diff options
Diffstat (limited to 'opt.py')
| -rwxr-xr-x | opt.py | 6 |
1 files changed, 1 insertions, 5 deletions
@@ -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]] |