mindcv_test/configs/dpn

• Dual Path Networks (DPN)
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment
  • References

Dual Path Networks (DPN)

Dual Path Networks

Introduction

Figure 1 shows the model architecture of ResNet, DenseNet and Dual Path Networks. By combining the feature reusage of ResNet and new feature introduction of DenseNet,
DPN could enjoy both benefits so that it could share common features and maintain the flexibility to explore new features. As a result, DPN could achieve better performance with
fewer computation cost compared with ResNet and DenseNet on ImageNet-1K dataset.[1]

Figure 1. Architecture of DPN [1]

Results

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

Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Recipe	Download
dpn92	D910x8-G	79.46	94.49	37.79	yaml	weights
dpn98	D910x8-G	79.94	94.57	61.74	yaml	weights
dpn107	D910x8-G	80.05	94.74	87.13	yaml	weights
dpn131	D910x8-G	80.07	94.72	79.48	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/dpn/dpn92_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/dpn/dpn92_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/dpn/dpn92_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Deployment

Please refer to the deployment tutorial in MindCV.

References

[1] Chen Y, Li J, Xiao H, et al. Dual path networks[J]. Advances in neural information processing systems, 2017, 30.