def _setup(self, config):
args = config.pop("args")
vars(args).update(config)
type_ = config.get('type', 'gainA')
use_dwt = config.get('dwt', False)
C = config.get('num_channels', 64)
dataset = config.get('dataset', args.dataset)
if hasattr(args, 'verbose'):
self._verbose = args.verbose
num_workers = 4
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
num_workers = 0
if self.use_cuda:
torch.cuda.manual_seed(args.seed)
# ######################################################################
# Data
kwargs = {'num_workers': num_workers, 'pin_memory': True} if self.use_cuda else {}
if args.dataset.startswith('cifar'):
self.train_loader, self.test_loader = cifar.get_data(
32, args.datadir, dataset=dataset,
batch_size=args.batch_size, trainsize=args.trainsize,
**kwargs)
elif args.dataset == 'tiny_imagenet':
self.train_loader, self.test_loader = tiny_imagenet.get_data(
64, args.datadir, val_only=False,
batch_size=args.batch_size, trainsize=args.trainsize,
distributed=False, **kwargs)
# ######################################################################
# Build the network based on the type parameter. θ are the optimal
# hyperparameters found by cross validation.
if type_.startswith('ref'):
θ = (0.1, 0.9, 1e-4)
else:
θ = (0.45, 0.8, 1e-4)
# raise ValueError('Unknown type')
lr, mom, wd = θ
# If the parameters were provided as an option, use them
lr = config.get('lr', lr)
mom = config.get('mom', mom)
wd = config.get('wd', wd)
wd1 = config.get('wd1', wd)
std = config.get('std', 1.0)
# Build the network
self.model = MixedNet(args.dataset, type_, use_dwt, C, wd, wd1)
init = lambda x: net_init(x, std)
self.model.apply(init)
# Split across GPUs
if torch.cuda.device_count() > 1 and config.get('num_gpus', 0) > 1:
self.model = nn.DataParallel(self.model)
model = self.model.module
else:
model = self.model
if self.use_cuda:
self.model.cuda()
# ######################################################################
# Build the optimizer - use separate parameter groups for the gain
# and convolutional layers
default_params = list(model.fc1.parameters())
gain_params = []
for name, module in model.net.named_children():
params = [p for p in module.parameters() if p.requires_grad]
if name.startswith('gain'):
gain_params += params
else:
default_params += params
self.optimizer, self.scheduler = optim.get_optim(
'sgd', default_params, init_lr=lr,
steps=args.steps, wd=wd, gamma=0.2, momentum=mom,
max_epochs=args.epochs)
if len(gain_params) > 0:
# Get special optimizer parameters
lr1 = config.get('lr1', lr)
gamma1 = config.get('gamma1', 0.2)
mom1 = config.get('mom1', mom)
opt1 = config.get('opt1', 'sgd')
if lr1 is None:
lr1 = lr
if mom1 is None:
mom1 = mom
# Do not use the optimizer's weight decay, call a special method to
# do it.
self.optimizer1, self.scheduler1 = optim.get_optim(
opt1, gain_params, init_lr=lr1,
steps=args.steps, wd=0, gamma=gamma1, momentum=mom1,
max_epochs=args.epochs)
if self.verbose:
print(self.model)
def _setup(self, config):
args = config.pop("args")
vars(args).update(config)
type_ = config.get('type')
dataset = config.get('dataset', args.dataset)
if hasattr(args, 'verbose'):
self._verbose = args.verbose
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
if self.use_cuda:
torch.cuda.manual_seed(args.seed)
# ######################################################################
# Data
kwargs = {
'num_workers': 0,
'pin_memory': True
} if self.use_cuda else {}
if dataset.startswith('cifar'):
self.train_loader, self.test_loader = cifar.get_data(
32,
args.datadir,
dataset=dataset,
batch_size=args.batch_size,
trainsize=args.trainsize,
seed=args.seed,
**kwargs)
elif dataset == 'tiny_imagenet':
self.train_loader, self.test_loader = tiny_imagenet.get_data(
64,
args.datadir,
val_only=False,
batch_size=args.batch_size,
trainsize=args.trainsize,
seed=args.seed,
distributed=False,
**kwargs)
# ######################################################################
# Build the network based on the type parameter. θ are the optimal
# hyperparameters found by cross validation.
if type_ == 'ref':
θ = (0.1, 0.9, 1e-4, 1)
else:
θ = (0.2, 0.9, 5e-5, 1.5)
# raise ValueError('Unknown type')
lr, mom, wd, std = θ
# If the parameters were provided as an option, use them
lr = config.get('lr', lr)
mom = config.get('mom', mom)
wd = config.get('wd', wd)
std = config.get('std', std)
bias = config.get('bias', 1e-2)
# drop_p = config.get('drop_p', drop_p)
# Build the network
self.model = ScatNet(args.dataset, type_, bias)
init = lambda x: net_init(x, std)
self.model.apply(init)
# Split across GPUs
if torch.cuda.device_count() > 1 and args.num_gpus > 1:
self.model = nn.DataParallel(self.model)
model = self.model.module
else:
model = self.model
if self.use_cuda:
self.model.cuda()
# ######################################################################
# Build the optimizer - use separate parameter groups for the invariant
# and convolutional layers
default_params = list(model.fc1.parameters())
inv_params = []
for name, module in model.net.named_children():
params = [p for p in module.parameters() if p.requires_grad]
if name.startswith('inv'):
inv_params += params
else:
default_params += params
self.optimizer, self.scheduler = optim.get_optim(
'sgd',
default_params,
init_lr=lr,
steps=args.steps,
wd=wd,
gamma=0.2,
momentum=mom,
max_epochs=args.epochs)
if len(inv_params) > 0:
# Get special optimizer parameters
lr1 = config.get('lr1', lr)
gamma1 = config.get('gamma1', 0.2)
mom1 = config.get('mom1', mom)
wd1 = config.get('wd1', wd)
self.optimizer1, self.scheduler1 = optim.get_optim(
'sgd',
inv_params,
init_lr=lr1,
steps=args.steps,
wd=wd1,
gamma=gamma1,
momentum=mom1,
max_epochs=args.epochs)
if self.verbose:
print(self.model)
def _setup(self, config):
args = config.pop("args")
vars(args).update(config)
type_ = config.get('type')
dataset = config.get('dataset', args.dataset)
num_gpus = config.get('num_gpus', args.num_gpus)
if hasattr(args, 'verbose'):
self._verbose = args.verbose
num_workers = 4
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
num_workers = 0
if self.use_cuda:
torch.cuda.manual_seed(args.seed)
else:
args.seed = random.randint(0, 10000)
# ######################################################################
# Data
kwargs = {
'num_workers': num_workers,
'pin_memory': True
} if self.use_cuda else {}
if dataset.startswith('cifar'):
self.train_loader, self.test_loader = cifar.get_data(
32,
args.datadir,
dataset=dataset,
batch_size=args.batch_size,
trainsize=args.trainsize,
**kwargs)
# θ = (0.5, 0.9, 1e-4, 1.5)
θ = (0.5, 0.85, 1e-4, 1.5)
elif dataset == 'tiny_imagenet':
self.train_loader, self.test_loader = tiny_imagenet.get_data(
64,
args.datadir,
val_only=False,
batch_size=args.batch_size,
trainsize=args.trainsize,
distributed=False,
**kwargs)
# θ = (0.2, 0.9, 1e-4, 1.5)
θ = (0.5, 0.85, 8e-5, 1.5)
# ######################################################################
# Build the network based on the type parameter. θ are the optimal
# hyperparameters found by cross validation.
lr, mom, wd, std = θ
# If the parameters were provided as an option, use them
lr = config.get('lr', lr)
mom = config.get('mom', mom)
wd = config.get('wd', wd)
std = config.get('std', std)
bias = config.get('bias', 1e-2)
alpha = config.get('alpha', None)
# drop_p = config.get('drop_p', drop_p)
# Build the network
self.model = ScatNet(dataset, type_, alpha, bias)
init = lambda x: net_init(x, std)
self.model.apply(init)
# Split across GPUs
if torch.cuda.device_count() > 1 and num_gpus > 1:
self.model = nn.DataParallel(self.model)
model = self.model.module
else:
model = self.model
if self.use_cuda:
self.model.cuda()
# ######################################################################
# Build the optimizer - use separate parameter groups for the invariant
# and convolutional layers
params = model.parameters()
self.optimizer, self.scheduler = optim.get_optim(
'sgd',
params,
init_lr=lr,
steps=args.steps,
wd=wd,
gamma=0.2,
momentum=mom,
max_epochs=args.epochs)
if self.verbose:
print(self.model)
def _setup(self, config):
args = config.pop("args")
vars(args).update(config)
# Parameters like learning rate and momentum can be speicified by the
# config search space. If not specified, fall back to the args
lr = config.get('lr', args.lr)
mom = config.get('mom', args.mom)
wd = config.get('wd', args.wd)
C = config.get('num_channels', args.C)
dataset = config.get('dataset', args.dataset)
# Get optimizer parameters for gainlayer
mom1 = config.get('mom1', args.mom1)
lr1 = config.get('lr1', args.lr1)
wd1 = config.get('wd1', args.wd1)
opt1 = config.get('opt1', args.opt1)
gamma1 = config.get('gamma1', 0.2)
if mom1 is None:
mom1 = mom
if lr1 is None:
lr1 = lr
# Get nonlinearity options
pixel_nl = config.get('pixel_nl', args.pixel_nl)
lp_nl = config.get('lp_nl', args.lp_nl)
bp_nl = config.get('bp_nl', args.bp_nl)
lp_q = config.get('lp_q', args.lp_q)
bp_q = config.get('bp_q', args.bp_q)
lp_thresh = config.get('lp_thresh', 1)
bp_thresh = config.get('bp_thresh', 1)
if hasattr(args, 'verbose'):
self._verbose = args.verbose
num_workers = 4
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
num_workers = 0
if self.use_cuda:
torch.cuda.manual_seed(args.seed)
# ######################################################################
# Data
kwargs = {
'num_workers': num_workers,
'pin_memory': True
} if self.use_cuda else {}
if args.dataset.startswith('cifar'):
self.train_loader, self.test_loader = cifar.get_data(
32,
args.datadir,
dataset=dataset,
batch_size=args.batch_size,
trainsize=args.trainsize,
**kwargs)
elif args.dataset == 'tiny_imagenet':
self.train_loader, self.test_loader = tiny_imagenet.get_data(
64,
args.datadir,
val_only=False,
batch_size=args.batch_size,
trainsize=args.trainsize,
distributed=False,
**kwargs)
# ######################################################################
# Build the network based on the type parameter. θ are the optimal
# hyperparameters found by cross validation.
# Build the network
self.model = MixedNet(args.dataset,
C,
wd,
wd1,
pixel_nl=pixel_nl,
lp_nl=lp_nl,
bp_nl=bp_nl,
lp_nl_kwargs=dict(q=lp_q, thresh=lp_thresh),
bp_nl_kwargs=dict(q=bp_q, thresh=bp_thresh))
init = lambda x: net_init(x, 1.0)
self.model.apply(init)
# Split across GPUs
if torch.cuda.device_count() > 1 and config.get('num_gpus', 0) > 1:
self.model = nn.DataParallel(self.model)
model = self.model.module
else:
model = self.model
if self.use_cuda:
self.model.cuda()
# ######################################################################
# Build the optimizer - use separate parameter groups for the gain
# and convolutional layers
default_params = list(model.fc1.parameters())
gain_params = []
for name, module in model.net.named_children():
params = [p for p in module.parameters() if p.requires_grad]
if name.startswith('wave'):
gain_params += params
else:
default_params += params
self.optimizer, self.scheduler = optim.get_optim(
'sgd',
default_params,
init_lr=lr,
steps=args.steps,
wd=0,
gamma=0.2,
momentum=mom,
max_epochs=args.epochs)
if len(gain_params) > 0:
# Do not use the optimizer's weight decay, call a special method to
# do it.
self.optimizer1, self.scheduler1 = optim.get_optim(
opt1,
gain_params,
init_lr=lr1,
steps=args.steps,
wd=0,
gamma=gamma1,
momentum=mom1,
max_epochs=args.epochs)
if self.verbose:
print(self.model)
