Add `--half` support for OpenVINO exports (#7615)

* feature(export): add half support for openvino models

* Update export.py

* Update export.py

Co-authored-by: Glenn Jocher <[email protected]>

export.py

@@ -168,7 +168,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
         LOGGER.info(f'{prefix} export failure: {e}')
 
 
-def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
+def export_openvino(model, im, file, half, prefix=colorstr('OpenVINO:')):
     # YOLOv5 OpenVINO export
     try:
         check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
@@ -177,7 +177,7 @@ def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
         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}"
+        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
         subprocess.check_output(cmd, shell=True)
 
         LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
@@ -477,7 +477,7 @@ def run(
     # Load PyTorch model
     device = select_device(device)
     if half:
-        assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
+        assert device.type != 'cpu' or coreml or xml, '--half only compatible with GPU export, i.e. use --device 0'
     model = attempt_load(weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
     nc, names = model.nc, model.names  # number of classes, class names
 
@@ -491,7 +491,7 @@ def run(
     im = torch.zeros(batch_size, 3, *imgsz).to(device)  # image size(1,3,320,192) BCHW iDetection
 
     # Update model
-    if half and not coreml:
+    if half and not (coreml or xml):
         im, model = im.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():
@@ -515,7 +515,7 @@ def run(
     if onnx or xml:  # OpenVINO requires ONNX
         f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
     if xml:  # OpenVINO
-        f[3] = export_openvino(model, im, file)
+        f[3] = export_openvino(model, im, file, half)
     if coreml:
         _, f[4] = export_coreml(model, im, file, int8, half)