def _predict(self, model: torch.nn.Module, dataset: Dataset, input_reader: BaseInputReader):
# create data loader
dataset.switch_mode(Dataset.EVAL_MODE)
data_loader = DataLoader(dataset, batch_size=self._args.eval_batch_size, shuffle=False, drop_last=False,
num_workers=self._args.sampling_processes, collate_fn=sampling.collate_fn_padding)
pred_entities = []
pred_relations = []
with torch.no_grad():
model.eval()
# iterate batches
total = math.ceil(dataset.document_count / self._args.eval_batch_size)
for batch in tqdm(data_loader, total=total, desc='Predict'):
# move batch to selected device
batch = util.to_device(batch, self._device)
# run model (forward pass)
result = model(encodings=batch['encodings'], context_masks=batch['context_masks'],
entity_masks=batch['entity_masks'], entity_sizes=batch['entity_sizes'],
entity_spans=batch['entity_spans'], entity_sample_masks=batch['entity_sample_masks'],
inference=True)
entity_clf, rel_clf, rels = result
# convert predictions
predictions = prediction.convert_predictions(entity_clf, rel_clf, rels,
batch, self._args.rel_filter_threshold,
input_reader)
batch_pred_entities, batch_pred_relations = predictions
pred_entities.extend(batch_pred_entities)
pred_relations.extend(batch_pred_relations)
prediction.store_predictions(dataset.documents, pred_entities, pred_relations, self._args.predictions_path)
def _predict(self, model: torch.nn.Module, dataset: Dataset, input_reader: JsonInputReader,
epoch: int = 0, updates_epoch: int = 0, iteration: int = 0):
if isinstance(model, DataParallel):
model = model.modules()
# create evaluator
evaluator = Evaluator(dataset, input_reader, self._tokenizer,
self.args.rel_filter_threshold, self.args.no_overlapping, self.args.output_path,
self._examples_path, self.args.example_count, epoch, dataset.label)
# create data loader
dataset.switch_mode(Dataset.EVAL_MODE)
data_loader = DataLoader(dataset, batch_size=self.args.eval_batch_size, shuffle=False, drop_last=False,
num_workers=self.args.sampling_processes, collate_fn=sampling.collate_fn_padding)
returned_predict= list()
with torch.no_grad():
model.eval()
# iteraate batches
total = math.ceil(dataset.document_count / self.args.eval_batch_size)
for batch in tqdm(data_loader, total=total, desc='Evaluate epoch %s' % epoch):
# move batch to selected device
batch = util.to_device(batch, self._device)
# run model (forward pass)
result = model(encodings=batch['encodings'], context_masks=batch['context_masks'],
entity_masks=batch['entity_masks'], entity_sizes=batch['entity_sizes'],
entity_spans=batch['entity_spans'], entity_sample_masks=batch['entity_sample_masks'],
evaluate=True)
entity_clf, rel_clf, rels = result
evaluator.predict_batch(entity_clf, rel_clf, rels, batch)
evaluator.store_predictions()
def _train_epoch(self, model: torch.nn.Module, compute_loss: Loss,
optimizer: Optimizer, dataset: Dataset,
updates_epoch: int, epoch: int):
self._logger.info("Train epoch: %s" % epoch)
# create data loader
dataset.switch_mode(Dataset.TRAIN_MODE)
data_loader = DataLoader(dataset,
batch_size=self.args.train_batch_size,
shuffle=True,
drop_last=True,
num_workers=self.args.sampling_processes,
collate_fn=sampling.collate_fn_padding)
model.zero_grad()
iteration = 0
total = dataset.document_count // self.args.train_batch_size
for batch in tqdm(data_loader,
total=total,
desc='Train epoch %s' % epoch):
model.train()
batch = util.to_device(batch, self._device)
# forward step
entity_logits, rel_logits, s1, s2 = model(
encodings=batch['encodings'],
context_masks=batch['context_masks'],
entity_masks=batch['entity_masks'],
entity_sizes=batch['entity_sizes'],
relations=batch['rels'],
rel_masks=batch['rel_masks'])
# print(s1)
# print(s2)
# os._exit(0)
# compute loss and optimize parameters
batch_loss = compute_loss.compute(
entity_logits=entity_logits,
rel_logits=rel_logits,
rel_types=batch['rel_types'],
entity_types=batch['entity_types'],
entity_sample_masks=batch['entity_sample_masks'],
rel_sample_masks=batch['rel_sample_masks'])
# logging
iteration += 1
global_iteration = epoch * updates_epoch + iteration
if global_iteration % self.args.train_log_iter == 0:
self._log_train(optimizer, batch_loss, epoch, iteration,
global_iteration, dataset.label)
return iteration
def _eval(self, model: torch.nn.Module, dataset: Dataset, input_reader: BaseInputReader,
epoch: int = 0, updates_epoch: int = 0, iteration: int = 0):
self._logger.info("Evaluate: %s" % dataset.label)
if isinstance(model, DataParallel):
# currently no multi GPU support during evaluation
model = model.module
# create evaluator
predictions_path = os.path.join(self._log_path, f'predictions_{dataset.label}_epoch_{epoch}.json')
examples_path = os.path.join(self._log_path, f'examples_%s_{dataset.label}_epoch_{epoch}.html')
evaluator = Evaluator(dataset, input_reader, self._tokenizer,
self._args.rel_filter_threshold, self._args.no_overlapping, predictions_path,
examples_path, self._args.example_count)
# create data loader
dataset.switch_mode(Dataset.EVAL_MODE)
data_loader = DataLoader(dataset, batch_size=self._args.eval_batch_size, shuffle=False, drop_last=False,
num_workers=self._args.sampling_processes, collate_fn=sampling.collate_fn_padding)
with torch.no_grad():
model.eval()
# iterate batches
total = math.ceil(dataset.document_count / self._args.eval_batch_size)
for batch in tqdm(data_loader, total=total, desc='Evaluate epoch %s' % epoch):
# move batch to selected device
batch = util.to_device(batch, self._device)
# run model (forward pass)
result = model(encodings=batch['encodings'], context_masks=batch['context_masks'],
entity_masks=batch['entity_masks'], entity_sizes=batch['entity_sizes'],
entity_spans=batch['entity_spans'], entity_sample_masks=batch['entity_sample_masks'],
inference=True)
entity_clf, rel_clf, rels = result
# evaluate batch
evaluator.eval_batch(entity_clf, rel_clf, rels, batch)
global_iteration = epoch * updates_epoch + iteration
ner_eval, rel_eval, rel_nec_eval = evaluator.compute_scores()
self._log_eval(*ner_eval, *rel_eval, *rel_nec_eval,
epoch, iteration, global_iteration, dataset.label)
if self._args.store_predictions and not self._args.no_overlapping:
evaluator.store_predictions()
if self._args.store_examples:
evaluator.store_examples()
def read(self, dataset_paths):
for dataset_label, dataset_path in dataset_paths.items():
dataset = Dataset(dataset_label, self)
self._parse_dataset(dataset_path, dataset)
self._datasets[dataset_label] = dataset
self._context_size = self._calc_context_size(self._datasets.values())
def read(self, dataset_path, dataset_label):
dataset = Dataset(dataset_label, self._relation_types,
self._entity_types, self._neg_entity_count,
self._neg_rel_count, self._max_span_size)
self._parse_dataset(dataset_path, dataset)
self._datasets[dataset_label] = dataset
return dataset
def read(self, dataset_paths):
for dataset_label, dataset_path in dataset_paths.items():
dataset = Dataset(dataset_label, self._relation_types, self._entity_types, self._neg_entity_count,
self._neg_rel_count, self._max_span_size)
self._parse_dataset(dataset_path, dataset)
self._datasets[dataset_label] = dataset
self._context_size = self._calc_context_size(self._datasets.values())
def dump_dataset(self, dataset_label, documents):
dataset = Dataset(dataset_label, self._relation_types,
self._entity_types, self._neg_entity_count,
self._neg_rel_count, self._max_span_size)
self._parse_dataset(documents, dataset)
self._datasets[dataset_label] = dataset
self._context_size = self._calc_context_size(self._datasets.values())
def read_for_infer(self, samples):
dataset_label = "infer"
dataset = Dataset(dataset_label, self._relation_types,
self._entity_types, self._neg_entity_count,
self._neg_rel_count, self._max_span_size)
for sample_tokens in samples:
self._parse_sample_for_infer(sample_tokens, dataset)
self._datasets[dataset_label] = dataset
self._context_size = self._calc_context_size(self._datasets.values())