Apply FReLU to YOLOv5

[glenn-jocher]

Thank you for your great contributions! In YOLOv5 we skipped over using Swish or Mish due to their expensive nature, especially during training, and retained the same LeakyReLU(0.1) as YOLOv3. We have a PR for the addition of FReLU https://github.com/ultralytics/yolov5/pull/556 as this. Is this a correct implementation? Should we use torch.max() or torch.maximum()?

# FReLU https://arxiv.org/abs/2007.11824 --------------------------------------
class FReLU(nn.Module):
    def __init__(self, c1, k=3):  # ch_in, kernel
        super().__init()__()
        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1)
        self.bn = nn.BatchNorm2d(c1)

    @staticmethod
    def forward(self, x):
        return torch.maximum(x, self.bn(self.conv(x)))

Secondly, I saw you recommended using FReLU only in the backbone of a detection network. So would you recommend replacing all of our LeakyReLU(0.1) activations in the backbone with FReLU() to start, and then doing the same with the head activations if we see positive results? Our main model structure is here:

https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, BottleneckCSP, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, BottleneckCSP, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, BottleneckCSP, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, BottleneckCSP, [1024, False]],  # 9
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, BottleneckCSP, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

Thank you in advance in advance for your time and any recommendations for our situation!

[nmaac]

Hi @glenn-jocher , (1) you should use torch.max(), which is different from MegEngine API. (2) yes you could try to use FReLU only in the backbone first, then try it in head. you could also replace all the LeakyReLU activations to FReLU because there is only one activation in each BottleneckCSP which are not originally many.

[glenn-jocher]

@nmaac great, thanks for the feedback! We'll try to experiment with this and see if it helps our results!

[clw5180]

We'll try to experiment with this and see if it helps our results!

Hi glenn, is there any improment on COCO or VOC dataset about FReLU ? Or how does it perform on the verification set? Thanks !

[glenn-jocher]

@clw5180 I've run some experiments with YOLOv5s, which seem to show some early improvement. I have not tried larger models, and improvements from one size to another sometimes does not extrapolate well, so I can't draw any conclusions across the entire v5 lineup, but so for small models it appears to help.

One warning is that FReLU increases training requirements greatly, similar to Swish perhaps, not quite as bad as Mish, so it's probably not a viable replacement for all activation functions in a model, but might be suitable for partial replacement.