254 lines
7.4 KiB
Python
254 lines
7.4 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=g-import-not-at-top
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"""Utilities related to disk I/O."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import os
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import sys
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import numpy as np
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import six
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import type_spec
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import keras_export
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try:
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import h5py
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except ImportError:
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h5py = None
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if sys.version_info >= (3, 6):
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def _path_to_string(path):
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if isinstance(path, os.PathLike):
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return os.fspath(path)
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return path
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elif sys.version_info >= (3, 4):
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def _path_to_string(path):
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import pathlib
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if isinstance(path, pathlib.Path):
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return str(path)
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return path
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else:
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def _path_to_string(path):
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return path
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def path_to_string(path):
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"""Convert `PathLike` objects to their string representation.
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If given a non-string typed path object, converts it to its string
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representation. Depending on the python version used, this function
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can handle the following arguments:
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python >= 3.6: Everything supporting the fs path protocol
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https://www.python.org/dev/peps/pep-0519
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python >= 3.4: Only `pathlib.Path` objects
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If the object passed to `path` is not among the above, then it is
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returned unchanged. This allows e.g. passthrough of file objects
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through this function.
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Args:
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path: `PathLike` object that represents a path
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Returns:
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A string representation of the path argument, if Python support exists.
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"""
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return _path_to_string(path)
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@keras_export('keras.utils.HDF5Matrix')
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class HDF5Matrix(object):
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"""Representation of HDF5 dataset to be used instead of a Numpy array.
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THIS CLASS IS DEPRECATED.
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Training with HDF5Matrix may not be optimized for performance, and might
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not work with every distribution strategy.
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We recommend using https://github.com/tensorflow/io to load your
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HDF5 data into a tf.data Dataset and passing that dataset to Keras.
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"""
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refs = collections.defaultdict(int)
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@deprecation.deprecated('2020-05-30', 'Training with '
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'HDF5Matrix is not optimized for performance. '
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'Instead, we recommend using '
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'https://github.com/tensorflow/io to load your '
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'HDF5 data into a tf.data Dataset and passing '
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'that dataset to Keras.')
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def __init__(self, datapath, dataset, start=0, end=None, normalizer=None):
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"""Representation of HDF5 dataset to be used instead of a Numpy array.
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Example:
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```python
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x_data = HDF5Matrix('input/file.hdf5', 'data')
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model.predict(x_data)
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```
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Providing `start` and `end` allows use of a slice of the dataset.
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Optionally, a normalizer function (or lambda) can be given. This will
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be called on every slice of data retrieved.
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Arguments:
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datapath: string, path to a HDF5 file
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dataset: string, name of the HDF5 dataset in the file specified
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in datapath
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start: int, start of desired slice of the specified dataset
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end: int, end of desired slice of the specified dataset
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normalizer: function to be called on data when retrieved
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Returns:
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An array-like HDF5 dataset.
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Raises:
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ImportError if HDF5 & h5py are not installed
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"""
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if h5py is None:
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raise ImportError('The use of HDF5Matrix requires '
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'HDF5 and h5py installed.')
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if datapath not in list(self.refs.keys()):
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f = h5py.File(datapath)
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self.refs[datapath] = f
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else:
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f = self.refs[datapath]
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self.data = f[dataset]
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self.start = start
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if end is None:
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self.end = self.data.shape[0]
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else:
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self.end = end
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self.normalizer = normalizer
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def __len__(self):
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return self.end - self.start
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def __getitem__(self, key):
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if isinstance(key, slice):
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start, stop = key.start, key.stop
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if start is None:
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start = 0
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if stop is None:
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stop = self.shape[0]
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if stop + self.start <= self.end:
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idx = slice(start + self.start, stop + self.start)
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else:
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raise IndexError
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elif isinstance(key, (int, np.integer)):
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if key + self.start < self.end:
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idx = key + self.start
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else:
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raise IndexError
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elif isinstance(key, np.ndarray):
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if np.max(key) + self.start < self.end:
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idx = (self.start + key).tolist()
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else:
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raise IndexError
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else:
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# Assume list/iterable
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if max(key) + self.start < self.end:
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idx = [x + self.start for x in key]
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else:
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raise IndexError
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if self.normalizer is not None:
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return self.normalizer(self.data[idx])
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else:
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return self.data[idx]
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@property
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def shape(self):
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"""Gets a numpy-style shape tuple giving the dataset dimensions.
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Returns:
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A numpy-style shape tuple.
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"""
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return (self.end - self.start,) + self.data.shape[1:]
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@property
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def dtype(self):
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"""Gets the datatype of the dataset.
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Returns:
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A numpy dtype string.
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"""
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return self.data.dtype
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@property
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def ndim(self):
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"""Gets the number of dimensions (rank) of the dataset.
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Returns:
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An integer denoting the number of dimensions (rank) of the dataset.
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"""
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return self.data.ndim
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@property
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def size(self):
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"""Gets the total dataset size (number of elements).
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Returns:
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An integer denoting the number of elements in the dataset.
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"""
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return np.prod(self.shape)
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@staticmethod
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def _to_type_spec(value):
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"""Gets the Tensorflow TypeSpec corresponding to the passed dataset.
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Args:
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value: A HDF5Matrix object.
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Returns:
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A tf.TensorSpec.
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"""
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if not isinstance(value, HDF5Matrix):
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raise TypeError('Expected value to be a HDF5Matrix, but saw: {}'.format(
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type(value)))
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return tensor_spec.TensorSpec(shape=value.shape, dtype=value.dtype)
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type_spec.register_type_spec_from_value_converter(HDF5Matrix,
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HDF5Matrix._to_type_spec) # pylint: disable=protected-access
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def ask_to_proceed_with_overwrite(filepath):
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"""Produces a prompt asking about overwriting a file.
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Arguments:
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filepath: the path to the file to be overwritten.
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Returns:
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True if we can proceed with overwrite, False otherwise.
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"""
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overwrite = six.moves.input('[WARNING] %s already exists - overwrite? '
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'[y/n]' % (filepath)).strip().lower()
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while overwrite not in ('y', 'n'):
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overwrite = six.moves.input('Enter "y" (overwrite) or "n" '
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'(cancel).').strip().lower()
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if overwrite == 'n':
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return False
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print('[TIP] Next time specify overwrite=True!')
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return True
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