inference_ram_openset.py

'''
 * The Recognize Anything Model (RAM) inference on unseen classes
 * Written by Xinyu Huang
'''
import argparse
import numpy as np
import random

import torch

from PIL import Image
from ram.models import ram
from ram import inference_ram_openset as inference
from ram import get_transform

from ram.utils import build_openset_label_embedding
from torch import nn

parser = argparse.ArgumentParser(
    description='Tag2Text inferece for tagging and captioning')
parser.add_argument('--image',
                    metavar='DIR',
                    help='path to dataset',
                    default='images/openset_example.jpg')
parser.add_argument('--pretrained',
                    metavar='DIR',
                    help='path to pretrained model',
                    default='pretrained/ram_swin_large_14m.pth')
parser.add_argument('--image-size',
                    default=384,
                    type=int,
                    metavar='N',
                    help='input image size (default: 448)')


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)

    #######load model
    model = ram(pretrained=args.pretrained,
                             image_size=args.image_size,
                             vit='swin_l')
    
    #######set openset interference
    openset_label_embedding, openset_categories = build_openset_label_embedding()

    model.tag_list = np.array(openset_categories)
    
    model.label_embed = nn.Parameter(openset_label_embedding.float())

    model.num_class = len(openset_categories)
    # the threshold for unseen categories is often lower
    model.class_threshold = torch.ones(model.num_class) * 0.5
    #######

    model.eval()

    model = model.to(device)

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

    res = inference(image, model)
    print("Image Tags: ", res)