import torch
import models

img = torch.randn(1, 3, 256, 256)
net = models.MobileViT_S()

# XXS: 1.3M 、 XS: 2.3M 、 S: 5.6M
print("MobileViT-S params: ", sum(p.numel() for p in net.parameters()))
print(f"Output shape: {net(img).shape}")

python main.py

CUDA_VISIBLE_DEVICES=3,4 python -m torch.distributed.launch --nproc_per_node=2 --master_port=6666 main_ddp.py

optional arguments:
  -h, --help            show this help message and exit
  --gpu_device GPU_DEVICE
                        Select specific GPU to run the model
  --batch-size N        Input batch size for training (default: 64)
  --epochs N            Number of epochs to train (default: 20)
  --num-class N         Number of classes to classify (default: 10)
  --lr LR               Learning rate (default: 0.01)
  --weight-decay WD     Weight decay (default: 1e-5)
  --model-path PATH     Path to save the model

@InProceedings{Sachin2021,
  title = {MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER},
  author = {Sachin Mehta and Mohammad Rastegari},
  booktitle = {},
  year = {2021}
}