RepMLP-pytorch

Pytorch implemention of RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

Pytorch implemention of RepVGG: Making VGG-style ConvNets Great Again

We add some annotations to make code easy to read !!!

Noting that dimention of input features $N$ should equal to dimention of output features $O$. Otherwise, the results of inference and training will be different !!!

Overview

RepMLP Usage

import torch
from torch import nn
from repmlp-pytorch import RepMLP

N=4 #batch size
C=512 #input dim
O=1024 #output dim
H=14 #image height
W=14 #image width
h=7 #patch height
w=7 #patch width
fc1_fc2_reduction=1 #reduction ratio
fc3_groups=8 # groups
repconv_kernels=[1,3,5,7] #kernel list
repmlp=RepMLP(C,H,W,h,w,fc1_fc2_reduction,fc3_groups,repconv_kernels=repconv_kernels)
x=torch.randn(N,C,H,W)
y=repmlp(x)

RepVGG Usage

import torch
from torch import nn
from repvgg-pytorch import RepBlock

input=torch.randn(50,512,49,49)
repblock=RepBlock(512,512)
repblock.eval()
out=repblock(input)
repblock._switch_to_deploy()
out2=repblock(input)
print('difference between vgg and repvgg')
print(((out2-out)**2).sum())

Reference

Official RepMLP

Official RepVGG