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test.py
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test.py
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import logging
from os import path as osp
from time import time
import torch
from neosr.data import build_dataloader, build_dataset
from neosr.models import build_model
from neosr.utils import get_root_logger, get_time_str, make_exp_dirs
from neosr.utils.options import parse_options
def test_pipeline(root_path):
# parse options, set distributed setting, set ramdom seed
opt, _ = parse_options(root_path, is_train=False)
torch.set_default_device("cuda")
torch.backends.cudnn.benchmark = True
# mkdir and initialize loggers
make_exp_dirs(opt)
log_file = osp.join(opt["path"]["log"], f"test_{opt['name']}_{get_time_str()}.log")
logger = get_root_logger(
logger_name="neosr", log_level=logging.INFO, log_file=log_file
)
# create test dataset and dataloader
test_loaders = []
for _, dataset_opt in sorted(opt["datasets"].items()):
test_set = build_dataset(dataset_opt)
num_gpu = opt.get("num_gpu", "auto")
test_loader = build_dataloader(
test_set,
dataset_opt,
num_gpu=num_gpu,
dist=opt["dist"],
sampler=None,
seed=opt["manual_seed"],
)
logger.info(f"Number of test images in {dataset_opt['name']}: {len(test_set)}")
test_loaders.append(test_loader)
# create model
model = build_model(opt)
for test_loader in test_loaders:
test_set_name = test_loader.dataset.opt["name"]
logger.info(f"Testing {test_set_name}...")
start_time = time()
model.validation(
test_loader,
current_iter=opt["name"],
tb_logger=None,
save_img=opt["val"]["save_img"],
)
end_time = time()
total_time = end_time - start_time
n_img = len(test_loader.dataset)
fps = n_img / total_time
logger.info(f"Inference took {total_time:.2f} seconds, at {fps:.2f} fps.")
if __name__ == "__main__":
root_path = osp.abspath(osp.join(__file__, osp.pardir))
test_pipeline(root_path)