def main(config: str, model_th: str, dataset: str, hypo_file: str, ref_file: str,
batch_size: int, no_gpu: bool):
logger = logging.getLogger(__name__)
logger.info("Loading configuration parameters")
params = Params.from_file(config)
vocab_params = params.pop("vocabulary")
vocab = Vocabulary.from_params(vocab_params)
reader_params = params.pop("dataset_reader")
reader_name = reader_params.pop("type")
# reader_params["lazy"] = True # make sure we do not load the entire dataset
reader = DatasetReader.by_name(reader_name).from_params(reader_params)
logger.info("Reading data from {}".format(dataset))
data = reader.read(dataset)
iterator = BasicIterator(batch_size=batch_size)
iterator.index_with(vocab)
batches = iterator._create_batches(data, shuffle=False)
logger.info("Loading model")
model_params = params.pop("model")
model_name = model_params.pop("type")
model = Model.by_name(model_name).from_params(model_params, vocab=vocab)
if not no_gpu:
model.cuda(0)
with open(model_th, 'rb') as f:
if no_gpu:
state_dict = torch.load(f, map_location=torch.device('cpu'))
else:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
predictor = Seq2SeqPredictor(model, reader)
model.eval()
with open(hypo_file, 'w') as hf, open(ref_file, 'w') as rf:
logger.info("Generating predictions")
for sample in tqdm(batches):
s = list(sample)
pred = predictor.predict_batch_instance(s)
for inst, p in zip(s, pred):
print(
" ".join(p["predicted_tokens"][0]),
file=hf
)
print(
" ".join(t.text for t in inst["target_tokens"][1:-1]),
file=rf
)
def main(config: str, model_th: str, dataset: str, out_file):
logger = logging.getLogger(__name__)
logger.info("Loading model and data")
params = Params.from_file(config)
vocab_params = params.pop("vocabulary")
vocab = Vocabulary.from_params(vocab_params)
reader_params = Params({
"source_token_indexers": {
"tokens": {
"type": "single_id",
"namespace": "tokens"
}
},
"target_namespace": "tokens"
})
# reader_name = reader_params.pop("type")
# reader_params["lazy"] = True # make sure we do not load the entire dataset
reader = UnsupervisedBTReader.from_params(reader_params)
logger.info("Reading data from {}".format(dataset))
data = reader.read(dataset)
iterator = BasicIterator(batch_size=32)
iterator.index_with(vocab)
batches = iterator._create_batches(data, shuffle=False)
logger.info("Loading model")
model_params = params.pop("model")
model_name = model_params.pop("type")
model = Model.by_name(model_name).from_params(model_params, vocab=vocab)
model.cuda(0)
with open(model_th, 'rb') as f:
model.load_state_dict(torch.load(f))
predictor = Seq2SeqPredictor(model, reader)
model.eval()
line_id = 0
writer = csv.writer(out_file, delimiter="\t")
logger.info("Generating predictions")
for sample in tqdm(batches):
s = list(sample)
pred = predictor.predict_batch_instance(s)
for inst, p in zip(s, pred):
writer.writerow((line_id, " ".join(
(t.text for t in inst["source_tokens"][1:-1])),
" ".join(p["predicted_tokens"][0])))
line_id += 1
def main(config: str, model_th: str, dataset: str, seed: int):
logger = logging.getLogger(__name__)
logger.info("Loading model and data")
params = Params.from_file(config)
vocab_params = params.pop("vocabulary")
vocab = Vocabulary.from_params(vocab_params)
reader_params = params.pop("dataset_reader")
reader_name = reader_params.pop("type")
reader_params["lazy"] = True # make sure we do not load the entire dataset
reader = DatasetReader.by_name(reader_name).from_params(reader_params)
data = reader.read(dataset)
iterator = BasicIterator(batch_size=10)
iterator.index_with(vocab)
batches = iterator._create_batches(data, shuffle=False)
model_params = params.pop("model")
model_name = model_params.pop("type")
model = Model.by_name(model_name).from_params(model_params, vocab=vocab)
# model.cuda(cuda_device)
with open(model_th, 'rb') as f:
model.load_state_dict(torch.load(f))
predictor = Seq2SeqPredictor(model, reader)
model.eval()
logger.info("Generating predictions")
random.seed(seed)
samples = []
for b in batches:
samples.append(b)
if random.random() > 0.6:
break
sample = list(random.choice(samples))
pred = predictor.predict_batch_instance(sample)
for inst, p in zip(sample, pred):
print()
print("SOURCE:", " ".join([t.text for t in inst["source_tokens"]]))
print("GOLD:", " ".join([t.text for t in inst["target_tokens"]]))
print("GEN:", p["predicted_tokens"])