def main(args):
'''
main function of FALCON
:param args: arguments for a model
'''
# choose dataset
if args.datasets == "svhn":
num_classes = 10
elif args.datasets == "cifar100":
num_classes = 100
else:
pass
# choose model ResNet
if "ResNet" in args.model:
if args.convolution == "FALCON":
net = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes)
if args.is_train:
if args.alpha == 1:
if args.init:
load_specific_model(net,
args,
convolution='StandardConv',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=args.init,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
if args.init:
net2 = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes)
net = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes,
alpha=args.alpha)
load_specific_model(net2,
args,
convolution='StandardConv',
input_path=args.stconv_path)
net = init_with_alpha_resnet(net2, net, args.alpha)
net.falcon(rank=args.rank,
init=args.init,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes,
alpha=args.alpha)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
if args.alpha == 1:
net = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes,
alpha=args.alpha)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes,
alpha=args.alpha)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == "StConvBranch":
net = ResNet_StConv_branch(layer_num=str(args.layer_num),
num_classes=num_classes,
alpha=args.alpha)
elif args.convolution == 'FALCONBranch':
net = ResNet_StConv_branch(layer_num=str(args.layer_num),
num_classes=num_classes,
alpha=args.alpha)
if args.init:
load_specific_model(net,
args,
convolution='StConvBranch',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == "StandardConv":
net = ResNet(layer_num=str(args.layer_num),
num_classes=num_classes)
else:
pass
# choose model VGG
elif "VGG" in args.model:
if args.convolution == "FALCON":
net = VGG(num_classes=num_classes, which=args.model)
if args.is_train:
if args.alpha == 1:
if args.init:
load_specific_model(net,
args,
convolution='StandardConv',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=args.init,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
if args.init:
net2 = VGG(num_classes=num_classes, which=args.model)
net = VGG(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
load_specific_model(net2,
args,
convolution='StandardConv',
input_path=args.stconv_path)
net = init_with_alpha_vgg(net2, net, args.alpha)
net.falcon(rank=args.rank,
init=args.init,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net = VGG(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
if args.alpha == 1:
net = VGG(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net = VGG(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == 'StConvBranch':
net = VGG_StConv_branch(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
elif args.convolution == 'FALCONBranch':
net = VGG_StConv_branch(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
if args.init:
load_specific_model(net,
args,
convolution='StConvBranch',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=args.is_train,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == "StandardConv":
net = VGG(num_classes=num_classes, which=args.model)
else:
pass
else:
pass
net = net.cuda()
print_model_parm_nums(net)
print_model_parm_flops(net)
if args.is_train:
# training
best = train(net,
lr=args.learning_rate,
optimizer_option=args.optimizer,
epochs=args.epochs,
batch_size=args.batch_size,
is_train=args.is_train,
data=args.datasets,
lrd=args.lr_decay_rate)
if not args.not_save:
save_specific_model(best, args)
test(net, batch_size=args.batch_size, data=args.datasets)
else:
# testing
load_specific_model(net, args, input_path=args.restore_path)
inference_time = 0
inference_time += \
test(net, batch_size=args.batch_size, data=args.datasets)
# print("Average Inference Time: %f" % (float(inference_time) / float(1)))
# calculate number of parameters & FLOPs
print_model_parm_nums(net)
print_model_parm_flops(net)
# time of forwarding 100 data sample (ms)
x = torch.rand(100, 3, 32, 32)
x = Variable(x.cuda())
net(x)
timer = Timer()
timer.tic()
for _ in range(100):
net(x)
timer.toc()
def main(args):
# choose dataset
if args.datasets == "cifar10" or args.datasets == "svhn" or args.datasets == "mnist":
num_classes = 10
elif args.datasets == "cifar100":
num_classes = 100
else:
pass
# choose model
if "ResNet" in args.model:
if args.convolution == "FALCON":
net = ResNet(layer_num="34", num_classes=num_classes)
if args.is_train:
load_specific_model(net,
args,
convolution='StandardConv',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=args.init,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == "StConvBranch":
net = ResNet_StConv_branch(layer_num='34',
num_classes=num_classes,
alpha=args.alpha)
elif args.convolution == 'FALCONBranch':
net = ResNet_StConv_branch(layer_num='34',
num_classes=num_classes,
alpha=args.alpha)
if args.is_train:
load_specific_model(net,
args,
convolution='StConvBranch',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == "StandardConv":
net = ResNet(layer_num="34", num_classes=num_classes)
else:
pass
elif "VGG" in args.model:
if args.convolution == "FALCON":
net = VGG(num_classes=num_classes, which=args.model)
if args.is_train:
load_specific_model(net,
args,
convolution='StandardConv',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=args.init,
bn=args.bn,
relu=args.relu,
groups=args.groups)
else:
net.falcon(rank=args.rank,
init=False,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == 'StConvBranch':
net = VGG_StConv_branch(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
elif args.convolution == 'FALCONBranch':
net = VGG_StConv_branch(num_classes=num_classes,
which=args.model,
alpha=args.alpha)
if args.is_train:
load_specific_model(net,
args,
convolution='StConvBranch',
input_path=args.stconv_path)
net.falcon(rank=args.rank,
init=args.is_train,
bn=args.bn,
relu=args.relu,
groups=args.groups)
elif args.convolution == "StandardConv":
net = VGG(num_classes=num_classes, which=args.model)
else:
pass
else:
pass
net = net.cuda()
print_model_parm_nums(net)
print_model_parm_flops(net)
if args.is_train:
# training
best = train(net,
lr=args.learning_rate,
optimizer_option=args.optimizer,
epochs=args.epochs,
batch_size=args.batch_size,
is_train=args.is_train,
data=args.datasets,
lrd=args.lr_decay_rate)
if not args.not_save:
save_specific_model(best, args)
test(net, batch_size=args.batch_size, data=args.datasets)
else:
# testing
load_specific_model(net, args, input_path=args.restore_path)
inference_time = 0
for i in range(1):
inference_time += test(net,
batch_size=args.batch_size,
data=args.datasets)
print("Average Inference Time: %f" %
(float(inference_time) / float(1)))
# calculate number of parameters & FLOPs
print_model_parm_nums(net)
print_model_parm_flops(net)
# time of forwarding 100 data sample (ms)
x = torch.rand(100, 3, 32, 32)
x = Variable(x.cuda())
net(x)
timer = Timer()
timer.tic()
for i in range(100):
net(x)
timer.toc()
print('Do once forward need %.3f ms.' % (timer.total_time * 1000 / 100.0))
