| | import argparse
|
| |
|
| | import cv2
|
| | import numpy as np
|
| | import torch
|
| |
|
| | from backbones import get_model
|
| |
|
| |
|
| | @torch.no_grad()
|
| | def inference(weight, name, img):
|
| | if img is None:
|
| | img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8)
|
| | else:
|
| | img = cv2.imread(img)
|
| | img = cv2.resize(img, (112, 112))
|
| |
|
| | img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
| | img = np.transpose(img, (2, 0, 1))
|
| | img = torch.from_numpy(img).unsqueeze(0).float()
|
| | img.div_(255).sub_(0.5).div_(0.5)
|
| | net = get_model(name, fp16=False)
|
| | net.load_state_dict(torch.load(weight))
|
| | net.eval()
|
| | feat = net(img).numpy()
|
| | print(feat)
|
| |
|
| |
|
| | if __name__ == "__main__":
|
| | parser = argparse.ArgumentParser(description='PyTorch ArcFace Training')
|
| | parser.add_argument('--network', type=str, default='r50', help='backbone network')
|
| | parser.add_argument('--weight', type=str, default='')
|
| | parser.add_argument('--img', type=str, default=None)
|
| | args = parser.parse_args()
|
| | inference(args.weight, args.network, args.img)
|
| |
|
