Create evaluate.py

evaluate.py

@@ -0,0 +1,16 @@
+from datasets import load_dataset
+import numpy as np
+import torch
+from pytorch3d.ops import box3d_overlap
+
+def run_evaluation(pred_path):
+    pred_boxes = np.load(pred_path)[:, :7]
+    pred_boxes = torch.tensor(pred_boxes).float().unsqueeze(0)
+
+    dataset = load_dataset("yourname/3d-iou-challenge-data", split="test")
+    gt_boxes = torch.tensor(dataset[0]["boxes"]).float().unsqueeze(0)
+
+    iou_matrix, _ = box3d_overlap(pred_boxes, gt_boxes)
+    iou = iou_matrix.diagonal(dim1=1, dim2=2).mean()
+
+    return float(iou)