def __init__(self,
num_classes=10,
in_features=256,
weights_init_path=None):
super(AlexNet, self).__init__()
self.num_classes = num_classes
self.in_features = in_features
self.num_channels = 3
self.image_size = 28
self.name = 'AlexNet'
self.dropout_keep_prob = 0.5
self.setup_net()
# if centroid:
# self.centroid = nn.Embedding(num_cls, self.out_dim)
if weights_init_path is not None:
init_weights(self)
self.load(weights_init_path)
else:
init_weights(self)
if self.in_features != 0:
self.fc9.weight.data.normal_(0, 0.005).clamp_(min=-0.01, max=0.01)
self.fc9.bias.data.fill_(0.1)
else:
self.fc8.weight.data.normal_(0, 0.005).clamp_(min=-0.01, max=0.01)
self.fc8.bias.data.fill_(0.1)
def __init__(self, in_features):
super(DigitDiscriminator, self).__init__()
self.in_features = in_features
self.discriminator = nn.Sequential(
nn.Linear(self.in_features, 500),
nn.ReLU(),
nn.Linear(500, 500),
nn.ReLU(),
nn.Linear(500, 1)
)
init_weights(self)
def __init__(self, num_classes=10, weights_init_path=None, in_features=0):
super(LeNet, self).__init__()
self.num_classes = num_classes
self.in_features = in_features
self.num_channels = 1
self.image_size = 28
self.name = 'LeNet'
self.setup_net()
if weights_init_path is not None:
init_weights(self)
self.load(weights_init_path)
else:
init_weights(self)
def __init__(self, num_classes=10, weights_init_path=None, in_features=0, num_domains=2, mmd=False):
super(DSBNLeNet, self).__init__()
self.num_classes = num_classes
self.in_features = in_features
self.num_channels = 1
self.image_size = 28
self.num_domains = num_domains
self.mmd = mmd
self.name = 'DSBNLeNet'
self.setup_net()
if weights_init_path is not None:
init_weights(self)
self.load(weights_init_path)
else:
init_weights(self)
def load(self, init_path):
net_init_dict = torch.load(init_path)
init_weights(self)
updated_state_dict = self.state_dict()
print('load {} params.'.format(init_path))
for k, v in updated_state_dict.items():
if k in net_init_dict:
if v.shape == net_init_dict[k].shape:
updated_state_dict[k] = net_init_dict[k]
else:
print(
"{0} params' shape not the same as pretrained params. Initialize with default settings.".format(
k))
else:
print("{0} params does not exist. Initialize with default settings.".format(k))
self.load_state_dict(updated_state_dict)
def __init__(self, in_features):
super(CPUADiscriminator, self).__init__()
self.in_features = in_features
self.discriminator = nn.Sequential(
nn.Linear(self.in_features, 1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Linear(1024, 1)
)
init_weights(self)
last_layer = self.discriminator[4]
last_layer.weight.data.normal_(0, 0.3).clamp_(min=-0.6, max=0.6)
last_layer.bias.data.zero_()
def __init__(self, num_classes, in_features):
super(ProjectionDiscriminator, self).__init__()
self.num_classes = num_classes
self.in_features = in_features
self.fc1 = nn.Linear(in_features, in_features)
self.fc2 = nn.Linear(in_features, in_features)
self.embed = nn.Embedding(num_classes, in_features)
self.linear = nn.Linear(in_features, 1)
# self.fc1 = spectral_norm(nn.Linear(in_features, in_features))
# self.fc2 = spectral_norm(nn.Linear(in_features, in_features))
# self.embed = spectral_norm(nn.Embedding(num_classes, in_features))
# self.linear = spectral_norm(nn.Linear(in_features, 1))
init_weights(self)
last_layer = self.linear
last_layer.weight.data.normal_(0, 0.3).clamp_(min=-0.6, max=0.6)
last_layer.bias.data.zero_()