78 lines
3.5 KiB
Python
78 lines
3.5 KiB
Python
import torch
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from utils.pose import PoseUtil
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import PytorchBoot.stereotype as stereotype
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import PytorchBoot.namespace as namespace
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def get_view_data(cam_pose, scene_name):
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pass
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@stereotype.evaluation_method("pose_diff", comment="not tested")
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class PoseDiff:
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def __init__(self, _):
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pass
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def evaluate(self, output_list, data_list):
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results = {namespace.TensorBoard.SCALAR: {}}
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rot_angle_list = []
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trans_dist_list = []
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for output, data in zip(output_list, data_list):
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gt_pose_9d = data['nbv_cam_pose']
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pred_pose_9d = output['pred_pose_9d']
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gt_rot_6d = gt_pose_9d[:, :6]
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gt_trans = gt_pose_9d[:, 6:]
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pred_rot_6d = pred_pose_9d[:, :6]
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pred_trans = pred_pose_9d[:, 6:]
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gt_rot_mat = PoseUtil.rotation_6d_to_matrix_tensor_batch(gt_rot_6d)
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pred_rot_mat = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_rot_6d)
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rotation_angles = PoseUtil.rotation_angle_distance(gt_rot_mat, pred_rot_mat)
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rot_angle_list.extend(list(rotation_angles))
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trans_dist = torch.norm(gt_trans-pred_trans)
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trans_dist_list.append(trans_dist)
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results[namespace.TensorBoard.SCALAR]["rot_diff"] = float(sum(rot_angle_list) / len(rot_angle_list))
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results[namespace.TensorBoard.SCALAR]["trans_diff"] = float(sum(trans_dist_list) / len(trans_dist_list))
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return results
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@stereotype.evaluation_method("coverage_rate_increase",comment="unfinished")
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class ConverageRateIncrease:
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def __init__(self, config):
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self.config = config
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def evaluate(self, output_list, data_list):
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results = {namespace.TensorBoard.SCALAR: {}}
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gt_coverate_increase_list = []
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pred_coverate_increase_list = []
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cr_diff_list = []
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for output, data in zip(output_list, data_list):
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scanned_cr = data['scanned_coverages_rate']
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gt_cr = data["nbv_coverage_rate"]
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scene_name_list = data['scene_name']
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scanned_view_pts_list = data['scanned_views_pts']
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pred_pose_9ds = output['pred_pose_9d']
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pred_rot_mats = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9ds[:, :6])
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pred_pose_mats = torch.cat([pred_rot_mats, pred_pose_9ds[:, 6:]], dim=-1)
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for idx in range(len(scanned_cr)):
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gt_coverate_increase_list.append(gt_cr-scanned_cr[idx])
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scene_name = scene_name_list[idx]
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pred_pose = pred_pose_mats[idx]
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scanned_view_pts = scanned_view_pts_list[idx]
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view_data = get_view_data(pred_pose, scene_name)
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pred_cr = self.compute_coverage_rate(pred_pose, scanned_view_pts, view_data)
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pred_coverate_increase_list.append(pred_cr-scanned_cr[idx])
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cr_diff_list.append(gt_cr-pred_cr)
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results[namespace.TensorBoard.SCALAR]["gt_cr_increase"] = float(sum(gt_coverate_increase_list) / len(gt_coverate_increase_list))
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results[namespace.TensorBoard.SCALAR]["pred_cr_increase"] = float(sum(pred_coverate_increase_list) / len(pred_coverate_increase_list))
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results[namespace.TensorBoard.SCALAR]["cr_diff"] = float(sum(cr_diff_list) / len(cr_diff_list))
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return results
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def compute_coverage_rate(self, pred_pose, scanned_view_pts, view_data):
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pass
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