Merge remote-tracking branch 'origin/master'

.github/workflows/greetings.yml

@@ -16,7 +16,7 @@ jobs:
           <a href="https://apps.apple.com/app/id1452689527" target="_blank">
           <img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="800"></a>
 
-          <img src="https://user-images.githubusercontent.com/26833433/84200349-729f2680-aa5b-11ea-8f9a-604c9e01a658.png" width="800">
+          <img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="800">
 
           To continue with this repo, please visit our [Custom Training Tutorial](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) to get started, and see our [Google Colab Notebook](https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb), [Docker Image](https://hub.docker.com/r/ultralytics/yolov3), and [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) for example environments.
             

models.py

@@ -438,7 +438,7 @@ def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
 
         target = weights.rsplit('.', 1)[0] + '.pt'
         torch.save(chkpt, target)
-        print("Success: converted '%s' to 's%'" % (weights, target))
+        print("Success: converted '%s' to '%s'" % (weights, target))
 
     else:
         print('Error: extension not supported.')

requirements.txt

@@ -3,6 +3,7 @@
 numpy == 1.17
 opencv-python >= 4.1
 torch >= 1.5
+torchvision
 matplotlib
 pycocotools
 tqdm

train.py

@@ -144,6 +144,15 @@ def train(hyp):
         # possible weights are '*.weights', 'yolov3-tiny.conv.15',  'darknet53.conv.74' etc.
         load_darknet_weights(model, weights)
     
+    if opt.freeze_layers:                                                                                                                                                            
+        output_layer_indices = [idx - 1 for idx, module in enumerate(model.module_list) if isinstance(module, YOLOLayer)]                                                                                                                      
+        freeze_layer_indices = [x for x in range(len(model.module_list)) if                                                                                                         
+                                (x not in output_layer_indices) and                                                                                                               
+                                (x - 1 not in output_layer_indices)]                                                                                                                 
+        for idx in freeze_layer_indices:                                                                                                                                             
+            for parameter in model.module_list[idx].parameters():                                                                                                                    
+                parameter.requires_grad_(False)                                                                                                                                      
+
     # Mixed precision training https://github.com/NVIDIA/apex
     if mixed_precision:
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
@@ -394,6 +403,7 @@ if __name__ == '__main__':
     parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
     parser.add_argument('--adam', action='store_true', help='use adam optimizer')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
+    parser.add_argument('--freeze-layers', action='store_true', help='Freeze non-output layers')  
     opt = parser.parse_args()
     opt.weights = last if opt.resume else opt.weights
     check_git_status()