fuse IDetect (#148)

models/yolo.py

@@ -106,7 +106,41 @@ class IDetect(nn.Module):
                 z.append(y.view(bs, -1, self.no))
 
         return x if self.training else (torch.cat(z, 1), x)
+    
+    def fuseforward(self, x):
+        # x = x.copy()  # for profiling
+        z = []  # inference output
+        self.training |= self.export
+        for i in range(self.nl):
+            x[i] = self.m[i](x[i])  # conv
+            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
+            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
 
+            if not self.training:  # inference
+                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
+                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+
+                y = x[i].sigmoid()
+                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
+                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
+                z.append(y.view(bs, -1, self.no))
+
+        return x if self.training else (torch.cat(z, 1), x)    
+    
+    def fuse(self):
+        print("IDetect.fuse")
+        # fuse ImplicitA and Convolution
+        for i in range(len(self.m)):
+            c1,c2,_,_ = self.m[i].weight.shape
+            c1_,c2_, _,_ = self.ia[i].implicit.shape
+            self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1,c2),self.ia[i].implicit.reshape(c2_,c1_)).squeeze(1)
+
+        # fuse ImplicitM and Convolution
+        for i in range(len(self.m)):
+            c1,c2, _,_ = self.im[i].implicit.shape
+            self.m[i].bias *= self.im[i].implicit.reshape(c2)
+            self.m[i].weight *= self.im[i].implicit.transpose(0,1)
+            
     @staticmethod
     def _make_grid(nx=20, ny=20):
         yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
@@ -537,6 +571,9 @@ class Model(nn.Module):
                 m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                 delattr(m, 'bn')  # remove batchnorm
                 m.forward = m.fuseforward  # update forward
+            elif isinstance(m, IDetect):
+                m.fuse()
+                m.forward = m.fuseforward
         self.info()
         return self