mindcv/configs/convit

• ConViT
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment
  • References

ConViT

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

Introduction

ConViT combines the strengths of convolutional architectures and Vision Transformers (ViTs).
ConViT introduces gated positional self-attention (GPSA), a form of positional self-attention
that can be equipped with a “soft” convolutional inductive bias.
ConViT initializes the GPSA layers to mimic the locality of convolutional layers,
then gives each attention head the freedom to escape locality by adjusting a gating parameter
regulating the attention paid to position versus content information.
ConViT, outperforms the DeiT (Touvron et al., 2020) on ImageNet,
while offering a much improved sample efficiency.[1]

Figure 1. Architecture of ConViT [1]

Results

Our reproduced model performance on ImageNet-1K is reported as follows.

Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Recipe	Download
convit_tiny	D910x8-G	73.66	91.72	5.71	yaml	weights
convit_tiny_plus	D910x8-G	77.00	93.60	9.97	yaml	weights
convit_small	D910x8-G	81.63	95.59	27.78	yaml	weights
convit_small_plus	D910x8-G	81.80	95.42	48.98	yaml	weights
convit_base	D910x8-G	82.10	95.52	86.54	yaml	weights
convit_base_plus	D910x8-G	81.96	95.04	153.13	yaml	weights

Notes

• Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
• Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.

Quick Start

Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

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

Training

• Distributed Training

It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run

# distrubted training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/imagenet

If the script is executed by the root user, the --allow-run-as-root parameter must be added to mpirun.

Similarly, you can train the model on multiple GPU devices with the above mpirun command.

For detailed illustration of all hyper-parameters, please refer to config.py.

Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.

• Standalone Training

If you want to train or finetune the model on a smaller dataset without distributed training, please run:

# standalone training on a CPU/GPU/Ascend device
python train.py --config configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False

Validation

To validate the accuracy of the trained model, you can use validate.py and parse the checkpoint path with --ckpt_path.

python validate.py -c configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Deployment

Please refer to the deployment tutorial in MindCV.

References

[1] d’Ascoli S, Touvron H, Leavitt M L, et al. Convit: Improving vision transformers with soft convolutional inductive biases[C]//International Conference on Machine Learning. PMLR, 2021: 2286-2296.