Source code for nengo_dl.utils

from __future__ import print_function

from functools import partial
import logging
import re
import sys
import threading
import time
import warnings

from nengo import Connection, Ensemble, Network, ensemble
from nengo.exceptions import SimulationError, ConfigError, NetworkContextError
from nengo.params import BoolParam, Parameter
import numpy as np
import progressbar
import tensorflow as tf
from tensorflow.python.framework.ops import get_gradient_function

logger = logging.getLogger(__name__)


[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 Name of ``func`` (optionally sanitized) """ 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. 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 Raises ------ :class:`~nengo:nengo.exceptions.SimulationError` If the function returns ``None`` or a non-finite value. """ 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)) try: if not np.all(np.isfinite(output)): raise SimulationError( "Function %r returned invalid value %r" % (function_name(func, sanitize=False), output)) except (TypeError, ValueError): raise SimulationError( "Function %r returned a value %r of invalid type %r" % (function_name(func, sanitize=False), output, type(output))) output = np.asarray(output, dtype=output_dtype) output = output.reshape(output_shape) return output return aligned_func return apply_align
[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: grad = get_gradient_function(o.op) if grad is None and len(o.op.inputs) > 0: # note: technically we're not sure that this op is # on the path to inputs. we could wait and propagate this # until we find inputs, but that can take a long time for # large graphs. it seems more useful to fail quickly, and # risk some false positives raise LookupError find_non_differentiable(inputs, o.op.inputs) except LookupError: raise SimulationError( "Graph contains non-differentiable " "elements: %s" % o.op)
[docs]class MessageBar(progressbar.BouncingBar): """ProgressBar widget for progress bars with possibly unknown duration. Parameters ---------- msg : str, optional A message to be displayed in the middle of the progress bar finish_msg : str, optional A message to be displayed when the progress bar is finished """ def __init__(self, msg="", finish_msg="", **kwargs): super(MessageBar, self).__init__(**kwargs) self.msg = msg self.finish_msg = finish_msg def __call__(self, progress, data, width): if progress.end_time: return self.finish_msg if progress.max_value is progressbar.UnknownLength: bar = progressbar.BouncingBar else: bar = progressbar.Bar line = bar.__call__(self, progress, data, width) if data["percentage"] is None: msg = self.msg else: msg = "%s (%d%%)" % (self.msg, data["percentage"]) offset = width // 2 - len(msg) // 2 return line[:offset] + msg + line[offset + len(msg):]
[docs]class ProgressBar(progressbar.ProgressBar): """Handles progress bar display for some tracked process. Parameters ---------- present : str, optional Description of process in present (e.g., "Simulating") past : str, optional Description of process in past (e.g., "Simulation") max_value : int or None, optional The maximum number of steps in the tracked process (or ``None`` if the maximum number of steps is unknown) vars : list of str, optional Extra variables that will be displayed at the end of the progress bar Notes ----- Launches a separate thread to handle the progress bar display updates. """ def __init__(self, present="", past=None, max_value=1, vars=None, **kwargs): self.present = present self.sub_bar = None self.finished = None if past is None: past = present self.msg_bar = MessageBar( msg=present, finish_msg="%s finished in" % past) widgets = [self.msg_bar, " "] if max_value is None: widgets.append(progressbar.Timer(format="%(elapsed)s")) else: widgets.append(progressbar.ETA( format="ETA: %(eta)s", format_finished="%(elapsed)s")) if vars is not None: self.var_vals = progressbar.FormatCustomText( " (" + ", ".join("%s: %%(%s)s" % (v, v) for v in vars) + ")", {v: "---" for v in vars}) widgets.append(self.var_vals) else: self.var_vals = None def update_thread(): while not self.finished: if self.sub_bar is None or self.sub_bar.finished: self.update() time.sleep(0.001) self.thread = threading.Thread(target=update_thread) self.thread.daemon = True if max_value is None: max_value = progressbar.UnknownLength super(ProgressBar, self).__init__( poll_interval=0.1, widgets=widgets, fd=sys.stdout, max_value=max_value, **kwargs)
[docs] def start(self, **kwargs): """Start tracking process, initialize display.""" super(ProgressBar, self).start(**kwargs) self.finished = False self.thread.start() return self
[docs] def finish(self, **kwargs): """Stop tracking process, finish display.""" if self.sub_bar is not None and self.sub_bar.finished is False: self.sub_bar.finish() self.finished = True self.thread.join() super(ProgressBar, self).finish(**kwargs)
[docs] def step(self, **vars): """Advance the progress bar one step. Parameters ---------- vars : dict of {str: str} Values for the extra variables displayed at the end of the progress bar (defined in :meth:`.ProgressBar.__init__`) """ if self.var_vals is not None: self.var_vals.update_mapping(**vars) self.value += 1
[docs] def sub(self, msg=None, **kwargs): """Creates a new progress bar for tracking a sub-process. Parameters ---------- msg : str, optional Description of sub-process """ if self.sub_bar is not None and self.sub_bar.finished is False: self.sub_bar.finish() self.sub_bar = ProgressBar( present="%s: %s" % (self.present, msg) if msg else self.present, **kwargs) self.sub_bar.finish = partial(self.sub_bar.finish, end="\r") return self.sub_bar
def __next__(self): """Wraps an iterable using this progress bar.""" try: if self.start_time is None: self.start() else: self.step() value = next(self._iterable) return value except StopIteration: self.finish() raise next = __next__ # for python 2.x
[docs]class NullProgressBar(progressbar.NullBar): """A progress bar that does nothing. Used to replace ProgressBar when we want to disable output. """ def sub(self, *args, **kwargs): return self
[docs]def minibatch_generator(inputs, targets, minibatch_size, shuffle=True, truncation=None, 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 truncation : int, optional If not None, divide the data up into sequences of ``truncation`` timesteps. rng : :class:`~numpy:numpy.random.RandomState`, optional Seeded random number generator Yields ------ offset : int The simulation step at which the returned data begins (will only be nonzero if ``truncation`` is not ``None``). 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, n_steps = next(iter(inputs.values())).shape[:2] if rng is None: rng = np.random if shuffle: perm = rng.permutation(n_inputs) else: perm = np.arange(n_inputs) if truncation is None: truncation = n_steps 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)] if n_steps % truncation != 0: warnings.warn(UserWarning( "Length of training data (%d) is not an even multiple of " "truncation length (%d); this may result in poor " "training results" % (n_steps, truncation))) for i in range(0, n_inputs - n_inputs % minibatch_size, minibatch_size): batch_inp = {n: inputs[n][perm[i:i + minibatch_size]] for n in inputs} batch_tar = {p: targets[p][perm[i:i + minibatch_size]] for p in targets} for j in range(0, n_steps, truncation): yield (j, {n: batch_inp[n][:, j:j + truncation] for n in inputs}, {p: batch_tar[p][:, j:j + truncation] for p in targets})
[docs]def configure_settings(**kwargs): """Pass settings to ``nengo_dl`` by setting them as parameters on the top-level Network config. The settings are passed as keyword arguments to ``configure_settings``; e.g., to set ``trainable`` use ``configure_settings(trainable=True)``. Parameters ---------- trainable : bool or None Adds a parameter to Nengo Ensembles/Connections/Networks that controls whether or not they will be optimized by :meth:`.Simulator.train`. Passing ``None`` will use the default ``nengo_dl`` trainable settings, or True/False will override the default for all objects. In either case trainability can be further configured on a per-object basis (e.g. ``net.config[my_ensemble].trainable = True``. See `the documentation <https://www.nengo.ai/nengo_dl/training.html#choosing-which-elements-to-optimize>`_ for more details. planner : graph planning algorithm Pass one of the `graph planners <https://www.nengo.ai/nengo_dl/graph_optimizer.html>`_ to change the default planner. """ # get the toplevel network if len(Network.context) > 0: config = Network.context[0].config else: raise NetworkContextError( "`configure_settings` must be called within a Network context " "(`with nengo.Network(): ...`)") try: params = config[Network] except ConfigError: config.configures(Network) params = config[Network] for attr, val in kwargs.items(): if attr == "trainable": for obj in (Ensemble, Connection, ensemble.Neurons, Network): try: obj_params = config[obj] except ConfigError: config.configures(obj) obj_params = config[obj] obj_params.set_param("trainable", BoolParam("trainable", val, optional=True)) else: params.set_param(attr, Parameter(attr, val))