4: 'still',
5: 'walking',
6: 'walkinglow'
}
device = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')
data_path = './data/RADAR'
train_data, train_labels, test_data, test_labels = lib.RADARLoader(
data_path, category, device, "")
train_data = lib.tensor_normalize(train_data)
train_labels = lib.tensor_normalize(train_labels)
test_data = lib.tensor_normalize(test_data)
test_labels = lib.tensor_normalize(test_labels)
gen = models.Generator_Conv(input_node_size=num_noise,
output_shape=data_shape).to(device)
disc = models.Discriminator_Conv(input_shape=data_shape).to(device)
lib.init_params(gen)
lib.init_params(disc)
optim_g = torch.optim.Adam(gen.parameters(), lr=1e-3, betas=(0, 0.9))
optim_d = torch.optim.Adam(disc.parameters(), lr=1e-3, betas=(0, 0.9))
for epoch in range(epochs):
gen.train()
disc.train()
for i in range(1):
for _train_data in train_data:
x = _train_data.view(-1, 1, 128, 128)
#======= Training ========
for t in range(num_tasks):
ratio = 1 / (t + 1) # current task's ratio
solver_acc_dict[t] = {}
pre_gen = None
pre_solver = None
if t > 0:
pre_gen = gen
pre_solver = deepcopy(solver.net)
lib.model_grad_switch(pre_gen, False)
lib.model_grad_switch(pre_solver, False)
gen = models.Generator_Conv(input_node_size=num_noise,
output_shape=(1, 28, 28),
hidden_node_size=256,
hidden_node_num=2).to(device)
disc = models.Discriminator_Conv(input_shape=(1, 28, 28)).to(device)
lib.init_params(gen)
lib.init_params(disc)
TrainDataLoader = TrainDataLoaders[t]
optim_g = torch.optim.Adam(gen.parameters(), lr=0.001, betas=(0, 0.9))
optim_d = torch.optim.Adam(disc.parameters(), lr=0.001, betas=(0, 0.9))
# Generator Training
for epoch in range(gen_epochs):
gen.train()
disc.train()