def benchmark_eval(args):
cfg = setup(args)
model = build_model(cfg)
model.eval()
logger.info("Model:\n{}".format(model))
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
dummy_data = list(itertools.islice(data_loader, 100))
def f():
while True:
yield from DatasetFromList(dummy_data, copy=False)
for _ in range(5): # warmup
model(dummy_data[0])
max_iter = 400
timer = Timer()
with tqdm.tqdm(total=max_iter) as pbar:
for idx, d in enumerate(f()):
if idx == max_iter:
break
model(d)
pbar.update()
logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds()))
def build_test_loader(cls, cfg, dataset_name):
"""
Returns:
iterable
It now calls :func:`detectron2.data.build_detection_test_loader`.
Overwrite it if you'd like a different data loader.
"""
return build_detection_test_loader(cfg, dataset_name)
def do_test(cfg, model):
results = OrderedDict()
for dataset_name in cfg.DATASETS.TEST:
data_loader = build_detection_test_loader(cfg, dataset_name)
evaluator = get_evaluator(
cfg, dataset_name,
os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name))
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
logger.info("Evaluation results for {} in csv format:".format(
dataset_name))
print_csv_format(results_i)
if len(results) == 1:
results = list(results.values())[0]
return results