MobileNetV4

MobileNetV4 -- Universal Models for the Mobile Ecosystem

This project is implemented in PyTorch, can be used to train your image-datasets for vision tasks.

official source code

For segmentation tasks, please refer this github warehouse

For detection tasks(Based on DETR Detector architecture), please refer this github warehouse

Preparation

Create conda virtual-environment

conda env create -f environment.yml

Download the dataset:

flower_dataset.

Project Structure

├── datasets: Load datasets
    ├── my_dataset.py: Customize reading data sets and define transforms data enhancement methods
    ├── split_data.py: Define the function to read the image dataset and divide the training-set and test-set
    ├── threeaugment.py: Additional data augmentation methods
├── models: MobileNetV4 Model
    ├── build_models.py: Construct MobileNetV4 models
├── util:
    ├── engine.py: Function code for a training/validation process
    ├── losses.py: Knowledge distillation loss, combined with teacher model (if any)
    ├── optimizer.py: Define Sophia optimizer
    ├── samplers.py: Define the parameter of "sampler" in DataLoader
    ├── utils.py: Record various indicator information and output and distributed environment
├── estimate_model.py: Visualized evaluation indicators ROC curve, confusion matrix, classification report, etc.
└── train_gpu.py: Training model startup file (including infer process)

Precautions

Before you use the code to train your own data set, please first enter the train_gpu.py file and modify the data_root, batch_size, num_workers and nb_classes parameters. If you want to draw the confusion matrix and ROC curve, you only need to set the predict parameter to True.
Moreover, you can set the opt_auc parameter to True if you want to optimize your model for a better performance(maybe~).

Use Sophia Optimizer (in util/optimizer.py)

You can use anther optimizer sophia, just need to change the optimizer in train_gpu.py, for this training sample, can achieve better results

# optimizer = create_optimizer(args, model_without_ddp)
optimizer = SophiaG(model.parameters(), lr=2e-4, betas=(0.965, 0.99), rho=0.01, weight_decay=args.weight_decay)

Train this model

Parameters Meaning:

1. nproc_per_node: <The number of GPUs you want to use on each node (machine/server)>
2. CUDA_VISIBLE_DEVICES: <Specify the index of the GPU corresponding to a single node (machine/server) (starting from 0)>
3. nnodes: <number of nodes (machine/server)>
4. node_rank: <node (machine/server) serial number>
5. master_addr: <master node (machine/server) IP address>
6. master_port: <master node (machine/server) port number>

Note:

If you want to use multiple GPU for training, whether it is a single machine with multiple GPUs or multiple machines with multiple GPUs, each GPU will divide the batch_size equally. For example, batch_size=4 in my train_gpu.py. If I want to use 2 GPUs for training, it means that the batch_size on each GPU is 4. Do not let batch_size=1 on each GPU, otherwise BN layer maybe report an error.

train model with single-machine single-GPU：

python train_gpu.py

train model with single-machine multi-GPU：

python -m torch.distributed.run --nproc_per_node=8 train_gpu.py

train model with single-machine multi-GPU:

(using a specified part of the GPUs: for example, I want to use the second and fourth GPUs)

CUDA_VISIBLE_DEVICES=1,3 python -m torch.distributed.run --nproc_per_node=2 train_gpu.py

train model with multi-machine multi-GPU:

(For the specific number of GPUs on each machine, modify the value of --nproc_per_node. If you want to specify a certain GPU, just add CUDA_VISIBLE_DEVICES= to specify the index number of the GPU before each command. The principle is the same as single-machine multi-GPU training)

On the first machine: python -m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=0 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py

On the second machine: python -m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=1 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py

ONNX Deployment

step 1: ONNX export (modify the param of output, model and checkpoint)

python onnx_export.py --model=mobilenetv4_small --output=./mobilenetv4_small.onnx --checkpoint=./output/mobilenetv4_small_best_checkpoint.pth

step2: ONNX optimise

python onnx_optimise.py --model=mobilenetv4_small --output=./mobilenetv4_small_optim.onnx'

step3: ONNX validate (modify the param of data_root and onnx-input)

python onnx_validate.py --data_root=/mnt/d/flower_data --onnx-input=./mobilenetv4_small_optim.onnx

Citation

@article{qin2024mobilenetv4,
  title={MobileNetV4-Universal Models for the Mobile Ecosystem},
  author={Qin, Danfeng and Leichner, Chas and Delakis, Manolis and Fornoni, Marco and Luo, Shixin and Yang, Fan and Wang, Weijun and Banbury, Colby and Ye, Chengxi and Akin, Berkin and others},
  journal={arXiv preprint arXiv:2404.10518},
  year={2024}
}