support HID

lib/utils/evaluation.py

@@ -140,3 +140,30 @@ def identification_real_scene(data, dataset, metric='euc'):
     msg_mgr.log_info('==Rank-5==')
     msg_mgr.log_info('%.3f' % (np.mean(acc[4])))
     return {"scalar/test_accuracy/Rank-1": np.mean(acc[0]), "scalar/test_accuracy/Rank-5": np.mean(acc[4])}
+
+
+def evaluate_HID(data, dataset, metric='euc'):
+    msg_mgr = get_msg_mgr()
+    msg_mgr.log_info("Evaluating HID")
+    feature, label, seq_type = data['embeddings'], data['labels'], data['types']
+    label = np.array(label)
+    seq_type = np.array(seq_type)
+    probe_mask = (label == "probe")
+    gallery_mask = (label != "probe")
+    gallery_x = feature[gallery_mask, :]
+    gallery_y = label[gallery_mask]
+    probe_x = feature[probe_mask, :]
+    probe_y = seq_type[probe_mask]
+    dist = cuda_dist(probe_x, gallery_x, metric)
+    idx = dist.cpu().sort(1)[1].numpy()
+    import os
+    from time import strftime, localtime
+    save_path = os.path.join(
+        "HID_result/"+strftime('%Y-%m%d-%H%M%S', localtime())+".csv")
+    os.makedirs("HID_result", exist_ok=True)
+    with open(save_path, "w") as f:
+        f.write("videoID,label\n")
+        for i in range(len(idx)):
+            f.write("{},{}\n".format(probe_y[i], gallery_y[idx[i, 0]]))
+        print("HID result saved to {}/{}".format(os.getcwd(), save_path))
+    return