| import os
|
| import argparse
|
| from glob import glob
|
| from tqdm import tqdm
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| import cv2
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| import torch
|
|
|
| from .dataset import MyData
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| from .models.birefnet import BiRefNet
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| from .utils import save_tensor_img, check_state_dict
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| from .config import Config
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|
|
|
|
| config = Config()
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|
|
|
|
| def inference(model, data_loader_test, pred_root, method, testset, device=0):
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| model_training = model.training
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| if model_training:
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| model.eval()
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| for batch in tqdm(data_loader_test, total=len(data_loader_test)) if 1 or config.verbose_eval else data_loader_test:
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| inputs = batch[0].to(device)
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|
|
| label_paths = batch[-1]
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| with torch.no_grad():
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| scaled_preds = model(inputs)[-1].sigmoid()
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|
|
| os.makedirs(os.path.join(pred_root, method, testset), exist_ok=True)
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|
|
| for idx_sample in range(scaled_preds.shape[0]):
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| res = torch.nn.functional.interpolate(
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| scaled_preds[idx_sample].unsqueeze(0),
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| size=cv2.imread(label_paths[idx_sample], cv2.IMREAD_GRAYSCALE).shape[:2],
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| mode='bilinear',
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| align_corners=True
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| )
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| save_tensor_img(res, os.path.join(os.path.join(pred_root, method, testset), label_paths[idx_sample].replace('\\', '/').split('/')[-1]))
|
| if model_training:
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| model.train()
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| return None
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|
|
|
|
| def main(args):
|
|
|
|
|
| device = config.device
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| if args.ckpt_folder:
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| print('Testing with models in {}'.format(args.ckpt_folder))
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| else:
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| print('Testing with model {}'.format(args.ckpt))
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|
|
| if config.model == 'BiRefNet':
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| model = BiRefNet(bb_pretrained=False)
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| weights_lst = sorted(
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| glob(os.path.join(args.ckpt_folder, '*.pth')) if args.ckpt_folder else [args.ckpt],
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| key=lambda x: int(x.split('epoch_')[-1].split('.pth')[0]),
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| reverse=True
|
| )
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| for testset in args.testsets.split('+'):
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| print('>>>> Testset: {}...'.format(testset))
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| data_loader_test = torch.utils.data.DataLoader(
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| dataset=MyData(testset, image_size=config.size, is_train=False),
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| batch_size=config.batch_size_valid, shuffle=False, num_workers=config.num_workers, pin_memory=True
|
| )
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| for weights in weights_lst:
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| if int(weights.strip('.pth').split('epoch_')[-1]) % 1 != 0:
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| continue
|
| print('\tInferencing {}...'.format(weights))
|
|
|
| state_dict = torch.load(weights, map_location='cpu')
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| state_dict = check_state_dict(state_dict)
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| model.load_state_dict(state_dict)
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| model = model.to(device)
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| inference(
|
| model, data_loader_test=data_loader_test, pred_root=args.pred_root,
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| method='--'.join([w.rstrip('.pth') for w in weights.split(os.sep)[-2:]]),
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| testset=testset, device=config.device
|
| )
|
|
|
|
|
| if __name__ == '__main__':
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|
|
| parser = argparse.ArgumentParser(description='')
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| parser.add_argument('--ckpt', type=str, help='model folder')
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| parser.add_argument('--ckpt_folder', default=sorted(glob(os.path.join('ckpt', '*')))[-1], type=str, help='model folder')
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| parser.add_argument('--pred_root', default='e_preds', type=str, help='Output folder')
|
| parser.add_argument('--testsets',
|
| default={
|
| 'DIS5K': 'DIS-VD+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4',
|
| 'COD': 'TE-COD10K+NC4K+TE-CAMO+CHAMELEON',
|
| 'HRSOD': 'DAVIS-S+TE-HRSOD+TE-UHRSD+TE-DUTS+DUT-OMRON',
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| 'General': 'DIS-VD',
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| 'Matting': 'TE-P3M-500-P',
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| 'DIS5K-': 'DIS-VD',
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| 'COD-': 'TE-COD10K',
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| 'SOD-': 'DAVIS-S+TE-HRSOD+TE-UHRSD',
|
| }[config.task + ''],
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| type=str,
|
| help="Test all sets: , 'DIS-VD+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'")
|
|
|
| args = parser.parse_args()
|
|
|
| if config.precisionHigh:
|
| torch.set_float32_matmul_precision('high')
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| main(args)
|
|
|
