mindcv_fixed/configs/repmlp

• RepMLPNet
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment (optional)

RepMLPNet

RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality

Introduction

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Accepted to CVPR-2022!

The latest version: https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_RepMLPNet_Hierarchical_Vision_MLP_With_Re-Parameterized_Locality_CVPR_2022_paper.pdf

Compared to the old version, we no longer use RepMLP Block as a plug-in component in traditional ConvNets. Instead, we build an MLP architecture with RepMLP Block with a hierarchical design. RepMLPNet shows favorable performance, compared to the other vision MLP models including MLP-Mixer, ResMLP, gMLP, S2-MLP, etc.

Of course, you may also use it in your model as a building block.

The overlap between the two versions is the Structural Re-parameterization method (Localtiy Injection) that equivalently merges conv into FC. The architectural designs presented in the latest version significantly differ from the old version (ResNet-50 + RepMLP).

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
repmlp_t224	D910x8	76.649		38.3	1011s/epoch	15.8ms/step	model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Quick Start

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Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Hyper-parameters. The hyper-parameter configurations for producing the reported results are stored in the yaml files in mindcv/configs/repmlp folder. For example, to train with one of these configurations, you can run:

  # train repmlp_t224 on 8 Ascends
  bash ./scripts/run_distribution_ascend.sh ./scripts/rank_table_8pcs.json [DATASET_PATH] ./config/repmlp/repmlp_T224.yaml
  

  Note that the number of GPUs/Ascends and batch size will influence the training results. To reproduce the training result at most, it is recommended to use the same number of GPUs/Ascneds with the same batch size.

Detailed adjustable parameters and their default value can be seen in config.py.

Validation

• To validate the trained model, you can use validate.py. Here is an example for densenet121 to verify the accuracy of
  pretrained weights.

  python validate.py --model=RepMLPNet_T224 --data_dir=imagenet_dir --val_split=val --ckpt_path
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.