ss_baselines/av_nav/ppo/ppo_trainer.py [363:441]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                        )
                    )

                    logger.info(
                        "update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
                        "frames: {}".format(
                            update, env_time, pth_time, count_steps
                        )
                    )

                    window_rewards = (
                        window_episode_reward[-1] - window_episode_reward[0]
                    ).sum()
                    window_counts = (
                        window_episode_counts[-1] - window_episode_counts[0]
                    ).sum()

                    if window_counts > 0:
                        logger.info(
                            "Average window size {} reward: {:3f}".format(
                                len(window_episode_reward),
                                (window_rewards / window_counts).item(),
                            )
                        )
                    else:
                        logger.info("No episodes finish in current window")

                # checkpoint model
                if update % self.config.CHECKPOINT_INTERVAL == 0:
                    self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
                    count_checkpoints += 1

            self.envs.close()

    def _eval_checkpoint(
        self,
        checkpoint_path: str,
        writer: TensorboardWriter,
        checkpoint_index: int = 0
    ) -> Dict:
        r"""Evaluates a single checkpoint.

        Args:
            checkpoint_path: path of checkpoint
            writer: tensorboard writer object for logging to tensorboard
            checkpoint_index: index of cur checkpoint for logging

        Returns:
            None
        """
        random.seed(self.config.SEED)
        np.random.seed(self.config.SEED)
        torch.manual_seed(self.config.SEED)
            
        # Map location CPU is almost always better than mapping to a CUDA device.
        ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")

        if self.config.EVAL.USE_CKPT_CONFIG:
            config = self._setup_eval_config(ckpt_dict["config"])
        else:
            config = self.config.clone()

        ppo_cfg = config.RL.PPO

        config.defrost()
        config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT
        if self.config.DISPLAY_RESOLUTION != config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH:
            model_resolution = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH
            config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH = config.TASK_CONFIG.SIMULATOR.RGB_SENSOR.HEIGHT = \
                config.TASK_CONFIG.SIMULATOR.RGB_SENSOR.WIDTH = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.HEIGHT = \
                self.config.DISPLAY_RESOLUTION
        else:
            model_resolution = self.config.DISPLAY_RESOLUTION
        config.freeze()

        if len(self.config.VIDEO_OPTION) > 0:
            config.defrost()
            config.TASK_CONFIG.TASK.MEASUREMENTS.append("TOP_DOWN_MAP")
            config.TASK_CONFIG.TASK.MEASUREMENTS.append("COLLISIONS")
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ss_baselines/av_wan/ppo/ppo_trainer.py [415:493]:
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                        )
                    )

                    logger.info(
                        "update: {}\tenv-time: {:.3f}s\tpth-time: {:.3f}s\t"
                        "frames: {}".format(
                            update, env_time, pth_time, count_steps
                        )
                    )

                    window_rewards = (
                            window_episode_reward[-1] - window_episode_reward[0]
                    ).sum()
                    window_counts = (
                            window_episode_counts[-1] - window_episode_counts[0]
                    ).sum()

                    if window_counts > 0:
                        logger.info(
                            "Average window size {} reward: {:3f}".format(
                                len(window_episode_reward),
                                (window_rewards / window_counts).item(),
                            )
                        )
                    else:
                        logger.info("No episodes finish in current window")

                # checkpoint model
                if update % self.config.CHECKPOINT_INTERVAL == 0:
                    self.save_checkpoint(f"ckpt.{count_checkpoints}.pth")
                    count_checkpoints += 1

            self.envs.close()

    def _eval_checkpoint(
            self,
            checkpoint_path: str,
            writer: TensorboardWriter,
            checkpoint_index: int = 0
    ) -> Dict:
        r"""Evaluates a single checkpoint.

        Args:
            checkpoint_path: path of checkpoint
            writer: tensorboard writer object for logging to tensorboard
            checkpoint_index: index of cur checkpoint for logging

        Returns:
            None
        """
        random.seed(self.config.SEED)
        np.random.seed(self.config.SEED)
        torch.manual_seed(self.config.SEED)

        # Map location CPU is almost always better than mapping to a CUDA device.
        ckpt_dict = self.load_checkpoint(checkpoint_path, map_location="cpu")

        if self.config.EVAL.USE_CKPT_CONFIG:
            config = self._setup_eval_config(ckpt_dict["config"])
        else:
            config = self.config.clone()

        ppo_cfg = config.RL.PPO

        config.defrost()
        config.TASK_CONFIG.DATASET.SPLIT = config.EVAL.SPLIT
        if self.config.DISPLAY_RESOLUTION != config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH:
            model_resolution = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH
            config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH = config.TASK_CONFIG.SIMULATOR.RGB_SENSOR.HEIGHT = \
                config.TASK_CONFIG.SIMULATOR.RGB_SENSOR.WIDTH = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.HEIGHT = \
                self.config.DISPLAY_RESOLUTION
        else:
            model_resolution = self.config.DISPLAY_RESOLUTION
        config.freeze()

        if len(self.config.VIDEO_OPTION) > 0:
            config.defrost()
            config.TASK_CONFIG.TASK.MEASUREMENTS.append("TOP_DOWN_MAP")
            config.TASK_CONFIG.TASK.MEASUREMENTS.append("COLLISIONS")
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