Fix MixConv2d() remove shortcut + apply depthwise (ultralytics#5410)

models/common.py

@@ -113,7 +113,7 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, nu
         self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
         self.cv4 = Conv(2 * c_, c2, 1, 1)
         self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
+        self.act = nn.SiLU()
         self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
 
     def forward(self, x):

models/experimental.py

@@ -2,7 +2,7 @@
 """
 Experimental modules
 """
-
+import math
 import numpy as np
 import torch
 import torch.nn as nn
@@ -48,26 +48,27 @@ def forward(self, x):
 
 class MixConv2d(nn.Module):
     # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch_out, kernel, stride, ch_strategy
         super().__init__()
-        groups = len(k)
+        n = len(k)  # number of convolutions
         if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
+            i = torch.linspace(0, n - 1E-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
         else:  # equal weight.numel() per group
-            b = [c2] + [0] * groups
-            a = np.eye(groups + 1, groups, k=-1)
+            b = [c2] + [0] * n
+            a = np.eye(n + 1, n, k=-1)
             a -= np.roll(a, 1, axis=1)
             a *= np.array(k) ** 2
             a[0] = 1
             c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
 
-        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+        self.m = nn.ModuleList(
+            [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
         self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
+        self.act = nn.SiLU()
 
     def forward(self, x):
-        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
 
 
 class Ensemble(nn.ModuleList):

utils/torch_utils.py

@@ -166,7 +166,7 @@ def initialize_weights(model):
         elif t is nn.BatchNorm2d:
             m.eps = 1e-3
             m.momentum = 0.03
-        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
+        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
             m.inplace = True