def test_sub_dataset(self):
with make_temp_directory("detectron2go_tmp_dataset") as tmp_dir:
image_dir, json_file = create_test_images_and_dataset_json(tmp_dir)
runner = Detectron2GoRunner()
cfg = runner.get_default_cfg()
cfg.merge_from_list(
[
str(x)
for x in [
"D2GO_DATA.DATASETS.COCO_INJECTION.NAMES",
["inj_ds"],
"D2GO_DATA.DATASETS.COCO_INJECTION.IM_DIRS",
[image_dir],
"D2GO_DATA.DATASETS.COCO_INJECTION.JSON_FILES",
[json_file],
"DATASETS.TEST",
("inj_ds",),
"D2GO_DATA.TEST.MAX_IMAGES",
1,
]
]
)
runner.register(cfg)
with maybe_subsample_n_images(cfg) as new_cfg:
test_loader = runner.build_detection_test_loader(
new_cfg, new_cfg.DATASETS.TEST[0]
)
self.assertEqual(len(test_loader), 1)
def do_test(self, cfg, model, train_iter=None):
"""do_test does not load the weights of the model.
If you want to use it outside the regular training routine,
you will have to load the weights through a checkpointer.
"""
results = OrderedDict()
with maybe_subsample_n_images(cfg) as new_cfg:
# default model
cur_results = self._do_test(new_cfg,
model,
train_iter=train_iter,
model_tag="default")
results.update(cur_results)
# model with ema weights
if cfg.MODEL_EMA.ENABLED and not isinstance(
model, PredictorWrapper):
logger.info("Run evaluation with EMA.")
with model_ema.apply_model_ema_and_restore(model):
cur_results = self._do_test(new_cfg,
model,
train_iter=train_iter,
model_tag="ema")
results.update(cur_results)
return results
def do_test(self, cfg, model, train_iter=None):
results = OrderedDict()
with maybe_subsample_n_images(cfg) as new_cfg:
# default model
cur_results = self._do_test(
new_cfg, model, train_iter=train_iter, model_tag="default"
)
results.update(cur_results)
# model with ema weights
if cfg.MODEL_EMA.ENABLED:
logger.info("Run evaluation with EMA.")
with model_ema.apply_model_ema_and_restore(model):
cur_results = self._do_test(
new_cfg, model, train_iter=train_iter, model_tag="ema"
)
results.update(cur_results)
return results
