LilT

Contrastive Aligned of Vision to Language Through Parameter-Efficient Transfer Learning [ICLR 23]

Setup

Dependencies

conda env create --name lilt --file environment.yaml

Data

See individual sections below for instructions.

Weights

The links to the weights all point to Google Drive. To download them without a browser, do pip install gdown use the ID of the file you want to download. For example, to download model weights at https://drive.google.com/file/d/1mdXQK9Jidk97FrLR2Bqzhbuptt07BPo4/view?usp=drive_link, you would use gdown 1mdXQK9Jidk97FrLR2Bqzhbuptt07BPo4. The weights were saved with torch.save() and can be loaded with torch.load().

• 500K Training Pairs

Pretraining

Pretraining Data

The data format used for pretraining looks like this:

pretraining_data: List[Dict]  = [
    {
        "image": "/media/coco2017/coco-images/000000203564.jpg",
        "image_id": 203564,
        "caption": "A bicycle replica with a clock as the front wheel."
    },
    {
        "image": "/media/coco2017/coco-images/000000322141.jpg",
        "image_id": 322141,
        "caption": "A room with blue walls and a white sink and door."
    }, 
    ...
]

The image_id does not need to correspond to anything for the pretraining (in this example, it is the COCO image IDs). The image field needs to contain an absolute path. This means the path should start with a slash /. You can download examples of pretraining files from Salesforce Research, but note that the paths for each image will need to be changed to match your local setup.

You can download COCO from https://cocodataset.org/#home.

The other pretraining datasets can be downloaded from Huggingface.

• Conceptual Captions
• SBU Captions

Pretraining Commands

See examples/pretrain_{clip, lilt, lit}.sh. In general, the pretraining commands look something like this:

python -m torch.distributed.launch --master_port=43770 --nproc_per_node=4 \
    --use_env PretrainHydra.py --config CLIPAdjustable \
    --output_dir ./storage/lilt_cache/clip_example \
    --overrides text_encoder=base vision_encoder=base \
    +save_last_only=True disable_wandb=False

The model checkpoints will be placed in --output_dir. The --overrides flag can be used to specify overrides for items in the configuration, following the syntax of Hydra. For example, the default size (in the config) for the text encoder is small, but we are overriding it to base from the command line.

The code was tested with V100, A6000, and A100 GPUs. You will need around 32GB of GPU memory (with 4x GPUs) to match the training settings of the CLIP model exactly, but the LilT and LiT models can be trained on 4x GPUs for much less memory. Of course, you can always lower the batch size if you want to train on a single GPU.

Evaluation

Classification

First, set up the ImageNetv2 dataset as described here. Next, edit classification.py, specifically this part:

test_dataset = ImageNetV2Dataset(
    location="./storage/10/imagenet_v2", transform=test_transform
)

to point to wherever you downloaded ImageNetV2. To run multimodal classification, you can use a command like the following:

python -m torch.distributed.launch --nproc_per_node=1 --use_env classification.py \
--config Retrieval_AdjustableCLIP_Flickr \
--output_dir ./clf_output \
 --checkpoint ./storage/lilt_example/checkpoint_14.pth \ --evaluate \
  --overrides text_encoder=base vision_encoder=base

The value of the --config flag is not a typo, we just reuse the same config for classification and retrieval.

Retrieval

Follow the instructions in codezakh/SIMLA#zero-shot-flickr to set up the data for Flickr. The only change is that you should edit configs-v2/Retrieval_AdjustableCLIP_Flickr.yaml to point to the downloaded dataset rather than configs/Retrieval_flickr.yaml.

See examples/evaluate_{clip, lilt, lit}.sh for evaluation scripts.

Multilingual Retrieval

Download the XD10 dataset from official repo. Then, run the script process_xd10.py, making sure to edit the paths at the top of the file:

XTD10_DIR = Path("/home/zkhan/Cross-lingual-Test-Dataset-XTD10/XTD10")
COCO_TRAIN_DIR = Path("./storage/10/coco2014/train2014")
OUTPUT_DIR = Path("./storage/10/multilingual_coco2014_xtd10")

so that XD10_DIR and COCO_TRAIN_DIR point to where you have downloaded the respective datasets.

You can then evaluate a pretrained model like this:

python -m torch.distributed.launch --master_port=40770 \
--nproc_per_node=1 \
--use_env zero_shot_retrieval.py \
--config Retrieval_AdjustableCLIP_COCO \
--output_dir ./eval_output \
--checkpoint ./path/to/checkpoint.pth \
--evaluate --overrides text_encoder=base_multilingual \
vision_encoder=base \
test_file=$XD10_OUTPUT_DIR/val.json"

where XD10_OUTPUT_DIR is the place you told the process_xd10.py script to put the preprocessed dataset.

To evaluate on a specific language as done in the paper, change test_file=$XD10_OUTPUT_DIR/val.json to test_file=$XD10_OUTPUT_DIR/val_{lang_abbrv}.json where lang_abbrv is one of the following:

LANGUAGES = ("es", "it", "ko", "pl", "ru", "tr", "zh")

Citation

@inproceedings{ContrastiveAlignmentVisionFu2023,
  title = {Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning},
  booktitle = {The Eleventh International Conference on Learning Representations},
  author = {Khan, Zaid and Fu, Yun},
  year = {2023},
}

Acknowledgements

This code borrows heavily from https://github.com/salesforce/ALBEF.