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