for i, (inp, target) in enumerate(test_loader):
target = target.cuda()
gt = torch.cat((gt, target), 0)
bs, n_crops, c, h, w = inp.size()
input_var = torch.autograd.Variable(inp.view(-1, c, h, w).cuda(), volatile=True)
output = model(input_var)
output_mean = output.view(bs, n_crops, -1).mean(1)
pred = torch.cat((pred, output_mean.data), 0)
AUROCs = compute_AUCs(gt, pred)
AUROC_avg = np.array(AUROCs).mean()