merlot_reserve

Code release for "MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound"

MERLOT Reserve (in submission) is a model for learning joint representations of vision, language, and sound from YouTube. The learned model can be used in a zero-shot or finetuned setting, where it does well on tasks like VCR and TVQA.

Visit our project page at rowanzellers.com/merlotreserve or read the full paper to learn more.

What's here

We are releasing the following:

• JAX code, and model checkpoints, for the MERLOT model
• Code for pretraining the model
• Code for finetuning the model on VCR and TVQA
• Code for doing zero-shot inference with the model

Environment and setup

There are two different ways to run MERLOT Reserve:

• Pretraining on videos You'll need a TPU Pod VM for this. This step shouldn't be necessary for most people, as we have released model checkpoints.
• Finetuning on VCR or TVQA I've done this on a TPU v3-8 VM. This should be possible on GPU(s), but I haven't tested this on such hardware.
• Zero-shot inference I've ran this on a GPU (even an older, Titan X from 2016 works.)

Installation on a GPU Machine

Install Cuda 11.4 (I used this link) and CUDNN 8.2. You might have to add something like this to your PATH:

export LD_LIBRARY_PATH=/usr/local/cuda/lib64

Create the environment:

conda create --name mreserve python=3.8 && conda activate mreserve
conda install -y python=3.8 tqdm numpy pyyaml scipy ipython cython typing h5py pandas matplotlib

# Install jax
pip install jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_releases.html
# If doing this on TPUs instead of locally...
# pip install "jax[tpu]>=0.2.18" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

# This is needed sometimes https://stackoverflow.com/questions/66060487/valueerror-numpy-ndarray-size-changed-may-indicate-binary-incompatibility-exp
pip uninstall numpy
pip install numpy==1.19.5

pip install -r requirements.txt

You can then try out the interactive script at demo/demo_video.py. It will handle downloading the model checkpoint for you.

Installation on a Cloud TPU VM

See the instructions in pretrain/ to set up your environment on a TPU v3-8 VM.

Checkpoints

These should get auto-downloaded if you use PretrainedMerlotReserve in mreserve/modeling.py. All are flax checkpoint files:

# pretrained checkpoints
gs://merlotreserve/ckpts/base
gs://merlotreserve/ckpts/base_resadapt
gs://merlotreserve/ckpts/large
gs://merlotreserve/ckpts/large_resadapt

# finetuned checkpoints
gs://merlotreserve/vcr_ckpts/vcr_finetune_base
gs://merlotreserve/vcr_ckpts/vcr_finetune_large

gs://merlotreserve/tvqa_ckpts/tvqa_finetune_base
gs://merlotreserve/tvqa_ckpts/tvqa_finetune_large

# TVQA Data
gs://merlotreserve/finetune_data/tvqa/

# VCR data
gs://merlotreserve/finetune_data/vcr/