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import json
import math
import time
from PIL import Image
import torch
from torchvision.transforms import transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = torch.load("path/to/your/model.pth")
model.to(device)
model.eval()
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
with open("tags_8034.json", "r") as f:
tags = json.load(f)
tags.append("placeholder0")
tags = sorted(tags)
image_path = "path/to/your/image.jpg"
start = time.time()
img = Image.open(image_path).convert('RGB')
aspect_ratio = img.width / img.height
new_height = math.sqrt(512 ** 2 / aspect_ratio)
new_width = aspect_ratio * new_height
img.thumbnail((int(new_width), int(new_height)), Image.LANCZOS)
tensor = transform(img).unsqueeze(0).to(device)
with torch.no_grad():
out = model(tensor)
probabilities = torch.nn.functional.sigmoid(out[0])
indices = torch.where(probabilities > 0.3)[0]
values = probabilities[indices]
for i in range(indices.size(0)):
print(tags[indices[i]], values[i].item())
end = time.time()
print(f'Executed in {end - start} seconds')
print("\n\n", end="")
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