@@ -124,9 +124,6 @@ def forward(self, x):
124124 return self .cv4 (self .act (self .bn (torch .cat ((y1 , y2 ), 1 ))))
125125
126126
127- from models .experimental import CrossConv
128-
129-
130127class C3 (nn .Module ):
131128 # CSP Bottleneck with 3 convolutions
132129 def __init__ (self , c1 , c2 , n = 1 , shortcut = True , g = 1 , e = 0.5 ): # ch_in, ch_out, number, shortcut, groups, expansion
@@ -135,8 +132,8 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, nu
135132 self .cv1 = Conv (c1 , c_ , 1 , 1 )
136133 self .cv2 = Conv (c1 , c_ , 1 , 1 )
137134 self .cv3 = Conv (2 * c_ , c2 , 1 ) # optional act=FReLU(c2)
138- # self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
139- self .m = nn .Sequential (* (CrossConv (c_ , c_ , 3 , 1 , g , 1.0 , shortcut ) for _ in range (n )))
135+ self .m = nn .Sequential (* (Bottleneck (c_ , c_ , shortcut , g , e = 1.0 ) for _ in range (n )))
136+ # self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
140137
141138 def forward (self , x ):
142139 return self .cv3 (torch .cat ((self .m (self .cv1 (x )), self .cv2 (x )), 1 ))
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