X_SSL_Net/tmp/VMamba-main/analyze/mmpretrain_configs/configs/repvgg

RepVGG

RepVGG: Making VGG-style ConvNets Great Again

Introduction

RepVGG is a VGG-style convolutional architecture. It has the following advantages:

1. The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes the output of its only preceding layer as input and feeds the output into its only following layer.
2. The model’s body uses only 3 × 3 conv and ReLU.
3. The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic search, manual refinement, compound scaling, nor other heavy designs.

Abstract

Show the paper's abstract

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet.

How to use it?

Predict image

from mmpretrain import inference_model, get_model

model = get_model('repvgg-A0_8xb32_in1k', pretrained=True)
model.backbone.switch_to_deploy()
predict = inference_model(model, 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('repvgg-A0_8xb32_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Train/Test Command

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/repvgg/repvgg-A0_8xb32_in1k.py

Test:

python tools/test.py configs/repvgg/repvgg-A0_8xb32_in1k.py https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_8xb32_in1k_20221213-60ae8e23.pth

Test with reparameterized model:

python tools/test.py configs/repvgg/repvgg-A0_8xb32_in1k.py repvgg_A0_deploy.pth --cfg-options model.backbone.deploy=True

Reparameterization

The checkpoints provided are all training-time models. Use the reparameterize tool to switch them to more efficient inference-time architecture, which not only has fewer parameters but also less calculations.

python tools/convert_models/reparameterize_model.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH}

${CFG_PATH} is the config file, ${SRC_CKPT_PATH} is the source chenpoint file, ${TARGET_CKPT_PATH} is the target deploy weight file path.

To use reparameterized weights, the config file must switch to the deploy config files.

python tools/test.py ${deploy_cfg} ${deploy_checkpoint} --metrics accuracy

You can also use backbone.switch_to_deploy() to switch to the deploy mode in Python code. For example:

from mmpretrain.models import RepVGG

backbone = RepVGG(arch='A0')
backbone.switch_to_deploy()

Models and results

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
repvgg-A0_8xb32_in1k	From scratch	8.31	1.36	72.37	90.56	config	model | log
repvgg-A1_8xb32_in1k	From scratch	12.79	2.36	74.23	91.80	config	model | log
repvgg-A2_8xb32_in1k	From scratch	25.50	5.12	76.49	93.09	config	model | log
repvgg-B0_8xb32_in1k	From scratch	3.42	15.82	75.27	92.21	config	model | log
repvgg-B1_8xb32_in1k	From scratch	51.83	11.81	78.19	94.04	config	model | log
repvgg-B1g2_8xb32_in1k	From scratch	41.36	8.81	77.87	93.99	config	model | log
repvgg-B1g4_8xb32_in1k	From scratch	36.13	7.30	77.81	93.77	config	model | log
repvgg-B2_8xb32_in1k	From scratch	80.32	18.37	78.58	94.23	config	model | log
repvgg-B2g4_8xb32_in1k	From scratch	55.78	11.33	79.44	94.72	config	model | log
repvgg-B3_8xb32_in1k	From scratch	110.96	26.21	80.58	95.33	config	model | log
repvgg-B3g4_8xb32_in1k	From scratch	75.63	16.06	80.26	95.15	config	model | log
repvgg-D2se_3rdparty_in1k*	From scratch	120.39	32.84	81.81	95.94	config	model

*Models with * are converted from the official repo. The config files of these models are only for inference. We haven't reproduce the training results.*

Citation

@inproceedings{ding2021repvgg,
  title={Repvgg: Making vgg-style convnets great again},
  author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13733--13742},
  year={2021}
}