support re-rank for HID

lib/utils/evaluation.py

@@ -40,7 +40,6 @@ def de_diag(acc, each_angle=False):
 
 def identification(data, dataset, metric='euc'):
     msg_mgr = get_msg_mgr()
-    
     feature, label, seq_type, view = data['embeddings'], data['labels'], data['types'], data['views']
     label = np.array(label)
     view_list = list(set(view))
@@ -158,8 +157,12 @@ def evaluate_HID(data, dataset, metric='euc'):
     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()
+
+    feat = np.concatenate([probe_x, gallery_x])
+    dist = cuda_dist(feat, feat, metric).cpu().numpy()
+    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(
@@ -171,3 +174,67 @@ def evaluate_HID(data, dataset, metric='euc'):
             f.write("{},{}\n".format(probe_y[i], gallery_y[idx[i, 0]]))
         print("HID result saved to {}/{}".format(os.getcwd(), save_path))
     return
+
+
+def re_ranking(original_dist, query_num, k1, k2, lambda_value):
+    # Modified from https://github.com/michuanhaohao/reid-strong-baseline/blob/master/utils/re_ranking.py
+    all_num = original_dist.shape[0]
+    original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
+    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]
+        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
+        fi = np.where(backward_k_neigh_index == i)[0]
+        k_reciprocal_index = forward_k_neigh_index[fi]
+        k_reciprocal_expansion_index = k_reciprocal_index
+        for j in range(len(k_reciprocal_index)):
+            candidate = k_reciprocal_index[j]
+            candidate_forward_k_neigh_index = initial_rank[candidate, :int(
+                np.around(k1 / 2)) + 1]
+            candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,
+                                                            :int(np.around(k1 / 2)) + 1]
+            fi_candidate = np.where(
+                candidate_backward_k_neigh_index == candidate)[0]
+            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
+            if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2 / 3 * len(
+                    candidate_k_reciprocal_index):
+                k_reciprocal_expansion_index = np.append(
+                    k_reciprocal_expansion_index, candidate_k_reciprocal_index)
+
+        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
+        weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
+        V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
+    original_dist = original_dist[:query_num, ]
+    if k2 != 1:
+        V_qe = np.zeros_like(V, dtype=np.float16)
+        for i in range(all_num):
+            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
+        V = V_qe
+        del V_qe
+    del initial_rank
+    invIndex = []
+    for i in range(all_num):
+        invIndex.append(np.where(V[:, i] != 0)[0])
+
+    jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
+
+    for i in range(query_num):
+        temp_min = np.zeros(shape=[1, all_num], dtype=np.float16)
+        indNonZero = np.where(V[i, :] != 0)[0]
+        indImages = [invIndex[ind] for ind in indNonZero]
+        for j in range(len(indNonZero)):
+            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
+                                                                               V[indImages[j], indNonZero[j]])
+        jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
+
+    final_dist = jaccard_dist * (1 - lambda_value) + \
+        original_dist * lambda_value
+    del original_dist
+    del V
+    del jaccard_dist
+    final_dist = final_dist[:query_num, query_num:]
+    return final_dist

misc/HID/README.md

@@ -1,6 +1,6 @@
 # HID Tutorial
 ![](http://hid2022.iapr-tc4.org/wp-content/uploads/sites/7/2022/03/%E5%9B%BE%E7%89%871-2.png)
-This is the official support for competition of [Human Identification at a Distance (HID)](http://hid2022.iapr-tc4.org/). We report our result is 68.7% using the baseline model. In order for participants to better start the first step, we provide a tutorial on how to use OpenGait for HID.
+This is the official support for competition of [Human Identification at a Distance (HID)](http://hid2022.iapr-tc4.org/). We report our result of 68.7% using the baseline model and 80.0% with re-ranking. In order for participants to better start the first step, we provide a tutorial on how to use OpenGait for HID.
 
 ## Preprocess the dataset
 Download the raw dataset from the [official link](http://hid2022.iapr-tc4.org/). You will get three compressed files, i.e. `train.tar`, `HID2022_test_gallery.zip` and `HID2022_test_probe.zip`.