def main():
args = parse_args()
checkpoint_path = Path(args.checkpoint)
checkpoint_dir = checkpoint_path.parent
params_path = checkpoint_dir / 'params.json'
vocab_dir = checkpoint_dir / 'vocab'
params = Params.from_file(params_path)
train_params, model_params = params.pop('train'), params.pop('model')
tokenizer = WordTokenizer(
start_tokens=['<s>'],
end_tokens=['</s>'],
)
token_indexer = SingleIdTokenIndexer(lowercase_tokens=True)
dataset_reader = SnliReader(tokenizer=tokenizer,
token_indexers={'tokens': token_indexer})
valid_dataset = dataset_reader.read(train_params.pop('valid_dataset_path'))
vocab = Vocabulary.from_files(vocab_dir)
random.shuffle(valid_dataset)
model_params['token_embedder']['pretrained_file'] = None
model = SNLIModel(params=model_params, vocab=vocab)
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'),
strict=False)
model.eval()
iterator = BasicIterator(batch_size=1)
iterator.index_with(vocab)
generator = iterator(valid_dataset)
for i in range(10):
batch = next(generator)
label_token_to_index = vocab.get_token_to_index_vocabulary('labels')
print('----')
print(' '.join(
model.convert_to_readable_text(batch['premise']['tokens'])[0]))
for label, label_index in label_token_to_index.items():
label_tensor = torch.tensor([label_index])
enc_embs = model.embed(batch['premise']['tokens'])
enc_mask = get_text_field_mask(batch['premise'])
enc_hidden = model.encode(inputs=enc_embs,
mask=enc_mask,
drop_start_token=True)
code, kld = model.sample_code_and_compute_kld(enc_hidden)
generated = model.generate(code=code,
label=label_tensor,
max_length=enc_mask.sum(1) * 2,
beam_size=10,
lp_alpha=args.lp_alpha)
text = model.convert_to_readable_text(generated[:, 0])[0]
print(label)
print(' '.join(text))
def main():
args = parse_args()
checkpoint_path = Path(args.checkpoint)
checkpoint_dir = checkpoint_path.parent
params_path = checkpoint_dir / 'params.json'
vocab_dir = checkpoint_dir / 'vocab'
params = Params.from_file(params_path)
train_params, model_params = params.pop('train'), params.pop('model')
tokenizer = WordTokenizer(start_tokens=['<s>'], end_tokens=['</s>'],)
token_indexer = SingleIdTokenIndexer(lowercase_tokens=True)
dataset_reader = SnliReader(
tokenizer=tokenizer, token_indexers={'tokens': token_indexer})
valid_dataset = dataset_reader.read(
train_params.pop('valid_dataset_path'))
vocab = Vocabulary.from_files(vocab_dir)
model_params['token_embedder']['pretrained_file'] = None
model = SNLIModel(params=model_params, vocab=vocab)
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'),
strict=False)
model.to(args.device)
model.eval()
iterator = BasicIterator(batch_size=args.batch_size)
iterator.index_with(vocab)
generator = iterator(valid_dataset, num_epochs=1, shuffle=False)
label_index_to_token = vocab.get_index_to_token_vocabulary('labels')
out_file = open(args.out, 'w')
for batch in tqdm(generator):
premise_tokens = batch['premise']['tokens']
enc_embs = model.embed(premise_tokens.to(args.device))
enc_mask = get_text_field_mask(batch['premise']).to(args.device)
enc_hidden = model.encode(inputs=enc_embs, mask=enc_mask,
drop_start_token=True)
code, kld = model.sample_code_and_compute_kld(enc_hidden)
pre_text = model.convert_to_readable_text(premise_tokens[:, 1:])
label_tensor = batch['label'].to(args.device)
generated = model.generate(
code=code, label=label_tensor, max_length=25,
beam_size=10, lp_alpha=args.lp_alpha)
text = model.convert_to_readable_text(generated[:, 0])
for pre_text_b, text_b, label_index_b in zip(pre_text, text, label_tensor):
obj = {'sentence1': ' '.join(pre_text_b), 'sentence2': ' '.join(text_b),
'gold_label': label_index_to_token[label_index_b.item()]}
out_file.write(json.dumps(obj))
out_file.write('\n')