A concise but complete implementation of CLIP with various experimental improvements from recent papers
$ pip install x-clipimport 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,
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)
decoupled_contrastive_learning = True, # use decoupled contrastive learning (DCL) objective function, removing positive pairs from the denominator of the InfoNCE loss (CLOOB + DCL)
extra_latent_projection = True, # whether to use separate projections for text-to-image vs image-to-text comparisons (CLOOB)
use_visual_ssl = True, # whether to do self supervised learning on iages
visual_ssl_type = 'simclr', # can be either 'simclr' or 'simsiam', depending on using DeCLIP or SLIP
use_mlm = False, # use masked language learning (MLM) on text (DeCLIP)
text_ssl_loss_weight = 0.05, # weight for text MLM loss
image_ssl_loss_weight = 0.05 # weight for image self-supervised learning loss
)
# mock data
text = torch.randint(0, 10000, (4, 256))
images = torch.randn(4, 3, 256, 256)
mask = torch.ones_like(text).bool()
# train
loss = clip(
text,
images,
text_mask = mask, # mask for text
freeze_image_encoder = False, # whether to freeze image encoder if using a pretrained image net, proposed by LiT paper
return_loss = True # needs to be set to True to return contrastive loss
)
loss.backward()You can also pass in an external visual transformer / residual net. You simply have to make sure your image encoder returns a set of embeddings in the shape of batch x seq x dim, and make sure dim_image is properly specified as the dimension of the returned embeddings. Below is an example using vision transformer from vit_pytorch
$ pip install vit_pytorch>=0.25.6import torch
from x_clip import CLIP
from vit_pytorch import ViT
from vit_pytorch.extractor import Extractor
base_vit = ViT(
image_size = 256,
patch_size = 32,
num_classes = 1000,
dim = 512,
depth = 6,
heads = 16,
mlp_dim = 2048,
dropout = 0.1,
emb_dropout = 0.1
)
vit = Extractor(
base_vit,
return_embeddings_only = True
)
clip = CLIP(
image_encoder = vit,
dim_image = 512, # must be set as the same dimensions as the vision transformer above
dim_text = 512,
dim_latent = 512,
num_text_tokens = 10000,
text_enc_depth = 6,
text_seq_len = 256,
text_heads = 8
)
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()Finally, one can also have the text transformer be externally defined. It will need to return the embeddings including the CLS token, for now.
import torch
from x_clip import CLIP, TextTransformer
from vit_pytorch import ViT
from vit_pytorch.extractor import Extractor
base_vit = ViT(
image_size = 256,
patch_size = 32,
num_classes = 1000,
dim = 512,
depth = 6,
heads = 16,
mlp_dim = 2048,
dropout = 0.1,
emb_dropout = 0.1
)
image_encoder = Extractor(
base_vit,
return_embeddings_only = True
)
text_encoder = TextTransformer(
dim = 512,
num_tokens = 10000,
max_seq_len = 256,
depth = 6,
heads = 8
)
clip = CLIP(
image_encoder = image_encoder,
text_encoder = text_encoder,
dim_image = 512,
dim_text = 512,
dim_latent = 512
)
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()@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}
}@misc{fürst2021cloob,
title = {CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP},
author = {Andreas Fürst and Elisabeth Rumetshofer and Viet Tran and Hubert Ramsauer and Fei Tang and Johannes Lehner and David Kreil and Michael Kopp and Günter Klambauer and Angela Bitto-Nemling and Sepp Hochreiter},
year = {2021},
eprint = {2110.11316},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{yeh2021decoupled,
title = {Decoupled Contrastive Learning},
author = {Chun-Hsiao Yeh and Cheng-Yao Hong and Yen-Chi Hsu and Tyng-Luh Liu and Yubei Chen and Yann LeCun},
year = {2021},
eprint = {2110.06848},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{zhai2021lit,
title = {LiT: Zero-Shot Transfer with Locked-image Text Tuning},
author = {Xiaohua Zhai and Xiao Wang and Basil Mustafa and Andreas Steiner and Daniel Keysers and Alexander Kolesnikov and Lucas Beyer},
year = {2021},
eprint = {2111.07991},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@misc{li2021supervision,
title = {Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm},
author = {Yangguang Li and Feng Liang and Lichen Zhao and Yufeng Cui and Wanli Ouyang and Jing Shao and Fengwei Yu and Junjie Yan},
year = {2021},
eprint = {2110.05208},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@Article{mu2021slip,
author = {Norman Mu and Alexander Kirillov and David Wagner and Saining Xie},
title = {SLIP: Self-supervision meets Language-Image Pre-training},
journal = {arXiv preprint arXiv:2112.12750},
year = {2021},
}