def get_evo_temp(cur_step):
"""randomly get different temperature according to current adversarial step"""
mu_temp_type = cfg.mu_temp.split()
all_temp = list()
# all_temp.append(get_fixed_temperature(1.0, 0, 0, 'no')) # temp=1.0
all_temp.append(
get_fixed_temperature(cfg.temperature, cur_step,
cfg.ADV_train_epoch,
random.choice(mu_temp_type))) # current step
all_temp.append(
get_fixed_temperature(cfg.temperature,
cur_step + cfg.evo_temp_step,
cfg.ADV_train_epoch,
random.choice(mu_temp_type)))
if cur_step > cfg.evo_temp_step:
all_temp.append(
get_fixed_temperature(cfg.temperature,
cur_step - cfg.evo_temp_step,
cfg.ADV_train_epoch,
random.choice(mu_temp_type)))
return torch.Tensor(all_temp) # three temp
def update_temperature(self, i, N):
self.gen.temperature = get_fixed_temperature(cfg.temperature, i, N, cfg.temp_adpt)
def update_temperature(self, i, N):
self.gen.temperature.data = torch.Tensor([get_fixed_temperature(cfg.temperature, i, N, cfg.temp_adpt)])
if cfg.CUDA:
self.gen.temperature.data = self.gen.temperature.data.cuda()
