mindspore.dataset.Schema¶
-
class
mindspore.dataset.
Schema
(schema_file=None)[source]¶ Class to represent a schema of a dataset.
- Parameters
schema_file (str) – Path of schema file (default=None).
- Returns
Schema object, schema info about dataset.
- Raises
RuntimeError – If schema file failed to load.
Example
>>> import mindspore.common.dtype as mstype >>> >>> # Create schema; specify column name, mindspore.dtype and shape of the column >>> schema = ds.Schema() >>> schema.add_column(name='col1', de_type=mstype.int64, shape=[2])
-
add_column
(name, de_type, shape=None)[source]¶ Add new column to the schema.
- Parameters
- Raises
ValueError – If column type is unknown.
-
from_json
(json_obj)[source]¶ Get schema file from JSON object.
- Parameters
json_obj (dictionary) – Object of JSON parsed.
- Raises
RuntimeError – if there is unknown item in the object.
RuntimeError – if dataset type is missing in the object.
RuntimeError – if columns are missing in the object.
-
parse_columns
(columns)[source]¶ Parse the columns and add it to self.
- Parameters
columns (Union[dict, list[dict], tuple[dict]]) –
Dataset attribute information, decoded from schema file.
list[dict], ‘name’ and ‘type’ must be in keys, ‘shape’ optional.
dict, columns.keys() as name, columns.values() is dict, and ‘type’ inside, ‘shape’ optional.
- Raises
RuntimeError – If failed to parse columns.
RuntimeError – If column’s name field is missing.
RuntimeError – If column’s type field is missing.
Example
>>> schema = Schema() >>> columns1 = [{'name': 'image', 'type': 'int8', 'shape': [3, 3]}, >>> {'name': 'label', 'type': 'int8', 'shape': [1]}] >>> schema.parse_columns(columns1) >>> columns2 = {'image': {'shape': [3, 3], 'type': 'int8'}, 'label': {'shape': [1], 'type': 'int8'}} >>> schema.parse_columns(columns2)