def test(cls, cfg, model):
"""
Args:
cfg (CfgNode):
model (nn.Module):
Returns:
dict: a dict of result metrics
"""
logger = logging.getLogger(__name__)
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):
logger.info("Prepare testing set")
try:
data_loader, evaluator = cls.build_evaluator(cfg, dataset_name)
except NotImplementedError:
logger.warn(
"No evaluator found. implement its `build_evaluator` method."
)
results[dataset_name] = {}
continue
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results)
print_csv_format(results)
if len(results) == 1: results = list(results.values())[0]
return results
def do_test(cfg, model):
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):
logger.info("Prepare testing set")
try:
data_loader, evaluator = get_evaluator(cfg, dataset_name)
except NotImplementedError:
logger.warn(
"No evaluator found. implement its `build_evaluator` method.")
results[dataset_name] = {}
continue
results_i = inference_on_dataset(model,
data_loader,
evaluator,
flip_test=cfg.TEST.FLIP_ENABLED)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results)
print_csv_format(results)
if len(results) == 1: results = list(results.values())[0]
return results
def test(cls, cfg, model, evaluators=None):
"""
Args:
cfg (CfgNode):
model (nn.Module):
evaluators (list[DatasetEvaluator] or None): if None, will call
:meth:`build_evaluator`. Otherwise, must have the same length as
`cfg.DATASETS.TEST`.
Returns:
dict: a dict of result metrics
"""
logger = logging.getLogger(__name__)
if isinstance(evaluators, DatasetEvaluator):
evaluators = [evaluators]
if evaluators is not None:
assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
len(cfg.DATASETS.TEST), len(evaluators)
)
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):
logger.info("Prepare testing set")
data_loader, num_query = cls.build_test_loader(cfg, dataset_name)
# When evaluators are passed in as arguments,
# implicitly assume that evaluators can be created before data_loader.
if evaluators is not None:
evaluator = evaluators[idx]
else:
try:
evaluator = cls.build_evaluator(cfg, num_query)
except NotImplementedError:
logger.warn(
"No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
"or implement its `build_evaluator` method."
)
results[dataset_name] = {}
continue
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results
)
print_csv_format(results)
if len(results) == 1: results = list(results.values())[0]
return results
def test(self, cfg, model):
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):
results_i = inference_on_dataset(
model, self.test_data_loader[dataset_name],
self.evaluator[dataset_name])
results[dataset_name] = results_i
self.eval_results = results
if comm.is_main_process():
assert isinstance(
results, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results)
print_csv_format(results)
if len(results) == 1: results = list(results.values())[0]
return results
def test(cls, cfg, model):
"""
Args:
cfg (CfgNode):
model (nn.Module):
Returns:
dict: a dict of result metrics
"""
logger = logging.getLogger('fastreid')
results = OrderedDict()
dataset_name = cfg.DATASETS.TGT
logger.info("Prepare testing set")
try:
data_loader, evaluator = cls.build_evaluator(cfg, dataset_name)
except NotImplementedError:
logger.warn(
"No evaluator found. implement its `build_evaluator` method.")
results[dataset_name] = {}
results_i = inference_on_dataset(model,
data_loader,
evaluator,
flip_test=cfg.TEST.FLIP.ENABLED)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results)
logger.info("Evaluation results for {} in csv format:".format(
dataset_name))
results_i['dataset'] = dataset_name
print_csv_format(results_i)
# if len(results) == 1:
# results = list(results.values())[0]
return results
