def __init__(self, online_network, target_network, conv_trans, TP_1, TP_2,
projection_1, projection_2, predictor, optimizer, device,
config, train_source, test_source, **params):
self.online_network = online_network
self.target_network = target_network
self.conv_trans = conv_trans
self.TP_1 = TP_1
self.TP_2 = TP_2
self.projection_1 = projection_1
self.projection_2 = projection_2
self.optimizer = optimizer
self.device = device
self.predictor = predictor
self.max_epochs = params['max_epochs']
self.writer = SummaryWriter()
self.config = config
self.m = params['m']
#self.batch_size = params['batch_size']
self.batch_size = (4, 16)
self.P, self.M = self.batch_size
#self.num_workers = params['num_workers']
self.num_workers = 0
self.train_source = train_source
self.test_source = test_source
self.checkpoint_interval = params['checkpoint_interval']
_create_model_training_folder(
self.writer,
files_to_same=["./config/config.yaml", "main.py", 'trainer.py'])
self.sample_type = 'all'
self.hard_or_full_trip = config['network']['hard_or_full_trip']
self.margin = config['network']['margin']
self.Gait_list = [self.online_network, self.conv_trans, self.TP_1]
self.Encode_list = [
self.online_network, self.conv_trans, self.TP_1, self.projection_1,
self.predictor
]
self.Target_list = [self.target_network, self.TP_2, self.projection_2]
self.triplet_loss = TripletLoss(self.P * self.M,
self.hard_or_full_trip,
self.margin).float()
self.triplet_loss = nn.DataParallel(self.triplet_loss)
self.triplet_loss.cuda()
self.hard_loss_metric = []
self.full_loss_metric = []
self.full_loss_num = []
self.dist_list = []
self.mean_dist = 0.01
self.niter = config['network']['restore_iter']
def __init__(self, online_network, target_network, predictor, optimizer, device, **params):
self.online_network = online_network
self.target_network = target_network
self.optimizer = optimizer
self.device = device
self.predictor = predictor
self.max_epochs = params['max_epochs']
self.writer = SummaryWriter()
self.m = params['m']
self.batch_size = params['batch_size']
self.num_workers = params['num_workers']
self.checkpoint_interval = params['checkpoint_interval']
_create_model_training_folder(self.writer, files_to_same=["./config/config.yaml", "main.py", 'trainer.py'])
def __init__(self, online_network, target_network, predictor, optimizer,
device, **params):
self.online_network = online_network
self.target_network = target_network
self.optimizer = optimizer
self.device = device
self.predictor = predictor
self.max_epochs = params['max_epochs']
self.writer = SummaryWriter()
self.m = params['m']
self.m_initial = params['m']
self.batch_size = params['batch_size']
self.num_workers = params['num_workers']
self.checkpoint_interval = params['checkpoint_interval']
_create_model_training_folder(
self.writer,
files_to_same=["./config/config.yaml", "main.py", 'trainer.py'])
self.center = None
self.bn = torch.nn.BatchNorm1d(256, affine=False).cuda()
self.C = torch.zeros(1, 256).cuda()
def __init__(self, log_dir, online_network, target_network, predictor,
optimizer, predictor_optimizer, device, **params):
self.online_network = online_network
self.target_network = target_network
self.optimizer = optimizer
self.predictor_optimizer = predictor_optimizer
self.device = device
self.predictor = predictor
self.params = params
self.writer = SummaryWriter(log_dir)
self.rand_pred_n_epoch = params["rand_pred_n_epoch"]
self.rand_pred_n_iter = params["rand_pred_n_iter"]
self.rand_pred_reg = params["rand_pred_reg"]
self.max_epochs = params['max_epochs']
self.m = params['m']
self.noise_blend = params["noise_blend"]
self.save_per_epoch = params["save_per_epoch"]
self.batch_size = params['batch_size']
self.num_workers = params['num_workers']
self.checkpoint_interval = params['checkpoint_interval']
self.target_noise = params['target_noise']
self.predictor_init = params["predictor_init"]
self.predictor_reg = params["predictor_reg"]
self.predictor_eig = params["predictor_eig"]
self.predictor_freq = params["predictor_freq"]
self.predictor_rank = params["predictor_rank"]
self.predictor_eps = params["predictor_eps"]
self.dyn_time = params["dyn_time"]
self.dyn_zero_mean = params["dyn_zero_mean"]
self.dyn_reg = params["dyn_reg"]
self.dyn_noise = params["dyn_noise"]
self.dyn_lambda = params["dyn_lambda"]
self.dyn_sym = params["dyn_sym"]
self.dyn_psd = params["dyn_psd"]
self.dyn_eps = params["dyn_eps"]
self.dyn_eps_inside = params["dyn_eps_inside"]
self.dyn_bn = params["dyn_bn"]
self.dyn_convert = params["dyn_convert"]
self.dyn_diagonalize = params["dyn_diagonalize"]
self.balance_type = params["balance_type"]
self.evaluator = params["evaluator"]
self.solve_direction = params["solve_direction"]
self.corr_collect = params["corr_collect"]
self.n_corr = params["n_corr"]
self.use_l2_normalization = params["use_l2_normalization"]
self.predictor_wd = params["predictor_wd"]
self.init_rand_pred = params["init_rand_pred"]
_create_model_training_folder(self.writer,
files_to_same=[
"./config/byol_config.yaml",
"main.py", 'byol_trainer.py',
"./models/mlp_head.py"
])
self.predictor_signaling = False
self.predictor_signaling_2 = False
self.cum_corr = Accumulator(dyn_lambda=self.dyn_lambda)
self.cum_cross_corr = Accumulator(dyn_lambda=self.dyn_lambda)
self.cum_mean1 = Accumulator(dyn_lambda=self.dyn_lambda)
self.cum_mean2 = Accumulator(dyn_lambda=self.dyn_lambda)
if self.dyn_noise is not None:
self.skew = torch.randn(128, 128).to(device=device)
self.skew = (self.skew - self.skew.t()) * self.dyn_noise
if self.predictor_reg == "partition":
# random partition.
self.partition_w = torch.randn(128, self.n_corr).to(device=device)
# accumulate according to random partitions.
self.cum_corrs_pos = [
Accumulator(dyn_lambda=self.dyn_lambda)
for i in range(self.n_corr)
]
self.cum_corrs_neg = [
Accumulator(dyn_lambda=self.dyn_lambda)
for i in range(self.n_corr)
]
self.counts_pos = [0 for i in range(self.n_corr)]
self.counts_neg = [0 for i in range(self.n_corr)]
