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434b0b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | 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)
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