@tensorleap_metadata

code_loader.inner_leap_binder.leapbinder_decorators.tensorleap_preprocess.tensorleap_metadata

The tensorleap_metadata decorates a Metadata Function.

@tensorleap_metadata(name='metadata_sample_index', metadata_type={"label":DatasetMetadataType.int, "is_circle": DatasetMetadataType.bool})
def metadata_sample_index(idx: int, preprocess: PreprocessResponse) -> 
    ...
    Union[int,str,bool,float,Dict[str,Union[int,str,bool,float]]]:
    pass

Args
metadata_type	(DatasetMetadataType, optional) This property helps visualize the metadata data. For a float ground truth, use DatasetMetadataType.float For a string ground truth, use DatasetMetadataType.string For a int ground truth, use DatasetMetadataType.int For a boolean ground truth, use DatasetMetadataType.boolean For a dictionary return a dictionary that maps key name to key type Providing MetadataType becomes mendatory if some of the samples has a "None" value for the metadata
name	(str) The given name of the metadata, e.g. label.

Examples

Basic Usage

from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.inner_leap_binder.leapbinder_decorators import tensorleap_custom_loss
import numpy as np
...
@tensorleap_metadata(name='metadata_label_description', metadata_type={"label":DatasetMetadataType.int, "is_circle": DatasetMetadataType.bool})
def metadata_label(idx: int, preprocess: PreprocessResponse) -> Dict[str,Union[int, bool]:
    return return {
        'label': int_metadata_creator(preprocess, idx),
        'is_circle': bool_metadata_creator(preprocess, idx),
    }

Usage within the full script can be found at Dataset Script.

Guides

Full examples can be found at the Dataset Integration section of the following guides:

• MNIST Guide

• IMDB Guide