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show_examples.py
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show_examples.py
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# coding=utf-8
# Copyright 2023 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Show example util."""
from __future__ import annotations
from collections.abc import Iterable
import typing
from typing import Any, Union
from tensorflow_datasets.core import dataset_info
from tensorflow_datasets.core import lazy_imports_lib
from tensorflow_datasets.core import splits
from tensorflow_datasets.core import utils
from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow as tf
from tensorflow_datasets.core.visualization import graph_visualizer
from tensorflow_datasets.core.visualization import image_visualizer
from tensorflow_metadata.proto.v0 import statistics_pb2
if typing.TYPE_CHECKING:
_Dataset = Union[
tf.data.Dataset,
Iterable,
]
else:
_Dataset = Any
_ALL_VISUALIZERS = [
image_visualizer.ImageGridVisualizer(),
graph_visualizer.GraphVisualizer(),
]
_DEFAULT_NUM_COLS = 3
_DEFAULT_NUM_ROWS = 3
def _to_tf_dataset(
ds: _Dataset,
min_length: int,
is_batched: bool = False,
) -> tf.data.Dataset:
"""Converts any iterable to a small tf.data.Dataset to use visualizations.
Warning: this util function is not optimized, so it should only be used for a
small number of records (i.e., small `min_length`).
Args:
ds: Any dataset as an iterable.
min_length: The minimum number of examples to generate.
is_batched: Whether the data is batched.
Returns:
the tf.data.Dataset of cardinality at least `min_length`.
"""
if isinstance(ds, tf.data.Dataset) and not isinstance(ds, Iterable):
if is_batched:
return ds.unbatch()
else:
return ds
tf_dataset = None
if is_batched:
from_tensor = tf.data.Dataset.from_tensor_slices
else:
from_tensor = tf.data.Dataset.from_tensors
for record in ds:
if tf_dataset is None:
tf_dataset = from_tensor(record)
else:
tf_dataset = tf_dataset.concatenate(from_tensor(record))
# Terminate if `tf_dataset` reached at least the expected `min_length`.
if tf_dataset.cardinality().numpy() >= min_length:
break
if tf_dataset is None:
raise ValueError(
'Empty dataset, could not generate a valid tf.data.Dataset.'
)
return tf_dataset
def show_examples(
ds: _Dataset,
ds_info: dataset_info.DatasetInfo,
is_batched: bool = False,
**options_kwargs: Any,
):
"""Visualize images (and labels) from an image classification dataset.
This function is for interactive use (Colab, Jupyter). It displays and return
a plot of (rows*columns) images from a tf.data.Dataset.
Usage:
```python
ds, ds_info = tfds.load('cifar10', split='train', with_info=True)
fig = tfds.show_examples(ds, ds_info)
```
Args:
ds: `tf.data.Dataset`. The tf.data.Dataset object to visualize. Examples
should not be batched. Examples will be consumed in order until (rows *
cols) are read or the dataset is consumed.
ds_info: The dataset info object to which extract the label and features
info. Available either through `tfds.load('mnist', with_info=True)` or
`tfds.builder('mnist').info`
is_batched: Whether the data is batched.
**options_kwargs: Additional display options, specific to the dataset type
to visualize. Are forwarded to `tfds.visualization.Visualizer.show`. See
the `tfds.visualization` for a list of available visualizers.
Returns:
fig: The `matplotlib.Figure` object
"""
rows = options_kwargs.pop('rows', _DEFAULT_NUM_ROWS)
cols = options_kwargs.pop('cols', _DEFAULT_NUM_COLS)
ds = _to_tf_dataset(ds, rows * cols, is_batched=is_batched)
if not isinstance(ds_info, dataset_info.DatasetInfo): # Arguments inverted
# `absl.logging` does not appear on Colab by default, so uses print instead.
print(
'WARNING: For consistency with `tfds.load`, the `tfds.show_examples` '
'signature has been modified from (info, ds) to (ds, info).\n'
'The old signature is deprecated and will be removed. '
'Please change your call to `tfds.show_examples(ds, info)`'
)
ds, ds_info = ds_info, ds
# Pack `as_supervised=True` datasets
ds = dataset_info.pack_as_supervised_ds(ds, ds_info)
for visualizer in _ALL_VISUALIZERS:
if visualizer.match(ds_info):
return visualizer.show(
ds, ds_info, **options_kwargs, rows=rows, cols=cols
)
raise ValueError(
'Visualisation not supported for dataset `{}`'.format(ds_info.name)
)
def show_statistics(
ds_info: dataset_info.DatasetInfo,
split: splits.Split = splits.Split.TRAIN,
disable_logging: bool = True,
) -> None:
"""Display the datasets statistics on a Colab/Jupyter notebook.
`tfds.show_statistics` is a wrapper around
[tensorflow_data_validation](https://www.tensorflow.org/tfx/data_validation/get_started)
which calls `tfdv.visualize_statistics`. Statistics are displayed using
[FACETS OVERVIEW](https://pair-code.github.io/facets/).
Usage:
```
builder = tfds.builder('mnist')
tfds.show_statistics(builder.info)
```
Or:
```
ds, ds_info = tfds.load('mnist', with_info)
tfds.show_statistics(ds_info)
```
Note: In order to work, `tensorflow_data_validation` must be installed and
the dataset info object must contain the statistics. For "official" datasets,
only datasets which have been added/updated recently will contains statistics.
For "custom" datasets, you need to generate the dataset with
`tensorflow_data_validation` installed to have the statistics.
Args:
ds_info: The `tfds.core.DatasetInfo` object containing the statistics.
split: Split for which generate the statistics.
disable_logging: `bool`, if True, disable the tfdv logs which can be too
verbose.
Returns:
`None`
"""
tfdv = lazy_imports_lib.lazy_imports.tensorflow_data_validation
if split not in ds_info.splits:
raise ValueError(
"Invalid requested split: '{}'. Only {} are availables.".format(
split, list(ds_info.splits)
)
)
# Creates the statistics.
statistics = statistics_pb2.DatasetFeatureStatisticsList()
statistics.datasets.add().CopyFrom(ds_info.splits[split].statistics) # pytype: disable=attribute-error # bind-properties
with utils.disable_logging() if disable_logging else utils.nullcontext():
return tfdv.visualize_statistics(statistics)