FROM nvcr.io/nvidia/pytorch:20.06-py3

models/{onnx_export.py → export.py}

@@ -1,7 +1,7 @@
-"""Exports a pytorch *.pt model to *.onnx format
+"""Exports a YOLOv5 *.pt model to *.onnx and *.torchscript formats
 
 Usage:
-    $ export PYTHONPATH="$PWD" && python models/onnx_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
+    $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
 """
 
 import argparse
@@ -17,27 +17,39 @@ if __name__ == '__main__':
     parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
     parser.add_argument('--batch-size', type=int, default=1, help='batch size')
     opt = parser.parse_args()
-    opt.img_size *= 2 if len(opt.img_size) == 1 else 1
+    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
     print(opt)
 
-    # Parameters
-    f = opt.weights.replace('.pt', '.onnx')  # onnx filename
+    # Input
     img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size, (1, 3, 320, 192) iDetection
 
-    # Load pytorch model
+    # Load PyTorch model
     google_utils.attempt_download(opt.weights)
     model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
     model.eval()
-    model.fuse()
-
-    # Export to onnx
     model.model[-1].export = True  # set Detect() layer export=True
     _ = model(img)  # dry run
-    torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'],
-                      output_names=['output'])  # output_names=['classes', 'boxes']
-
-    # Check onnx model
-    model = onnx.load(f)  # load onnx model
-    onnx.checker.check_model(model)  # check onnx model
-    print(onnx.helper.printable_graph(model.graph))  # print a human readable representation of the graph
-    print('Export complete. ONNX model saved to %s\nView with https://github.com/lutzroeder/netron' % f)
+
+    # Export to torchscript
+    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.')
+
+    # Export to ONNX
+    try:
+        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'],
+                          output_names=['output'])  # output_names=['classes', 'boxes']
+
+        # Checks
+        onnx_model = onnx.load(f)  # load onnx model
+        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.')

models/torchscript_export.py

@@ -1,38 +0,0 @@
-"""Exports a pytorch *.pt model to *.torchscript format
-
-Usage:
-    $ export PYTHONPATH="$PWD" && python models/torchscript_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
-"""
-
-import argparse
-
-
-from models.common import *
-from utils import google_utils
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    opt = parser.parse_args()
-    print(opt)
-
-    # Parameters
-    f = opt.weights.replace('.pt', '.torchscript')  # onnx filename
-    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)
-    model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
-    model.eval()
-
-    # Don't fuse layers, it won't work with torchscript exports
-    #model.fuse()
-
-    # Export to jit/torchscript
-    model.model[-1].export = True  # set Detect() layer export=True
-    _ = model(img)  # dry run
-
-    traced_script_module = torch.jit.trace(model, img)
-    traced_script_module.save(f)