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generate.py
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generate.py
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# -*- coding: utf-8 -*-
import argparse
import os
import dill
from tqdm import tqdm
import torch
from torchtext import data
from options import generate_opts
import utils
from models.transformer import TranslationLM
def main(args):
device = torch.device('cuda' if args.gpu else 'cpu')
load_vars = torch.load(args.model)
lm_args = load_vars['args']
weights = load_vars['weights']
dirname = os.path.dirname(args.model)
TEXT = utils.load_field(os.path.join(dirname, 'text.field'))
fields = [('src', TEXT), ('tgt', TEXT)]
with open(args.input, 'r') as f:
examples = [data.Example.fromlist([line], [('src', TEXT)]) for line in f]
test_data = data.Dataset(examples, [('src', TEXT)])
test_iter = data.Iterator(
test_data,
batch_size=args.batch_size,
train=False,
shuffle=False,
sort=False,
)
model = TranslationLM(TEXT, lm_args).to(device)
model.load_state_dict(weights)
model.eval()
for samples in tqdm(test_iter, total=len(test_iter)):
srcs = samples.src.to(device)
outs = model.generate(srcs, args.maxlen).transpose(0, 1)
sents = [utils.id2w(out, TEXT) for out in outs]
print('\n'.join(sents))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
generate_opts(parser)
args = parser.parse_args()
main(args)