def getNetwork(args):
if (args.net_type == 'alexnet'):
net = AlexNet(num_classes, inputs)
file_name = 'alexnet-'
elif (args.net_type == 'resnet'):
net = ResNet(args.depth, num_classes, inputs)
file_name = 'resnet-' + str(args.depth)
else:
print(
'Error : Network should be either [LeNet / AlexNet /SqueezeNet/ ResNet / Wide_ResNet'
)
sys.exit(0)
return net, file_name
def getNetwork(args):
if (args.net_type == 'alexnet'):
#net = models.alexnet(pretrained=True)
#net.classifier[6] = nn.Linear(4096,num_classes)
net = AlexNet(num_classes, inputs)
file_name = 'alexnet-'
elif (args.net_type == 'resnet'):
#net = models.resnet18(pretrained=True)
#net.fc = nn.Linear(512, num_classes)
net = ResNet(args.depth, num_classes, inputs)
file_name = 'resnet-18'
else:
print('Error : Network should be either [AlexNet / ResNet ')
sys.exit(0)
return net, file_name
def load_model():
global model
model = ResNet(34, 2, 3)
#model = resnet50(pretrained=False)
model_path = "./checkpoint/business_cards/resnet-34.t7"
checkpoint = torch.load(model_path, map_location='cpu')
#model.load_state_dict(checkpoint)
model = checkpoint['net']
if use_cuda:
model.cuda()
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
cudnn.benchmark = True
model.eval()
def main():
os.chdir(os.path.dirname(__file__))
args = get_arguments()
constr_weight = get_constraint(args.weight_bits, 'weight')
constr_activation = get_constraint(args.activation_bits, 'activation')
if args.dataset == 'cifar10':
network = resnet20
dataloader = dataloader_cifar10
elif args.dataset == 'cifar100':
t_net = ResNet(depth=56, num_classes=100)
state = torch.load("/prj/neo_lv/user/ybhalgat/LSQ-KD/cifar100_pretrained/resnet56.pth.tar")
t_net.load_state_dict(state)
network = resnet20
dataloader = dataloader_cifar100
else:
if args.network == 'resnet18':
network = resnet18
elif args.network == 'resnet50':
network = resnet50
elif args.network == 'efficientnet-b0':
t_net = EfficientNet.from_pretrained("efficientnet-b3")
network = efficientnet_b0
else:
print('Not Support Network Type: %s' % args.network)
return
dataloader = dataloader_imagenet
train_loader = dataloader(args.data_root, split='train', batch_size=args.batch_size)
test_loader = dataloader(args.data_root, split='test', batch_size=args.batch_size)
net = network(quan_first=args.quan_first,
quan_last=args.quan_last,
constr_activation=constr_activation,
preactivation=args.preactivation,
bw_act=args.activation_bits)
model_path = os.path.join(args.model_root, args.model_name + '.pth.tar')
if not os.path.exists(model_path):
model_path = model_path[:-4]
name_weights_old = torch.load(model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
load_checkpoint(net, name_weights_new)
# net.load_state_dict(name_weights_new, strict=False)
if not args.haq:
add_lsqmodule(net, bit_width=args.weight_bits)
else:
if args.network == 'resnet50':
strategy = [6, 6, 5, 5, 5, 5, 4, 5, 5, 4, 5, 5, 5, 5, 5, 5, 3, 5, 4, 3, 5, 4, 3, 4, 4, 4, 2, 5,
4, 3, 3, 5, 3, 2, 5, 3, 2, 4, 3, 2, 5, 3, 2, 5, 3, 4, 2, 5, 2, 3, 4, 2, 3, 4]
elif args.network == 'efficientnet-b0':
strategy = [7, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 6, 6, 6,
6, 6, 6, 6, 6, 7, 6, 7, 6, 7, 6, 5, 6, 5, 6, 4, 5, 6, 5, 6, 4, 4, 5, 4, 5, 2,
3, 4, 3, 4, 2, 3, 4, 4, 7, 5, 2, 4, 2, 5, 5, 2, 4, 2, 5, 5, 2, 4, 2, 5, 5, 2,
4, 3, 3, 2]
add_lsqmodule(net, strategy=strategy)
print(net)
net = net.cuda()
net = nn.DataParallel(net, device_ids=range(cuda.device_count()))
t_net = t_net.cuda()
t_net = nn.DataParallel(t_net, device_ids=range(cuda.device_count()))
quan_activation = isinstance(constr_activation, np.ndarray)
postfix = '_w' if not quan_activation else '_a'
new_model_name = args.prefix + args.model_name + '_lsq' + postfix
cache_root = os.path.join('.', 'cache')
train_loger = LogHelper(new_model_name, cache_root, quan_activation, args.resume)
optimizer, lr_scheduler, optimizer_t = get_optimizer(s_net=net,
t_net=t_net,
optimizer=args.optimizer,
lr_base=args.learning_rate,
weight_decay=args.weight_decay,
lr_scheduler=args.lr_scheduler,
total_epoch=args.total_epoch,
quan_activation=quan_activation,
act_lr_factor=args.act_lr_factor,
weight_lr_factor=args.weight_lr_factor)
trainer = Trainer(net=net,
t_net=t_net,
train_loader=train_loader,
test_loader=test_loader,
optimizer=optimizer,
optimizer_t=optimizer_t,
lr_scheduler=lr_scheduler,
model_name=new_model_name,
train_loger=train_loger)
trainer(total_epoch=args.total_epoch,
save_check_point=True,
resume=args.resume)
def main():
## Epochs, lr, Dataset={"FashionMNIST","CIFAR10"}
args = {'epochs': 30, 'lr': 0.05, 'ensemble': 5, 'dataset': "FashionMNIST"}
loss_fn = F.nll_loss
#Selecting Main Dataset
#FashionMNIST-Mnist
#CIFAR10-SVHN
ds = all_datasets[args['dataset']]()
input_size, num_classes, train_dataset, test_dataset = ds
kwargs = {"num_workers": 4, "pin_memory": True}
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=128,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=5000,
shuffle=False,
**kwargs)
#Selecting model CNN for FashionMNIST and Resnet for CIFAR10
if args['dataset'] == "FashionMNIST":
milestones = [10, 20]
ensemble = [
CNN(input_size, num_classes).cuda()
for _ in range(args['ensemble'])
]
else:
milestones = [25, 50]
ensemble = [
ResNet(input_size, num_classes).cuda()
for _ in range(args['ensemble'])
]
ensemble = torch.nn.ModuleList(ensemble)
#ensemble.load_state_dict(torch.load("FM_5_ensemble_30.pt"))
optimizers = []
schedulers = []
for model in ensemble:
# Need different optimisers to apply weight decay and momentum properly
# when only optimising one element of the ensemble
optimizers.append(
torch.optim.SGD(model.parameters(),
lr=args['lr'],
momentum=0.9,
weight_decay=5e-4))
schedulers.append(
torch.optim.lr_scheduler.MultiStepLR(optimizers[-1],
milestones=milestones,
gamma=0.1))
for epoch in range(1, args['epochs'] + 1):
#####Train#####
for i, model in enumerate(ensemble):
train(model, train_loader, optimizers[i], epoch, loss_fn)
schedulers[i].step()
#####Test######
#Test on testset of main dataset
test(ensemble, test_loader, loss_fn)
#####AUROC######
#AUROC on Main + ood
if (args['dataset'] == "FashionMNIST"):
accuracy, auroc = get_fm_mnist_ood_ensemble(ensemble)
print({'mnist_ood_auroc': auroc})
else:
accuracy, auroc = get_cifar10_svhn_ood_ensemble(ensemble)
print({'cifar10_ood_auroc': auroc})
#Save
path = f"model{args['dataset']}_{len(ensemble)}"
torch.save(ensemble.state_dict(), path + "_ensemble.pt")
class_mode="categorical",
target_size=(64, 64),
color_mode="rgb",
shuffle=False,
batch_size=BS)
# initialise the testing generator
testGen = valAug.flow_from_directory(config.TEST_PATH,
class_mode="categorical",
target_size=(64, 64),
color_mode="rgb",
shuffle=False,
batch_size=BS)
# initialise our ResNet model and compile it
model = ResNet.build(64, 64, 3, 2, (3, 4, 6), (64, 128, 256, 512), reg=0.0005)
opt = SGD(lr=INIT_LR, momentum=0.9)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# define our set of callbacks and fit the model
callbacks = [LearningRateScheduler(poly_decay)]
H = model.fit_generator(trainGen,
steps_per_epoch=totalTrain // BS,
validation_data=valGen,
validation_steps=totalVal // BS,
epochs=NUM_EPOCHS,
callbacks=callbacks)
# reset the testing generator and use the trained model to
# make predictions on the data
def loadModel(config):
net = ResNet(config)
net.load_state_dict(torch.load('results/checkpoint.pt'))
return net
from utils.resnet import ResNet from utils.dataTool import train_val_test_loader from utils.trainer import train_test_model from utils.baseline import testLastHourRegression, testArima from config import Config import torch # 全局设置 torch.set_default_tensor_type(torch.DoubleTensor) config = Config() # 读入训练集，验证集 train_loader, test_loader = train_val_test_loader(config) # 定义网络，训练并测试 net = ResNet(config).to(config.device) train_test_model(net, train_loader, test_loader, config) # 测试baseline testLastHourRegression(test_loader, config.device, config.metric, hour_type='last_day') # 前一天 testLastHourRegression(test_loader, config.device, config.metric, hour_type='last_week') # 前一周 testArima(config) # arima