BiRefNet_github/inference.py

import os
import argparse
from glob import glob
from tqdm import tqdm
import cv2
import torch

from dataset import MyData
from models.birefnet import BiRefNet
from utils import save_tensor_img, check_state_dict
from config import Config


config = Config()


def inference(model, data_loader_test, pred_root, method, testset, device=0):
 model_training = model.training
 if model_training:
 model.eval()
 for batch in tqdm(data_loader_test, total=len(data_loader_test)) if 1 or config.verbose_eval else data_loader_test:
 inputs = batch[0].to(device)
 # gts = batch[1].to(device)
 label_paths = batch[-1]
 with torch.no_grad():
 scaled_preds = model(inputs)[-1].sigmoid()

 os.makedirs(os.path.join(pred_root, method, testset), exist_ok=True)

 for idx_sample in range(scaled_preds.shape[0]):
 res = torch.nn.functional.interpolate(
 scaled_preds[idx_sample].unsqueeze(0),
 size=cv2.imread(label_paths[idx_sample], cv2.IMREAD_GRAYSCALE).shape[:2],
 mode='bilinear',
 align_corners=True
 )
 save_tensor_img(res, os.path.join(os.path.join(pred_root, method, testset), label_paths[idx_sample].replace('\\', '/').split('/')[-1])) # test set dir + file name
 if model_training:
 model.train()
 return None


def main(args):
 # Init model

 device = config.device
 if args.ckpt_folder:
 print('Testing with models in {}'.format(args.ckpt_folder))
 else:
 print('Testing with model {}'.format(args.ckpt))

 if config.model == 'BiRefNet':
 model = BiRefNet(bb_pretrained=False)
 weights_lst = sorted(
 glob(os.path.join(args.ckpt_folder, '*.pth')) if args.ckpt_folder else [args.ckpt],
 key=lambda x: int(x.split('epoch_')[-1].split('.pth')[0]),
 reverse=True
 )
 for testset in args.testsets.split('+'):
 print('>>>> Testset: {}...'.format(testset))
 data_loader_test = torch.utils.data.DataLoader(
 dataset=MyData(testset, image_size=config.size, is_train=False),
 batch_size=config.batch_size_valid, shuffle=False, num_workers=config.num_workers, pin_memory=True
 )
 for weights in weights_lst:
 if int(weights.strip('.pth').split('epoch_')[-1]) % 1 != 0:
 continue
 print('\tInferencing {}...'.format(weights))
 # model.load_state_dict(torch.load(weights, map_location='cpu'))
 state_dict = torch.load(weights, map_location='cpu')
 state_dict = check_state_dict(state_dict)
 model.load_state_dict(state_dict)
 model = model.to(device)
 inference(
 model, data_loader_test=data_loader_test, pred_root=args.pred_root,
 method='--'.join([w.rstrip('.pth') for w in weights.split(os.sep)[-2:]]),
 testset=testset, device=config.device
 )


if __name__ == '__main__':
 # Parameter from command line
 parser = argparse.ArgumentParser(description='')
 parser.add_argument('--ckpt', type=str, help='model folder')
 parser.add_argument('--ckpt_folder', default=sorted(glob(os.path.join('ckpt', '*')))[-1], type=str, help='model folder')
 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',
 'DIS5K+HRSOD+HRS10K': 'DIS-VD',
 'P3M-10k': 'TE-P3M-500-P+TE-P3M-500-NP',
 'DIS5K-': 'DIS-VD',
 'COD-': 'TE-COD10K',
 'SOD-': 'DAVIS-S+TE-HRSOD+TE-UHRSD',
 }[config.task + ''],
 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')
 main(args)