from __future__ import print_function
import datetime
import logging
import re
import sys
import time
import warnings
from nengo.exceptions import SimulationError
import numpy as np
import tensorflow as tf
from tensorflow.python.framework.ops import get_gradient_function
logger = logging.getLogger(__name__)
if sys.version_info[:2] < (3, 3):
def print_and_flush(*args, **kwargs):
print(*args, **kwargs)
file = kwargs.get('file', sys.stdout)
file.flush()
else:
def print_and_flush(*args, **kwargs):
print(*args, flush=True, **kwargs)
[docs]def sanitize_name(name):
"""Remove illegal Tensorflow name characters from string.
Valid Tensorflow name characters are ``[A-Za-z0-9_.\\-/]``
Parameters
----------
name : str
name to be sanitized
Returns
-------
str
sanitized name
"""
if not isinstance(name, str):
name = str(name)
name = name.replace(" ", "_")
name = name.replace(":", "_")
valid_exp = re.compile(r"[A-Za-z0-9_.\-/]")
return "".join([c for c in name if valid_exp.match(c)])
[docs]def function_name(func, sanitize=True):
"""Get the name of the callable object ``func``.
Parameters
----------
func : callable
callable object (e.g., function, callable class)
sanitize : bool, optional
if True, remove any illegal Tensorflow name characters from name
Returns
-------
str
(sanitized) name of ``func``
"""
name = getattr(func, "__name__", func.__class__.__name__)
if sanitize:
name = sanitize_name(name)
return name
[docs]def align_func(output_shape, output_dtype):
"""Decorator that ensures the output of ``func`` is an
:class:`~numpy:numpy.ndarray` with the given shape and dtype.
Raises a ``SimulationError`` if the
function returns ``None``.
Parameters
----------
output_shape : tuple of int
desired shape for function output (must have the same size as actual
function output)
output_dtype : ``tf.DType`` or :class:`~numpy:numpy.dtype`
desired dtype of function output
"""
if isinstance(output_dtype, tf.DType):
output_dtype = output_dtype.as_numpy_dtype
def apply_align(func):
def aligned_func(*args):
output = func(*args)
if output is None:
raise SimulationError(
"Function %r returned None" %
function_name(func, sanitize=False))
elif not np.all(np.isfinite(output)):
raise SimulationError(
"Function %r returned invalid value %r" %
(function_name(func, sanitize=False), output))
output = np.asarray(output, dtype=output_dtype)
output = output.reshape(output_shape)
return output
return aligned_func
return apply_align
[docs]def print_op(input, message):
"""Inserts a print statement into the tensorflow graph.
Parameters
----------
input : ``tf.Tensor``
the value of this tensor will be printed whenever it is computed
in the graph
message : str
string prepended to the value of ``input``, to help with logging
Returns
-------
``tf.Tensor``
new tensor representing the print operation applied to ``input``
Notes
-----
This is what ``tf.Print`` is supposed to do, but it doesn't seem to work
consistently.
"""
def print_func(x):
print(message, str(x))
return x
with tf.device("/cpu:0"):
output = tf.py_func(print_func, [input], input.dtype)
output.set_shape(input.get_shape())
return output
[docs]def cast_dtype(dtype, target):
"""Changes float dtypes to the target dtype, leaves others unchanged.
Used to map all float values to a target precision. Also casts numpy
dtypes to Tensorflow dtypes.
Parameters
----------
dtype : ``tf.DType`` or :class:`~numpy:numpy.dtype`
input dtype to be converted
target : ``tf.DType``
floating point dtype to which all floating types should be converted
Returns
-------
``tf.DType``
input dtype, converted to ``target`` type if necessary
"""
if not isinstance(dtype, tf.DType):
dtype = tf.as_dtype(dtype)
if dtype.is_floating:
dtype = target
return dtype
[docs]def find_non_differentiable(inputs, outputs):
"""Searches through a Tensorflow graph to find non-differentiable elements
between ``inputs`` and ``outputs`` (elements that would prevent us from
computing ``d_outputs / d_inputs``.
Parameters
----------
inputs : list of ``tf.Tensor``
input tensors
outputs : list of ``tf.Tensor``
output tensors
"""
for o in outputs:
if o in inputs:
continue
else:
try:
get_gradient_function(o.op)
find_non_differentiable(inputs, o.op.inputs)
except LookupError as e:
logger.exception(e)
raise SimulationError(
"Graph contains non-differentiable "
"elements: %s" % o.op)
[docs]class ProgressBar(object):
"""Displays a progress bar and ETA for tracked steps.
Parameters
----------
max_steps : int
number of steps required to complete the tracked process
label : str, optional
a description of what is being tracked
"""
def __init__(self, max_steps, label=None):
self.max_steps = max_steps
self.width = 30
self.label = label
self.reset()
[docs] def reset(self):
"""Reset the tracker to initial conditions."""
self.curr_step = 0
self.start_time = time.time()
self.progress = -1
print_and_flush("[%s] ETA: unknown" % (" " * self.width), end="")
[docs] def stop(self):
"""Stop the progress tracker.
Normally this will be called automatically when ``max_steps`` is
reached, but it can be called manually to trigger an early finish.
"""
line = "\n"
line += ("Completed" if self.label is None else
self.label + " completed")
line += " in %s" % datetime.timedelta(
seconds=int(time.time() - self.start_time))
print(line)
self.curr_step = None
[docs] def step(self, msg=None):
"""Increment the progress tracker one step.
Parameters
----------
msg : str, optional
display the given string at the end of the progress bar
"""
self.curr_step += 1
tmp = int(self.width * self.curr_step / self.max_steps)
if tmp > self.progress:
self.progress = tmp
else:
return
eta = int((time.time() - self.start_time) *
(self.max_steps - self.curr_step) / self.curr_step)
line = "\r[%s%s] ETA: %s" % ("#" * self.progress,
" " * (self.width - self.progress),
datetime.timedelta(seconds=eta))
if msg is not None or self.label is not None:
line += " (%s)" % self.label if msg is None else msg
print_and_flush(line, end="")
if self.curr_step == self.max_steps:
self.stop()
[docs]def minibatch_generator(inputs, targets, minibatch_size, shuffle=True,
rng=None):
"""Generator to yield ``minibatch_sized`` subsets from ``inputs`` and
``targets``.
Parameters
----------
inputs : dict of {:class:`~nengo:nengo.Node`: \
:class:`~numpy:numpy.ndarray`}
input values for Nodes in the network
targets : dict of {:class:`~nengo:nengo.Probe`: \
:class:`~numpy:numpy.ndarray`}
desired output value at Probes, corresponding to each value in
``inputs``
minibatch_size : int
the number of items in each minibatch
shuffle : bool, optional
if True, the division of items into minibatches will be randomized each
time the generator is created
rng : :class:`~numpy:numpy.random.RandomState`, optional
random number generator
Yields
------
inputs : dict of {:class:`~nengo:nengo.Node`: \
:class:`~numpy:numpy.ndarray`}
the same structure as ``inputs``, but with each array reduced to
``minibatch_size`` elements along the first dimension
targets : dict of {:class:`~nengo:nengo.Probe`: \
:class:`~numpy:numpy.ndarray`}
the same structure as ``targets``, but with each array reduced to
``minibatch_size`` elements along the first dimension
"""
n_inputs = next(iter(inputs.values())).shape[0]
if rng is None:
rng = np.random
if shuffle:
perm = rng.permutation(n_inputs)
else:
perm = np.arange(n_inputs)
if n_inputs % minibatch_size != 0:
warnings.warn(UserWarning(
"Number of inputs (%d) is not an even multiple of "
"minibatch size (%d); inputs will be truncated" %
(n_inputs, minibatch_size)))
perm = perm[:-(n_inputs % minibatch_size)]
for i in range(0, n_inputs - n_inputs % minibatch_size,
minibatch_size):
yield ({n: inputs[n][perm[i:i + minibatch_size]] for n in inputs},
{p: targets[p][perm[i:i + minibatch_size]] for p in targets})