Awesome

Generative Robustness

This repo contains the code for the experiments in the 'Leaving Reality to Imagination: Robust Classification via Generated Datasets' paper. Arxiv link: https://arxiv.org/pdf/2302.02503.pdf

Colab

Accepted as Oral Presentation at RTML ICLR 2023. Accepted at SPIGM ICML 2023.

Link to Generated ImageNet-1K dataset

You can download the Base-Generated-ImageNet-1K dataset from here. Even though we discuss three variants of generated data in the paper, we make generations using captions of the class labels public for novel usecases by the community.

You can download the Finetuned-Generated-ImageNet-1K dataset from here.

Structure of the dataset looks like:

* train (1000 folders)
    * n01440764 (1300 images)
        * image1.jpeg
        *  .
        * imageN.jpeg
    * .
    * .
* val (1000 images)
    * n01440764 (50 images)
        * image1.jpeg
        *  .
        * imageN.jpeg
    * .
    * .

Finetuning Stable Diffusion on the Real ImageNet-1K

We provide the finetuning details and as well as the finetuned Stable Diffusion model at https://huggingface.co/hbXNov/ucla-mint-finetune-sd-im1k.

Colab Notebook: here

The finetuning code could be found in this folder: sd_finetune. Most of this code is adopted from the diffusers library - text-to-image. We are really thankful to the authors!!

The rest of the README will focus on generating data from Stable Diffusion in a zero-shot manner, and training ImageNet classifiers efficiently using FFCV.

Data Generation Using Stable Diffusion

Stable Diffusion is a popular text-to-image generative model. Most of the code is adapted from the very popular diffusers library from HuggingFace.

However, it might not be straightforward to generate images from Stable Diffusion on multiple GPUs. To that end, we use the accelerate package from Huggingface.

Requirements

• Both Linux and Windows are supported, but we strongly recommend Linux for performance and compatibility reasons.
• 64-bit Python 3.7+ installation.
• We used 5 A6000s 24GB DRAM GPUs for generation.

Setup

1. git clone https://github.com/Hritikbansal/leaving_reality_to_imagination.git
2. cd leaving_reality_to_imagination
3. conda env create -f environment.yml
4. pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0+cu117 -f https://download.pytorch.org/whl/torch_stable.html (Replace based on your computer's hardware)
5. accelerate config
- This machine
- multi-GPU
- (How many machines?) 1
- (optimize with dynamo?) NO
- (Deepspeed?) NO
- (FullyShardedParallel?) NO
- (MegatronLM) NO
- (Num of GPUs) 5
- (device ids) 0,1,2,3,4
- (np/fp16/bp16) no

Files

1. generate_images_captions generates the images conditioned on the diverse text prompts (Class Labels).
2. generate_images generates the images conditioned on the images (Real Images).
3. generate_images_i2i generates the images conditioned on the encoded images and text (Real Images and Class Labels).
4. conditional ldm generates images from the class-conditional latent diffusion model. You can download the model ckpt from Stable Diffusion repo.

Move the classes.py and folder_to_class.csv to the imagenet_dir.

Commands

accelerate launch --num_cpu_threads_per_process 8 -m generation.generate_images_captions --batch_size 8 --data_dir <imagenet_dir> --save_image_gen <save dir> --diversity --split val

accelerate launch --num_cpu_threads_per_process 8 -m generation.generate_images --batch_size 2 --eval_test_data_dir <imagenet_dir> --save_image_gen <save dir> --split val

accelerate launch --num_cpu_threads_per_process 8 -m generation.generate_images_i2i --batch_size 12 --data_dir <imagenet_dir> --save_image_gen <save dir> --split val --diversity

accelerate launch --num_cpu_threads_per_process 8 conditional_ldm.py --config cin256-v2.yaml --checkpoint <model checkpoint> --save_image_gen <save dir>

Training ImageNet Models Using FFCV

We suggest the users to create a separate FFCV conda environment for training ImageNet models.

Preparing the Dataset

Following the ImageNet training pipeline of FFCV for ResNet50, generate the dataset with the following command (IMAGENET_DIR should point to a PyTorch style ImageNet dataset):

# Required environmental variables for the script:
cd train/
export IMAGENET_DIR=/path/to/pytorch/format/imagenet/directory/
export WRITE_DIR=/your/path/here/

# Serialize images with:
# - 500px side length maximum
# - 50% JPEG encoded, 90% raw pixel values
# - quality=90 JPEGs
./write_imagenet.sh 500 0.50 90

Note that we prepare the dataset with the following FFCV configuration:

• ResNet-50 training: 50% JPEG 500px side length (train_500_0.50_90.ffcv)
• ResNet-50 evaluation: 0% JPEG 500px side length (val_500_uncompressed.ffcv)

• We have made some custom edits to write_imagenet.py to generate augmented imagenet data

Training

CUDA_VISIBLE_DEVICES=0,1,2,3,4 python train_imagenet.py --config-file resnet_configs/resnext50.yaml --data.train_dataset=<path to train ffcv> --data.val_dataset=<path to validation ffcv> --data.num_workers=8 --logging.folder=<logging folder> --model.num_classes=100 (if imagenet 100) --training.distributed=1 --dist.world_size=5 

Evaluation

CUDA_VISIBLE_DEVICES=0,1,2,3,4 python train_imagenet.py --config-file resnet_configs/rn18_88_epochs.yaml --data.train_dataset=<path to train ffcv> --data.val_dataset=<path to validation ffcv> --data.num_workers=8 --training.path=<path to final_weights.pt> --model.num_classes=1000 --training.distributed=1 --dist.world_size=5 --training.eval_only=1

Note

1. Since ImageNet-R and ObjectNet do not share all the classes with ImageNet1K, we use an additional validation.imr or validation.obj flag while evaluating on these datasets.
2. create_imagenet_subset is used to create the random subset containing 100 classes. mappings contains the relevant imagenet100.txt file.

Natural Distribution Shift Datasets

We use (a) ImageNet-Sketch, (b) ImageNet-R, (c) ImageNet-V2, and (d) ObjectNet in our work. The users can navigate to their respective sources to download the data.

1. rename_imagenetv2 renames the imagenetv2 folders that are original named based on indices 0-1000 to original imagenet folder names n0XXXXX.
2. subset_objectnet_im is used to create a subset of ObjectNet classes that overlap with ImageNet-100/1000.