Merge remote-tracking branch 'origin/master'

detect.py

@@ -158,7 +158,7 @@ if __name__ == '__main__':
     with torch.no_grad():
         detect()
 
-        # Update all models
+        # # Update all models
         # for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
-        #    detect()
-        #    create_pretrained(opt.weights, opt.weights)
+        #     detect()
+        #     create_pretrained(opt.weights, opt.weights)

models/export.py

@@ -1,4 +1,4 @@
-"""Exports a YOLOv5 *.pt model to *.onnx and *.torchscript formats
+"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
 
 Usage:
     $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
@@ -6,8 +6,6 @@ Usage:
 
 import argparse
 
-import onnx
-
 from models.common import *
 from utils import google_utils
 
@@ -21,7 +19,7 @@ if __name__ == '__main__':
     print(opt)
 
     # Input
-    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size, (1, 3, 320, 192) iDetection
+    img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection
 
     # Load PyTorch model
     google_utils.attempt_download(opt.weights)
@@ -30,20 +28,22 @@ if __name__ == '__main__':
     model.model[-1].export = True  # set Detect() layer export=True
     _ = model(img)  # dry run
 
-    # Export to torchscript
+    # TorchScript export
     try:
         f = opt.weights.replace('.pt', '.torchscript')  # filename
         ts = torch.jit.trace(model, img)
         ts.save(f)
-        print('Torchscript export success, saved as %s' % f)
-    except:
-        print('Torchscript export failed.')
+        print('TorchScript export success, saved as %s' % f)
+    except Exception as e:
+        print('TorchScript export failed: %s' % e)
 
-    # Export to ONNX
+    # ONNX export
     try:
+        import onnx
+
         f = opt.weights.replace('.pt', '.onnx')  # filename
         model.fuse()  # only for ONNX
-        torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'],
+        torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
                           output_names=['output'])  # output_names=['classes', 'boxes']
 
         # Checks
@@ -51,5 +51,5 @@ if __name__ == '__main__':
         onnx.checker.check_model(onnx_model)  # check onnx model
         print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable representation of the graph
         print('ONNX export success, saved as %s\nView with https://github.com/lutzroeder/netron' % f)
-    except:
-        print('ONNX export failed.')
+    except Exception as e:
+        print('ONNX export failed: %s' % e)