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()