" "]
- len_max = max([len(x) for x in train_sequences])
- self.train_input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in train_sequences
- ]
- )
- ],
- 0,
- ).to(device)
-
- len_max = max([len(x) for x in test_sequences])
- self.test_input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in test_sequences
- ]
- )
- ],
- 0,
- ).to(device)
+ self.train_input = self.tensorize(train_sequences)
+ self.test_input = self.tensorize(test_sequences)
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
return "".join([self.id2char[k.item()] for k in s])
def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ self,
+ n_epoch,
+ model,
+ result_dir,
+ logger,
+ deterministic_synthesis,
+ input_file=None,
):
with torch.autograd.no_grad():
t = model.training
##############################################################
# Log a few generated sequences
- input = self.test_input[:10]
+ if input_file is None:
+ input = self.test_input[:10]
+ else:
+ with open(input_file, "r") as f:
+ sequences = [e.strip() for e in f.readlines()]
+ sequences = [s + " " + "#" * 50 for s in sequences]
+ input = self.tensorize(sequences)
+
result = input.clone()
ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
result = (1 - ar_mask) * result + ar_mask * self.filler