diff --git a/lib/utils/evaluation.py b/lib/utils/evaluation.py
index 335bcc8..fde4d17 100644
--- a/lib/utils/evaluation.py
+++ b/lib/utils/evaluation.py
@@ -1,7 +1,9 @@
+import os
+from time import strftime, localtime
 import torch
 import numpy as np
 import torch.nn.functional as F
-from utils import get_msg_mgr
+from utils import get_msg_mgr, mkdir
 
 
 def cuda_dist(x, y, metric='euc'):
@@ -150,16 +152,14 @@ def identification_real_scene(data, dataset, metric='euc'):
     msg_mgr.log_info('%.3f' % (np.mean(acc[19])))
     return {"scalar/test_accuracy/Rank-1": np.mean(acc[0]), "scalar/test_accuracy/Rank-5": np.mean(acc[4])}
 
+
 def identification_GREW_submission(data, dataset, metric='euc'):
-    msg_mgr = get_msg_mgr()
+    get_msg_mgr().log_info("Evaluating GREW")
     feature, label, seq_type, view = data['embeddings'], data['labels'], data['types'], data['views']
     label = np.array(label)
-    view  = np.array(view)
-    gallery_seq_type = {'GREW': ['01','02']}
+    view = np.array(view)
+    gallery_seq_type = {'GREW': ['01', '02']}
     probe_seq_type = {'GREW': ['03']}
-
-    num_rank = 20
-    acc = np.zeros([num_rank]) - 1.
     gseq_mask = np.isin(seq_type, gallery_seq_type[dataset])
     gallery_x = feature[gseq_mask, :]
     gallery_y = label[gseq_mask]
@@ -170,11 +170,9 @@ def identification_GREW_submission(data, dataset, metric='euc'):
     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(
         "GREW_result/"+strftime('%Y-%m%d-%H%M%S', localtime())+".csv")
-    os.makedirs("GREW_result", exist_ok=True)
+    mkdir("GREW_result")
     with open(save_path, "w") as f:
         f.write("videoId,rank1,rank2,rank3,rank4,rank5,rank6,rank7,rank8,rank9,rank10,rank11,rank12,rank13,rank14,rank15,rank16,rank17,rank18,rank19,rank20\n")
         for i in range(len(idx)):
@@ -182,8 +180,8 @@ def identification_GREW_submission(data, dataset, metric='euc'):
             output_row = '{}'+',{}'*20+'\n'
             f.write(output_row.format(probe_y[i], *r_format))
         print("GREW result saved to {}/{}".format(os.getcwd(), save_path))
+    return
 
-    return 
 
 def evaluate_HID(data, dataset, metric='euc'):
     msg_mgr = get_msg_mgr()
@@ -200,14 +198,13 @@ def evaluate_HID(data, dataset, metric='euc'):
 
     feat = np.concatenate([probe_x, gallery_x])
     dist = cuda_dist(feat, feat, metric).cpu().numpy()
+    msg_mgr.log_info("Starting Re-ranking")
     re_rank = re_ranking(dist, probe_x.shape[0], k1=6, k2=6, lambda_value=0.3)
     idx = np.argsort(re_rank, axis=1)
 
-    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)
+    mkdir("HID_result")
     with open(save_path, "w") as f:
         f.write("videoID,label\n")
         for i in range(len(idx)):
@@ -223,7 +220,6 @@ def re_ranking(original_dist, query_num, k1, k2, lambda_value):
     V = np.zeros_like(original_dist).astype(np.float16)
     initial_rank = np.argsort(original_dist).astype(np.int32)
 
-    print('starting re_ranking')
     for i in range(all_num):
         # k-reciprocal neighbors
         forward_k_neigh_index = initial_rank[i, :k1 + 1]