def _synchronize_configs()

in occant_baselines/rl/occant_nav_trainer.py [0:0]

• 46 lines of code
• 6 McCabe index (conditional complexity)

    def _synchronize_configs(self, config):
        r"""Matches configs for different parts of the model as well as the simulator.
        """
        config.defrost()
        config.RL.ANS.PLANNER.nplanners = config.NUM_PROCESSES
        config.RL.ANS.MAPPER.thresh_explored = config.RL.ANS.thresh_explored
        config.RL.ANS.pyt_random_seed = config.PYT_RANDOM_SEED
        config.RL.ANS.OCCUPANCY_ANTICIPATOR.pyt_random_seed = config.PYT_RANDOM_SEED
        # Compute the EGO_PROJECTION options based on the
        # depth sensor information and agent parameters.
        map_size = config.RL.ANS.MAPPER.map_size
        map_scale = config.RL.ANS.MAPPER.map_scale
        min_depth = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.MIN_DEPTH
        max_depth = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.MAX_DEPTH
        hfov = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.HFOV
        width = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.WIDTH
        height = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.HEIGHT
        hfov_rad = np.radians(float(hfov))
        vfov_rad = 2 * np.arctan((height / width) * np.tan(hfov_rad / 2.0))
        vfov = np.degrees(vfov_rad).item()
        camera_height = config.TASK_CONFIG.SIMULATOR.DEPTH_SENSOR.POSITION[1]
        height_thresholds = [0.2, 1.5]
        # Set the EGO_PROJECTION options
        ego_proj_config = config.RL.ANS.OCCUPANCY_ANTICIPATOR.EGO_PROJECTION
        ego_proj_config.local_map_shape = (2, map_size, map_size)
        ego_proj_config.map_scale = map_scale
        ego_proj_config.min_depth = min_depth
        ego_proj_config.max_depth = max_depth
        ego_proj_config.hfov = hfov
        ego_proj_config.vfov = vfov
        ego_proj_config.camera_height = camera_height
        ego_proj_config.height_thresholds = height_thresholds
        config.RL.ANS.OCCUPANCY_ANTICIPATOR.EGO_PROJECTION = ego_proj_config
        # Set the GT anticipation options
        wall_fov = config.RL.ANS.OCCUPANCY_ANTICIPATOR.GP_ANTICIPATION.wall_fov
        config.TASK_CONFIG.TASK.GT_EGO_MAP_ANTICIPATED.WALL_FOV = wall_fov
        config.TASK_CONFIG.TASK.GT_EGO_MAP_ANTICIPATED.MAP_SIZE = map_size
        config.TASK_CONFIG.TASK.GT_EGO_MAP_ANTICIPATED.MAP_SCALE = map_scale
        config.TASK_CONFIG.TASK.GT_EGO_MAP_ANTICIPATED.MAX_SENSOR_RANGE = -1
        # Set the correct image scaling values
        config.RL.ANS.MAPPER.image_scale_hw = config.RL.ANS.image_scale_hw
        config.RL.ANS.LOCAL_POLICY.image_scale_hw = config.RL.ANS.image_scale_hw
        # Set the agent dynamics for the local policy
        config.RL.ANS.LOCAL_POLICY.AGENT_DYNAMICS.forward_step = (
            config.TASK_CONFIG.SIMULATOR.FORWARD_STEP_SIZE
        )
        config.RL.ANS.LOCAL_POLICY.AGENT_DYNAMICS.turn_angle = (
            config.TASK_CONFIG.SIMULATOR.TURN_ANGLE
        )
        # Enable GT anticipation sensor for baseline evaluation
        if config.RL.ANS.OCCUPANCY_ANTICIPATOR.type == "occant_ground_truth":
            if 'GT_EGO_MAP_ANTICIPATED' not in config.TASK_CONFIG.TASK.SENSORS:
                config.TASK_CONFIG.TASK.SENSORS.append('GT_EGO_MAP_ANTICIPATED')

        config.freeze()