success

evaluations/evaluation_methods/eval_delta_pose.py

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+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