Refactor `export.py` (#4080)

* Refactor `export.py`

* cleanup

* Update check_requirements()

* Update export.py

export.py

@@ -24,6 +24,78 @@ from utils.general import colorstr, check_img_size, check_requirements, file_siz
 from utils.torch_utils import select_device
 
 
+def export_torchscript(model, img, file, optimize):
+    # TorchScript model export
+    prefix = colorstr('TorchScript:')
+    try:
+        print(f'\n{prefix} starting export with torch {torch.__version__}...')
+        f = file.with_suffix('.torchscript.pt')
+        ts = torch.jit.trace(model, img, strict=False)
+        (optimize_for_mobile(ts) if optimize else ts).save(f)
+        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+        return ts
+    except Exception as e:
+        print(f'{prefix} export failure: {e}')
+
+
+def export_onnx(model, img, file, opset_version, train, dynamic, simplify):
+    # ONNX model export
+    prefix = colorstr('ONNX:')
+    try:
+        check_requirements(('onnx', 'onnx-simplifier'))
+        import onnx
+
+        print(f'{prefix} starting export with onnx {onnx.__version__}...')
+        f = file.with_suffix('.onnx')
+        torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version,
+                          training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
+                          do_constant_folding=not train,
+                          input_names=['images'],
+                          output_names=['output'],
+                          dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
+                                        'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
+                                        } if dynamic else None)
+
+        # Checks
+        model_onnx = onnx.load(f)  # load onnx model
+        onnx.checker.check_model(model_onnx)  # check onnx model
+        # print(onnx.helper.printable_graph(model_onnx.graph))  # print
+
+        # Simplify
+        if simplify:
+            try:
+                import onnxsim
+
+                print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
+                model_onnx, check = onnxsim.simplify(
+                    model_onnx,
+                    dynamic_input_shape=dynamic,
+                    input_shapes={'images': list(img.shape)} if dynamic else None)
+                assert check, 'assert check failed'
+                onnx.save(model_onnx, f)
+            except Exception as e:
+                print(f'{prefix} simplifier failure: {e}')
+        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+    except Exception as e:
+        print(f'{prefix} export failure: {e}')
+
+
+def export_coreml(ts_model, img, file, train):
+    # CoreML model export
+    prefix = colorstr('CoreML:')
+    try:
+        import coremltools as ct
+
+        print(f'{prefix} starting export with coremltools {ct.__version__}...')
+        f = file.with_suffix('.mlmodel')
+        assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
+        model = ct.convert(ts_model, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
+        model.save(f)
+        print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+    except Exception as e:
+        print(f'{prefix} export failure: {e}')
+
+
 def run(weights='./yolov5s.pt',  # weights path
         img_size=(640, 640),  # image (height, width)
         batch_size=1,  # batch size
@@ -40,12 +112,13 @@ def run(weights='./yolov5s.pt',  # weights path
     t = time.time()
     include = [x.lower() for x in include]
     img_size *= 2 if len(img_size) == 1 else 1  # expand
+    file = Path(weights)
 
     # Load PyTorch model
     device = select_device(device)
     assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
     model = attempt_load(weights, map_location=device)  # load FP32 model
-    labels = model.names
+    names = model.names
 
     # Input
     gs = int(max(model.stride))  # grid size (max stride)
@@ -57,7 +130,6 @@ def run(weights='./yolov5s.pt',  # weights path
         img, model = img.half(), model.half()  # to FP16
     model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
     for k, m in model.named_modules():
-        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
         if isinstance(m, Conv):  # assign export-friendly activations
             if isinstance(m.act, nn.Hardswish):
                 m.act = Hardswish()
@@ -72,73 +144,13 @@ def run(weights='./yolov5s.pt',  # weights path
         y = model(img)  # dry runs
     print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
 
-    # TorchScript export -----------------------------------------------------------------------------------------------
-    if 'torchscript' in include or 'coreml' in include:
-        prefix = colorstr('TorchScript:')
-        try:
-            print(f'\n{prefix} starting export with torch {torch.__version__}...')
-            f = weights.replace('.pt', '.torchscript.pt')  # filename
-            ts = torch.jit.trace(model, img, strict=False)
-            (optimize_for_mobile(ts) if optimize else ts).save(f)
-            print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-        except Exception as e:
-            print(f'{prefix} export failure: {e}')
-
-    # ONNX export ------------------------------------------------------------------------------------------------------
+    # Exports
     if 'onnx' in include:
-        prefix = colorstr('ONNX:')
-        try:
-            import onnx
-
-            print(f'{prefix} starting export with onnx {onnx.__version__}...')
-            f = weights.replace('.pt', '.onnx')  # filename
-            torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version,
-                              training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
-                              do_constant_folding=not train,
-                              input_names=['images'],
-                              output_names=['output'],
-                              dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
-                                            'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
-                                            } if dynamic else None)
-
-            # Checks
-            model_onnx = onnx.load(f)  # load onnx model
-            onnx.checker.check_model(model_onnx)  # check onnx model
-            # print(onnx.helper.printable_graph(model_onnx.graph))  # print
-
-            # Simplify
-            if simplify:
-                try:
-                    check_requirements(['onnx-simplifier'])
-                    import onnxsim
-
-                    print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
-                    model_onnx, check = onnxsim.simplify(
-                        model_onnx,
-                        dynamic_input_shape=dynamic,
-                        input_shapes={'images': list(img.shape)} if dynamic else None)
-                    assert check, 'assert check failed'
-                    onnx.save(model_onnx, f)
-                except Exception as e:
-                    print(f'{prefix} simplifier failure: {e}')
-            print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-        except Exception as e:
-            print(f'{prefix} export failure: {e}')
-
-    # CoreML export ----------------------------------------------------------------------------------------------------
-    if 'coreml' in include:
-        prefix = colorstr('CoreML:')
-        try:
-            import coremltools as ct
-
-            print(f'{prefix} starting export with coremltools {ct.__version__}...')
-            assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
-            model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
-            f = weights.replace('.pt', '.mlmodel')  # filename
-            model.save(f)
-            print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-        except Exception as e:
-            print(f'{prefix} export failure: {e}')
+        export_onnx(model, img, file, opset_version, train, dynamic, simplify)
+    if 'torchscript' in include or 'coreml' in include:
+        ts = export_torchscript(model, img, file, optimize)
+        if 'coreml' in include:
+            export_coreml(ts, img, file, train)
 
     # Finish
     print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')