# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(cut_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
transform_test = transforms.Compose([
transforms.TenCrop(cut_size),
transforms.Lambda(lambda crops: torch.stack(
[transforms.ToTensor()(crop) for crop in crops])),
])
trainset = CK(split='Training', fold=opt.fold, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=opt.bs,
shuffle=True,
num_workers=1)
testset = CK(split='Testing', fold=opt.fold, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=5,
shuffle=False,
num_workers=1)
# Model
if opt.model == 'VGG19':
net = VGG('VGG19')
elif opt.model == 'Resnet18':
net = ResNet18()
elif opt.model == 'Resnet18':
net = ResNet18()
correct = 0
total = 0
all_target = []
for i in xrange(10):
print("%d fold" % (i + 1))
path = os.path.join(opt.dataset + '_' + opt.model, '%d' % (i + 1))
checkpoint = torch.load(os.path.join(path, 'Test_model.t7'))
net.load_state_dict(checkpoint['net'])
net.cuda()
net.eval()
testset = CK(split='Testing', fold=i + 1, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=5,
shuffle=False,
num_workers=1)
for batch_idx, (inputs, targets) in enumerate(testloader):
bs, ncrops, c, h, w = np.shape(inputs)
inputs = inputs.view(-1, c, h, w)
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs, volatile=True), Variable(targets)
outputs = net(inputs)
outputs_avg = outputs.view(bs, ncrops, -1).mean(1) # avg over crops
_, predicted = torch.max(outputs_avg.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()