OpenAI——CLIPs（代码使用示例）

OpenAI——CLIPs(打通NLP与CV)

Open AI在2021年1月份发布Contrastive Language-Image Pre-training(CLIP),基于对比文本-图像对对比学习的多模态模型，通过图像和它对应的文本描述对比学习，模型能够学习到文本-图像对的匹配关系。它开源、多模态、zero-shot、few-shot、监督训练均可。
原文原理图：
在这里插入图片描述
原文算法思想伪代码：

OpenAI CLIP 原项目：

https://github.com/openai/CLIP

使用

（一）原版
安装：

$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install ftfy regex tqdm
$ pip install git+https://github.com/openai/CLIP.git

当然没有GPU和cuda，直接CPU也可以
源码：

import torch
import clip
from PIL import Image

device ="cuda"if torch.cuda.is_available()else"cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

image = preprocess(Image.open("cat.png")).unsqueeze(0).to(device)# CLIP.png为本文中图一，即CLIP的流程图
text = clip.tokenize(["cat in basket","python","a cute cat","pytorch","code of CLIP","code of pytorch ","code"]).to(device)# 将这三句话向量化with torch.no_grad():
    image_features = model.encode_image(image)# 将图片进行编码
    text_features = model.encode_text(text)# 将文本进行编码# print("image_features shape:",image_features.shape,image_features.size(),image_features.ndim)# print("text_features shape:", text_features.shape)

    logits_per_image, logits_per_text = model(image, text)# print("logits_per_image shape:",logits_per_image.shape)# print("logits_per_text shape:", logits_per_text.shape)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()print("Label probs:", probs)# prints: [[0.9927937  0.00421068 0.00299572]] # 图片"CLIP.png",text["a diagram", "a dog", "a cat"] 对应"a diagram"的概率为0.9927937####(2)接前:矩阵相乘分类import pandas as pd
with torch.no_grad():
    score =[]
    image_features = model.encode_image(image)# 将图片进行编码
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features = model.encode_text(text)# 将文本进行编码
    text_features /= text_features.norm(dim=-1, keepdim=True)# texts = ["cat in basket", "python", "a cute cat","pytorch","code of CLIP","code of pytorch ","code"]
    texts =["cat in basket","python","a cat","pytorch","code","pytorch code"]for text in texts:
        textp = clip.tokenize(text)# 问题文本编码
        textp_embeddings = model.encode_text(textp)
        textp_embeddings /= textp_embeddings.norm(dim=-1, keepdim=True)# 计算图片和问题之间的匹配分数(矩阵相乘)
        sc =float((image_features  @ textp_embeddings.T).cpu().numpy())
        score.append(sc)print(pd.DataFrame({'texts': texts,'score': score}).sort_values('score', ascending=False))print('')print('-------------------------')print('')

（二）transformer库版本
Transformers 库的基本使用：
https://blog.csdn.net/benzhujie1245com/article/details/125279229
安装：

pip install transformers

CLIP源码：

####基本用法二:利用transformer库from PIL import Image
from transformers import CLIPProcessor,CLIPModel

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")#这里加入自己图片的地址就行
image = Image.open('cat.png')#这里加入类别的标签类别
text =["cat in basket","python","a cute cat","pytorch","code of CLIP","code of pytorch ","code"]
inputs = processor(text=text,images = image,return_tensors="pt",padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)for i inrange(len(text)):print(text[i],":",probs[0][i])

输入图片：
在这里插入图片描述
结果：