OpenVINO Export (#6057)

* OpenVINO export

* Remove timeout

* Add 3 files

* str

* Constrain opset to 12

* Default ONNX opset to 12

* Make dir

* Make dir

* Cleanup

* Cleanup

* check_requirements(('openvino-dev',))

export.py

 TorchScript             | yolov5s.torchscript       | `torchscript`
 ONNX                    | yolov5s.onnx              | `onnx`
 CoreML                  | yolov5s.mlmodel           | `coreml`
+OpenVINO                | yolov5s_openvino_model/   | `openvino`
 TensorFlow SavedModel   | yolov5s_saved_model/      | `saved_model`
 TensorFlow GraphDef     | yolov5s.pb                | `pb`
 TensorFlow Lite         | yolov5s.tflite            | `tflite`
 TensorRT                | yolov5s.engine            | `engine`
 
 Usage:
-    $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml saved_model pb tflite tfjs
+    $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml openvino saved_model tflite tfjs
 
 Inference:
     $ python path/to/detect.py --weights yolov5s.pt
                                          yolov5s.torchscript
                                          yolov5s.onnx
                                          yolov5s.mlmodel  (under development)
+                                         yolov5s_openvino_model  (under development)
                                          yolov5s_saved_model
                                          yolov5s.pb
                                          yolov5s.tflite
     return ct_model
 
 
+def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
+    # YOLOv5 OpenVINO export
+    try:
+        check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+        import openvino.inference_engine as ie
+
+        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
+        f = str(file).replace('.pt', '_openvino_model' + os.sep)
+
+        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
+        subprocess.check_output(cmd, shell=True)
+
+        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+    except Exception as e:
+        LOGGER.info(f'\n{prefix} export failure: {e}')
+
+
 def export_saved_model(model, im, file, dynamic,
                        tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
                        conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
         imgsz=(640, 640),  # image (height, width)
         batch_size=1,  # batch size
         device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        include=('torchscript', 'onnx', 'coreml'),  # include formats
+        include=('torchscript', 'onnx'),  # include formats
         half=False,  # FP16 half-precision export
         inplace=False,  # set YOLOv5 Detect() inplace=True
         train=False,  # model.train() mode
         int8=False,  # CoreML/TF INT8 quantization
         dynamic=False,  # ONNX/TF: dynamic axes
         simplify=False,  # ONNX: simplify model
-        opset=14,  # ONNX: opset version
+        opset=12,  # ONNX: opset version
         verbose=False,  # TensorRT: verbose log
         workspace=4,  # TensorRT: workspace size (GB)
         nms=False,  # TF: add NMS to model
     t = time.time()
     include = [x.lower() for x in include]
     tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'tfjs'))  # TensorFlow exports
-    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
     file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
 
+    # Checks
+    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
+    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12
+
     # 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'
     # Exports
     if 'torchscript' in include:
         export_torchscript(model, im, file, optimize)
-    if 'onnx' in include:
+    if ('onnx' in include) or ('openvino' in include):  # OpenVINO requires ONNX
         export_onnx(model, im, file, opset, train, dynamic, simplify)
     if 'engine' in include:
         export_engine(model, im, file, train, half, simplify, workspace, verbose)
     if 'coreml' in include:
         export_coreml(model, im, file)
+    if 'openvino' in include:
+        export_openvino(model, im, file)
 
     # TensorFlow Exports
     if any(tf_exports):
     parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
     parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
     parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
-    parser.add_argument('--opset', type=int, default=14, help='ONNX: opset version')
+    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
     parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
     parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
     parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')

requirements.txt

 # scikit-learn==0.19.2  # CoreML quantization
 # tensorflow>=2.4.1  # TFLite export
 # tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
 
 # Extras --------------------------------------
 # albumentations>=1.0.3