PyTorch Hub cv2 .save() .show() bug fix (#2831)

* PyTorch Hub cv2 .save() .show() bug fix

cv2.rectangle() was failing on non-contiguous np array inputs. This checks for contiguous arrays and applies is necessary:
```python
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
```

* Update plots.py

```python
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
```

* Update hubconf.py

Expand CI tests to OpenCV image.

models/common.py

@@ -240,7 +240,7 @@ class autoShape(nn.Module):
     @torch.no_grad()
     def forward(self, imgs, size=640, augment=False, profile=False):
         # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
-        #   filename:   imgs = 'data/samples/zidane.jpg'
+        #   filename:   imgs = 'data/images/zidane.jpg'
         #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
         #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
         #   PIL:             = Image.open('image.jpg')  # HWC x(640,1280,3)
@@ -271,7 +271,7 @@ class autoShape(nn.Module):
             shape0.append(s)  # image shape
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
-            imgs[i] = im  # update
+            imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
         shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
         x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
         x = np.stack(x, 0) if n > 1 else x[0][None]  # stack