def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d'
% (j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d'
% (j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt, device_id, model, fields,
optim, data_type, model_saver=model_saver)
def train_iter_fct(): return build_dataset_iter(
lazily_load_dataset("train", opt), fields, opt)
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def bulid_nmt_model(opt):
# opt = training_opt_postprocessing(opt)
# init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.model_path_G:
logger.info('Loading checkpoint from %s' % opt.model_path_G)
checkpoint = torch.load(opt.model_path_G, map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
raise(AssertionError("no nmt model"))
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d'
% (j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d'
% (j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint, additional_device0)
# model = build_model(model_opt, opt, fields, checkpoint, "cpu")
return model
def main(opt):
opt = training_opt_postprocessing(opt)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
print('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
# I don't like reassigning attributes of opt: it's not clear.
opt.start_epoch = checkpoint['epoch'] + 1
else:
checkpoint = None
model_opt = opt
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
_collect_report_features(fields)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
_tally_parameters(model)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt), fields,
opt)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps, opt.save_checkpoint_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def monitor_iter_fct():
monitor_data = dict()
for src, tgt in zip(opt.monitor_src, opt.monitor_tgt):
fname = src.split("/" if "/" in src else "\\")[-1].split(
".")[0].replace("_src", "")
monitor_data[fname] = build_dataset_iter(lazily_load_dataset(
"monitor", opt, fname),
fields,
opt,
is_train=False)
return monitor_data
def get_speech_iterator(self, name, lang1, lang2, is_train=True): # name: train or valid
"""
Create a new iterator for a dataset.
"""
key = ','.join([x for x in ['speech', name, lang1, lang2] if x is not None])
logger.info("Creating new training %s iterator ..." % key)
speech_direction = (lang1, lang2)
iterator = build_dataset_iter(
lazily_load_dataset(name, self.params.speech_dataset[speech_direction][0]),
self.speech_fields[speech_direction], self.params, is_train)
iterator = iter(iterator)
self.iterators[key] = iterator
return iterator
def get_speech_iterator(self, data_type, lang1, lang2):
"""
Create a new iterator for a dataset.
"""
assert data_type in ['valid']
speech_direction = (lang1, lang2)
lang2_id = self.params.lang2id[lang2]
iterator = build_dataset_iter(
lazily_load_dataset(data_type, self.params.speech_dataset[speech_direction][0]),
self.params.speech_fields[speech_direction], self.params, False)
iterator = iter(iterator)
for batch in iterator:
bos_index = self.params.bos_index[lang2_id]
batch.tgt[0] = bos_index
yield batch
def main(opt, device_id):
#TODO delete all these lines related to WALS features
#begin
SimulationLanguages = [opt.wals_src, opt.wals_tgt]
print('Loading WALS features from databases...')
cwd = os.getcwd()
db = sqlite3.connect(cwd + '/onmt/WalsValues.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM WalsValues')
WalsValues = cursor.fetchall()
db = sqlite3.connect(cwd + '/onmt/FeaturesList.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM FeaturesList')
FeaturesList = cursor.fetchall()
db = sqlite3.connect(cwd + '/onmt/FTInfos.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM FTInfos')
FTInfos = cursor.fetchall()
db = sqlite3.connect(cwd + '/onmt/FTList.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM FTList')
FTList = cursor.fetchall()
ListLanguages = []
for i in WalsValues:
ListLanguages.append(i[0])
FeatureTypes = []
for i in FTList:
FeatureTypes.append((i[0], i[1].split(',')))
FeatureNames = []
for i in FeatureTypes:
FeatureNames += i[1]
FeatureTypesNames = []
for i in FeatureTypes:
FeatureTypesNames.append(i[0])
FeatureValues, FeatureTensors = get_feat_values(SimulationLanguages, WalsValues, FeaturesList, ListLanguages, FeatureTypes, FeatureNames)
print('WALS databases loaded!')
#end
#TODO: load wals features from command-line (wals.npz)
# FeatureValues: defaultdict with feature values, per language.
# FeatureTensors: tensor of possible outputs, per feature.
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d'
% (j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d'
% (j, len(fields[feat].vocab)))
# Build model.
#TODO: remove all parameters related to WALS features: FeatureValues, FeatureTensors, FeatureTypes, FeaturesList, FeatureNames, FTInfos, FeatureTypesNames, SimulationLanguages
#TODO: include four parameter related to WALS features: the four numpy arrays separately
model = build_model(model_opt, opt, fields, checkpoint, FeatureValues, FeatureTensors, FeatureTypes, FeaturesList, FeatureNames, FTInfos, FeatureTypesNames, SimulationLanguages)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt, device_id, model, fields,
optim, data_type, model_saver=model_saver)
def train_iter_fct(): return build_dataset_iter(
lazily_load_dataset("train", opt), fields, opt)
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def train_iter_fct():
model._vivisect["iteration"] += 1
model._vivisect["mode"] = "train"
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = load_fields(first_dataset, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt),
fields,
opt,
is_train=False)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
if opt.no_base == False:
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
if opt.comparable:
logger.info('')
logger.info('Beginning comparable data extraction and training.')
# 1. Initialize Comparable object
comp = Comparable(model, trainer, fields, logger, opt)
# 2. Infer similarity threshold from training data
for epoch in range(opt.comp_epochs):
# 3. Update threshold if dynamic
if opt.threshold_dynamics != 'static' and epoch != 0:
comp.update_threshold(opt.threshold_dynamics,
opt.infer_threshold)
# 4. Extract parallel data and train
#if opt.match_articles:
# comparable_data = comp.match_articles(opt.match_articles)
# train_stats = comp.extract_and_train(comparable_data)
#else:
train_stats = comp.extract_and_train(opt.comparable_data)
# 5. Validate on validation set
if opt.no_valid == False:
valid_iter = build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt)
valid_stats = comp.validate(valid_iter)
# 6. Drop a checkpoint if needed
comp.trainer.model_saver._save(epoch)
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
def main(opt, device_id):
# opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
raise Exception('You need to load a model')
logger.info('Loading data from %s' % opt.data)
dataset = next(lazily_load_dataset("train", opt))
data_type = dataset.data_type
logger.info('Data type %s' % data_type)
# Load fields generated from preprocess phase.
fields = _load_fields(dataset, data_type, opt, checkpoint)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
dataset_iter = build_dataset_iter(lazily_load_dataset("train", opt),
fields, opt)
out_file = codecs.open(opt.output, 'w+', 'utf-8')
scorer = onmt.translate.GNMTGlobalScorer(opt.alpha, opt.beta,
opt.coverage_penalty,
opt.length_penalty)
translation_builder = TranslationBuilder(dataset,
fields,
n_best=opt.n_best,
replace_unk=opt.replace_unk,
has_tgt=False)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
translator = Translator(trainer.model,
fields,
opt.beam_size,
global_scorer=scorer,
out_file=out_file,
report_score=False,
copy_attn=model_opt.copy_attn,
logger=logger)
for i, batch in enumerate(dataset_iter):
unprocessed_translations = translator.translate_batch(batch, dataset)
translations = translation_builder.from_batch(unprocessed_translations)
print "Translations: ", ' '.join(translations[0].pred_sents[0])
trainer.train_from_data(batch, train_steps=1)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def meta_valid_iter_fct(task_id, is_log=False):
return build_dataset_iter(
lazily_load_dataset("meta_valid", opt, task_id=task_id, is_log=is_log), fields_list[task_id], opt)
def __init__(self, encoder, decoder, discriminator, lm, data, params):
"""
Initialize trainer.
"""
super().__init__(device_ids=tuple(range(params.otf_num_processes)))
self.encoder = encoder
self.decoder = decoder
self.discriminator = discriminator
self.lm = lm
self.data = data
self.params = params
self.train_iter = None
self.speech_fields = dict()
params.speech_fields = self.speech_fields
# training variables
self.best_metrics = {metric: -1e12 for metric in self.VALIDATION_METRICS}
self.epoch = 0
self.n_total_iter = 0
self.freeze_enc_emb = self.params.freeze_enc_emb
self.freeze_dec_emb = self.params.freeze_dec_emb
self.reload_model()
# initialization for on-the-fly generation/training
if len(params.pivo_directions) > 0:
self.otf_start_multiprocessing()
# define encoder parameters (the ones shared with the
# decoder are optimized by the decoder optimizer)
enc_params = list(encoder.parameters())
for i in range(params.n_langs):
if params.share_lang_emb and i >= 0:
break
#assert enc_params[i].size() == (params.n_words[i], params.emb_dim)
if self.params.share_encdec_emb:
to_ignore = 1 if params.share_lang_emb else params.n_langs
enc_params = enc_params[to_ignore:]
# optimizers
if params.dec_optimizer == 'enc_optimizer':
params.dec_optimizer = params.enc_optimizer
self.enc_optimizer = get_optimizer(enc_params, params.enc_optimizer) if len(enc_params) > 0 else None
self.dec_optimizer = get_optimizer(decoder.parameters(), params.dec_optimizer)
self.dis_optimizer = get_optimizer(discriminator.parameters(), params.dis_optimizer) if discriminator is not None else None
self.lm_optimizer = get_optimizer(lm.parameters(), params.enc_optimizer) if lm is not None else None
# models / optimizers
self.model_opt = {
'enc': (self.encoder, self.enc_optimizer),
'dec': (self.decoder, self.dec_optimizer),
'dis': (self.discriminator, self.dis_optimizer),
'lm': (self.lm, self.lm_optimizer),
}
# define validation metrics / stopping criterion used for early stopping
logger.info("Stopping criterion: %s" % params.stopping_criterion)
if params.stopping_criterion == '':
for lang1, lang2 in self.data['para'].keys():
for data_type in ['valid', 'test']:
self.VALIDATION_METRICS.append('bleu_%s_%s_%s' % (lang1, lang2, data_type))
self.VALIDATION_METRICS.append('speech_bleu_%s_%s_%s' % (lang1, lang2, data_type))
for lang1, lang2, lang3 in self.params.pivo_directions:
if lang1 == lang3:
continue
for data_type in ['valid', 'test']:
self.VALIDATION_METRICS.append('bleu_%s_%s_%s_%s' % (lang1, lang2, lang3, data_type))
self.stopping_criterion = None
self.best_stopping_criterion = None
else:
split = params.stopping_criterion.split(',')
assert len(split) == 2 and split[1].isdigit()
self.decrease_counts_max = int(split[1])
self.decrease_counts = 0
self.stopping_criterion = split[0]
self.best_stopping_criterion = -1e12
assert len(self.VALIDATION_METRICS) == 0
self.VALIDATION_METRICS.append(self.stopping_criterion)
# training statistics
self.n_iter = 0
self.n_sentences = 0
self.stats = {
'dis_costs': [],
'processed_s': 0,
'processed_w': 0,
}
for lang1, lang2 in params.speech_dataset.keys():
self.stats['xe_costs_sp_%s_%s' % (lang1, lang2)] = []
for speech_direction in params.speech_dataset.keys():
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", params.speech_dataset[speech_direction][0]))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields_vocab(first_dataset, data_type, params.speech_vocabs[speech_direction[1]], None,
'vocab')
self.speech_fields[speech_direction] = fields
for lang in params.mono_directions:
self.stats['xe_costs_%s_%s' % (lang, lang)] = []
for lang1, lang2 in params.para_directions:
self.stats['xe_costs_%s_%s' % (lang1, lang2)] = []
for lang1, lang2 in params.back_directions:
self.stats['xe_costs_bt_%s_%s' % (lang1, lang2)] = []
for lang1, lang2, lang3 in params.pivo_directions:
self.stats['xe_costs_%s_%s_%s' % (lang1, lang2, lang3)] = []
for lang in params.langs:
self.stats['lme_costs_%s' % lang] = []
self.stats['lmd_costs_%s' % lang] = []
self.stats['lmer_costs_%s' % lang] = []
self.stats['enc_norms_%s' % lang] = []
self.last_time = time.time()
if len(params.pivo_directions) > 0:
self.gen_time = 0
# data iterators
self.iterators = {}
# initialize BPE subwords
self.init_bpe()
# initialize lambda coefficients and their configurations
parse_lambda_config(params, 'lambda_xe_mono')
parse_lambda_config(params, 'lambda_xe_para')
parse_lambda_config(params, 'lambda_xe_back')
parse_lambda_config(params, 'lambda_xe_otfd')
parse_lambda_config(params, 'lambda_xe_otfa')
parse_lambda_config(params, 'lambda_dis')
parse_lambda_config(params, 'lambda_lm')
parse_lambda_config(params, 'lambda_speech')
def extract_and_train(self, comparable_data_list):
""" Manages the alternating extraction of parallel sentences and training.
Args:
comparable_data_list(str): path to list of mapped documents
Returns:
train_stats(:obj:'onmt.Trainer.Statistics'): epoch loss statistics
"""
# Start first epoch
self.trainer.next_epoch()
self.accepted_file = \
open('{}_accepted-e{}.txt'.format(self.comp_log, self.trainer.cur_epoch), 'w+', encoding='utf8')
self.status_file = '{}_status-e{}.txt'.format(self.comp_log,
self.trainer.cur_epoch)
if self.write_dual:
self.embed_file = '{}_accepted_embed-e{}.txt'.format(
self.comp_log, self.trainer.cur_epoch)
self.hidden_file = '{}_accepted_hidden-e{}.txt'.format(
self.comp_log, self.trainer.cur_epoch)
epoch_similarities = []
epoch_scores = []
counter = 0
src_sents = []
tgt_sents = []
src_embeds = []
tgt_embeds = []
# Go through comparable data
with open(comparable_data_list, encoding='utf8') as c:
comp_list = c.read().split('\n')
num_articles = len(comp_list)
cur_article = 0
for article_pair in comp_list:
cur_article += 1
# Update status
with open(self.status_file, 'a', encoding='utf8') as sf:
sf.write('{} / {}\n'.format(cur_article, num_articles))
articles = article_pair.split('\t')
# Discard malaligned documents
if len(articles) != 2:
continue
# Prepare iterator objects for current src/tgt document
src_article = self._get_iterator(articles[0])
tgt_article = self._get_iterator(articles[1])
# Get sentence representations
try:
if self.representations == 'embed-only':
# C_e
src_sents += self.get_article_coves(src_article,
'embed',
fast=self.fast)
tgt_sents += self.get_article_coves(tgt_article,
'embed',
fast=self.fast)
else:
# C_h
src_sents += self.get_article_coves(src_article,
fast=self.fast)
tgt_sents += self.get_article_coves(tgt_article,
fast=self.fast)
# C_e
src_embeds += self.get_article_coves(src_article,
'embed',
fast=self.fast)
tgt_embeds += self.get_article_coves(tgt_article,
'embed',
fast=self.fast)
except:
# Skip document pair in case of errors
src_sents = []
tgt_sents = []
src_embeds = []
tgt_embeds = []
continue
# Ensure enough sentences are accumulated (otherwise scoring becomes unstable)
if len(src_sents) < 15 or len(tgt_sents) < 15:
continue
# Score src and tgt sentences
src2tgt, tgt2src, similarities, scores = self.score_sents(
src_sents, tgt_sents)
# Keep statistics
epoch_similarities += similarities
epoch_scores += scores
src_sents = []
tgt_sents = []
# Filter candidates (primary filter)
try:
if self.representations == 'dual':
# For dual representation systems, filter C_h...
candidates = self.filter_candidates(src2tgt,
tgt2src,
second=self.second)
# ...and C_e
comparison_pool, cand_embed = self.get_comparison_pool(
src_embeds, tgt_embeds)
src_embeds = []
tgt_embeds = []
if self.write_dual:
self.write_embed_only(candidates, cand_embed)
else:
# Filter C_e or C_h for single representation system
candidates = self.filter_candidates(src2tgt, tgt2src)
comparison_pool = None
except:
# Skip document pair in case of errors
print('Error occured in: {}\n'.format(article_pair),
flush=True)
src_embeds = []
tgt_embeds = []
continue
# Extract parallel samples (secondary filter)
self.extract_parallel_sents(candidates, comparison_pool)
# Check if enough parallel sentences were collected
while self.similar_pairs.contains_batch():
# Get a batch of extracted parrallel sentences and train
try:
training_batch = self.similar_pairs.yield_batch()
except:
print('Error creating batch. Continuing...',
flush=True)
continue
# Statistics
train_stats = self.trainer.train(training_batch)
self.trainstep += 1
# Validate
if self.trainstep % self.valid_steps == 0:
if self.no_valid == False:
valid_iter = build_dataset_iter(
lazily_load_dataset('valid', self.opt),
self.fields, self.opt)
valid_stats = self.validate(valid_iter)
# Create checkpoint
if self.trainstep % 5000 == 0:
self.trainer.model_saver._save(self.trainstep)
# Train on remaining partial batch
if len((self.similar_pairs.pairs)) > 0:
train_stats = self.trainer.train(
self.similar_pairs.yield_batch())
self.trainstep += 1
# Write epoch statistics
self.write_similarities(epoch_similarities,
'e{}_comp'.format(self.trainer.cur_epoch))
self.write_similarities(
epoch_scores, 'e{}_comp_scores'.format(self.trainer.cur_epoch))
self.trainer.report_epoch()
self.logger.info(
'Accepted parrallel sentences from comparable data: %d / %d' %
(self.accepted, self.total))
self.logger.info(
'Acceptable parrallel sentences from comparable data (out of limit): %d / %d'
% (self.accepted_limit, self.total))
self.logger.info('Declined sentences from comparable data: %d / %d' %
(self.declined, self.total))
# Reset epoch statistics
self.accepted = 0
self.accepted_limit = 0
self.declined = 0
self.total = 0
self.accepted_file.close()
return train_stats
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
if opt.train_from and opt.reset_optim != 'all':
logger.info('* checkpoint training not considered by me yet')
else:
# warmup_steps and rnn_size are parameters for Noam decay (transformer):
# https://arxiv.org/pdf/1706.03762.pdf (Section 3)
decay_method = opt.decay_method if opt.decay_method else "standard"
logger.info(
'* Opt: %s (rate %.5f, maxgnorm %.1f, %s decay, '
'decay_rate %.1f, start_decay_at %d, decay_every %d, '
'ab1 %.5f, ab2 %.5f, adagradaccum %.1f, '
'warmupsteps %d, hiddensize %d)' %
(opt.optim, opt.learning_rate, opt.max_grad_norm, decay_method,
opt.learning_rate_decay, opt.start_decay_steps, opt.decay_steps,
opt.adam_beta1, opt.adam_beta2, opt.adagrad_accumulator_init,
opt.warmup_steps, opt.rnn_size))
optim = build_optim(model, opt, checkpoint)
# Build model saver v
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
logger.info('* model_saver built, using it to build trainer with ')
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
#---------------------------------------------------------------------------
# 1. lazily_load_dataset = for pt in pts: yield torch.load(pt)
# 2. build_dataset_iter = return DatasetLazyIter (train_iter_fct)
# 3. train_iter_fct() = iterator over torchtext.data.batch.Batches
#---------------------------------------------------------------------------
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt),
fields,
opt,
is_train=False)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def train_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("train", opt, task_id=task_id), fields_list[task_id], opt)
train_dataset_files = build_save_dataset('train', fields, opt, logger)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt, logger)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt, logger)
opt = train_args
opt = training_opt_postprocessing(opt)
model_opt = opt
checkpoint = None
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
_collect_report_features(fields)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
remove(args.host, args.port, "OpenNMT")
probe(
args.name,
model,
def valid_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("valid", opt, task_id=task_id), fields_list[task_id], opt)
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
out_file = None
best_test_score, best_ckpt = -10000, None
dummy_parser = argparse.ArgumentParser(description='all_dev.py')
opts.model_opts(dummy_parser)
dummy_opt = dummy_parser.parse_known_args([])[0]
for i in range(0, opt.train_epochs, 10):
ckpt_path = '{}_epoch_{}.pt'.format(opt.save_model, i + 1)
logger.info('Loading checkpoint from %s' % ckpt_path)
checkpoint = torch.load(ckpt_path,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
fields = load_fields_from_vocab(checkpoint['vocab'], data_type="text")
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
assert opt.train_from == '' # do not load optimizer state
optim = build_optim(model, opt, checkpoint)
# Build model saver, no need to create task dir for dev
if not os.path.exists('experiments/all_dev'):
os.mkdir('experiments/all_dev')
os.mkdir('experiments/all_dev/' + opt.meta_dev_task)
elif not os.path.exists('experiments/all_dev/' + opt.meta_dev_task):
os.mkdir('experiments/all_dev/' + opt.meta_dev_task)
model_saver = build_model_saver(
model_opt, 'experiments/all_dev/' + opt.meta_dev_task + '/model',
opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
"text",
model_saver=model_saver)
train_iter = list(
build_dataset_iter(lazily_load_dataset("train", opt), fields, opt))
# do training on trainset of meta-dev task
trainer.train(train_iter, opt.inner_iterations)
# do evaluation on devset of meta-dev task
best_dev_score, best_model_path = -10000, None
for model_path in os.listdir('experiments/all_dev/' +
opt.meta_dev_task):
if model_path.find('.pt') == -1:
continue
if out_file is None:
out_file = codecs.open(opt.output, 'w+', 'utf-8')
fields, model, model_opt = onmt.model_builder.load_test_model(
opt,
dummy_opt.__dict__,
model_path='experiments/all_dev/' + opt.meta_dev_task + '/' +
model_path)
scorer = onmt.translate.GNMTGlobalScorer(opt.alpha, opt.beta,
opt.coverage_penalty,
opt.length_penalty)
kwargs = {
k: getattr(opt, k)
for k in [
"beam_size", "n_best", "max_length", "min_length",
"stepwise_penalty", "block_ngram_repeat",
"ignore_when_blocking", "dump_beam", "report_bleu",
"replace_unk", "gpu", "verbose", "fast", "mask_from"
]
}
fields['graph'] = torchtext.data.Field(sequential=False)
translator = Translator(model,
fields,
global_scorer=scorer,
out_file=out_file,
report_score=False,
copy_attn=model_opt.copy_attn,
logger=logger,
log_probs_out_file=None,
**kwargs)
# make translation and save result
all_scores, all_predictions = translator.translate(
src_path='processed_data/meta-dev/' + opt.meta_dev_task +
'/src-dev.txt',
tgt_path=None,
src_dir=None,
batch_size=opt.translate_batch_size,
attn_debug=False)
# dump predictions
f = open('experiments/all_dev/' + opt.meta_dev_task +
'/dev_predictions.csv',
'w',
encoding='utf-8')
f.write('smiles,property\n')
for n_best_mols in all_predictions:
for mol in n_best_mols:
f.write(mol.replace(' ', '') + ',0\n')
f.close()
# call chemprop to get scores
test_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/dev_predictions.csv' + '\"'
checkpoint_path = '\"' + 'scorer_ckpts/' + opt.meta_dev_task + '/model.pt' + '\"'
preds_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/dev_scores.csv' + '\"'
# in case of all mols are invalid (will produce not output file by chemprop)
# the predictions are copied into score file
cmd = 'cp {} {}'.format(test_path, preds_path)
result = os.popen(cmd)
result.close()
cmd = 'python chemprop/predict.py --test_path {} --checkpoint_path {} --preds_path {} --num_workers 0'.format(
test_path, checkpoint_path, preds_path)
scorer_result = os.popen(cmd)
scorer_result.close()
# read score file and get score
score = read_score_csv('experiments/all_dev/' + opt.meta_dev_task +
'/dev_scores.csv')
assert len(score) % opt.beam_size == 0
# dev_scores = []
# for i in range(0, len(score), opt.beam_size):
# dev_scores.append(sum([x[1] for x in score[i:i+opt.beam_size]]) / opt.beam_size)
# report dev score
dev_metrics = calculate_metrics(opt.meta_dev_task, 'dev', 'dev',
score)
logger.info('dev metrics: ' + str(dev_metrics))
dev_score = dev_metrics['success_rate']
if dev_score > best_dev_score:
logger.info('New best dev success rate: {:.4f} by {}'.format(
dev_score, model_path))
best_model_path = model_path
best_dev_score = dev_score
else:
logger.info('dev success rate: {:.4f} by {}'.format(
dev_score, model_path))
del fields
del model
del model_opt
del scorer
del translator
gc.collect()
assert best_model_path != None
# do testing on testset of meta-dev task
if out_file is None:
out_file = codecs.open(opt.output, 'w+', 'utf-8')
fields, model, model_opt = onmt.model_builder.load_test_model(
opt,
dummy_opt.__dict__,
model_path='experiments/all_dev/' + opt.meta_dev_task + '/' +
best_model_path)
scorer = onmt.translate.GNMTGlobalScorer(opt.alpha, opt.beta,
opt.coverage_penalty,
opt.length_penalty)
kwargs = {
k: getattr(opt, k)
for k in [
"beam_size", "n_best", "max_length", "min_length",
"stepwise_penalty", "block_ngram_repeat",
"ignore_when_blocking", "dump_beam", "report_bleu",
"replace_unk", "gpu", "verbose", "fast", "mask_from"
]
}
fields['graph'] = torchtext.data.Field(sequential=False)
translator = Translator(model,
fields,
global_scorer=scorer,
out_file=out_file,
report_score=False,
copy_attn=model_opt.copy_attn,
logger=logger,
log_probs_out_file=None,
**kwargs)
# make translation and save result
all_scores, all_predictions = translator.translate(
src_path='processed_data/meta-dev/' + opt.meta_dev_task +
'/src-test.txt',
tgt_path=None,
src_dir=None,
batch_size=opt.translate_batch_size,
attn_debug=False)
# dump predictions
f = open('experiments/all_dev/' + opt.meta_dev_task +
'/test_predictions.csv',
'w',
encoding='utf-8')
f.write('smiles,property\n')
for n_best_mols in all_predictions:
for mol in n_best_mols:
f.write(mol.replace(' ', '') + ',0\n')
f.close()
# call chemprop to get scores
test_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/test_predictions.csv' + '\"'
checkpoint_path = '\"' + 'scorer_ckpts/' + opt.meta_dev_task + '/model.pt' + '\"'
preds_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/test_scores.csv' + '\"'
# in case of all mols are invalid (will produce not output file by chemprop)
# the predictions are copied into score file
cmd = 'cp {} {}'.format(test_path, preds_path)
result = os.popen(cmd)
result.close()
cmd = 'python chemprop/predict.py --test_path {} --checkpoint_path {} --preds_path {} --num_workers 0'.format(
test_path, checkpoint_path, preds_path)
scorer_result = os.popen(cmd)
# logger.info('{}'.format('\n'.join(scorer_result.readlines())))
scorer_result.close()
# read score file and get score
score = read_score_csv('experiments/all_dev/' + opt.meta_dev_task +
'/test_scores.csv')
assert len(score) % opt.beam_size == 0
# test_scores = []
# for i in range(0, len(score), opt.beam_size):
# test_scores.append(sum([x[1] for x in score[i:i+opt.beam_size]]) / opt.beam_size)
# report if it is the best on test
test_metrics = calculate_metrics(opt.meta_dev_task, 'dev', 'test',
score)
logger.info('test metrics: ' + str(test_metrics))
test_score = test_metrics['success_rate']
if test_score > best_test_score:
best_ckpt = ckpt_path
logger.info('New best test success rate: {:.4f} by {}'.format(
test_score, ckpt_path))
best_test_score = test_score
else:
logger.info('test success rate: {:.4f} by {}'.format(
test_score, ckpt_path))
del model_opt
del fields
del checkpoint
del model
del optim
del model_saver
del trainer
gc.collect()
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Gather information related to the training script and commit version
script_path = os.path.abspath(__file__)
script_dir = os.path.dirname(os.path.dirname(script_path))
logger.info('Train script dir: %s' % script_dir)
git_commit = str(subprocess.check_output(['bash', script_dir + '/cluster_scripts/git_version.sh']))
logger.info("Git Commit: %s" % git_commit[2:-3])
# Load checkpoint if we resume from a previous training.
if opt.train_from:
# TODO: load MTL model
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
num_tasks = len(opt.data.split(','))
opt.num_tasks = num_tasks
checkpoint_list=[]
if opt.warm_model:
base_name=opt.warm_model
for task_id in range(num_tasks):
chkpt_path=base_name.replace("X",str(task_id))
if not os.path.isfile(chkpt_path):
chkpt_path = base_name.replace("X", str(0))
logger.info('Loading a checkpoint from %s' % chkpt_path)
checkpoint = torch.load(chkpt_path,
map_location=lambda storage, loc: storage)
checkpoint_list.append(checkpoint)
else:
for task_id in range(num_tasks):
checkpoint_list.append(None)
fields_list = []
data_type=None
for task_id in range(num_tasks):
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt, task_id=task_id))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
if opt.mtl_shared_vocab and task_id > 0:
logger.info(' * vocabulary size. Same as the main task!')
fields = fields_list[0]
else:
fields = load_fields(first_dataset, opt, checkpoint_list[task_id], task_id=task_id)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * (Task %d) src feature %d size = %d'
% (task_id, j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * (Task %) tgt feature %d size = %d'
% (task_id, j, len(fields[feat].vocab)))
fields_list.append(fields)
if opt.epochs > -1:
total_num_batch = 0
for task_id in range(num_tasks):
train_iter = build_dataset_iter(lazily_load_dataset("train", opt, task_id=task_id), fields_list[task_id], opt)
for i, batch in enumerate(train_iter):
num_batch = i
total_num_batch+=num_batch
if opt.mtl_schedule < 10:
break
num_batch = total_num_batch
opt.train_steps = (num_batch * opt.epochs) + 1
# Do the validation and save after each epoch
opt.valid_steps = num_batch
opt.save_checkpoint_steps = 1
# logger.info(opt_to_string(opt))
logger.info(opt)
# Build model(s).
models_list = []
for task_id in range(num_tasks):
if opt.mtl_fully_share and task_id > 0:
# Since we only have one model, copy the pointer to the model for all
models_list.append(models_list[0])
else:
main_model = models_list[0] if task_id > 0 else None
model = build_model(model_opt, opt, fields_list[task_id], checkpoint_list[task_id], main_model=main_model, task_id=task_id)
n_params, enc, dec = _tally_parameters(model)
logger.info('(Task %d) encoder: %d' % (task_id, enc))
logger.info('(Task %d) decoder: %d' % (task_id, dec))
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
models_list.append(model)
# combine parameters of different models and consider shared parameters just once.
def combine_named_parameters(named_params_list):
observed_params = []
for model_named_params in named_params_list:
for name, p in model_named_params:
is_observed = False
# Check whether we observed this parameter before
for param in observed_params:
if p is param:
is_observed = True
break
if not is_observed:
observed_params.append(p)
yield name, p
# Build optimizer.
optims_list = []
all_models_params=[]
for task_id in range(num_tasks):
if not opt.mtl_shared_optimizer:
optim = build_optim(models_list[task_id], opt, checkpoint)
optims_list.append(optim)
else:
all_models_params.append(models_list[task_id].named_parameters())
# Extract the list of shared parameters among the models of all tasks.
observed_params = []
shared_params = []
for task_id in range(num_tasks):
for name, p in models_list[task_id].named_parameters():
is_observed = False
# Check whether we observed this parameter before
for param in observed_params:
if p is param:
shared_params.append(name)
is_observed = True
break
if not is_observed:
observed_params.append(p)
opt.shared_params = shared_params
if opt.mtl_shared_optimizer:
optim = build_optim_mtl_params(combine_named_parameters(all_models_params), opt, checkpoint)
optims_list.append(optim)
# Build model saver
model_saver = build_mtl_model_saver(model_opt, opt, models_list, fields_list, optims_list)
trainer = build_trainer(opt, device_id, models_list, fields_list,
optims_list, data_type, model_saver=model_saver)
def train_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("train", opt, task_id=task_id), fields_list[task_id], opt)
def valid_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("valid", opt, task_id=task_id), fields_list[task_id], opt)
def meta_valid_iter_fct(task_id, is_log=False):
return build_dataset_iter(
lazily_load_dataset("meta_valid", opt, task_id=task_id, is_log=is_log), fields_list[task_id], opt)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps, meta_valid_iter_fct=meta_valid_iter_fct)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
logger.info('* batch_size: %d' % opt.batch_size)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def data_iter_fct(data_stage):
"""data_stage: train / valid"""
pt_file = opt.data + '.' + data_stage + '.pt'
logger.info('Loading {} dataset'.format(data_stage))
dataset = torch.load(pt_file)
logger.info('Loaded {} dataset'.format(data_stage))
dataset.fields = fields
is_train = True if data_stage == "train" else False
batch_size = opt.batch_size if is_train else opt.valid_batch_size
repeat = True if data_stage == "train" else False
if opt.gpuid != -1:
device = "cuda"
else:
device = "cpu"
def sort_key(ex):
""" Sort using length of source sentences. """
return ex.total_tokens
return torchtext.data.Iterator(dataset=dataset,
batch_size=batch_size,
device=device,
train=is_train,
sort=False,
sort_key=sort_key,
repeat=repeat)
# Do training.
trainer.train(data_iter_fct, opt.train_steps, opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt, logger),
fields, opt)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = load_fields(first_dataset, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt),
fields,
opt,
is_train=False)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct():
model._vivisect["mode"] = "dev"
return build_dataset_iter(lazily_load_dataset("valid", opt), fields,
opt)
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
if len(opt.gpu_ranks):
def main(opt):
opt = training_opt_postprocessing(opt)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
if opt.load_pretrained_selector_from:
logger.info('Loading selector checkpoint from %s' %
opt.load_pretrained_selector_from)
sel_checkpoint = torch.load(opt.load_pretrained_selector_from,
map_location=lambda storage, loc: storage)
else:
sel_checkpoint = None
if opt.load_pretrained_s2s_generator_from:
logger.info('Loading s2s generator checkpoint from %s' %
opt.load_pretrained_s2s_generator_from)
s2s_gen_checkpoint = torch.load(
opt.load_pretrained_s2s_generator_from,
map_location=lambda storage, loc: storage)
else:
s2s_gen_checkpoint = None
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint, sel_checkpoint,
s2s_gen_checkpoint)
# Fix the pretrained selector parameters if needed
if model_opt.fix_sel_all:
assert opt.load_pretrained_selector_from
assert opt.sel_lambda == 0.0
assert not model_opt.fix_sel_classifier
for name, param in model.named_parameters():
if 'selector' in name:
param.requires_grad = False
# only fix the classifier of the selector
if model_opt.fix_sel_classifier:
assert opt.load_pretrained_selector_from
assert not model_opt.fix_sel_all
for name, param in model.named_parameters():
if 'selector' in name and 'rnn' not in name and 'embeddings' not in name:
param.requires_grad = False
n_params, sel, enc, dec, gen = _my_tally_parameters(model)
logger.info('selector: %d' % sel)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('generator: %d' % gen)
logger.info('* number of parameters: %d' % n_params)
print_trainable_parameters(model)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt), fields,
opt)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt),
fields,
opt,
is_train=False)
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.start_epoch, opt.epochs)
