def infer(self, test_sentence):
# read raw data to list
test_sentence = self.convert(test_sentence)
print(test_sentence)
lines_raw = [test_sentence]
lines_prep = [self.preprocess(test_sentence)]
# prepare dataset
print("Reading test data...")
test = Seq2SeqDataset.from_list(lines_prep)
test.src_field.vocab = self.src_vocab
# prepare iterator
test_iterator = BucketIterator(dataset=test,
batch_size=1,
train=False,
sort=False,
sort_within_batch=False,
shuffle=False,
device=device)
# predict
with torch.no_grad():
for i, batch in enumerate(test_iterator):
# forward through model
_, _, output = self.model(batch,
has_targets=False,
mask_softmax=1.0,
teacher_forcing=1.0)
# get top-1
predicted_values, predicted_indices = torch.max(output, dim=-1)
# convert predicted vocab indices to an actual sentence
predicted_seq = [
self.tgt_vocab.itos[c]
for c in predicted_indices.squeeze(0).tolist()
]
# output is log_softmax so do exp()
predicted_values = predicted_values.exp()
# convert to list
predicted_values_ = predicted_values.squeeze(0).tolist()
# beam search
predicted_seq = self.beam_lm(''.join(predicted_seq[1:-1]),
predicted_values_[1:-1],
lines_raw[i])
# match case and punctuations
predicted_seq = self.match_case(predicted_seq, lines_raw[i])
# do some post-processing to match submission output
print("{} {}".format(i, predicted_seq))
return predicted_seq
def predict(self, test_path, test_cleaned_path, out_path):
# read raw data to list
lines_id = []
lines_raw = []
lines_cleaned = []
lines_prep = []
with open(test_path, 'r') as f, open(test_cleaned_path, 'r') as fc:
for line in f:
line_id = line[:3]
line_seq = line[4:]
lines_id.append(line_id)
lines_raw.append(line_seq)
lines_prep.append(self.preprocess(line_seq))
for line in fc:
lines_cleaned.append(line[4:])
# prepare dataset
print("Reading test data...")
test = Seq2SeqDataset.from_list(lines_prep)
test.src_field.vocab = self.src_vocab
# prepare iterator
test_iterator = BucketIterator(dataset=test,
batch_size=1,
train=False,
sort=False,
sort_within_batch=False,
shuffle=False,
device=device)
# predict
with open(out_path, 'w') as writer:
with torch.no_grad():
for i, batch in enumerate(test_iterator):
# forward through model
_, _, output = self.model(batch,
has_targets=False,
mask_softmax=1.0,
teacher_forcing=1.0)
print(output.shape)
# get top-1
predicted_values, predicted_indices = torch.max(output,
dim=-1)
print(predicted_values.shape)
print(predicted_indices.shape)
# convert predicted vocab indices to an actual sentence
predicted_seq = [
self.tgt_vocab.itos[c]
for c in predicted_indices.squeeze(0).tolist()
]
# print('predicted_seq')
# print(predicted_seq)
# output is log_softmax so do exp()
predicted_values = predicted_values.exp()
# print('predicted_values')
# print(predicted_values)
# convert to list
predicted_values_ = predicted_values.squeeze(0).tolist()
# beam search
predicted_seq = self.beam_lm(''.join(predicted_seq[1:-1]),
predicted_values_[1:-1],
lines_raw[i])
# match case and punctuations
predicted_seq = self.match_case(predicted_seq,
lines_raw[i])
# do some post-processing to match submission output
predicted_seq = self.match_output(predicted_seq,
lines_cleaned[i])
print("{} {}".format(i, predicted_seq))
# write to file with line_id
writer.write(lines_id[i] + ',' + predicted_seq + '\n')
