inference_tag2text.py

'''
 * The Tag2Text Model
 * Written by Xinyu Huang
'''
import argparse
import numpy as np
import random

import torch

from PIL import Image
from ram.models import tag2text
from ram import inference_tag2text as inference
from ram import get_transform


parser = argparse.ArgumentParser(
    description='Tag2Text inferece for tagging and captioning')
parser.add_argument('--image',
                    metavar='DIR',
                    help='path to dataset',
                    default='images/1641173_2291260800.jpg')
parser.add_argument('--pretrained',
                    metavar='DIR',
                    help='path to pretrained model',
                    default='pretrained/tag2text_swin_14m.pth')
parser.add_argument('--image-size',
                    default=384,
                    type=int,
                    metavar='N',
                    help='input image size (default: 448)')
parser.add_argument('--thre',
                    default=0.68,
                    type=float,
                    metavar='N',
                    help='threshold value')
parser.add_argument('--specified-tags',
                    default='None',
                    help='User input specified tags')


if __name__ == "__main__":

    args = parser.parse_args()

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

    transform = get_transform(image_size=args.image_size)

    # delete some tags that may disturb captioning
    # 127: "quarter"; 2961: "back", 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one"
    delete_tag_index = [127,2961, 3351, 3265, 3338, 3355, 3359]

    #######load model
    model = tag2text(pretrained=args.pretrained,
                             image_size=args.image_size,
                             vit='swin_b',
                             delete_tag_index=delete_tag_index)
    model.threshold = args.thre  # threshold for tagging
    model.eval()

    model = model.to(device)

    image = transform(Image.open(args.image)).unsqueeze(0).to(device)

    res = inference(image, model, args.specified_tags)
    print("Model Identified Tags: ", res[0])
    print("User Specified Tags: ", res[1])
    print("Image Caption: ", res[2])