mindcv/configs/visformer

• Visformer
• • Introduction
  • Results
  • ImageNet-1k
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment
  • References

Visformer

Visformer: The Vision-friendly Transformer

Introduction

Visformer, or Vision-friendly Transformer, is an architecture that combines Transformer-based architectural features with those from convolutional neural network architectures. Visformer adopts the stage-wise design for higher base performance. But self-attentions are only utilized in the last two stages, considering that self-attention in the high-resolution stage is relatively inefficient even when the FLOPs are balanced. Visformer employs bottleneck blocks in the first stage and utilizes group 3 × 3 convolutions in bottleneck blocks inspired by ResNeXt. It also introduces BatchNorm to patch embedding modules as in CNNs. [2]

Figure 1. Network Configuration of Visformer [1]

Results

ImageNet-1k

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

Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Recipe	Download
visformer_tiny	D910x8-G	78.28	94.15	10.33	yaml	weights
visformer_tiny_v2	D910x8-G	78.82	94.41	9.38	yaml	weights
visformer_small	D910x8-G	81.76	95.88	40.25	yaml	weights
visformer_small_v2	D910x8-G	82.17	95.90	23.52	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

# distributed training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/visformer/visformer_tiny_ascend.yaml --data_dir /path/to/imagenet

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/visformer/visformer_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/visformer/visformer_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Deployment

To deploy online inference services with the trained model efficiently, please refer to the deployment tutorial.

References

[1] Chen Z, Xie L, Niu J, et al. Visformer: The vision-friendly transformer. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 589-598.

[2] Visformer, https://paperswithcode.com/method/visformer