CLIP-图文预训练模型

"Ref：https://github.com/openai/CLIP"
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)
#cifar每个类别，输入图片，检索匹配的类别

# 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}%")

"""
Top predictions:
           snake: 65.31%
          turtle: 12.29%
    sweet_pepper: 3.83%
          lizard: 1.88%
       crocodile: 1.75%
"""

"Ref：https://github.com/openai/CLIP"
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) # c自定义
classifier.fit(train_features, train_labels)

# Evaluate using the logistic regression classifier
predictions = classifier.predict(test_features)
accuracy = np.mean((test_labels == predictions).astype(np.float)) * 100.
print(f"Accuracy = {accuracy:.3f}")