z

README.md

@@ -1,12 +1,18 @@
 
 <img src="./FightingCVimg/LOGO.gif" height="200" width="400"/>
 
+
 # FightingCV Codebase For [***Attention***](#attention-series),[***Backbone***](#backbone-series), [***MLP***](#mlp-series), [***Re-parameter***](#re-parameter-series), [**Convolution**](#convolution-series)
 
 ![](https://img.shields.io/badge/fightingcv-v0.0.1-brightgreen)
 ![](https://img.shields.io/badge/python->=v3.0-blue)
 ![](https://img.shields.io/badge/pytorch->=v1.4-red)
 
+
+*If this project is helpful to you, welcome to give a ***star***.* 
+
+*Don't forget to ***follow*** me to learn about project updates.*
+
 Hello，大家好，我是小马🚀🚀🚀
 
 ***For 小白（Like Me）：***
@@ -40,29 +46,8 @@ Hello，大家好，我是小马🚀🚀🚀
 ![](./FightingCVimg/wechat.jpg)
 
 强烈推荐大家关注[**知乎**](https://www.zhihu.com/people/jason-14-58-38/posts)账号和[**FightingCV公众号**](https://mp.weixin.qq.com/s/sgNw6XFBPcD20Ef3ddfE1w)，可以快速了解到最新优质的干货资源。
-***
-
-
 
 
-*If this project is helpful to you, welcome to give a ***star***.* 
-
-*Don't forget to ***follow*** me to learn about project updates.*
-
-
- <!-- # Installation ***(Optional)***
-
-
-```
-$ pip install git+https://github.com/xmu-xiaoma666/External-Attention-pytorch
-```
-
-Or（But Not Recommended）：
-
-```
-$ pip install fightingcv
-``` -->
-
 
 
 ***
@@ -135,6 +120,7 @@ $ pip install fightingcv
 
     - [3. MobileViT Usage](#3-MobileViT-Usage)
 
+    - [4. ConvMixer Usage](#4-ConvMixer-Usage)
 - [MLP Series](#mlp-series)
 
     - [1. RepMLP Usage](#1-RepMLP-Usage)
@@ -988,6 +974,8 @@ if __name__ == '__main__':
 
 - Pytorch implementation of [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer---ArXiv 2020.10.05](https://arxiv.org/abs/2103.02907)
 
+- Pytorch implementation of [Patches Are All You Need?---ICLR2022 (Under Review)](https://openreview.net/forum?id=TVHS5Y4dNvM)
+
 
 ### 1. ResNet Usage
 #### 1.1. Paper
@@ -1081,6 +1069,32 @@ if __name__ == '__main__':
 
 
 
+### 4. ConvMixer Usage
+#### 4.1. Paper
+[Patches Are All You Need?---ICLR2022 (Under Review)](https://openreview.net/forum?id=TVHS5Y4dNvM)
+#### 4.2. Overview
+![](./fightingcv/img/ConvMixer.png)
+
+#### 4.3. Usage Code
+```python
+
+from fightingcv.backbone.ConvMixer import *
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+if __name__ == '__main__':
+    x=torch.randn(1,3,224,224)
+    convmixer=ConvMixer(dim=512,depth=12)
+    out=convmixer(x)
+    print(out.shape)  #[1, 1000]
+
+
+```
+
+
+
+
 
 
 

fightingcv/backbone/ConvMixer.py

@@ -0,0 +1,39 @@
+import torch.nn as nn
+from torch.nn.modules.activation import GELU
+import torch
+from torch.nn.modules.pooling import AdaptiveAvgPool2d
+
+class Residual(nn.Module):
+    def __init__(self,fn):
+        super().__init__()
+        self.fn=fn
+    def forward(self,x):
+        return x+self.fn(x)
+
+def ConvMixer(dim,depth,kernel_size=9,patch_size=7,num_classes=1000):
+    return nn.Sequential(
+        nn.Conv2d(3,dim,kernel_size=patch_size,stride=patch_size),
+        nn.GELU(),
+        nn.BatchNorm2d(dim),
+        *[nn.Sequential(
+            Residual(nn.Sequential(
+                nn.Conv2d(dim,dim,kernel_size=kernel_size,groups=dim,padding=kernel_size//2),
+                nn.GELU(),
+                nn.BatchNorm2d(dim)
+            )),
+            nn.Conv2d(dim,dim,kernel_size=1),
+            nn.GELU(),
+            nn.BatchNorm2d(dim)
+        ) for _ in range(depth)],
+        nn.AdaptiveAvgPool2d(1),
+        nn.Flatten(),
+        nn.Linear(dim,num_classes)
+    )
+
+if __name__ == '__main__':
+    x=torch.randn(1,3,224,224)
+    convmixer=ConvMixer(dim=512,depth=12)
+    out=convmixer(x)
+    print(out.shape)  #[1, 1000]
+
+

main.py

@@ -1,32 +1,10 @@
-from fightingcv.backbone.MobileViT import *
+from fightingcv.backbone.ConvMixer import *
 import torch
 from torch import nn
 from torch.nn import functional as F
 
 if __name__ == '__main__':
-    input=torch.randn(1,3,224,224)
-
-    ### mobilevit_xxs
-    mvit_xxs=mobilevit_xxs()
-    out=mvit_xxs(input)
-    print(out.shape)
-
-    ### mobilevit_xs
-    mvit_xs=mobilevit_xs()
-    out=mvit_xs(input)
-    print(out.shape)
-
-
-    ### mobilevit_s
-    mvit_s=mobilevit_s()
-    out=mvit_s(input)
-    print(out.shape)
-
-
-
-
-
-
-
-
-
+    x=torch.randn(1,3,224,224)
+    convmixer=ConvMixer(dim=512,depth=12)
+    out=convmixer(x)
+    print(out.shape)  #[1, 1000]