engine/BiRefNet/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, BiRefNetC2F
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)
    elif config.model == "BiRefNetC2F":
        model = BiRefNetC2F(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))
            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:]]
                ),
                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=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)