Use export_formats() in export.py (ultralytics#6705)

* Use `export_formats()` in export.py

* list fix

export.py

@@ -433,9 +433,12 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
         conf_thres=0.25  # TF.js NMS: confidence threshold
         ):
     t = time.time()
-    include = [x.lower() for x in include]
-    tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs'))  # TensorFlow exports
-    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
+    include = [x.lower() for x in include]  # to lowercase
+    formats = tuple(export_formats()['Argument'][1:])  # --include arguments
+    flags = [x in include for x in formats]
+    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {formats}'
+    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags  # export booleans
+    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights
 
     # Load PyTorch model
     device = select_device(device)
@@ -475,20 +478,19 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
     # Exports
     f = [''] * 10  # exported filenames
     warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
-    if 'torchscript' in include:
+    if jit:
         f[0] = export_torchscript(model, im, file, optimize)
-    if 'engine' in include:  # TensorRT required before ONNX
+    if engine:  # TensorRT required before ONNX
         f[1] = export_engine(model, im, file, train, half, simplify, workspace, verbose)
-    if ('onnx' in include) or ('openvino' in include):  # OpenVINO requires ONNX
+    if onnx or xml:  # OpenVINO requires ONNX
         f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
-    if 'openvino' in include:
+    if xml:  # OpenVINO
         f[3] = export_openvino(model, im, file)
-    if 'coreml' in include:
+    if coreml:
         _, f[4] = export_coreml(model, im, file)
 
     # TensorFlow Exports
-    if any(tf_exports):
-        pb, tflite, edgetpu, tfjs = tf_exports[1:]
+    if any((saved_model, pb, tflite, edgetpu, tfjs)):
         if int8 or edgetpu:  # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
             check_requirements(('flatbuffers==1.12',))  # required before `import tensorflow`
         assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'