Method save_checkpoint

improved_diffusion/train_util.py:272–281

(rate, params)

    def save(self):
        def save_checkpoint(rate, params):
            state_dict = self._master_params_to_state_dict(params)
            if dist.get_rank() == 0:
                logger.log(f"saving model {rate}...")
                if not rate:
                    filename = f"model{(self.step+self.resume_step):06d}.pt"
                else:
                    filename = f"ema_{rate}_{(self.step+self.resume_step):06d}.pt"
                with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f:
                    th.save(state_dict, f)

        save_checkpoint(0, self.master_params)
        for rate, params in zip(self.ema_rate, self.ema_params):