def service(pytestconfig):
test_svc_bundle = pytestconfig.getoption("bento_dist") or os.path.join(
sys.argv[1], "build", "dist")
import bentoml
return bentoml.load_from_dir(test_svc_bundle)
def test_detectron2_artifact_pack(detectron2_classifier_class):
cfg = get_cfg()
# add project-specific config (e.g., TensorMask)
# here if you're not running a model in detectron2's core library
cfg.merge_from_file(
model_zoo.get_config_file(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
)
)
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
# Find a model from detectron2's model zoo.
# You can use the https://dl.fbaipublicfiles... url as well
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
)
clone_cfg = cfg.clone() # cfg can be modified by model
clone_cfg.MODEL.DEVICE = "cpu"
model = build_model(clone_cfg)
model.eval()
checkpointer = DetectionCheckpointer(model)
checkpointer.load(cfg.MODEL.WEIGHTS)
image = imageio.imread('http://images.cocodataset.org/val2017/000000439715.jpg')
image = image[:, :, ::-1]
svc = detectron2_classifier_class()
svc.pack(
'model',
model,
metadata={"device": "cpu"},
input_model_yaml=model_zoo.get_config_file(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
),
)
response = svc.predict(image)
assert response['scores'][0] > 0.9
comparison = np.array(response['classes']) == np.array(
[17, 0, 0, 0, 0, 0, 0, 0, 25, 0, 25, 25, 0, 0, 24]
)
assert comparison.all()
saved_bundle = svc.save()
svc = load_from_dir(saved_bundle)
response = svc.predict(image)