* ACON Activation batch-size 1 bug path

This is not a great solution to https://github.com/nmaac/acon/issues/4 but it's all I could think of at the moment.

WARNING: YOLOv5 models with MetaAconC() activations are incapable of running inference at batch-size 1 properly due to a known bug in https://github.com/nmaac/acon/issues/4 with no known solution.

* Update activations.py

* Update activations.py

* Update activations.py

* Update activations.py

* ACON Activation Function

## 🚀 Feature

There is a new activation function [ACON (CVPR 2021)](https://arxiv.org/pdf/2009.04759.pdf) that unifies ReLU and Swish. 
ACON is simple but very effective, code is here: https://github.com/nmaac/acon/blob/main/acon.py#L19

![image](https://user-images.githubusercontent.com/5032208/115676962-a38dfe80-a382-11eb-9883-61fa3216e3e6.png)

The improvements are very significant:
![image](https://user-images.githubusercontent.com/5032208/115680180-eac9be80-a385-11eb-9c7a-8643db552c69.png)

## Alternatives

It also has an enhanced version meta-ACON that uses a small network to learn beta explicitly, which may influence the speed a bit.

## Additional context

[Code](https://github.com/nmaac/acon) and [paper](https://arxiv.org/pdf/2009.04759.pdf).

* Update activations.py

* fix #541 #542

* Update train.py

* Add Frelu

* Update activations.py

PEP8 and format updates for commonality with models.common.Conv()

* Update activations.py

Update case

* Update activations.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>