Simplify autoshape() post-process (#1653)

* Simplify autoshape() post-process

* cleanup

* cleanup

hubconf.py

@@ -108,7 +108,7 @@ def yolov5x(pretrained=False, channels=3, classes=80):
 
 if __name__ == '__main__':
     model = create(name='yolov5s', pretrained=True, channels=3, classes=80)  # example
-    model = model.fuse().autoshape()  # for PIL/cv2/np inputs and NMS
+    model = model.autoshape()  # for PIL/cv2/np inputs and NMS
 
     # Verify inference
     from PIL import Image

models/common.py

@@ -167,8 +167,7 @@ class autoShape(nn.Module):
 
         # Post-process
         for i in batch:
-            if y[i] is not None:
-                y[i][:, :4] = scale_coords(shape1, y[i][:, :4], shape0[i])
+            scale_coords(shape1, y[i][:, :4], shape0[i])
 
         return Detections(imgs, y, self.names)
 
@@ -177,13 +176,13 @@ class Detections:
     # detections class for YOLOv5 inference results
     def __init__(self, imgs, pred, names=None):
         super(Detections, self).__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
         self.xyxy = pred  # xyxy pixels
         self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
         self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
         self.n = len(self.pred)

requirements.txt

@@ -9,8 +9,8 @@ Pillow
 PyYAML>=5.3
 scipy>=1.4.1
 tensorboard>=2.2
-torch>=1.6.0
-torchvision>=0.7.0
+torch>=1.7.0
+torchvision>=0.8.1
 tqdm>=4.41.0
 
 # logging -------------------------------------
@@ -26,5 +26,5 @@ pandas
 # scikit-learn==0.19.2  # for coreml quantization
 
 # extras --------------------------------------
-# thop  # FLOPS computation
-# pycocotools>=2.0  # COCO mAP
+thop  # FLOPS computation
+pycocotools>=2.0  # COCO mAP

utils/general.py

@@ -258,7 +258,7 @@ def wh_iou(wh1, wh2):
     return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
 
 
-def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, classes=None, agnostic=False, labels=()):
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
     """Performs Non-Maximum Suppression (NMS) on inference results
 
     Returns: