|
|
@@ -70,18 +70,20 @@ class Yolov7PaFPN(nn.Module):
|
|
|
norm_type=norm_type,
|
|
|
depthwise=depthwise
|
|
|
)
|
|
|
-
|
|
|
+ self.head_conv_1 = Conv(round(128*width), round(256*width), k=1, act_type=act_type, norm_type=norm_type)
|
|
|
+ self.head_conv_2 = Conv(round(256*width), round(512*width), k=1, act_type=act_type, norm_type=norm_type)
|
|
|
+ self.head_conv_3 = Conv(round(512*width), round(1024*width), k=1, act_type=act_type, norm_type=norm_type)
|
|
|
# output proj layers
|
|
|
if out_dim is not None:
|
|
|
self.out_layers = nn.ModuleList([
|
|
|
Conv(in_dim, out_dim, k=1,
|
|
|
norm_type=norm_type, act_type=act_type)
|
|
|
- for in_dim in [round(128*width), round(256*width), round(512*width)]
|
|
|
+ for in_dim in [round(256*width), round(512*width), round(1024*width)]
|
|
|
])
|
|
|
self.out_dim = [out_dim] * 3
|
|
|
else:
|
|
|
self.out_layers = None
|
|
|
- self.out_dim = [round(128*width), round(256*width), round(512*width)]
|
|
|
+ self.out_dim = [round(256*width), round(512*width), round(1024*width)]
|
|
|
|
|
|
|
|
|
def forward(self, features):
|
|
|
@@ -109,7 +111,10 @@ class Yolov7PaFPN(nn.Module):
|
|
|
c18 = torch.cat([c17, c5], dim=1)
|
|
|
c19 = self.bottom_up_layer_2(c18)
|
|
|
|
|
|
- out_feats = [c13, c16, c19] # [P3, P4, P5]
|
|
|
+ c20 = self.head_conv_1(c13)
|
|
|
+ c21 = self.head_conv_2(c16)
|
|
|
+ c22 = self.head_conv_3(c19)
|
|
|
+ out_feats = [c20, c21, c22] # [P3, P4, P5]
|
|
|
|
|
|
# output proj layers
|
|
|
if self.out_layers is not None:
|
yjh0410