- result = input.clone()
- ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- for n in range(result.size(0)):
- logger(f"test_before {self.seq2str(result[n])}")
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- correct = (1 - ar_mask) * self.space + ar_mask * input
- for n in range(result.size(0)):
- comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
- logger(f"test_after {self.seq2str(result[n])} {comment}")
- logger(f"correct {self.seq2str(correct[n])}")
- ##############################################################
-
- model.train(t)
+ 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()
+ s = (result == self.space).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.filler
+
+ for n in range(result.size(0)):
+ logger(f"test_before {self.seq2str(result[n])}")
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ correct = (1 - ar_mask) * self.space + ar_mask * input
+ for n in range(result.size(0)):
+ comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
+ logger(f"test_after {self.seq2str(result[n])} {comment}")
+ logger(f"truth {self.seq2str(correct[n])}")
+ ##############################################################