Update Evaluate.py

Evaluate.py

@@ -32,18 +32,15 @@
 parser.add_argument('--gpus', nargs='+', type=str, help='gpus')
 parser.add_argument('--batch_size', type=int, default=4, help='batch size for training')
 parser.add_argument('--test_batch_size', type=int, default=1, help='batch size for test')
-parser.add_argument('--epochs', type=int, default=60, help='number of epochs for training')
-parser.add_argument('--loss_compact', type=float, default=0.01, help='weight of the feature compactness loss')
-parser.add_argument('--loss_separate', type=float, default=0.01, help='weight of the feature separateness loss')
 parser.add_argument('--h', type=int, default=256, help='height of input images')
 parser.add_argument('--w', type=int, default=256, help='width of input images')
 parser.add_argument('--c', type=int, default=3, help='channel of input images')
-parser.add_argument('--lr', type=float, default=2e-4, help='initial learning rate')
 parser.add_argument('--t_length', type=int, default=5, help='length of the frame sequences')
 parser.add_argument('--fdim', type=int, default=512, help='channel dimension of the features')
 parser.add_argument('--mdim', type=int, default=512, help='channel dimension of the memory items')
 parser.add_argument('--msize', type=int, default=10, help='number of the memory items')
-parser.add_argument('--alpha', type=float, default=0.7, help='weight for the anomality score')
+parser.add_argument('--alpha', type=float, default=0.6, help='weight for the anomality score')
+parser.add_argument('--th', type=float, default=0.01, help='threshold for test updating')
 parser.add_argument('--num_workers', type=int, default=2, help='number of workers for the train loader')
 parser.add_argument('--num_workers_test', type=int, default=1, help='number of workers for the test loader')
 parser.add_argument('--dataset_type', type=str, default='ped2', help='type of dataset: ped2, avenue, shanghai')
@@ -77,6 +74,7 @@
 test_batch = data.DataLoader(test_dataset, batch_size = args.test_batch_size, 
                              shuffle=False, num_workers=args.num_workers_test, drop_last=False)
 
+loss_func_mse = nn.MSELoss(reduction='none')
 
 # Loading the trained model
 model = torch.load(args.model_dir)
@@ -102,7 +100,7 @@
 print('Evaluation of', args.dataset_type)
 
 # Setting for video anomaly detection
-for video in sorted(videos_new):
+for video in sorted(videos_list):
     video_name = video.split('/')[-1]
     labels_list = np.append(labels_list, labels[0][4+label_length:videos[video_name]['length']+label_length])
     label_length += videos[video_name]['length']
@@ -131,7 +129,7 @@
     # Calculating the threshold for updating at the test time
     point_sc = point_score(outputs, imgs[:,3*4:])
 
-    if  point_sc < threshold:
+    if  point_sc < args.th:
         query = F.normalize(feas, dim=1)
         query = query.permute(0,2,3,1) # b X h X w X d
         m_items_test = model.memory.update(query, m_items_test, False)
@@ -142,7 +140,7 @@
 
 # Measuring the abnormality score and the AUC
 anomaly_score_total_list = []
-for video in sorted(videos_new):
+for video in sorted(videos_list):
     video_name = video.split('/')[-1]
     anomaly_score_total_list += score_sum(anomaly_score_list(psnr_list[video_name]), 
                                      anomaly_score_list_inv(feature_distance_list[video_name]), args.alpha)