def test(args, ckpt_file):
batch_size = args["batch_size"]
testloader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=generate_batch)
predictions, targets = [], []
net = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUN_CLASS).to(device)
ckpt = torch.load(os.path.join(args["EXPT_DIR"], ckpt_file))
net.load_state_dict(ckpt["model"])
net.eval()
correct, total = 0, 0
with torch.no_grad():
for data in tqdm(testloader, desc="Testing"):
text, offsets, cls = data
text, offsets, cls = text.to(device), offsets.to(device), cls.to(
device)
outputs = net(text, offsets)
_, predicted = torch.max(outputs.data, 1)
predictions.extend(predicted.cpu().numpy().tolist())
targets.extend(cls.cpu().numpy().tolist())
total += cls.size(0)
correct += (predicted == cls).sum().item()
return {"predictions": predictions, "labels": targets}
def infer(args, unlabeled, ckpt_file):
unlabeled = Subset(train_dataset, unlabeled)
unlabeled_loader = torch.utils.data.DataLoader(
unlabeled,
batch_size=args["batch_size"],
shuffle=False,
num_workers=2,
collate_fn=generate_batch)
net = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUN_CLASS).to(device)
ckpt = torch.load(os.path.join(args["EXPT_DIR"], ckpt_file))
net.load_state_dict(ckpt["model"])
net.eval()
correct, total = 0, 0
outputs_fin = {}
with torch.no_grad():
for i, data in tqdm(enumerate(unlabeled_loader), desc="Inferring"):
text, offsets, cls = data
text, offsets, cls = text.to(device), offsets.to(device), cls.to(
device)
outputs = net(text, offsets)
_, predicted = torch.max(outputs.data, 1)
total += cls.size(0)
correct += (predicted == cls).sum().item()
for j in range(len(outputs)):
outputs_fin[j] = {}
outputs_fin[j]["prediction"] = predicted[j].item()
outputs_fin[j]["pre_softmax"] = outputs[j].cpu().numpy()
return {"outputs": outputs_fin}