args = parser.parse_args()
# setup device
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# get the dataset
data = Cifar10Provider(root=args.root, workers=8)
# neuron configure
neurons = {
'relu': nn.ReLU(True),
'leaky_relu': nn.LeakyReLU(negative_slope=args.neg_slope, inplace=True),
'tanh': nn.Tanh(),
'prelu': nn.PReLU(),
'sprelu': sPReLU(),
'selu': nn.SELU(),
'seluv2': SeLUv2(gamma_=np.square(args.gain), fixpoint=args.fixpoint, epsilon_=args.epsilon)
}
# initialization method configure
initializer = Initializer(method=args.init_fn, nonlinearity=args.neuron,
neg_slope=args.neg_slope, manual=args.gain)
# normalization method configure
norm_fns = {
'none': None,
'bn': nn.BatchNorm2d
}
# get the dataset
data = Cifar10Provider(root=args.root, workers=8)
# neuron configure
neurons = {
'relu':
nn.ReLU(True),
'leaky_relu':
nn.LeakyReLU(negative_slope=args.neg_slope, inplace=True),
'tanh':
nn.Tanh(),
'prelu':
nn.PReLU(),
'sprelu':
sPReLU(),
'selu':
nn.SELU(),
'seluv2':
SeLUv2(gamma_=np.square(args.gain),
fixpoint=args.fixpoint,
epsilon_=args.epsilon)
}
# initialization method configure
initializer = Initializer(method=args.init_fn,
nonlinearity=args.neuron,
neg_slope=args.neg_slope,
manual=args.gain)
# normalization method configure