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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Methods to allow dict of numpy arrays."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from six import string_types
from tensorflow.python.estimator.inputs.queues import feeding_functions
# Key name to pack the target into dict of `features`. See
# `_get_unique_target_key` for details.
_TARGET_KEY = '__target_key__'
def _get_unique_target_key(features):
"""Returns a key not existed in the input dict `features`.
Caller of `input_fn` usually provides `features` (dict of numpy arrays) and
`target`, but the underlying feeding module expects a single dict of numpy
arrays as input. So, the `target` needs to be packed into the `features`
temporarily and unpacked after calling the feeding function. Toward this goal,
this function returns a key not existed in the `features` to pack the
`target`.
"""
target_key = _TARGET_KEY
while target_key in features:
target_key += '_n'
return target_key
def numpy_input_fn(x,
y=None,
batch_size=128,
num_epochs=1,
shuffle=None,
queue_capacity=1000,
num_threads=1):
"""Returns input function that would feed dict of numpy arrays into the model.
This returns a function outputting `features` and `targets` based on the dict
of numpy arrays. The dict `features` has the same keys as the `x`. The dict
`targets` has the same keys as the `y` if `y` is a dict.
Example:
```python
age = np.arange(4) * 1.0
height = np.arange(32, 36)
x = {'age': age, 'height': height}
y = np.arange(-32, -28)
with tf.Session() as session:
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
```
Args:
x: dict of numpy array object.
y: numpy array object or dict of numpy array object. `None` if absent.
batch_size: Integer, size of batches to return.
num_epochs: Integer, number of epochs to iterate over data. If `None` will
run forever.
shuffle: Boolean, if True shuffles the queue. Avoid shuffle at prediction
time.
queue_capacity: Integer, size of queue to accumulate.
num_threads: Integer, number of threads used for reading and enqueueing. In
order to have predicted and repeatable order of reading and enqueueing,
such as in prediction and evaluation mode, `num_threads` should be 1.
Returns:
Function, that has signature of ()->(dict of `features`, `targets`)
Raises:
ValueError: if the shape of `y` mismatches the shape of values in `x` (i.e.,
values in `x` have same shape).
ValueError: if duplicate keys are in both `x` and `y` when `y` is a dict.
ValueError: if x or y is an empty dict.
TypeError: `x` is not a dict or `shuffle` is not bool.
"""
if not isinstance(shuffle, bool):
raise TypeError('shuffle must be explicitly set as boolean; '
'got {}'.format(shuffle))
def input_fn():
"""Numpy input function."""
if not isinstance(x, dict):
raise TypeError('x must be dict; got {}'.format(type(x).__name__))
if not x:
raise ValueError('x cannot be empty')
# Make a shadow copy and also ensure the order of iteration is consistent.
ordered_dict_data = collections.OrderedDict(
sorted(x.items(), key=lambda t: t[0]))
# Deep copy keys which is a view in python 3
feature_keys = list(ordered_dict_data.keys())
if y is None:
target_keys = None
elif isinstance(y, dict):
if not y:
raise ValueError('y cannot be empty dict, use None instead.')
ordered_dict_y = collections.OrderedDict(
sorted(y.items(), key=lambda t: t[0]))
target_keys = list(ordered_dict_y.keys())
duplicate_keys = set(feature_keys).intersection(set(target_keys))
if len(duplicate_keys):
raise ValueError('{} duplicate keys are found in both x and y: '
'{}'.format(len(duplicate_keys), duplicate_keys))
ordered_dict_data.update(ordered_dict_y)
else:
target_keys = _get_unique_target_key(ordered_dict_data)
ordered_dict_data[target_keys] = y
if len(set(v.shape[0] for v in ordered_dict_data.values())) != 1:
shape_dict_of_x = {k: ordered_dict_data[k].shape
for k in feature_keys}
if target_keys is None:
shape_of_y = None
elif isinstance(target_keys, string_types):
shape_of_y = y.shape
else:
shape_of_y = {k: ordered_dict_data[k].shape
for k in target_keys}
raise ValueError('Length of tensors in x and y is mismatched. All '
'elements in x and y must have the same length.\n'
'Shapes in x: {}\n'
'Shapes in y: {}\n'.format(shape_dict_of_x, shape_of_y))
queue = feeding_functions._enqueue_data( # pylint: disable=protected-access
ordered_dict_data,
queue_capacity,
shuffle=shuffle,
num_threads=num_threads,
enqueue_size=batch_size,
num_epochs=num_epochs)
batch = (queue.dequeue_many(batch_size) if num_epochs is None
else queue.dequeue_up_to(batch_size))
# Remove the first `Tensor` in `batch`, which is the row number.
if len(batch) > 0:
batch.pop(0)
features = dict(zip(feature_keys, batch[:len(feature_keys)]))
if target_keys is None:
# TODO(martinwicke), return consistent result
return features
elif isinstance(target_keys, string_types):
target = batch[-1]
return features, target
else:
target = dict(zip(target_keys, batch[-len(target_keys):]))
return features, target
return input_fn
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