inference.py

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

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


config = Config()

mixed_precision = config.mixed_precision
if mixed_precision == 'fp16':
    mixed_dtype = torch.float16
elif mixed_precision == 'bf16':
    mixed_dtype = torch.bfloat16
else:
    mixed_dtype = None

autocast_ctx = torch.amp.autocast(device_type='cuda', dtype=mixed_dtype) if mixed_dtype else nullcontext()


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 config.verbose_eval else data_loader_test:
        inputs = batch[0].to(device)
        label_paths = batch[-1]
        with autocast_ctx, torch.no_grad():
            scaled_preds = model(inputs)[-1].sigmoid().to(torch.float32)

        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):
    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)
    else:
        print('Undefined model: {}.'.format(config.model))
        return None
    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
    )
    try:
        if args.resolution in [None, 'None', 0, '']:
            # Use original resolution for inference.
            data_size = None
        elif args.resolution in ['config.size']:
            data_size = config.size
        else:
            data_size = [int(l) for l in args.resolution.split('x')]
    except Exception as e:
        print(f"Exception: {type(e).__name__} at line {e.__traceback__.tb_lineno} of {__file__}: {e}")
        # default as the config.size.
        data_size = config.size

    for testset in args.testsets.split('+'):
        print('>>>> Testset: {}...'.format(testset))
        data_loader_test = torch.utils.data.DataLoader(
            dataset=MyData(testset, data_size=data_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))
            state_dict = torch.load(weights, map_location='cpu', weights_only=True)
            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:]]) + '-reso_{}'.format('x'.join([str(s) for s in data_size])),
                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('ckpts', '*')))[-1], type=str, help='model folder')
    parser.add_argument('--pred_root', default='e_preds', type=str, help='Output folder')
    parser.add_argument('--resolution', default='default', type=str, help='WeixHei')
    parser.add_argument('--testsets',
                        default=config.testsets.replace(',', '+'),
                        type=str,
                        help="Test all sets: DIS5K -> '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)