x-clip (wip)

A concise but complete implementation of CLIP with various experimental improvements from recent papers

Install

$ pip install x-clip

Usage

import torch
from x_clip import CLIP

clip = CLIP(
    dim_text = 512,
    dim_image = 512,
    dim_latent = 512,
    num_text_tokens = 10000,
    text_enc_depth = 6,
    text_seq_len = 256,
    text_heads = 8,
    num_visual_tokens = 512,
    visual_enc_depth = 6,
    visual_image_size = 256,
    visual_patch_size = 32,
    visual_heads = 8,
    use_all_token_embeds = True   # whether to use fine-grained contrastive learning (FILIP)
)

text = torch.randint(0, 10000, (4, 256))
images = torch.randn(4, 3, 256, 256)
mask = torch.ones_like(text).bool()

loss = clip(text, images, text_mask = mask, return_loss = True)
loss.backward()

Citations

@misc{radford2021learning,
    title   = {Learning Transferable Visual Models From Natural Language Supervision}, 
    author  = {Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
    year    = {2021},
    eprint  = {2103.00020},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{yao2021filip,
    title   = {FILIP: Fine-grained Interactive Language-Image Pre-Training}, 
    author  = {Lewei Yao and Runhui Huang and Lu Hou and Guansong Lu and Minzhe Niu and Hang Xu and Xiaodan Liang and Zhenguo Li and Xin Jiang and Chunjing Xu},
    year    = {2021},
    eprint  = {2111.07783},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}