add delta_pose_diff

core/evaluation.py

@@ -1,13 +1,25 @@
-import torch
+from utils.pose import PoseUtil
 import PytorchBoot.stereotype as stereotype
+import PytorchBoot.namespace as namespace
 
-@stereotype.evaluation_method("delta_pose_diff", comment="unfinished")
+@stereotype.evaluation_method("delta_pose_diff")
 class DeltaPoseDiff:
-    def __init__(self, config):
+    def __init__(self, _):
         pass
-
     def evaluate(self, output_list, data_list):
-        return
+        results = {namespace.TensorBoard.SCALAR: {}}
+        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 = PoseUtil.rotation_angle_distance(gt_delta_rot_mat, est_delta_rot_mat)
+            rot_angle_list.extend(list(rotation_angles))
+
+        results[namespace.TensorBoard.SCALAR]["delta_rotation"] = float(sum(rot_angle_list) / len(rot_angle_list))
+        return results
+    
 
 
 @stereotype.evaluation_method("coverage_rate_increase",comment="unfinished")

utils/pose.py

@@ -221,6 +221,13 @@ class PoseUtil:
     def matrix_to_rotation_6d_numpy(matrix):
         return np.copy(matrix[:2, :]).reshape((6,))
     
+    @staticmethod
+    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
+
 
 """ ------------ Debug ------------ """