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This is a warehouse for GroupMixFormer-pytorch-model, can be used to train your image datasets for classification tasks.
├── 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: GroupMixFormer Model
├── build_model.py: Construct "GroupMixFormer" model
├── 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
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 and nb_classes parameters. If you want to draw the confusion matrix and ROC curve, you only need to remove the comments of Plot_ROC and Predictor at the end of the code. For the third parameter, you should change it to the path of your own model weights file(.pth). Taking the model(groupmixformer_tiny) as an example, inputting a 3-channel image with a height and width of 224, the number of model parameters that need to be trained is as follows:
===================================================================================================================
Total params: 10,709,357
Trainable params: 10,709,357
Non-trainable params: 0
Total mult-adds (M): 466.37
===================================================================================================================
Input size (MB): 0.60
Forward/backward pass size (MB): 342.21
Params size (MB): 42.84
Estimated Total Size (MB): 385.65
===================================================================================================================
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)
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>
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, each GPU will divide the batch_size. That means batch_size=2 on each GPU. Do not let batch_size=1 on each GPU, otherwise BN layer maybe report an error. If you recive an error like "ONE-PEACE training and evaluation script: error: unrecognized arguments: --local-rank=1" when you use distributed multi-GPUs training, just replace the command "torch.distributed.launch" to "torch.distributed.run".
python train_gpu.py
python -m torch.distributed.launch --nproc_per_node=8 train_gpu.py
(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.launch --nproc_per_node=2 train_gpu.py
(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.launch --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.launch --nproc_per_node=1 --nnodes=2 --node_rank=1 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py
@inproceedings{Ge2023AdvancingVT,
title={Advancing Vision Transformers with Group-Mix Attention},
author={Chongjian Ge and Xiaohan Ding and Zhan Tong and Li Yuan and Jiangliu Wang and Yibing Song and Ping Luo},
year={2023},
url={https://api.semanticscholar.org/CorpusID:265456206}
}
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