def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Train alignment model')
parser.add_argument('--train', help='training corpus', required=True)
parser.add_argument('--iter',
help='number of iterations',
type=int,
required=True)
parser.add_argument('--charlm', help='character language model')
parser.add_argument('--pyp',
help='G_w^0 is PYP(CharLM)',
action='store_true')
parser.add_argument('--output', help='model output path')
parser.add_argument(
'--reverse',
help='train model in reverse direction (but output f-e)',
action='store_true')
args = parser.parse_args()
source_vocabulary = Vocabulary()
source_vocabulary[NULL]
target_vocabulary = Vocabulary()
logging.info('Reading parallel training data')
with open(args.train) as train:
training_corpus = read_parallel_corpus(train, source_vocabulary,
target_vocabulary, args.reverse)
if args.charlm:
logging.info('Preloading character language model')
if args.charlm == 'pu':
char_lm = PoissonUniformCharLM(target_vocabulary)
else:
char_lm = CharLM(args.charlm, target_vocabulary)
if args.pyp:
t_base = PYP(char_lm, PYPPrior(1.0, 1.0, 1.0, 1.0, 0.1, 1.0))
else:
t_base = char_lm
else:
t_base = Uniform(len(target_vocabulary))
model = AlignmentModel(len(source_vocabulary), t_base)
logging.info('Training alignment model')
alignments = run_sampler(model, training_corpus, args.iter)
if args.output:
with open(args.output, 'w') as f:
model.source_vocabulary = source_vocabulary
model.target_vocabulary = target_vocabulary
cPickle.dump(model, f, protocol=-1)
fmt = ('{e}-{f}' if args.reverse else '{f}-{e}')
for a, (f, e) in izip(alignments, training_corpus):
#f_sentence = ' '.join(source_vocabulary[w] for w in f[1:])
#e_sentence = ' '.join(target_vocabulary[w] for w in e)
print(' '.join(
fmt.format(f=j - 1, e=i) for i, j in enumerate(a) if j > 0))
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
parser = argparse.ArgumentParser(description='Train alignment model')
parser.add_argument('--train', help='training corpus', required=True)
parser.add_argument('--iter', help='number of iterations', type=int, required=True)
parser.add_argument('--charlm', help='character language model')
parser.add_argument('--pyp', help='G_w^0 is PYP(CharLM)', action='store_true')
parser.add_argument('--output', help='model output path')
parser.add_argument('--reverse', help='train model in reverse direction (but output f-e)',
action='store_true')
args = parser.parse_args()
source_vocabulary = Vocabulary()
source_vocabulary[NULL]
target_vocabulary = Vocabulary()
logging.info('Reading parallel training data')
with open(args.train) as train:
training_corpus = read_parallel_corpus(train, source_vocabulary, target_vocabulary,
args.reverse)
if args.charlm:
logging.info('Preloading character language model')
if args.charlm == 'pu':
char_lm = PoissonUniformCharLM(target_vocabulary)
else:
char_lm = CharLM(args.charlm, target_vocabulary)
if args.pyp:
t_base = PYP(char_lm, PYPPrior(1.0, 1.0, 1.0, 1.0, 0.1, 1.0))
else:
t_base = char_lm
else:
t_base = Uniform(len(target_vocabulary))
model = AlignmentModel(len(source_vocabulary), t_base)
logging.info('Training alignment model')
alignments = run_sampler(model, training_corpus, args.iter)
if args.output:
with open(args.output, 'w') as f:
model.source_vocabulary = source_vocabulary
model.target_vocabulary = target_vocabulary
cPickle.dump(model, f, protocol=-1)
fmt = ('{e}-{f}' if args.reverse else '{f}-{e}')
for a, (f, e) in izip(alignments, training_corpus):
#f_sentence = ' '.join(source_vocabulary[w] for w in f[1:])
#e_sentence = ' '.join(target_vocabulary[w] for w in e)
print(' '.join(fmt.format(f=j-1, e=i) for i, j in enumerate(a) if j > 0))
def run_sampler(model, corpus, n_iter):
n_words = sum(len(e) for f, e in corpus)
alignments = [None] * len(corpus)
samples = []
for it in range(n_iter):
logging.info('Iteration %d/%d', it + 1, n_iter)
for i, (f, e) in enumerate(corpus):
if it > 0: model.decrement(f, e, alignments[i])
alignments[i] = list(model.increment(f, e))
if it % 10 == 0:
logging.info('Model: %s', model)
ll = model.log_likelihood()
ppl = math.exp(-ll / n_words)
logging.info('LL=%.0f ppl=%.3f', ll, ppl)
if it % 30 == 29:
logging.info('Resampling hyperparameters...')
acceptance, rejection = model.resample_hyperparemeters(mh_iter)
arate = acceptance / float(acceptance + rejection)
logging.info('Metropolis-Hastings acceptance rate: %.4f', arate)
logging.info('Model: %s', model)
if it > n_iter / 10 and it % 10 == 0:
logging.info('Estimating sample')
samples.append(model.map_estimate())
logging.info('Combining %d samples', len(samples))
align = AlignmentModel.combine(samples)
for i, (f, e) in enumerate(corpus):
alignments[i] = list(align(f, e))
return alignments
def run_sampler(model, corpus, n_iter):
n_words = sum(len(e) for f, e in corpus)
alignments = [None] * len(corpus)
samples = []
for it in range(n_iter):
logging.info('Iteration %d/%d', it+1, n_iter)
for i, (f, e) in enumerate(corpus):
if it > 0: model.decrement(f, e, alignments[i])
alignments[i] = list(model.increment(f, e))
if it % 10 == 0:
logging.info('Model: %s', model)
ll = model.log_likelihood()
ppl = math.exp(-ll / n_words)
logging.info('LL=%.0f ppl=%.3f', ll, ppl)
if it % 30 == 29:
logging.info('Resampling hyperparameters...')
acceptance, rejection = model.resample_hyperparemeters(mh_iter)
arate = acceptance / float(acceptance + rejection)
logging.info('Metropolis-Hastings acceptance rate: %.4f', arate)
logging.info('Model: %s', model)
if it > n_iter/10 and it % 10 == 0:
logging.info('Estimating sample')
samples.append(model.map_estimate())
logging.info('Combining %d samples', len(samples))
align = AlignmentModel.combine(samples)
for i, (f, e) in enumerate(corpus):
alignments[i] = list(align(f, e))
return alignments
import sys
sys.path.append('../')
import torch
import numpy as np
import argparse
from model import AlignmentModel
from torch.utils.data import DataLoader
from cc_dataset import CCDataset
device = "cuda:1"
model_path = "../checkpoints/2020_12_23_1/epch0_bidx6400.pt"
model = AlignmentModel().to(device)
model_data = torch.load(model_path)["model_state_dict"]
model_data.pop("text_encoder.model.embeddings.position_ids")
model.load_state_dict(model_data)
model.eval()
class Namespace:
def __init__(self, opts):
self.__dict__.update(opts)
def collate_fn(batch):
image_feats = []
text_feats = []
for sample in batch:
image_feats.append(sample["img_feat"])
text_feats.append(sample["text_feat"])
image_feats = torch.mean(torch.stack(image_feats), dim=1).to(device)
