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/yolo.py

@@ -297,8 +297,12 @@ class ClassificationModel(BaseModel):
 
 
 def parse_model(d, ch):  # model_dict, input_channels(3)
+    # Parse a YOLOv5 model.yaml dictionary
     LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
-    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
+    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
+    if act:
+        Conv.act = eval(act)  # redefine default activation, i.e. Conv.act = nn.SiLU()
+        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
     na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
     no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)