Two dimensional size=(h,w) AutoShape support (#9072)

* Two dimensional `size=(h,w)` AutoShape support

May resolve https://github.com/ultralytics/yolov5/issues/9039

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

* Update hubconf.py

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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

hubconf.py

@@ -30,7 +30,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
 
     from models.common import AutoShape, DetectMultiBackend
     from models.experimental import attempt_load
-    from models.yolo import DetectionModel
+    from models.yolo import ClassificationModel, DetectionModel
     from utils.downloads import attempt_download
     from utils.general import LOGGER, check_requirements, intersect_dicts, logging
     from utils.torch_utils import select_device
@@ -45,8 +45,12 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
         if pretrained and channels == 3 and classes == 80:
             try:
                 model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
-                if autoshape and isinstance(model.model, DetectionModel):
-                    model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
+                if autoshape:
+                    if model.pt and isinstance(model.model, ClassificationModel):
+                        LOGGER.warning('WARNING: YOLOv5 v6.2 ClassificationModel is not yet AutoShape compatible. '
+                                       'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
+                    else:
+                        model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
             except Exception:
                 model = attempt_load(path, device=device, fuse=False)  # arbitrary model
         else:

models/common.py

@@ -589,7 +589,7 @@ class AutoShape(nn.Module):
 
     @smart_inference_mode()
     def forward(self, ims, size=640, augment=False, profile=False):
-        # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
+        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
         #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
         #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
         #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
@@ -600,6 +600,8 @@ class AutoShape(nn.Module):
 
         dt = (Profile(), Profile(), Profile())
         with dt[0]:
+            if isinstance(size, int):  # expand
+                size = (size, size)
             p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
             autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
             if isinstance(ims, torch.Tensor):  # torch
@@ -622,10 +624,10 @@ class AutoShape(nn.Module):
                 im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
                 s = im.shape[:2]  # HWC
                 shape0.append(s)  # image shape
-                g = (size / max(s))  # gain
+                g = max(size) / max(s)  # gain
                 shape1.append([y * g for y in s])
                 ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-            shape1 = [make_divisible(x, self.stride) if self.pt else size for x in np.array(shape1).max(0)]  # inf shape
+            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size  # 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