New model.yaml activation: field (#9371)

* New model.yaml `activation:` field

Add optional model yaml activation field to define model-wide activations, i.e.:

```yaml
activation: nn.LeakyReLU(0.1)  # activation with arguments
activation: nn.SiLU()  # activation with no arguments
```

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update yolo.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Add example models

* l to m models

* update

* Add yolov5s-LeakyReLU.yaml

* Update yolov5s-LeakyReLU.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/common.py

@@ -39,11 +39,13 @@ def autopad(k, p=None, d=1):  # kernel, padding, dilation
 
 class Conv(nn.Module):
     # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+    act = nn.SiLU()  # default activation
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
         super().__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
         self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+        self.act = self.act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
 
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
@@ -54,8 +56,8 @@ class Conv(nn.Module):
 
 class DWConv(Conv):
     # Depth-wise convolution
-    def __init__(self, c1, c2, k=1, s=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
+        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
 
 
 class DWConvTranspose2d(nn.ConvTranspose2d):