AMP check improvements backup YOLOv5n pretrained (#7959)

* Reduce AMP check to detections verification

More robust and faster

* Update general.py

* Update general.py

utils/general.py

@@ -506,27 +506,27 @@ def check_dataset(data, autodownload=True):
 
 def check_amp(model):
     # Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation
-    from models.common import AutoShape
+    from models.common import AutoShape, DetectMultiBackend
+
+    def amp_allclose(model, im):
+        # All close FP32 vs AMP results
+        m = AutoShape(model, verbose=False)  # model
+        a = m(im).xywhn[0]  # FP32 inference
+        m.amp = True
+        b = m(im).xywhn[0]  # AMP inference
+        return a.shape == b.shape and torch.allclose(a, b, atol=0.1)  # close to 10% absolute tolerance
 
-    if next(model.parameters()).device.type == 'cpu':  # get model device
-        return False
     prefix = colorstr('AMP: ')
-    file = ROOT / 'data' / 'images' / 'bus.jpg'  # image to test
-    if file.exists():
-        im = cv2.imread(file)[..., ::-1]  # OpenCV image (BGR to RGB)
-    elif check_online():
-        im = 'https://ultralytics.com/images/bus.jpg'
-    else:
-        LOGGER.warning(emojis(f'{prefix}checks skipped ⚠️, not online.'))
-        return True
-    m = AutoShape(model, verbose=False)  # model
-    a = m(im).xywhn[0]  # FP32 inference
-    m.amp = True
-    b = m(im).xywhn[0]  # AMP inference
-    if (a.shape == b.shape) and torch.allclose(a, b, atol=0.05):  # close to 5% absolute tolerance
+    device = next(model.parameters()).device  # get model device
+    if device.type == 'cpu':
+        return False  # AMP disabled on CPU
+    f = ROOT / 'data' / 'images' / 'bus.jpg'  # image to check
+    im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3))
+    try:
+        assert amp_allclose(model, im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im)
         LOGGER.info(emojis(f'{prefix}checks passed ✅'))
         return True
-    else:
+    except Exception:
         help_url = 'https://github.com/ultralytics/yolov5/issues/7908'
         LOGGER.warning(emojis(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}'))
         return False