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import matplotlib.pyplot as plt

from pgvector.psycopg import register_vector

import psycopg

import tempfile

import torch

import torchvision

from tqdm import tqdm

seed = True

# establish connection

conn = psycopg.connect(dbname='pgvector_example')

conn.autocommit = True

conn.execute('CREATE EXTENSION IF NOT EXISTS vector')

register_vector(conn)

# load images

transform = torchvision.transforms.Compose([

torchvision.transforms.ToTensor(),

torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

])

dataset = torchvision.datasets.CIFAR10(root=tempfile.gettempdir(), train=True, download=True, transform=transform)

dataloader = torch.utils.data.DataLoader(dataset, batch_size=1000)

# load pretrained model

model = torchvision.models.resnet18(pretrained=True)

model.fc = torch.nn.Identity()

model.eval()

def generate_embeddings(inputs):

return model(inputs).detach().numpy()

# generate, save, and index embeddings

if seed:

conn.execute('DROP TABLE IF EXISTS image')

conn.execute('CREATE TABLE image (id bigserial primary key, embedding vector(512))')

for data in tqdm(dataloader):

embeddings = generate_embeddings(data[0])

sql = 'INSERT INTO image (embedding) VALUES ' + ','.join(['(%s)' for _ in embeddings])

params = [embedding for embedding in embeddings]

conn.execute(sql, params)

conn.execute('CREATE INDEX ON image USING ivfflat (embedding vector_cosine_ops)')

def show_images(dataset_images):

grid = torchvision.utils.make_grid(dataset_images)

img = (grid / 2 + 0.5).permute(1, 2, 0).numpy()

plt.imshow(img)

plt.waitforbuttonpress()

# load 5 random unseen images

queryset = torchvision.datasets.CIFAR10(root=tempfile.gettempdir(), train=False, download=True, transform=transform)

queryloader = torch.utils.data.DataLoader(queryset, batch_size=5, shuffle=True)

images = next(iter(queryloader))[0]

# generate and query embeddings

embeddings = generate_embeddings(images)

for image, embedding in zip(images, embeddings):

result = conn.execute('SELECT id FROM image ORDER BY embedding <=> %s LIMIT 15', (embedding,)).fetchall()

show_images([image] + [dataset[row[0] - 1][0] for row in result])