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mocking.py
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mocking.py
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# coding=utf-8
# Copyright 2022 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.
"""Mock util for tfds."""
import contextlib
import enum
import functools
import os
import random
from typing import Callable, Iterator, Optional
from unittest import mock
from absl import logging
import numpy as np
import tensorflow as tf
from tensorflow_datasets.core import dataset_builder
from tensorflow_datasets.core import decode
from tensorflow_datasets.core import features as features_lib
from tensorflow_datasets.core import read_only_builder
from tensorflow_datasets.core import tfrecords_reader
from tensorflow_datasets.testing import test_utils
class MockPolicy(enum.Enum):
"""Strategy to use with `tfds.testing.mock_data` to mock the dataset.
Attributes:
AUTO: Try `USE_FILES`, and fallback to `USE_CODE` if the metadata files
(`dataset_info.json`,...) are not found.
USE_FILES: Load dataset from the metadata files (present in the `data_dir`
kwarg of `tfds.testing.mock_data`, raise error if data_dir is not
reachable.
USE_CODE: Load the data from the original dataset generation class. Do not
use any generated files. More is more convenient but less safe than
`USE_FILES`. Not all features might be available (e.g. no split-names).
"""
AUTO = enum.auto()
USE_CODE = enum.auto()
USE_FILES = enum.auto()
@contextlib.contextmanager
def mock_data(
num_examples: int = 1,
num_sub_examples: int = 1,
max_value: Optional[int] = None,
*,
policy: MockPolicy = MockPolicy.AUTO,
as_dataset_fn: Optional[Callable[..., tf.data.Dataset]] = None,
data_dir: Optional[str] = None,
) -> Iterator[None]:
"""Mock tfds to generate random data.
### Usage
* Usage (automated):
```py
with tfds.testing.mock_data(num_examples=5):
ds = tfds.load('some_dataset', split='train')
for ex in ds: # ds will yield randomly generated examples.
ex
```
* Usage (manual):
For more control over the generated examples, you can
manually overwrite the `DatasetBuilder._as_dataset` method:
```py
def as_dataset(self, *args, **kwargs):
return tf.data.Dataset.from_generator(
lambda: ({
'image': np.ones(shape=(28, 28, 1), dtype=np.uint8),
'label': i % 10,
} for i in range(num_examples)),
output_types=self.info.features.dtype,
output_shapes=self.info.features.shape,
)
with mock_data(as_dataset_fn=as_dataset):
ds = tfds.load('some_dataset', split='train')
for ex in ds: # ds will yield the fake data example of 'as_dataset'.
ex
```
### Policy
For improved results, you can copy the true metadata files
(`dataset_info.json`, `label.txt`, vocabulary files) in
`data_dir/dataset_name/version`. This will allow the mocked dataset to use
the true metadata computed during generation (split names,...).
If metadata files are not found, then info from the original class will be
used, but the features computed during generation won't be available (e.g.
unknown split names, so any splits are accepted).
### Miscellaneous
* The examples are deterministically generated. Train and test split will
yield the same examples.
* The actual examples will be randomly generated using
`builder.info.features.get_tensor_info()`.
* Download and prepare step will always be a no-op.
* Warning: `info.split['train'].num_examples` won't match
`len(list(ds_train))`
Some of those points could be improved. If you have suggestions, issues with
this functions, please open a new issue on our Github.
Args:
num_examples: Number of fake example to generate.
num_sub_examples: Number of examples to generate in nested Dataset features.
max_value: The maximum value present in generated tensors; if max_value is
None or it is set to 0, then random numbers are generated from the range
from 0 to 255.
policy: Strategy to use to generate the fake examples. See
`tfds.testing.MockPolicy`.
as_dataset_fn: If provided, will replace the default random example
generator. This function mock the `FileAdapterBuilder._as_dataset`
data_dir: Folder containing the metadata file (searched in
`data_dir/dataset_name/version`). Overwrite `data_dir` kwargs from
`tfds.load`. Used in `MockPolicy.USE_FILES` mode.
Yields:
None
"""
original_init_fn = dataset_builder.DatasetBuilder.__init__
original_as_dataset_fn = dataset_builder.DatasetBuilder.as_dataset
original_builder_from_files = read_only_builder.builder_from_files
def mock_download_and_prepare(self, *args, **kwargs):
"""`builder.download_and_prepare` is a no-op."""
del self, args, kwargs # Unused
def mock_as_dataset_base(self, **kwargs):
"""Function which overwrite `builder.as_dataset`."""
# When `USE_FILES` is used, make sure the metadata actually exists.
if tf.io.gfile.exists(self.data_dir):
logging.info('Metadata found for %s at %s', self.name, self.data_dir)
else:
if policy == MockPolicy.USE_FILES:
raise ValueError(
'TFDS has been mocked with `MockPolicy.USE_FILES`, but metadata '
f'files were not found in {self.data_dir}. '
'You should copy the real metadata files, so that the dataset '
'can be loaded properly, or set the data_dir kwarg of '
'tfds.testing.mock_tfds(data_dir=...).\n'
)
if policy == MockPolicy.AUTO:
logging.info(
'Metadata NOT found for %s at %s. Will use `MockPolicy.USE_CODE.`',
self.name,
self.data_dir,
)
# Info is already restored at this point, so can mock the file system
# safely in case of `USE_CODE` mode.
# The only gfile access is to check `self.data_dir` existance.
with test_utils.MockFs() as fs:
fs.add_file(os.path.join(self.data_dir, 'tmp.txt'))
return original_as_dataset_fn(self, **kwargs)
def mock_as_dataset(self, split, decoders=None, read_config=None, **kwargs):
"""Function which overwrite `builder._as_dataset`."""
del split
del kwargs
# Partial decoding
if isinstance(decoders, decode.PartialDecoding):
# TODO(epot): Should be moved inside `features.decode_example`
features = decoders.extract_features(self.info.features)
decoders = decoders.decoders
# Full decoding (all features decoded)
else:
features = self.info.features
decoders = decoders # pylint: disable=self-assigning-variable
has_nested_dataset = any(
isinstance(f, features_lib.Dataset)
for f in features._flatten(features)) # pylint: disable=protected-access
if decoders is not None or has_nested_dataset:
# If a decoder is passed, encode/decode the examples.
generator_cls = EncodedRandomFakeGenerator
specs = features.get_serialized_info()
decode_fn = functools.partial(features.decode_example, decoders=decoders)
else:
generator_cls = RandomFakeGenerator
specs = features.get_tensor_info()
decode_fn = lambda ex: ex # identity
ds = tf.data.Dataset.from_generator(
# `from_generator` takes a callable with signature () -> iterable
# Recreating a new generator each time ensure that all pipelines are
# using the same examples
# pylint: disable=g-long-lambda]
lambda: generator_cls(
features=features,
num_examples=num_examples,
num_sub_examples=num_sub_examples,
max_value=max_value),
# pylint: enable=g-long-lambda]
output_types=tf.nest.map_structure(lambda t: t.dtype, specs),
output_shapes=tf.nest.map_structure(lambda t: t.shape, specs),
)
ds = ds.apply(tf.data.experimental.assert_cardinality(num_examples))
ds = ds.map(decode_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
if read_config and read_config.add_tfds_id:
ds_id = tfrecords_reader._make_id_dataset( # pylint: disable=protected-access
filename=f'{self.name}-split.tfrecord-00000-of-00001',
start_index=0, # pytype: disable=wrong-arg-types
)
ds = tf.data.Dataset.zip((ds, ds_id))
ds = ds.map(lambda ex, id: {'tfds_id': id, **ex})
return ds
if not as_dataset_fn:
as_dataset_fn = mock_as_dataset
if policy == MockPolicy.USE_CODE:
mock_data_dir = '/tmp/non-existing/tfds/data_dir/'
elif not data_dir:
mock_data_dir = os.path.join(os.path.dirname(__file__), 'metadata')
else: # AUTO or USE_FILES with explicitly given `data_dir`
mock_data_dir = data_dir
def mock_init(*args, data_dir=None, **kwargs):
del data_dir # Unused. Inject `mock_data_dir` instead.
return original_init_fn(*args, data_dir=mock_data_dir, **kwargs)
def new_builder_from_files(*args, **kwargs):
# Replace the user-given data dir by the mocked one
kwargs.pop('data_dir', None)
# `DatasetBuilder.__init__` is mocked above to inject the wrong data_dir.
# So we restore the original `DatasetBuilder.__init__` inside
# `builder_from_files` calls.
with mock.patch(f'{core}.dataset_builder.DatasetBuilder.__init__',
original_init_fn):
return original_builder_from_files(
*args, data_dir=mock_data_dir, **kwargs)
core = 'tensorflow_datasets.core'
with contextlib.ExitStack() as stack:
for path, mocked_fn in [
# All GCS access should fail (is_dataset_on_gcs,...).
(f'{core}.utils.gcs_utils.exists', lambda path: False),
# Patch `data_dir`: `data_dir` explicitly set by users will be ignored.
# `data_dir` is used at two places:
# * `builder_from_files` to search read-only datasets loaded from config
# * `DatasetBuilder.__init__` otherwise
(f'{core}.dataset_builder.DatasetBuilder.__init__', mock_init),
(
f'{core}.read_only_builder.builder_from_files',
new_builder_from_files,
),
# Patch DatasetBuilder
(
f'{core}.dataset_builder.DatasetBuilder.download_and_prepare',
mock_download_and_prepare,
),
(
f'{core}.dataset_builder.DatasetBuilder.as_dataset',
mock_as_dataset_base,
),
(
f'{core}.dataset_builder.FileReaderBuilder._as_dataset',
as_dataset_fn,
),
]:
stack.enter_context(mock.patch(path, mocked_fn))
yield
class RandomFakeGenerator(object):
"""Generator of fake examples randomly and deterministically generated."""
def __init__(self,
features,
num_examples: int,
num_sub_examples: int = 1,
max_value: Optional[int] = None,
seed: int = 0):
self._rgn = np.random.RandomState(seed) # Could use the split name as seed
self._py_rng = random.Random(seed)
self._features = features
self._num_examples = num_examples
self._num_sub_examples = num_sub_examples
self._max_value = max_value
def _generate_random_string_array(self, shape):
"""Generates an array of random strings."""
def rand_str():
return ''.join(
self._rgn.choice(
list(' abcdefghij'), size=(self._py_rng.randint(10, 20))))
if not shape:
return rand_str()
return np.array([rand_str() for _ in range(np.prod(shape, dtype=np.int32))
]).reshape(shape)
def _generate_random_obj(self, feature, tensor_info):
"""Generates a random tensor for a single feature."""
# TODO(tfds): Could improve the fake generatiion:
# * Use the feature statistics (min, max)
# * For Sequence features
# * For Text
# First we deal with the case of sub-datasets:
if isinstance(feature, features_lib.Dataset):
# For sub-datasets self._num_sub_examples examples are generated.
generator = RandomFakeGenerator(
feature.feature,
num_examples=self._num_sub_examples,
num_sub_examples=1,
max_value=self._max_value)
# Returns the list of examples in the nested dataset.
return list(generator)
shape = [ # Fill dynamic shape with random values
self._rgn.randint(5, 50) if s is None else s for s in tensor_info.shape
]
if isinstance(feature, features_lib.ClassLabel):
max_value = feature.num_classes
elif isinstance(feature, features_lib.Text) and feature.vocab_size:
max_value = feature.vocab_size
elif self._max_value:
max_value = self._max_value
else:
max_value = 255
# We cast the data to make sure `encode_example` don't raise errors
dtype = tensor_info.dtype
# Generate some random values, depending on the dtype
if dtype.is_integer:
return self._rgn.randint(0, max_value, shape).astype(dtype.as_numpy_dtype)
elif dtype.is_floating:
return self._rgn.random_sample(shape).astype(dtype.as_numpy_dtype)
elif dtype.is_bool:
return (self._rgn.random_sample(shape) < .5).astype(dtype.as_numpy_dtype)
elif dtype == tf.string:
return self._generate_random_string_array(shape)
raise ValueError('Fake generation not supported for {}'.format(dtype))
def _generate_example(self):
"""Generate the next example."""
root_feature = self._features
flat_features = root_feature._flatten(root_feature) # pylint: disable=protected-access
flat_tensor_info = root_feature._flatten(root_feature.get_tensor_info()) # pylint: disable=protected-access
flat_objs = [
self._generate_random_obj(feature, tensor_info)
for feature, tensor_info in zip(flat_features, flat_tensor_info)
]
return root_feature._nest(flat_objs) # pylint: disable=protected-access
def __iter__(self):
"""Yields all fake examples."""
for _ in range(self._num_examples):
yield self._generate_example()
class EncodedRandomFakeGenerator(RandomFakeGenerator):
"""Generator of fake encoded examples."""
def __iter__(self):
"""Yields all fake examples."""
for ex in super(EncodedRandomFakeGenerator, self).__iter__():
yield self._features.encode_example(ex)