Add CoreML inference (#6195)

* Add Apple CoreML inference

* Cleanup

detect.py

@@ -17,7 +17,7 @@ Usage - formats:
                                          yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                          yolov5s.xml                # OpenVINO
                                          yolov5s.engine             # TensorRT
-                                         yolov5s.mlmodel            # CoreML (under development)
+                                         yolov5s.mlmodel            # CoreML (MacOS-only)
                                          yolov5s_saved_model        # TensorFlow SavedModel
                                          yolov5s.pb                 # TensorFlow GraphDef
                                          yolov5s.tflite             # TensorFlow Lite

export.py

@@ -25,7 +25,7 @@ Inference:
                                          yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                          yolov5s.xml                # OpenVINO
                                          yolov5s.engine             # TensorRT
-                                         yolov5s.mlmodel            # CoreML (under development)
+                                         yolov5s.mlmodel            # CoreML (MacOS-only)
                                          yolov5s_saved_model        # TensorFlow SavedModel
                                          yolov5s.pb                 # TensorFlow GraphDef
                                          yolov5s.tflite             # TensorFlow Lite
@@ -156,7 +156,6 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
         LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
         f = file.with_suffix('.mlmodel')
 
-        model.train()  # CoreML exports should be placed in model.train() mode
         ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
         ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
         ct_model.save(f)

models/common.py

@@ -420,9 +420,12 @@ class DetectMultiBackend(nn.Module):
             im = Image.fromarray((im[0] * 255).astype('uint8'))
             # im = im.resize((192, 320), Image.ANTIALIAS)
             y = self.model.predict({'image': im})  # coordinates are xywh normalized
-            box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
-            conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
-            y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
+            if 'confidence' in y:
+                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
+                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
+                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
+            else:
+                y = y[list(y)[-1]]  # last output
         else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             if self.saved_model:  # SavedModel

val.py

@@ -11,7 +11,7 @@ Usage - formats:
                                       yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                       yolov5s.xml                # OpenVINO
                                       yolov5s.engine             # TensorRT
-                                      yolov5s.mlmodel            # CoreML (under development)
+                                      yolov5s.mlmodel            # CoreML (MacOS-only)
                                       yolov5s_saved_model        # TensorFlow SavedModel
                                       yolov5s.pb                 # TensorFlow GraphDef
                                       yolov5s.tflite             # TensorFlow Lite