Rewrite some paper

1 files changed, 1 insertions, 5 deletions

diff --git a/opt.py b/opt.py
index ee63cc0..29acea4 100755
--- a/opt.py
+++ b/opt.py
@@ -87,7 +87,6 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                                MemorySave = False, Minibatch = 2000)
     else:
         if args.mahalanobis:
-            # metric = 'jaccard' is also valid
             cov_inv = np.linalg.inv(np.cov(gallery_data.T))
             distances = np.zeros((probe_data.shape[0], gallery_data.shape[0]))
             for i in range(int(probe_data.shape[0]/10)):
@@ -118,7 +117,7 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                   probe_label[probe_idx] == gallery_label[row[n]]):
                     n += 1
                 nneighbors[probe_idx][q] = gallery_label[row[n]]
-                nnshowrank[probe_idx][q] = showfiles_train[row[n]] #
+                nnshowrank[probe_idx][q] = showfiles_train[row[n]] 
                 q += 1
                 n += 1
 
@@ -160,10 +159,8 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
             for i in range(probe_label.shape[0]):
                 for j in range(11):
                     max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))])
-                    #print(mAP[i])
             for i in range(probe_label.shape[0]):
                 mAP[i] = sum(max_level_precision[i])/11
-                #mAP[i] = sum(precision[i])/args.neighbors
             print('mAP:',np.mean(mAP))
             return np.mean(mAP)
         
@@ -177,7 +174,6 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
         labs = labels[train_idx].reshape((labels[train_idx].shape[0],1))
         tt = np.hstack((train_idx, cam)) 
         train, test, train_label, test_label = train_test_split(tt, labs, test_size=0.3, random_state=0)
-        #to make it smaller we do a double split
         del labs
         del cam
         train_data = feature_vectors[train[:,0]]