EVA-CLIP-8B-SAFETENSORS

[!IMPORTANT] Original Model Link : https://huggingface.co/BAAI/EVA-CLIP-8B

name: EVA-CLIP-8B-SFT
base_model: BAAI/EVA-CLIP-8B
license: apache-2.0
pipeline_tag: image-to-text
size:
- 30100210576
- 30GB
- 10GB x 3
- 3GB x 1
tasks :
 - image-to-text
 - image-text-to-text
 - visual-question-answering
 - zero-shot-image-classification
language: en
datasets :
 - laion/laion2B-en
 - kakaobrain/coyo-700m

EVA-CLIP-8B-SFT

EVA-CLIP-8B in Safetensors format with no quantization

Usage

<details><summary>Huggingface Version</summary>

from PIL import Image
from transformers import AutoModel, AutoConfig
from transformers import CLIPImageProcessor, pipeline, CLIPTokenizer
import torch
import torchvision.transforms as T
from torchvision.transforms import InterpolationMode

image_path = "CLIP.png"
model_name_or_path = "BAAI/EVA-CLIP-8B" # or /path/to/local/EVA-CLIP-8B
image_size = 224

processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")

# use image processor with conig
# processor = CLIPImageProcessor(size={"shortest_edge":image_size}, do_center_crop=True, crop_size=image_size)

## you can also directly use the image processor by torchvision
## squash
# processor = T.Compose(
#     [
#         T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
#         T.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC),
#         T.ToTensor(),
#         T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
#     ]
# )
## shortest
## processor = T.Compose(
#     [
#         T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
#         T.Resize(image_size, interpolation=InterpolationMode.BICUBIC),
#         T.CenterCrop(image_size),
#         T.ToTensor(),
#         T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
#     ]
# )

model = AutoModel.from_pretrained(
    model_name_or_path, 
    torch_dtype=torch.float16,
    trust_remote_code=True).to('cuda').eval()

image = Image.open(image_path)
captions = ["a diagram", "a dog", "a cat"]
tokenizer = CLIPTokenizer.from_pretrained(model_name_or_path)
input_ids = tokenizer(captions,  return_tensors="pt", padding=True).input_ids.to('cuda')
input_pixels = processor(images=image, return_tensors="pt", padding=True).pixel_values.to('cuda')

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(input_pixels)
    text_features = model.encode_text(input_ids)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)

label_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print(f"Label probs: {label_probs}")

</details> <details><summary>PyTorch Version</summary>

GitHub

import torch
from eva_clip import create_model_and_transforms, get_tokenizer
from PIL import Image

model_name = "EVA-CLIP-8B" 
pretrained = "eva_clip" # or "/path/to/EVA_CLIP_18B_psz14_s6B.fp16.pt"

image_path = "CLIP.png"
caption = ["a diagram", "a dog", "a cat"]

device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, processor = create_model_and_transforms(model_name, pretrained, force_custom_clip=True)
tokenizer = get_tokenizer(model_name)
model = model.to(device)

image = processor(Image.open(image_path)).unsqueeze(0).to(device)
text = tokenizer(["a diagram", "a dog", "a cat"]).to(device)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)

    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)

You can leverage deepspeed.zero.Init() with deepspeed zero stage 3 if you have limited CPU memory. For loading a pretrained checkpoint in the context of using deepspeed.zero.Init(), it's advised to use the load_zero_partitions() function in eva_clip/factory.py.

</details>

BibTeX & Citation

@article{EVA-CLIP-8B,
  title={EVA-CLIP-8B: Scaling CLIP to 18 Billion Parameters}, 
  author={Quan Sun and Jinsheng Wang and Qiying Yu and Yufeng Cui and Fan Zhang and Xiaosong Zhang and Xinlong Wang},
  journal={arXiv preprint arXiv:2402.04252},
  year={2023}
}