def __init__(self, sentences, extra={}, **kwargs):
self.sentences = sentences
self.config = config = get_config(get_default_config(), **kwargs)
self.extra = extra
self.loader = None
self.cuda = config.get('cuda')
self.logger = get_logger()
def run(options):
logger = get_logger()
experiment_logger = ExperimentLogger()
train_dataset, validation_dataset = get_train_and_validation(options)
train_iterator = get_train_iterator(options, train_dataset)
validation_iterator = get_validation_iterator(options, validation_dataset)
embeddings = train_dataset['embeddings']
word2idx = train_dataset['word2idx']
logger.info('Initializing model.')
trainer = build_net(options, embeddings, train_iterator, word2idx=word2idx)
logger.info('Model:')
for name, p in trainer.net.named_parameters():
logger.info('{} {} {}'.format(name, p.shape, p.requires_grad))
# Evaluation.
context = {}
context['dataset'] = validation_dataset
context['batch_iterator'] = validation_iterator
model_evaluation = ModelEvaluation(get_eval_components(options, context, config_lst=options.eval_config))
if options.eval_only_mode:
info = dict()
info['experiment_path'] = options.experiment_path
info['step'] = 0
run_evaluation(options, trainer, model_evaluation, info, metadata=dict(step=0))
sys.exit()
if options.save_init:
logger.info('Saving model (init).')
trainer.save_model(os.path.join(options.experiment_path, 'model_init.pt'))
run_train(options, train_iterator, trainer, model_evaluation)
def __init__(self, data_source, batch_size, include_partial=False, rng=None):
self.data_source = data_source
self.active = False
if rng is None:
rng = np.random.RandomState(seed=11)
self.rng = rng
self.batch_size = batch_size
self.include_partial = include_partial
self._batch_size_cache = { 0: self.batch_size }
self.logger = get_logger()
def configure(options):
# Configure output paths for this experiment.
configure_experiment(options.experiment_path)
# Get logger.
logger = get_logger()
# Print flags.
logger.info(stringify_flags(options))
save_flags(options, options.experiment_path)
def run_evaluation(options, trainer, model_evaluation, info, metadata):
logger = get_logger()
lst = []
for eval_result_dict in model_evaluation.run(trainer, info, metadata):
eval_result = eval_result_dict['result']
eval_name = eval_result['name']
for k, v in eval_result['meta'].items():
logger.info('eval[{}] {}={}'.format(eval_name, k, v))
lst.append(eval_result_dict)
return lst
def __init__(self, **kwargs):
super().__init__()
# defaults
self.enabled = True
self.is_initialized = True
self.write = False
self.logger = get_logger()
self.init_defaults()
# override
for k, v in kwargs.items():
setattr(self, k, v)
def build_net(options, context=dict(), net_components=dict()):
logger = get_logger()
logger.info('build net')
# Components.
projection_layer = net_components['projection_layer']
diora = net_components['diora']
loss_funcs = net_components['loss_funcs']
# Context.
cuda = context['cuda']
lr = options.lr
word2idx = context['word2idx']
# Net
net = Net(projection_layer,
diora,
loss_funcs=loss_funcs,
size=options.hidden_dim)
# Load model.
if options.load_model_path is not None:
logger.info('Loading model: {}'.format(options.load_model_path))
Trainer.load_model(net, options.load_model_path)
# CUDA-support
if cuda:
net.cuda()
diora.cuda()
# Trainer
trainer_kwargs = {}
trainer_kwargs['net'] = net
trainer_kwargs['cuda'] = cuda
trainer_kwargs['mlp_reg'] = options.mlp_reg
trainer_kwargs['clip_threshold'] = options.clip_threshold
trainer_kwargs['word2idx'] = word2idx
trainer = Trainer(**trainer_kwargs)
trainer.experiment_name = options.experiment_name
trainer.init_optimizer(options)
logger.info('Architecture =\n {}'.format(str(trainer.net)))
return trainer
def get_iterator(self):
options = self.options
logger = get_logger()
logger.info('Generating seeds for {} epochs using initial seed {}.'.format(options.max_epoch, options.seed))
seeds = generate_seeds(options.max_epoch, options.seed)
step = 0
for epoch, seed in zip(range(options.max_epoch), seeds):
logger.info('epoch={} seed={}'.format(epoch, seed))
it = self.batch_iterator.get_iterator(random_seed=seed, epoch=epoch)
wrap_it = self.get_iterator_standard(it)
def wrap_again(it):
last_batch_idx, last_batch_map, end_of_epoch = None, None, False
for batch_idx, batch_map in it:
if last_batch_idx is not None:
yield last_batch_idx, last_batch_map, end_of_epoch
last_batch_idx, last_batch_map = batch_idx, batch_map
end_of_epoch = True
yield last_batch_idx, last_batch_map, end_of_epoch
for epoch_step, (batch_idx, batch_map, end_of_epoch) in enumerate(wrap_again(wrap_it)):
should = {}
should['end_of_epoch'] = end_of_epoch
should['log'] = step % options.log_every_batch == 0
should['periodic'] = step % options.save_latest == 0 and step >= options.save_after
should['distinct'] = step % options.save_distinct == 0 and step >= options.save_after
should_eval = (
options.eval_every_batch > 0 and
step % options.eval_every_batch == 0 and
step >= options.eval_after
)
should_eval = should_eval or (
end_of_epoch and
epoch % options.eval_every_epoch == 0 and
step >= options.eval_after
)
should['eval'] = should_eval
yield epoch, step, batch_idx, batch_map, should
step += 1
if options.max_step >= 0 and step >= options.max_step:
logger.info('Max-Step={} Quitting.'.format(options.max_step))
sys.exit()
def build_net(options, embeddings, batch_iterator=None, word2idx=None):
cuda = options.cuda
context = {}
context['cuda'] = cuda
context['embeddings'] = embeddings
context['batch_iterator'] = batch_iterator
context['word2idx'] = word2idx
net_components = get_net_components(options, context)
trainer = trainer_build_net(options, context=context, net_components=net_components)
logger = get_logger()
logger.info('# of params = {}'.format(count_params(trainer.net)))
return trainer
def __init__(self, **kwargs):
super().__init__()
# defaults
self.enabled = True
self.unregistered = {}
self.logger = get_logger()
self.init_defaults()
# override
for k, v in kwargs.items():
if k in self.unregistered:
self.unregistered[k] = v
continue
setattr(self, k, v)
self.init_parameters()
self.reset_parameters()
def __init__(self,
net=None,
cuda=None,
word2idx=None,
mlp_reg=0,
clip_threshold=5.0):
super(Trainer, self).__init__()
self.net = net
self.optimizer = None
self.optimizer_cls = None
self.optimizer_kwargs = None
self.cuda = cuda
self.word2idx = word2idx
self._embedding_keys = None
self.mlp_reg = mlp_reg
self.clip_threshold = clip_threshold
self.logger = get_logger()
def __init__(self, name, kwargs_dict):
self.name = name
self.kwargs_dict = kwargs_dict
self.logger = get_logger()
def run_train(options, train_iterator, trainer, model_evaluation):
logger = get_logger()
experiment_logger = ExperimentLogger()
logger.info('Running train.')
step = 0
best_step = 0
best_metric = math.inf
best_dict = {}
my_iterator = MyTrainIterator(options, train_iterator)
for epoch, step, batch_idx, batch_map, should in my_iterator.get_iterator():
# HACK: Weird fix that counteracts other libraries (i.e. allennlp) modifying
# the global logger. Needed after adding tensorboard.
while len(logger.parent.handlers) > 0:
logger.parent.handlers.pop()
batch_map['step'] = step
result = trainer.step(batch_map)
experiment_logger.record(result)
del result
if should['log']:
metrics = experiment_logger.log_batch(epoch, step, batch_idx)
# -- Periodic Checkpoints -- #
if should['periodic']:
logger.info('Saving model (periodic).')
trainer.save_model(os.path.join(options.experiment_path, 'model_periodic.pt'))
save_experiment(os.path.join(options.experiment_path, 'experiment_periodic.json'),
dict(step=step, epoch=epoch, best_step=best_step, best_metric=best_metric))
if should['distinct']:
logger.info('Saving model (distinct).')
trainer.save_model(os.path.join(options.experiment_path, 'model.step_{}.pt'.format(step)))
save_experiment(os.path.join(options.experiment_path, 'experiment.step_{}.json'.format(step)),
dict(step=step, epoch=epoch, best_step=best_step, best_metric=best_metric))
# -- Validation -- #
if should['eval']:
logger.info('Evaluation.')
info = dict()
info['experiment_path'] = options.experiment_path
info['step'] = step
info['epoch'] = epoch
for eval_result_dict in run_evaluation(options, trainer, model_evaluation, info, metadata=dict(step=step)):
result = eval_result_dict['result']
func = eval_result_dict['component']
name = func.name
for key, val, is_best in func.compare(best_dict, result):
best_dict_key = 'best__{}__{}'.format(name, key)
# Early stopping.
if is_best:
if best_dict_key in best_dict:
prev_val = best_dict[best_dict_key]['value']
else:
prev_val = None
# Update running result.
best_dict[best_dict_key] = {}
best_dict[best_dict_key]['eval'] = name
best_dict[best_dict_key]['metric'] = key
best_dict[best_dict_key]['value'] = val
best_dict[best_dict_key]['step'] = step
best_dict[best_dict_key]['epoch'] = epoch
logger.info('Recording, best eval, key = {}, val = {} -> {}, json = {}'.format(
best_dict_key, prev_val, val, json.dumps(best_dict[best_dict_key])))
if step >= options.save_after:
logger.info('Saving model, best eval, key = {}, json = {}'.format(
best_dict_key, json.dumps(best_dict[best_dict_key])))
logger.info('checkpoint_dir = {}'.format(options.experiment_path))
# Save result and model.
trainer.save_model(
os.path.join(options.experiment_path, 'model.{}.pt'.format(best_dict_key)))
save_experiment(os.path.join(options.experiment_path, 'experiment.{}.json'.format(best_dict_key)),
best_dict[best_dict_key])
# END OF EPOCH
if should['end_of_epoch']:
experiment_logger.log_epoch(epoch, step)
trainer.end_of_epoch(best_dict)
def __init__(self, datasets):
self.datasets = datasets
self.logger = get_logger()
def __init__(self, reader):
super(ReaderManager, self).__init__()
self.reader = reader
self.logger = get_logger()
def __init__(self, lowercase=True, filter_length=0, for_nopunct=False):
self.lowercase = lowercase
self.filter_length = filter_length
self.for_nopunct = for_nopunct
self.logger = get_logger()
