cleanup

models/yolo.py

@@ -38,34 +38,6 @@ class Detect(nn.Module):
 
         return x if self.training else (torch.cat(z, 1), x)
 
-    def forward_(self, x):
-        if hasattr(self, 'nx'):
-            z = []  # inference output
-            for (y, gi, agi, si, nyi, nxi) in zip(x, self.grid, self.ag, self.stride, self.ny, self.nx):
-                m = self.na * nxi * nyi
-                y = y.view(1, self.na, self.no, nyi, nxi).permute(0, 1, 3, 4, 2).contiguous().view(m, self.no).sigmoid()
-
-                xy = (y[..., 0:2] * 2. - 0.5 + gi) * si  # xy
-                wh = (y[..., 2:4] * 2) ** 2 * agi  # wh
-                p_cls = y[:, 4:5] if self.nc == 1 else y[:, 5:self.no] * y[:, 4:5]  # conf
-                z.append([p_cls, xy, wh])
-
-            z = [torch.cat(x, 0) for x in zip(*z)]
-            return z[0], torch.cat(z[1:3], 1)  # scores, boxes: 3780x80, 3780x4
-
-        else:  # dry run
-            self.nx = [0] * self.nl
-            self.ny = [0] * self.nl
-            self.ag = [0] * self.nl
-            for i in range(self.nl):
-                bs, _, ny, nx = x[i].shape
-                m = self.na * nx * ny
-                self.grid[i] = self._make_grid(nx, ny).repeat(1, self.na, 1, 1, 1).view(m, 2) / torch.tensor([[nx, ny]])
-                self.ag[i] = self.anchor_grid[i].repeat(1, 1, nx, ny, 1).view(m, 2)
-                self.nx[i] = nx
-                self.ny[i] = ny
-            return None
-
     @staticmethod
     def _make_grid(nx=20, ny=20):
         yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])