本文分享自华为云社区《多模态对比语言图像预训练CLIP：打破语言与视觉的界限》，作者：汀丶。

一种基于多模态（图像、文本）对比训练的神经网络。它可以在给定图像的情况下，使用自然语言来预测最相关的文本片段，而无需为特定任务进行优化。CLIP的设计类似于GPT-2和GPT-3，具备出色的零射击能力，可以应用于多种多模态任务。

CLIP(对比语言图像预训练)是一种基于多种(图像、文本)对进行训练的神经网络。在给定图像的情况下，它可以用自然语言来预测最相关的文本片段，而无需直接针对任务进行优化，类似于GPT-2和gpt - 3的零射击能力。我们发现CLIP在不使用任何原始的1.28M标记示例的情况下，在ImageNet“零射击”上匹配原始ResNet50的性能，克服了计算机视觉中的几个主要挑战。

1.安装

ftfy

regex

tqdm

torch

torchvision

$ 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

Replace cudatoolkit=11.0 above with the appropriate CUDA version on your machine or cpuonly when installing on a machine without a GPU.

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("CLIP.png")).unsqueeze(0).to(device)

text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)

with torch.no_grad():

image_features = model.encode_image(image)

text_features = model.encode_text(text)



logits_per_image, logits_per_text = model(image, text)

probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs) # prints: [[0.9927937 0.00421068 0.00299572]]

The CLIP module clip provides the following methods:

Returns the names of the available CLIP models.

返回模型和模型所需的TorchVision转换，由’ clip.available_models() ‘返回的模型名指定。它将根据需要下载模型。’ name '参数也可以是本地检查点的路径。

可以选择性地指定运行模型的设备，默认是使用第一个CUDA设备(如果有的话)，否则使用CPU。当’ jit ‘为’ False '时，将加载模型的非jit版本。

返回一个LongTensor，其中包含给定文本输入的标记化序列。这可以用作模型的输入

’ clip.load() '返回的模型支持以下方法:

给定一批图像，返回由CLIP模型的视觉部分编码的图像特征。

给定一批文本tokens，返回由CLIP模型的语言部分编码的文本特征。

给定一批图像和一批文本标记，返回两个张量，包含对应于每个图像和文本输入的logit分数。其值是对应图像和文本特征之间的相似度的余弦值，乘以100。

2.案例介绍

2.1 零样本能力

下面的代码使用CLIP执行零样本预测，如本文附录B所示。本例从CIFAR-100数据集获取图像，并在数据集的100个文本标签中预测最可能的标签。

import os

import clip

import torch

from torchvision.datasets import CIFAR100

#Load the model

device = "cuda" if torch.cuda.is_available() else "cpu"

model, preprocess = clip.load('ViT-B/32', device)

#Download the dataset

cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)

#Prepare the inputs

image, class_id = cifar100[3637]

image_input = preprocess(image).unsqueeze(0).to(device)

text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)

#Calculate features

with torch.no_grad():

image_features = model.encode_image(image_input)

text_features = model.encode_text(text_inputs)

#Pick the top 5 most similar labels for the image

image_features /= image_features.norm(dim=-1, keepdim=True)

text_features /= text_features.norm(dim=-1, keepdim=True)

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

values, indices = similarity[0].topk(5)

#Print the result

print("\nTop predictions:\n")

for value, index in zip(values, indices):

print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%")

输出将如下所示(具体数字可能因计算设备的不同而略有不同):

Top predictions:

snake: 65.31%

turtle: 12.29%

sweet_pepper: 3.83%

lizard: 1.88%

crocodile: 1.75%

Note that this example uses the encode_image() and encode_text() methods that return the encoded features of given inputs.

2.2 Linear-probe 评估

The example below uses scikit-learn to perform logistic regression on image features.

import os

import clip

import torch

import numpy as np

from sklearn.linear_model import LogisticRegression

from torch.utils.data import DataLoader

from torchvision.datasets import CIFAR100

from tqdm import tqdm

#Load the model

device = "cuda" if torch.cuda.is_available() else "cpu"

model, preprocess = clip.load('ViT-B/32', device)

#Load the dataset

root = os.path.expanduser("~/.cache")

train = CIFAR100(root, download=True, train=True, transform=preprocess)

test = CIFAR100(root, download=True, train=False, transform=preprocess)

def get_features(dataset):

all_features = []

all_labels = []



with torch.no_grad():

for images, labels in tqdm(DataLoader(dataset, batch_size=100)):

features = model.encode_image(images.to(device))

all_features.append(features)

all_labels.append(labels)

return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy()

#Calculate the image features

train_features, train_labels = get_features(train)

test_features, test_labels = get_features(test)

#Perform logistic regression

classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1)

classifier.fit(train_features, train_labels)

#Evaluate using the logistic regression classifier

predictions = classifier.predict(test_features)

accuracy = np.mean((test_labels == predictions).astype(float)) * 100.

print(f"Accuracy = {accuracy:.3f}")

Note that the C value should be determined via a hyperparameter sweep using a validation split.

3.更多资料参考

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