def __init__(self,
input_size,
input_channels,
feature_dim,
num_classes,
use_l2_norm,
use_dropout,
use_batch_norm,
device='cuda'):
super(SimpleClassifier, self).__init__()
self.input_size = input_size
self.num_classes = num_classes
self.use_l2_norm = use_l2_norm
self.use_dropout = use_dropout
self.use_batch_norm = use_batch_norm
self.input_channels = input_channels
self.feature_dim = feature_dim
self.device = device
self.encoder = Encoder(input_size, input_channels, feature_dim)
final_fc = []
if use_batch_norm:
final_fc.append(nn.BatchNorm1d(feature_dim))
if use_dropout:
final_fc.append(nn.Dropout(0.5))
final_fc.append(nn.Linear(feature_dim, num_classes))
self.final_fc = nn.Sequential(*final_fc)
def __init__(self, input_size, input_channels, feature_dim, pred_steps,
num_class, use_l2_norm, use_dropout, use_batch_norm, device):
super(CPCClassifier, self).__init__()
self.input_size = input_size
self.input_channels = input_channels
self.feature_dim = feature_dim
self.pred_steps = pred_steps
self.device = device
self.use_l2_norm = use_l2_norm
self.use_dropout = use_dropout
self.use_batch_norm = use_batch_norm
self.encoder = Encoder(input_size, input_channels, feature_dim)
# self.agg = nn.GRU(input_size=feature_dim, hidden_size=feature_dim, batch_first=True)
# self.relu = nn.ReLU(inplace=True)
final_fc = []
if use_batch_norm:
final_fc.append(nn.BatchNorm1d(self.feature_size))
if use_dropout:
final_fc.append(nn.Dropout(0.5))
final_fc.append(nn.Linear(feature_dim, num_class))
self.final_fc = nn.Sequential(*final_fc)
def __init__(self, input_size, input_channels, hidden_channels, feature_dim, device='cuda'):
super(TemporalShuffling, self).__init__()
self.input_size = input_size
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.feature_dim = feature_dim
self.device = device
self.encoder = Encoder(input_size, input_channels, feature_dim)
self.linear_head = nn.Linear(2 * feature_dim, 1, bias=True)
def __init__(self, input_size, input_channels, hidden_channels, feature_dim, device='cuda'):
super(RelativePosition, self).__init__()
self.input_size = input_size
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.feature_dim = feature_dim
self.device = device
self.encoder = Encoder(input_size, input_channels, feature_dim)
self.linear_head = nn.Linear(feature_dim, 1, bias=True)
def run(run_id, train_patients, test_patients, args):
print('Train patient ids:', train_patients)
print('Test patient ids:', test_patients)
if args.data_name == 'SEED':
input_size = 200
elif args.data_name == 'DEAP':
input_size = 128
elif args.data_name == 'AMIGOS':
input_size = 128
else:
raise ValueError
encoder = Encoder(input_size=input_size, input_channel=args.input_channel, feature_dim=args.feature_dim)
encoder.cuda(args.device)
discriminator = Discriminator(args.feature_dim, args.device)
discriminator.cuda(args.device)
dataset = TNCDataset()
pretrain(run_id, encoder, discriminator, dataset, args.device, args)
def __init__(self,
input_size,
input_channels,
hidden_channels,
feature_dim,
device='cuda'):
super(TLoss, self).__init__()
self.input_size = input_size
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.feature_dim = feature_dim
self.device = device
self.encoder = Encoder(input_size, input_channels, feature_dim)
def __init__(self, input_size, input_channels, feature_dim, pred_steps, use_temperature, temperature,
device):
super(DPC, self).__init__()
self.input_size = input_size
self.input_channels = input_channels
self.feature_dim = feature_dim
self.pred_steps = pred_steps
self.use_temperature = use_temperature
self.temperature = temperature
self.device = device
self.encoder = Encoder(input_size, input_channels, feature_dim)
self.agg = nn.GRU(input_size=feature_dim, hidden_size=feature_dim, batch_first=True)
self.predictor = nn.Linear(feature_dim, feature_dim)
self.relu = nn.ReLU(inplace=True)
self.targets = None
