AltCLIP-m18
名称 Name 任务 Task 语言 Language(s) 模型 Model Github AltCLIP-m18 Text-Image Multilingual CLIP FlagAI
简介 Brief Introduction
继双语模型AltCLIP与9语模型AltCLIP-m9之后，我们训练了18语CLIP模型。命名为AltCLIP-m18。它支持英语、中文、日语、泰语、韩语、印地语、乌克兰语、阿拉伯语、土耳其语、越南语、波兰语、荷兰语、葡萄牙语、意大利语、西班牙语、德语、法语和俄语。
AltCLIP-m18模型可以为AltDiffusion-m18模型提供支持，关于AltDiffusion模型的具体信息可查看
此教程 。
模型代码已经在
FlagAI 上开源，权重位于我们搭建的
modelhub 上。我们还提供了微调，推理，验证的脚本，欢迎试用。
Following the bilingual model AltCLIP and the nine-language model AltCLIP-m9, we trained the eighteen-language CLIP model, Named AltCLIP-m18. It supports English, Chinese, Japanese, Thai, Korean, Hindi, Ukrainian, Arabic, Turkish, Vietnamese, Polish, Dutch, Portuguese, Italian, Spanish, German, French, and Russian.
The AltCLIP-m18 model can provide support for the AltDiffusion-m18 model. Specific information on the AltDiffusion modle can be found in
this tutorial .
The model code has been open sourced on
FlagAI and the weights are located on
modelhub . We also provide scripts for fine-tuning, inference, and validation, so feel free to try them out.
训练数据集 Training datasets
[1] WuDaoMM数据集仅用于学术研究，任何使用该数据集都应该遵循以下要求。WuDaoMM不拥有这些图片的版权。 图片的使用必须遵守
Flickr使用条款 。 图像的用户对使用数据集承担全部责任，不私自传播上面的图片。 如果图片的版权受到侵犯，请联系我们，我们将立即删除。
[1] WuDaoMMdataset is only used for academic research, any use of this dataset should follow the following requirements. WuDaoMM does not own the copyright of these pictures. Use of images is subject to the
Flickr term of use . Users of the images take full responsibility for using the dataset and do not distribute the above images privately. If the copyright of the image is violated, please contact us and it will be removed immediately.
阶段1使用平行语料库数据。
阶段2和3主要使用Laion-Aesthetics的一个子集。中文数据集采用wudaoMM数据集(CC-BY-SA 4.0)。
Stage 1 uses parallel corpus data.
Stage2&3 mainly use a subset of Laion-Aesthetics. The wudaoMM data set (CC-BY-SA 4.0) is used as a Chinese data set.
引用 Citation
关于AltCLIP，我们已经推出了相关报告，有更多细节可以查阅，如对您的工作有帮助，欢迎引用。
If you find this work helpful, please consider to cite
@article{https://doi.org/10.48550/arxiv.2211.06679,
doi = {10.48550/ARXIV.2211.06679},
url = {https://arxiv.org/abs/2211.06679},
author = {Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences},
title = {AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}AltCLIP-m18评测 AltCLIP-m18 evaluation
部分数据集评测结果展示:
Partial dataset evaluation results are displayed:
ImageNet
ImageNet-adv ImageNet-adv-cn ImageNet-adv-es ImageNet-adv-fr ImageNet-adv-it ImageNet-adv-jp ImageNet-adv-ko ImageNet-adv-ru ImageNet-ren ImageNet-ren-cn imageNet-ren-es ImageNet-ren-fr ImageNet-ren-it ImageNet-ren-jp ImageNet-ren-ko ImageNet-ren-ru ImageNet-ske ImageNet-ske-cn ImageNet-ske-es ImageNet-ske-fr ImageNet-ske-it ImageNet-ske-jp ImageNet-ske-ko ImageNet-ske-ru ImageNet-1k ImageNet-1k-cn ImageNet-1k-es ImageNet-1k-fr ImageNet-1k-it ImageNet-1k-jp ImageNet-1k-ko ImageNet-1k-ru ImageNet-v2 ImageNet-v2-cn ImageNet-v2-es ImageNet-v2-fr ImageNet-v2-it ImageNet-v2-jp ImageNet-v2-ko ImageNet-v2-ru AltCLIP-M18 58 50.35 43.56 44.07 48.25 36.48 38.48 40.57 89.53 81.36 71.78 74.96 76.44 67.68 69.27 75.53 65.42 51.26 97.44 84.83 30.52 68.62 67.46 54.4 76.71 57.12 54.22 54.84 52.29 51.71 53.65 51.53 65.45 51.76 48.91 49.24 47.27 46.76 48.1 46.53
其他分类 Other classification
caltech101 cars cifar10 cifar100 country211 dtd eurosat fer2013 Fgvc-aircraft flowers food101 gtsrb Hateful-memes Kitti-distance Mnist pcam pets Renderedsst2 Resisc45 Voc2007 AltCLIP-M18 88.25 92.75 97.44 84.83 30.52 68.62 67.46 54.4 40.41 71.64 92.49 56.35 50.8 14.91 78.46 54.76 94.11 65.95 70.83 81.62
检索 Retrieval
Multi30k-de-I2T Multi30k-de-T2I Multi30k-en-I2T Multi30k-en-T2I Multi30k-fr-I2T Multi30k-fr-I2T Xtd-de-I2T Xtd-de-T2I Xtd-en-I2T Xtd-en-T2I Xtd-es-I2T Xtd-es-T2I Xtd-fr-I2T Xtd-fr-T2I Xtd- it- I2T Xtd-it- T2I Xtd-jp-I2T Xtd-jp-T2I Xtd-ko-I2T Xtd-ko-T2I Xtd-pl-I2T Xtd-pl-T2I Xtd-ru-I2T Xtd-ru-T2I Xtd-tr-I2T Xtd-tr-T2I Xtd-zh-I2T Xtd-zh-T2I AltCLIP-M18 84.4 65.82 91.1 77.76 74.5 75.4 64.76 66.57 72.17 72.67 65.83 65.03 67.17 67.47 66.63 66.03 58.96 62.96 61.42 64.43 67.23 69.14 60.22 61.02 65.03 64.23 64.53 65.43
Cifar10 dataset evaluation code
1 # Copyright © 2022 BAAI. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License")
4 import torch
5 from flagai . auto_model . auto_loader import AutoLoader
6 import zeroshot_classification
7 import json
8 import os
9 from torchvision . datasets import CIFAR10
10
11 device = torch . device ( "cuda" if torch . cuda . is_available ( ) else "cpu" )
12 maxlen = 256
13
14 dataset_root = "./clip_benchmark_datasets/"
15 dataset_name = "cifar10"
16
17 auto_loader = AutoLoader (
18 task_name = "txt_img_matching" ,
19 model_dir = "./checkpoints/" ,
20 model_name = "AltCLIP-XLMR-L-m18" # Load the checkpoints from Modelhub(model.baai.ac.cn/models)
21 )
22
23 model = auto_loader . get_model ( )
24 model . to ( device )
25 model . eval ( )
26 tokenizer = auto_loader . get_tokenizer ( )
27 transform = auto_loader . get_transform ( )
28
29 dataset = CIFAR10 ( root = os . path . join ( dataset_root , dataset_name ) ,
30 transform = transform ,
31 download = True )
32 batch_size = 128
33 num_workers = 4
34
35 template = { "cifar10" : [
36 "a photo of a {c}." ,
37 "a blurry photo of a {c}." ,
38 "a black and white photo of a {c}." ,
39 "a low contrast photo of a {c}." ,
40 "a high contrast photo of a {c}." ,
41 "a bad photo of a {c}." ,
42 "a good photo of a {c}." ,
43 "a photo of a small {c}." ,
44 "a photo of a big {c}." ,
45 "a photo of the {c}." ,
46 "a blurry photo of the {c}." ,
47 "a black and white photo of the {c}." ,
48 "a low contrast photo of the {c}." ,
49 "a high contrast photo of the {c}." ,
50 "a bad photo of the {c}." ,
51 "a good photo of the {c}." ,
52 "a photo of the small {c}." ,
53 "a photo of the big {c}."
54 ] ,
55 }
56 def evaluate ( ) :
57 if dataset :
58 dataloader = torch . utils . data . DataLoader (
59 dataset ,
60 batch_size = batch_size ,
61 shuffle = False ,
62 num_workers = num_workers ,
63 )
64
65 zeroshot_templates = template [ "cifar10" ]
66 classnames = dataset . classes if hasattr ( dataset , "classes" ) else None
67
68 metrics = zeroshot_classification . evaluate (
69 model ,
70 dataloader ,
71 tokenizer ,
72 classnames ,
73 zeroshot_templates ,
74 device = device ,
75 amp = True ,
76 )
77
78 dump = {
79 "dataset" : dataset_name ,
80 "metrics" : metrics
81 }
82
83 print ( dump )
84 with open ( "./result.txt" , "w" ) as f :
85 json . dump ( dump , f )
86 return metrics
87
88 if __name__ == "__main__" :
89 evaluate ( )
90
推理脚本 inference
1 import torch
2 from PIL import Image
3 from flagai . auto_model . auto_loader import AutoLoader
4
5 device = torch . device ( "cuda" if torch . cuda . is_available ( ) else "cpu" )
6
7 loader = AutoLoader (
8 task_name = "txt_img_matching" ,
9 model_name = "AltCLIP-XLMR-L-m18" , # Load the checkpoints from Modelhub(model.baai.ac.cn/models)
10 model_dir = "./checkpoints"
11 )
12
13 model = loader . get_model ( )
14 tokenizer = loader . get_tokenizer ( )
15 transform = loader . get_transform ( )
16
17 model . eval ( )
18 model . to ( device )
19 tokenizer = loader . get_tokenizer ( )
20
21 def inference ( ) :
22 image = Image . open ( "./dog.jpeg" )
23 image = transform ( image )
24 image = torch . tensor ( image [ "pixel_values" ] ) . to ( device )
25 tokenizer_out = tokenizer ( [ "a rat" , "a dog" , "a cat" ] ,
26 padding = True ,
27 truncation = True ,
28 max_length = 77 ,
29 return_tensors = 'pt' )
30
31 text = tokenizer_out [ "input_ids" ] . to ( device )
32 attention_mask = tokenizer_out [ "attention_mask" ] . to ( device )
33 with torch . no_grad ( ) :
34 image_features = model . get_image_features ( image )
35 text_features = model . get_text_features ( text , attention_mask = attention_mask )
36 text_probs = ( image_features @ text_features . T ) . softmax ( dim = - 1 )
37
38 print ( text_probs . cpu ( ) . numpy ( ) [ 0 ] . tolist ( ) )
39
40 if __name__ == "__main__" :
41 inference ( )
微调 fintuning
Cifar10 dataset
1 # Copyright © 2022 BAAI. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License")
4 import torch
5 from flagai . auto_model . auto_loader import AutoLoader
6 import os
7 from flagai . trainer import Trainer
8 from torchvision . datasets import (
9 CIFAR10
10 )
11
12 device = torch . device ( "cuda" if torch . cuda . is_available ( ) else "cpu" )
13
14 dataset_root = "./clip_benchmark_datasets"
15 dataset_name = "cifar10"
16
17 batch_size = 4
18 classes = [ 'airplane' , 'automobile' , 'bird' , 'cat' , 'deer' , 'dog' , 'frog' , 'horse' , 'ship' , 'truck' ]
19
20 auto_loader = AutoLoader (
21 task_name = "txt_img_matching" ,
22 model_dir = "./checkpoints" ,
23 model_name = "AltCLIP-XLMR-L-m18" # Load the checkpoints from Modelhub(model.baai.ac.cn/models)
24 )
25
26 model = auto_loader . get_model ( )
27 model . to ( device )
28 model . eval ( )
29 tokenizer = auto_loader . get_tokenizer ( )
30 transform = auto_loader . get_transform ( )
31
32 trainer = Trainer ( env_type = "pytorch" ,
33 pytorch_device = device ,
34 experiment_name = "clip_finetuning" ,
35 batch_size = 4 ,
36 lr = 1e-4 ,
37 epochs = 10 ,
38 log_interval = 10 )
39
40 dataset = CIFAR10 ( root = os . path . join ( dataset_root , dataset_name ) ,
41 transform = transform ,
42 download = True )
43
44 def cifar10_collate_fn ( batch ) :
45 # image shape is (batch, 3, 224, 224)
46 images = torch . tensor ( [ b [ 0 ] [ "pixel_values" ] [ 0 ] for b in batch ] )
47 # text_id shape is (batch, n)
48 input_ids = torch . tensor ( [ tokenizer ( f"a photo of a { b [ 1 ] } " ,
49 padding = True ,
50 truncation = True ,
51 max_length = 77 ) [ "input_ids" ] for b in batch ] )
52
53 attention_mask = torch . tensor ( [ tokenizer ( f"a photo of a { b [ 1 ] } " ,
54 padding = True ,
55 truncation = True ,
56 max_length = 77 ) [ "attention_mask" ] for b in batch ] )
57
58 return {
59 "pixel_values" : images ,
60 "input_ids" : input_ids ,
61 "attention_mask" : attention_mask ,
62 }
63
64 if __name__ == "__main__" :
65 trainer . train ( model = model , train_dataset = dataset , collate_fn = cifar10_collate_fn )
