def save_model(self, src_loader, trg_loader, save_best, save_every):
"""
checkoint model, optimizer and history
"""
params = self.params
modelname = params['modelname']
checkpoint_path = osp.join(modelname, 'model.pth')
torch.save(self.model.state_dict(), checkpoint_path)
self.log("model saved to {}".format(checkpoint_path))
optimizer_path = osp.join(modelname, 'optimizer.pth')
torch.save(self.optimizer.state_dict(), optimizer_path)
self.log("optimizer saved to {}".format(optimizer_path))
self.trackers['src_iterators'] = src_loader.iterators
self.trackers['trg_iterators'] = trg_loader.iterators
self.trackers['iteration'] = self.iteration
self.trackers['epoch'] = self.epoch
pdump(self.trackers, osp.join(modelname, 'trackers.pkl'))
if save_best:
checkpoint_path = osp.join(modelname, 'model-best.pth')
torch.save(self.model.state_dict(), checkpoint_path)
self.log("model saved to {}".format(checkpoint_path))
optimizer_path = osp.join(modelname, 'optimizer-best.pth')
torch.save(self.optimizer.state_dict(), optimizer_path)
self.log("optimizer saved to {}".format(optimizer_path))
pdump(self.trackers, osp.join(modelname, 'trackers-best.pkl'))
if save_every:
checkpoint_path = osp.join(modelname, 'model-%d.pth' % self.iteration)
torch.save(self.model.state_dict(), checkpoint_path)
self.log("model saved to {}".format(checkpoint_path))
def build_vocab(sentences, max_words, vocab_file):
"""
Build vocabulary
"""
# count up the number of words
counts = {}
# lets look at the distribution of lengths as well
sent_lengths = {}
for txt in sentences:
nw = len(txt)
sent_lengths[nw] = sent_lengths.get(nw, 0) + 1
for w in txt:
counts[w] = counts.get(w, 0) + 1
cw = sorted([(count, w) for w, count in counts.items()], reverse=True)
print('top words and their counts:')
print('\n'.join(map(str, cw[:20])))
# print some stats
total_words = sum(counts.values())
print('total words:', total_words)
vocab = [w for (c, w) in cw[:max_words]]
bad_words = [w for (c, w) in cw[max_words:]]
bad_count = sum(counts[w] for w in bad_words)
print('number of bad words: %d/%d = %.2f%%' %
(len(bad_words), len(counts), len(bad_words) * 100.0 / len(counts)))
print('number of words in vocab would be %d' % (len(vocab), ))
print('number of UNKs: %d/%d = %.2f%%' %
(bad_count, total_words, bad_count * 100.0 / total_words))
max_len = max(sent_lengths.keys())
print('max length sentence in raw data: ', max_len)
# print('sentence length distribution (count, number of words):')
# sum_len = sum(sent_lengths.values())
# for i in range(max_len+1):
# print('%2d: %10d %f%%' % (i, sent_lengths.get(i, 0), sent_lengths.get(i, 0)*100.0/sum_len))
# additional special UNK token we will use below to map infrequent words to
print('inserting the special UNK token')
vocab.insert(0, "<BOS>")
vocab.insert(0, "<EOS>")
vocab.insert(0, "<UNK>")
vocab.insert(0, "<PAD>")
# writing a vocab file:
with open(vocab_file, 'w') as fv:
for word in vocab:
fv.write(word + '\n')
# Dump the statistics for later use:
pdump(
{
"counts": counts,
"vocab": vocab,
"bad words": bad_words,
"lengths": sent_lengths
}, vocab_file + ".stats")
return vocab
def main_trg(params, train_order, val_order, test_order, vocab=None):
"""
Main preprocessing
"""
max_length = params.max_length
train_trg = 'data/%s/train.%s' % (params.data_dir, params.trg)
val_trg = 'data/%s/valid.%s' % (params.data_dir, params.trg)
test_trg = 'data/%s/test.%s' % (params.data_dir, params.trg)
with open(train_trg, 'r') as f:
sentences = f.readlines()
sentences = [sent.strip().split()[:max_length] for sent in sentences]
if train_order is not None:
sentences = [sentences[k] for k in train_order]
print("Read %d lines from %s" % (len(sentences), train_trg))
if vocab is None:
vocab_file = "data/%s/vocab.%s" % (params.data_dir, params.trg)
if osp.exists(vocab_file):
print('...Reading vocabulary file (%s)' % vocab_file)
vocab = []
for line in open(vocab_file, 'r'):
vocab.append(line.strip())
if '<BOS>' not in vocab:
print('Inserting BOS')
vocab.insert(0, "<BOS>")
if '<EOS>' not in vocab:
print('Inserting EOS')
vocab.insert(0, "<EOS>")
if '<UNK>' not in vocab:
print('Inserting UNK')
vocab.insert(0, "<UNK>")
if '<PAD>' not in vocab:
print('Inserting PAD')
vocab.insert(0, "<PAD>")
else:
print('...Creating vocabulary of the %d frequent tokens' %
params.max_words_trg)
vocab = build_vocab(sentences, params.max_words_trg, vocab_file)
print('...Vocabulary size:', len(vocab))
itow = {i: w for i, w in enumerate(vocab)}
wtoi = {w: i for i, w in enumerate(vocab)}
# encode captions in large arrays, ready to ship to hdf5 file
IL_train, Mask_train, Lengths_train = encode_sentences(
sentences, params, wtoi)
with open(val_trg, 'r') as f:
sentences = f.readlines()
sentences = [sent.strip().split()[:max_length] for sent in sentences]
if val_order is not None:
sentences = [sentences[k] for k in val_order]
print("Read %d lines from %s" % (len(sentences), val_trg))
IL_val, Mask_val, Lengths_val = encode_sentences(sentences, params, wtoi)
with open(test_trg, 'r') as f:
sentences = f.readlines()
sentences = [sent.strip().split()[:max_length] for sent in sentences]
if test_order is not None:
sentences = [sentences[k] for k in test_order]
print("Read %d lines from %s" % (len(sentences), test_trg))
IL_test, Mask_test, Lengths_test = encode_sentences(
sentences, params, wtoi)
# create output h5 file
f = h5py.File('data/%s/%s.h5' % (params.data_dir, params.trg), "w")
f.create_dataset("labels_train", dtype='uint32', data=IL_train)
f.create_dataset("lengths_train", dtype='uint32', data=Lengths_train)
f.create_dataset("labels_val", dtype='uint32', data=IL_val)
f.create_dataset("lengths_val", dtype='uint32', data=Lengths_val)
f.create_dataset("labels_test", dtype='uint32', data=IL_test)
f.create_dataset("lengths_test", dtype='uint32', data=Lengths_test)
print('Wrote h5file for the target langauge')
pdump({
'itow': itow,
'params': params
}, 'data/%s/%s.infos' % (params.data_dir, params.trg))
def main_src(params):
"""
Main preprocessing
"""
max_length = params.max_length
batch_size = params.batch_size # 32
train_src = 'data/%s/train.%s' % (params.data_dir, params.src)
val_src = 'data/%s/valid.%s' % (params.data_dir, params.src)
test_src = 'data/%s/test.%s' % (params.data_dir, params.src)
with open(train_src, 'r') as f:
sentences = f.readlines()
sentences = [sent.strip().split()[:max_length] for sent in sentences]
print("Read %d lines from %s" % (len(sentences), train_src))
if params.sort:
print('...Sorting by length')
train_order = sorted(range(len(sentences)),
key=lambda k: len(sentences[k]),
reverse=True)
sentences = [sentences[k] for k in train_order]
elif params.shuffle_sort:
print('...Batching by length')
train_order = sorted(range(len(sentences)),
key=lambda k: len(sentences[k]),
reverse=True)
batched_order = np.array_split(train_order,
len(train_order) // batch_size)
print('...Shuffling')
np.random.shuffle(batched_order)
train_order = np.concatenate(batched_order)
sentences = [sentences[k] for k in train_order]
else:
train_order = None
vocab_file = "data/%s/vocab.%s" % (params.data_dir, params.src)
if osp.exists(vocab_file):
print('...Reading vocabulary file (%s)' % vocab_file)
vocab = []
for line in open(vocab_file, 'r'):
vocab.append(line.strip())
if '<BOS>' not in vocab:
print('Inserting BOS')
vocab.insert(0, "<BOS>")
if '<EOS>' not in vocab:
print('Inserting EOS')
vocab.insert(0, "<EOS>")
if '<UNK>' not in vocab:
vocab.insert(0, "<UNK>")
if '<PAD>' not in vocab:
vocab.insert(0, "<PAD>")
else:
print('...Creating vocabulary of the %d frequent tokens' %
params.max_words_src)
vocab = build_vocab(sentences, params.max_words_src, vocab_file)
print('...Vocabulary size:', len(vocab))
itow = {i: w for i, w in enumerate(vocab)}
wtoi = {w: i for i, w in enumerate(vocab)}
# encode captions in large arrays, ready to ship to hdf5 file
IL_train_src, _, Lengths_train = encode_sentences(sentences, params, wtoi)
with open(val_src, 'r') as f:
sentences = f.readlines()
sentences = [sent.strip().split()[:max_length] for sent in sentences]
print("Read %d lines from %s" % (len(sentences), val_src))
if params.shuffle_sort_eval:
print('...Batching by length')
val_order = sorted(range(len(sentences)),
key=lambda k: len(sentences[k]),
reverse=True)
batched_order = np.array_split(val_order, len(val_order) // batch_size)
np.random.shuffle(batched_order)
val_order = np.concatenate(batched_order)
sentences = [sentences[k] for k in val_order]
else:
val_order = None
IL_val_src, _, Lengths_val = encode_sentences(sentences, params, wtoi)
with open(test_src, 'r') as f:
sentences = f.readlines()
sentences = [sent.strip().split()[:max_length] for sent in sentences]
print("Read %d lines from %s" % (len(sentences), test_src))
if params.shuffle_sort_eval:
print('...Batching by length')
test_order = sorted(range(len(sentences)),
key=lambda k: len(sentences[k]),
reverse=True)
batched_order = np.array_split(test_order,
len(test_order) // batch_size)
np.random.shuffle(batched_order)
test_order = np.concatenate(batched_order)
sentences = [sentences[k] for k in test_order]
else:
test_order = None
IL_test_src, _, Lengths_test = encode_sentences(sentences, params, wtoi)
# HDF5 encoding
f = h5py.File('data/%s/%s.h5' % (params.data_dir, params.src), "w")
f.create_dataset("labels_train", dtype='uint32', data=IL_train_src)
f.create_dataset("lengths_train", dtype='uint32', data=Lengths_train)
f.create_dataset("labels_val", dtype='uint32', data=IL_val_src)
f.create_dataset("lengths_val", dtype='uint32', data=Lengths_val)
f.create_dataset("labels_test", dtype='uint32', data=IL_test_src)
f.create_dataset("lengths_test", dtype='uint32', data=Lengths_test)
print('Wrote h5 file for the source langauge')
pdump({
'itow': itow,
'params': params
}, 'data/%s/%s.infos' % (params.data_dir, params.src))
return train_order, val_order, test_order, vocab
