inference.py

import time
from PIL import Image
from timm.data import resolve_data_config
import torch
from torchvision.transforms import transforms

model = torch.load('path/to/model.pth')
model.eval()

config = resolve_data_config({}, model=model)
transform = transforms.Compose([
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize image
            ])


with open("tags.txt", "r") as f:
    categories = [s.strip() for s in f.readlines()]
    categories=sorted(categories)


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

images=["your_image_here.jpg", "your_second_image_here.jpg"]

for item in images:
    start = time.time()
    img = Image.open(item).convert('RGB')
    tensor = transform(img).unsqueeze(0).to(device) # transform and add batch dimension

    with torch.no_grad():
        out = model(tensor)
    probabilities = torch.nn.functional.sigmoid(out[0])
    print(probabilities.shape)


    top10_prob, top10_catid = torch.topk(probabilities, 10)
    for i in range(top10_prob.size(0)):
        print(categories[top10_catid[i]], top10_prob[i].item())

    end = time.time()
    print(f'Executed in {end - start} seconds')
    print("\n\n", end="")