Apply make_divisible for ONNX models in Autoshape (#10172)

* Apply make_divisible for onnx models in Autoshape

At line 697 we have this `make_divisible` function for pytorch models. 
* Context: we want to run inference on varied input sizes instead of fixed image size.
* When I test an image of size [720, 720] for a pytorch model (e.g., yolov5n.pt), we can see that it will be reshaped to [736, 736] by the function. This is as expected.
* When I test the same image for the onnx model (e.g., yolov5n.onnx, exported with `--dynamic`), I got an error and it's due to the indivisible problem
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running Concat node. Name:'Concat_143' Status Message: concat.cc:156 PrepareForCompute Non concat axis dimensions must match: Axis 3 has mismatched dimensions of 45 and 46
```

The simple solution is to enable the `make_divisible` function for onnx model too.

Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com>

* revise indent

Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com>

* Apply make_divisible to all formats

All formats from DetectMultiBackend should have default stride=32

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/common.py

@@ -694,7 +694,7 @@ class AutoShape(nn.Module):
                 g = max(size) / max(s)  # gain
                 shape1.append([int(y * g) for y in s])
                 ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size  # inf shape
+            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
             x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
             x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
             x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32