Skip to content

# lipdp.pipeline module

## `DatasetMetadata` `dataclass`

class that handle dataset metadata that will be used to compute privacy guarantees

Source code in `lipdp/pipeline.py`

    31
    32
    33
    34
    35
    36
    37
    38
    39
    40
    41
    42
    43
    44
    45
    @dataclass
    class DatasetMetadata:
        """
        class that handle dataset metadata that will be used
        to compute privacy guarantees
        """

        input_shape: Tuple[int, int, int]
        nb_classes: int
        nb_samples_train: int
        nb_samples_test: int
        class_names: List[str]
        nb_steps_per_epochs: int
        batch_size: int
        max_norm: float

## `load_and_prepare_data(dataset_name='mnist', batch_size=256, colorspace='RGB', drop_remainder=True, augmentation_fct=None, bound_fct=None)`

load dataset_name data using tensorflow datasets.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`dataset_name` | `str` | name of the dataset to load. | `'mnist'`
`batch_size` | `int` | batch size | `256`
`colorspace` | `str` | one of RGB, HSV, YIQ, YUV | `'RGB'`
`drop_remainder` | `bool` | when true drop the last batch if it has less than batch_size elements. Defaults to True. | `True`
`augmentation_fct` | `callable` | data augmentation to be applied to train. the function must take a tuple (img, label) and return a tuple of (img, label). Defaults to None. | `None`
`bound_fct` | `callable` | function that is responsible of bounding the inputs. Can be None, bound_normalize or bound_clip_value. None means that no clipping is performed, and max theoretical value is reported ( sqrt(whc) ). bound_normalize means that each input is normalized setting the bound to 1. bound_clip_value will clip norm to defined value. | `None`

Returns:

Type | Description
--- | ---
| ds_train, ds_test, metadat: two dataset, with data preparation, augmentation, shuffling and batching. Also return an DatasetMetadata object with infos about the dataset.

Source code in `lipdp/pipeline.py`

     77
     78
     79
     80
     81
     82
     83
     84
     85
     86
     87
     88
     89
     90
     91
     92
     93
     94
     95
     96
     97
     98
     99
    100
    101
    102
    103
    104
    105
    106
    107
    108
    109
    110
    111
    112
    113
    114
    115
    116
    117
    118
    119
    120
    121
    122
    123
    124
    125
    126
    127
    128
    129
    130
    131
    132
    133
    134
    135
    136
    137
    138
    139
    140
    141
    142
    143
    144
    145
    146
    147
    148
    149
    150
    151
    152
    153
    154
    155
    156
    157
    158
    159
    160
    161
    162
    163
    164
    165
    166
    167
    168
    169
    170
    171
    172
    173
    174
    175
    176
    177
    178
    179
    180
    181
    182
    183
    184
    185
    def load_and_prepare_data(
        dataset_name: str = "mnist",
        batch_size: int = 256,
        colorspace: str = "RGB",
        drop_remainder=True,
        augmentation_fct=None,
        bound_fct=None,
    ):
        """
        load dataset_name data using tensorflow datasets.

        Args:
            dataset_name (str): name of the dataset to load.
            batch_size (int): batch size
            colorspace (str): one of RGB, HSV, YIQ, YUV
            drop_remainder (bool, optional): when true drop the last batch if it
                has less than batch_size elements. Defaults to True.
            augmentation_fct (callable, optional): data augmentation to be applied
                to train. the function must take a tuple (img, label) and return a
                tuple of (img, label). Defaults to None.
            bound_fct (callable, optional): function that is responsible of
                bounding the inputs. Can be None, bound_normalize or bound_clip_value.
                None means that no clipping is performed, and max theoretical value is
                reported ( sqrt(w*h*c) ). bound_normalize means that each input is
                normalized setting the bound to 1. bound_clip_value will clip norm to
                defined value.

        Returns:
            ds_train, ds_test, metadat: two dataset, with data preparation,
                augmentation, shuffling and batching. Also return an
                DatasetMetadata object with infos about the dataset.
        """
        # load data
        (ds_train, ds_test), ds_info = tfds.load(
            dataset_name,
            split=["train", "test"],
            shuffle_files=True,
            as_supervised=True,
            with_info=True,
        )
        # handle case where functions are None
        if augmentation_fct is None:
            augmentation_fct = lambda x, y: (x, y)
        # None bound yield default trivial bound
        nb_classes = ds_info.features["label"].num_classes
        input_shape = ds_info.features["image"].shape
        if bound_fct is None:
            bound_fct = (
                lambda x, y: (x, y),
                input_shape[-3] * input_shape[-2] * input_shape[-1],
            )
        bound_callable, bound_val = bound_fct
        # train pipeline
        ds_train = (
            ds_train.map(  # map to 0,1 and one hot encode
                lambda x, y: (
                    tf.cast(x, tf.float32) / 255.0,
                    tf.one_hot(y, nb_classes),
                ),
                num_parallel_calls=tf.data.AUTOTUNE,
            )
            .shuffle(  # shuffle
                min(batch_size * 10, max(batch_size, ds_train.cardinality())),
                reshuffle_each_iteration=True,
            )
            .map(augmentation_fct, num_parallel_calls=tf.data.AUTOTUNE)  # augment
            .map(  # map colorspace
                get_colorspace_function(colorspace),
                num_parallel_calls=tf.data.AUTOTUNE,
            )
            .map(bound_callable, num_parallel_calls=tf.data.AUTOTUNE)  # apply bound
            .batch(batch_size, drop_remainder=drop_remainder)  # batch
            .prefetch(tf.data.AUTOTUNE)
        )

        ds_test = (
            ds_test.map(
                lambda x, y: (
                    tf.cast(x, tf.float32) / 255.0,
                    tf.one_hot(y, nb_classes),
                ),
                num_parallel_calls=tf.data.AUTOTUNE,
            )
            .map(
                get_colorspace_function(colorspace),
                num_parallel_calls=tf.data.AUTOTUNE,
            )
            .shuffle(
                min(batch_size * 10, max(batch_size, ds_test.cardinality())),
                reshuffle_each_iteration=True,
            )
            .batch(batch_size, drop_remainder=drop_remainder)
            .prefetch(tf.data.AUTOTUNE)
        )
        # get dataset metadata
        metadata = DatasetMetadata(
            input_shape=ds_info.features["image"].shape,
            nb_classes=ds_info.features["label"].num_classes,
            nb_samples_train=ds_info.splits["train"].num_examples,
            nb_samples_test=ds_info.splits["test"].num_examples,
            class_names=ds_info.features["label"].names,
            nb_steps_per_epochs=ds_train.cardinality().numpy()
            if ds_train.cardinality() > 0  # handle case cardinality return -1 (unknown)
            else ds_info.splits["train"].num_examples / batch_size,
            batch_size=batch_size,
            max_norm=bound_val,
        )

        return ds_train, ds_test, metadata