Awesome

FDSL on VisualAtom

Summary

The repository contains VisualAtom Construction, Pre-training and Fine-tuning in Python/PyTorch. The repository is based on the paper: Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka and Rio Yokota, "Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023. [Project] [PDF] [Dataset] [Poster] [Supp]

Updates

Update (Mar. 24, 2023)

VisualAtom Construction & Pre-training & Fine-tuning scripts are shared here.
Downloadable pre-training models : [Pre-trained Models]

Update (Feb. 28, 2023)

Our paper was accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023. [PDF (preprint)]

Citation

If you use this scripts, please cite the following paper:

@InProceedings{takashima2023visual,
    author    = {Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka and Rio Yokota},
    title     = {Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {18579-18588}
}

Requirements

Python 3.x (worked at 3.8.2)
CUDA (worked at 10.2)
CuDNN (worked at 8.0)
NCCL (worked at 2.7)
OpenMPI (worked at 4.1.3)
Graphic board (worked at single/four NVIDIA V100)

Please install packages with the following command.

$ pip install --upgrade pip
$ pip install -r requirements.txt

VisualAtom Construction (README)

$ cd visual_atomic_renderer
$ bash make_VisualAtom.sh

You can also download raw VisualAtom-1k here : zenodo

Pre-training

We used almost the same scripts as in Kataoka_2022_CVPR for our pre-training.

Run the python script pretrain.py, you can pre-train with your dataset.

Basically, you can run the python script pretrain.py with the following command.

Example : with deit_base, pre-train VisualAtom-21k, 4 GPUs (Batch Size = 64×4 = 256)

$ mpirun -npernode 4 -np 4 \
  python pretrain.py /PATH/TO/VisualAtom21000 \
    --model deit_base_patch16_224 --experiment pretrain_deit_base_VisualAtom21000_1.0e-3 \
    --input-size 3 224 224 \
    --sched cosine_iter --epochs 90 --lr 1.0e-3 --weight-decay 0.05 \
    --min-lr 1.0e-5 --warmup-lr 1.0e-6 --warmup-iter 5000 --cooldown-epochs 0 \
    --batch-size 64 --opt adamw --num-classes 21000 \
    --smoothing 0.1 --drop-path 0.1 --aa rand-m9-mstd0.5-inc1 \
    --repeated-aug --mixup 0.8 --cutmix 1.0 --reprob 0.25 \
    --remode pixel --interpolation bicubic --hflip 0.0 \
    -j 16 --pin-mem --eval-metric loss \
    --interval_saved_epochs 10 --output ./output/pretrain \
    --amp \
    --log-wandb

Note

--batch-size means batch size per process. In the above script, for example, you use 4 GPUs (4 process), so overall batch size is 64×4(=256).

In our paper research, for datasets with more than 21k categories, we basically pre-trained with overall batch size of 8192 (64×128).

If you wish to distribute pre-train across multiple nodes, the following must be done.

Set the MASTER_ADDR environment variable which is the IP address of the machine in rank 0.

Set the -npernode and -np arguments of mpirun command.

-npernode means GPUs (processes) per node and -np means overall the number of GPUs (processes).

Or you can run the job script scripts/pretrain.sh (support multiple nodes training with OpenMPI). Note, the setup is multiple nodes and using a large number of GPUs (32 nodes and 128 GPUs for pre-train).

When running with the script above, please make your dataset structure as following.

/PATH/TO/VisualAtom21000/
    image/
        00000/
        00000_0000.png
        00000_0001.png
        ...
        00001/
        00001_0000.png
        00001_0001.png
        ...
        ...
    ...

After above pre-training, trained models are created like output/pretrain/pretrain_deit_base_VisualAtom21000_1.0e-3/model_best.pth.tar and output/pretrain/pretrain_deit_base_VisualAtom21000_1.0e-3/last.pth.tar. Moreover, you can resume the training from a checkpoint by setting --resume parameter.

Please see the script and code files for details on each arguments.

Pre-training with shard dataset

Shard dataset is also available for accelerating IO processing. To make shard dataset, please refer to this repository: https://github.com/webdataset/webdataset. Here is an Example of training with shard dataset.

Example : with deit_base, pre-train VisualAtom-21k(shard), 4 GPUs (Batch Size = 64×4 = 256)

$ mpirun -npernode 4 -np 4 \
  python pretrain.py /NOT/WORKING \
    -w --trainshards /PATH/TO/VisualAtom21000/SHARDS-{000000..002099}.tar \
    --model deit_base_patch16_224 --experiment pretrain_deit_base_VisualAtom21000_1.0e-3_shards \
    --input-size 3 224 224 \
    --sched cosine_iter --epochs 90 --lr 1.0e-3 --weight-decay 0.05 \
    --min-lr 1.0e-5 --warmup-lr 1.0e-6 --warmup-iter 5000 --cooldown-epochs 0 \
    --batch-size 64 --opt adamw --num-classes 21000 \
    --smoothing 0.1 --drop-path 0.1 --aa rand-m9-mstd0.5-inc1 \
    --repeated-aug --mixup 0.8 --cutmix 1.0 --reprob 0.25 \
    --remode pixel --interpolation bicubic --hflip 0.0 \
    -j 1 --eval-metric loss --no-prefetcher \
    --interval_saved_epochs 10 --output ./output/pretrain \
    --amp \
    --log-wandb

When running with the script above with shard dataset, please make your shard dataset structure as following.

/PATH/TO/VisualAtom21000/
    SHARDS-000000.tar
    SHARDS-000001.tar
    ...
    SHARDS-002099.tar

Pre-trained models

Our pre-trained models are available in this [Link].

We have mainly prepared three different pre-trained models. These pre-trained models are ViT-Tiny/Base (patch size of 16, input size of 224) pre-trained on VisualAtom-1k/21k and Swin-Base (patch size of 7, window size of 7, input size of 224) pre-trained on VisualAtom-21k.

vit_tiny_with_visualatom_1k.pth.tar: timm model is deit_tiny_patch16_224, pre-trained on VisualAtom-1k
vit_base_with_visualatom_21k.pth.tar: timm model is deit_base_patch16_224, pre-trained on VisualAtom-21k
swin_base_with_visualatom_21k.pth.tar: timm model is swin_base_patch4_window7_224, pre-trained on VisualAtom-21k

Fine-tuning

We used fine-tuning scripts based on Nakashima_2022_AAAI.

Run the python script finetune.py, you additionally train other datasets from your pre-trained model.

In order to use the fine-tuning code, you must prepare a fine-tuning dataset (e.g., CIFAR-10/100, ImageNet-1k, Pascal VOC 2012). You should set the dataset as the following structure.

/PATH/TO/DATASET/
  train/
    class1/
      img1.jpeg
      ...
    class2/
      img2.jpeg
      ...
    ...
  val/
    class1/
      img3.jpeg
      ...
    class2/
      img4.jpeg
      ...
    ...

Basically, you can run the python script finetune.py with the following command.

Example : with deit_base, fine-tune CIFAR10 from pre-trained model (with VisualAtom-21k), 8 GPUs (Batch Size = 96×8 = 768)

$ mpiexec -npernode 4 -np 8 \
    python -B finetune.py data=colorimagefolder \
    data.baseinfo.name=CIFAR10 data.baseinfo.num_classes=10 \
    data.trainset.root=/PATH/TO/CIFAR10/TRAIN data.baseinfo.train_imgs=50000 \
    data.valset.root=/PATH/TO/CIFAR10/VAL data.baseinfo.val_imgs=10000 \
    data.loader.batch_size=96 \
    ckpt=./output/pretrain/pretrain_deit_base_VisualAtom21000_1.0e-3/last.pth.tar \
    model=vit model.arch.model_name=vit_base_patch16_224 \
    model.optim.optimizer_name=sgd model.optim.learning_rate=0.01 \
    model.optim.weight_decay=1.0e-4 model.scheduler.args.warmup_epochs=10 \
    epochs=1000 mode=finetune \
    logger.entity=YOUR_WANDB_ENTITY_NAME logger.project=YOUR_WANDB_PROJECT_NAME logger.group=YOUR_WANDB_GROUP_NAME \
    logger.experiment=finetune_deit_base_CIFAR10_batch768_from_VisualAtom21000_1.0e-3 \
    logger.save_epoch_freq=100 \
    output_dir=./output/finetune

Or you can run the job script scripts/finetune.sh (support multiple nodes training with OpenMPI).