load_pretrained_model(path, model, mode=mode)
# data
testset = Testset(root='./data/test')
testloader = DataLoader(dataset=testset,
batch_size=128,
shuffle=False,
num_workers=8,
pin_memory=True)
submit = {}
TTA_times = 10
fnames = []
model.eval()
with torch.no_grad():
results = np.zeros((len(testset), 9691))
for n in range(TTA_times):
print('{:>3}/{:>3}'.format(n, TTA_times))
t1 = time.time()
preds = []
for idx, (data, fname) in enumerate(testloader):
if n == 0:
fnames.extend(fname)
print(idx, end=',')
data = data.to(device)
out = model(data)
_, pred = torch.max(out, dim=1)
preds.extend(pred.cpu().tolist())
