def test_dumps(self):
contents = {
"CLASSIFICATION": {"category": "cat", "attributes": {"gender": "male"}},
"BOX2D": [
{
"box2d": {"xmin": 1, "ymin": 1, "xmax": 2, "ymax": 2},
"category": "dog",
"attributes": {"gender": "female"},
}
],
}
label = Label()
label.classification = Classification.loads(contents["CLASSIFICATION"])
label.box2d = [LabeledBox2D.loads(contents["BOX2D"][0])]
assert label.dumps() == contents
from tensorbay.dataset import Dataset
from tensorbay.label import Classification
# Use AuthData to organize a dataset by the "Dataset" class before importing.
dataset = Dataset("<DATASET_NAME>")
# TensorBay uses "segment" to separate different parts in a dataset.
segment = dataset.create_segment()
images = cloud_client.list_auth_data("<data/images/>")
labels = cloud_client.list_auth_data("<data/labels/>")
for auth_data, label in zip(images, labels):
with label.open() as fp:
auth_data.label.classification = Classification.loads(json.load(fp))
segment.append(auth_data)
dataset_client = gas.upload_dataset(dataset, jobs=8)
""""""
"""Create local storage config"""
gas.create_local_storage_config(
name="<LOCAL_STORAGE_CONFIG>",
file_path="<path/to/dataset>",
endpoint="<external IP address of the local storage service>",
)
""""""
"""Create authorized local storage dataset"""
dataset_client = gas.create_dataset("<DATASET_NAME>",
config_name="<LOCAL_STORAGE_CONFIG>")
""""""
def test_loads(self):
contents = {"category": "cat", "attributes": {"gender": "male"}}
classification = Classification.loads(contents)
assert classification.category == "cat"
assert classification.attributes == {"gender": "male"}
