| ''' |
| * The Recognize Anything Plus Model (RAM++) |
| * Written by Xinyu Huang |
| ''' |
| import argparse |
| import numpy as np |
| import random |
|
|
| import torch |
|
|
| from PIL import Image |
| from ram.models import ram_plus |
| from ram import inference_ram 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/demo/demo1.jpg') |
| parser.add_argument('--pretrained', |
| metavar='DIR', |
| help='path to pretrained model', |
| default='pretrained/ram_plus_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) |
|
|
| |
| model = ram_plus(pretrained=args.pretrained, |
| image_size=args.image_size, |
| vit='swin_l') |
| model.eval() |
|
|
| model = model.to(device) |
|
|
| image = transform(Image.open(args.image)).unsqueeze(0).to(device) |
|
|
| res = inference(image, model) |
| print("Image Tags: ", res[0]) |
| print("图像标签: ", res[1]) |
|
|
