Clip TTA Augmented Tails (#5028)

* Clip TTA Augmented Tails

Experimental TTA update.

* Update yolo.py

* Update yolo.py

* Update yolo.py

* Update yolo.py

models/yolo.py

@@ -134,6 +134,7 @@ class Model(nn.Module):
             # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
             yi = self._descale_pred(yi, fi, si, img_size)
             y.append(yi)
+        y = self._clip_augmented(y)  # clip augmented tails
         return torch.cat(y, 1), None  # augmented inference, train
 
     def _forward_once(self, x, profile=False, visualize=False):
@@ -166,6 +167,17 @@ class Model(nn.Module):
             p = torch.cat((x, y, wh, p[..., 4:]), -1)
         return p
 
+    def _clip_augmented(self, y):
+        # Clip YOLOv5 augmented inference tails
+        nl = self.model[-1].nl  # number of detection layers (P3-P5)
+        g = sum(4 ** x for x in range(nl))  # grid points
+        e = 1  # exclude layer count
+        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
+        y[0] = y[0][:, :-i]  # large
+        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
+        y[-1] = y[-1][:, i:]  # small
+        return y
+
     def _profile_one_layer(self, m, x, dt):
         c = isinstance(m, Detect)  # is final layer, copy input as inplace fix
         o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs