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 the schema file. Default: None.
- Returns
Schema object, schema info about dataset.
- Raises
RuntimeError – If schema file failed to load.
Examples
>>> from mindspore import 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.
Examples
>>> from mindspore.dataset import Schema >>> 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)