def test():
# define default variables
args = get_args() # divide args part and call it as function
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
state = {k: v for k, v in args._get_kwargs()}
# prepare test data parts
if args.train_set == 0:
phase = False
else:
phase = True
test_transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean, std)])
test_data = dset.CIFAR100(args.data_path, train=phase, transform=test_transform, download=True)
if args.dataset == 'cifar10':
test_data = dset.CIFAR10(args.data_path, train=phase, transform=test_transform, download=True)
nlabels = 10
else:
test_data = dset.CIFAR100(args.data_path, train=phase, transform=test_transform, download=True)
nlabels = 100
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.test_bs, shuffle=False,
num_workers=args.prefetch, pin_memory=True)
# initialize model and load from checkpoint
net = CifarResNeXt(args.cardinality, args.depth, nlabels, args.widen_factor)
loaded_state_dict = torch.load(args.load, map_location='cpu')
temp = {}
for key, val in list(loaded_state_dict.iteritems()):
# parsing keys for ignoring 'module.' in keys
temp[key[7:]] = val
loaded_state_dict = temp
net.load_state_dict(loaded_state_dict)
# paralleize model
if args.ngpu > 1:
net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))
if args.ngpu > 0:
net.cuda()
# use network for evaluation
net.eval()
# calculation part
start = time.clock()
result = np.zeros((len(test_data), net.stages[3]), dtype=np.float32)
result_target = np.zeros(len(test_data))
print(result.shape)
for batch_idx, (data, target) in enumerate(test_loader):
result_target[batch_idx * args.test_bs:(batch_idx + 1) * args.test_bs] = target.numpy()
data, target = torch.autograd.Variable(data), torch.autograd.Variable(target)
# forward
output = net(data).detach().numpy()
result[batch_idx*args.test_bs:(batch_idx+1)*args.test_bs,:] = output
print("batch: {0}, average_time: {1}s, time_left:{2}s".format(batch_idx, (time.clock()-start)/(batch_idx+1), (time.clock()-start)/(batch_idx+1)*(len(test_loader)-batch_idx-1)))
if batch_idx % 1000==0:
np.save(args.savename+".npy",result)
np.save(args.savename, result)
np.save(args.savename+"_target", result_target)
def test():
# define default variables
args = get_args() # divide args part and call it as function
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
state = {k: v for k, v in args._get_kwargs()}
# prepare test data parts
test_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)])
test_data = dset.CIFAR100(args.data_path,
train=False,
transform=test_transform,
download=True)
if args.dataset == 'cifar10':
test_data = dset.CIFAR10(args.data_path,
train=False,
transform=test_transform,
download=True)
nlabels = 10
else:
test_data = dset.CIFAR100(args.data_path,
train=False,
transform=test_transform,
download=True)
nlabels = 100
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=args.test_bs,
shuffle=False,
num_workers=args.prefetch,
pin_memory=True)
# initialize model and load from checkpoint
net = CifarResNeXt(args.cardinality, args.depth, nlabels,
args.widen_factor)
loaded_state_dict = torch.load(args.load)
temp = {}
for key, val in list(loaded_state_dict.iteritems()):
# parsing keys for ignoring 'module.' in keys
temp[key[7:]] = val
loaded_state_dict = temp
net.load_state_dict(loaded_state_dict)
# paralleize model
if args.ngpu > 1:
net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))
if args.ngpu > 0:
net.cuda()
# use network for evaluation
net.eval()
# calculation part
loss_avg = 0.0
correct = 0.0
for batch_idx, (data, target) in enumerate(test_loader):
data, target = torch.autograd.Variable(
data.cuda()), torch.autograd.Variable(target.cuda())
# forward
output = net(data)
loss = F.cross_entropy(output, target)
# accuracy
pred = output.data.max(1)[1]
correct += pred.eq(target.data).sum()
# test loss average
loss_avg += loss.data[0]
state['test_loss'] = loss_avg / len(test_loader)
state['test_accuracy'] = correct / len(test_loader.dataset)
# finally print state dictionary
print(state)
num_workers=args.prefetch,
pin_memory=True)
# Init checkpoints
if not os.path.isdir(args.save):
os.makedirs(args.save)
# Init model, criterion, and optimizer
net = CifarResNeXt(args.cardinality, args.depth, nlabels,
args.widen_factor)
print(net)
if args.ngpu > 1:
net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))
if args.ngpu > 0:
net.cuda()
optimizer = torch.optim.SGD(net.parameters(),
state['learning_rate'],
momentum=state['momentum'],
weight_decay=state['decay'],
nesterov=True)
# train function (forward, backward, update)
def train():
net.train()
loss_avg = 0.0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = torch.autograd.Variable(
data.cuda()), torch.autograd.Variable(target.cuda())