def loop_through_data_for_eval(self,
dataset: DataLoader,
model: Model,
graph: Graph) -> Result:
graph.to(self.device)
model.to(device=self.device)
model.eval()
result = Result(entity_dict=self.entity_id_to_str_dict, relation_dict=self.relation_id_to_str_dict)
for idx, (paths, mask, _, triplet, num_paths) in enumerate(tqdm(dataset)):
labels = triplet[:, 1]
assert len(triplet) == len(labels)
if num_paths.size() == torch.Size([1, 1]) and num_paths.item() == 0:
score = torch.randn(1, self.num_relations)
else:
paths = paths.to(device=self.device)
mask = mask.to(device=self.device)
triplet = triplet.to(device=self.device)
score = model(triplet, graph, paths=paths, masks=mask, num_paths=num_paths)
result.append(score.cpu(), labels.cpu())
return result
def loop_through_data_for_eval(
self,
dataset: np.ndarray, # assuming batch_size * 3
model: Model,
graph: Graph,
batch_size: int) -> Result:
graph.to(self.device)
model.to(device=self.device)
model.eval()
result = Result()
num_batches = ceil(batch_size / len(dataset))
for batch_idx in range(num_batches):
start_idx, end_idx = batch_idx * batch_size, batch_idx * batch_size + batch_size
batch = torch.from_numpy(
dataset[start_idx:end_idx]).long().to(device=self.device)
if self.config.link_predict:
labels = batch[:, 1]
else:
labels = batch[:,
2] # the objects in <subject, relation, object>
scores = model(batch, graph, link_predict=self.config.link_predict)
result.append(scores.cpu(), labels.cpu())
return result
