27 lines
1.1 KiB
Python
Executable File
27 lines
1.1 KiB
Python
Executable File
import torch
|
|
import annotations.stereotype as stereotype
|
|
from utils.pose_util import PoseUtil
|
|
|
|
|
|
@stereotype.evaluation_method("delta_pose")
|
|
def evaluate(output_list, data_list):
|
|
results = {"scalars": {}}
|
|
rot_angle_list = []
|
|
for output, data in zip(output_list, data_list):
|
|
gt_delta_rot_6d = data['delta_rot_6d']
|
|
est_delta_rot_6d = output['estimated_delta_rot_6d']
|
|
gt_delta_rot_mat = PoseUtil.rotation_6d_to_matrix_tensor_batch(gt_delta_rot_6d)
|
|
est_delta_rot_mat = PoseUtil.rotation_6d_to_matrix_tensor_batch(est_delta_rot_6d)
|
|
rotation_angles = rotation_angle_distance(gt_delta_rot_mat, est_delta_rot_mat)
|
|
rot_angle_list.extend(list(rotation_angles))
|
|
|
|
results["scalars"]["delta_rotation"] = float(sum(rot_angle_list) / len(rot_angle_list))
|
|
return results
|
|
|
|
|
|
def rotation_angle_distance(R1, R2):
|
|
R = torch.matmul(R1, R2.transpose(1, 2))
|
|
trace = torch.diagonal(R, dim1=1, dim2=2).sum(-1)
|
|
angle = torch.acos(torch.clamp((trace - 1) / 2, -1.0, 1.0))/torch.pi*180
|
|
return angle
|