558 lines
19 KiB
Python
558 lines
19 KiB
Python
|
# Copyright 2015 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""Python utilities required by Keras."""
|
||
|
|
|
||
|
|
import binascii
|
||
|
|
import codecs
|
||
|
|
import importlib
|
||
|
|
import marshal
|
||
|
|
import os
|
||
|
|
import re
|
||
|
|
import sys
|
||
|
|
import time
|
||
|
|
import types as python_types
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import tensorflow.compat.v2 as tf
|
||
|
|
|
||
|
|
from keras.utils import io_utils
|
||
|
|
from keras.utils import tf_inspect
|
||
|
|
|
||
|
|
# isort: off
|
||
|
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
|
||
|
|
|
||
|
|
def func_dump(func):
|
||
|
|
"""Serializes a user defined function.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
func: the function to serialize.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple `(code, defaults, closure)`.
|
||
|
|
"""
|
||
|
|
if os.name == "nt":
|
||
|
|
raw_code = marshal.dumps(func.__code__).replace(b"\\", b"/")
|
||
|
|
code = codecs.encode(raw_code, "base64").decode("ascii")
|
||
|
|
else:
|
||
|
|
raw_code = marshal.dumps(func.__code__)
|
||
|
|
code = codecs.encode(raw_code, "base64").decode("ascii")
|
||
|
|
defaults = func.__defaults__
|
||
|
|
if func.__closure__:
|
||
|
|
closure = tuple(c.cell_contents for c in func.__closure__)
|
||
|
|
else:
|
||
|
|
closure = None
|
||
|
|
return code, defaults, closure
|
||
|
|
|
||
|
|
|
||
|
|
def func_load(code, defaults=None, closure=None, globs=None):
|
||
|
|
"""Deserializes a user defined function.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
code: bytecode of the function.
|
||
|
|
defaults: defaults of the function.
|
||
|
|
closure: closure of the function.
|
||
|
|
globs: dictionary of global objects.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A function object.
|
||
|
|
"""
|
||
|
|
if isinstance(code, (tuple, list)): # unpack previous dump
|
||
|
|
code, defaults, closure = code
|
||
|
|
if isinstance(defaults, list):
|
||
|
|
defaults = tuple(defaults)
|
||
|
|
|
||
|
|
def ensure_value_to_cell(value):
|
||
|
|
"""Ensures that a value is converted to a python cell object.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
value: Any value that needs to be casted to the cell type
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A value wrapped as a cell object (see function "func_load")
|
||
|
|
"""
|
||
|
|
|
||
|
|
def dummy_fn():
|
||
|
|
|
||
|
|
value # just access it so it gets captured in .__closure__
|
||
|
|
|
||
|
|
cell_value = dummy_fn.__closure__[0]
|
||
|
|
if not isinstance(value, type(cell_value)):
|
||
|
|
return cell_value
|
||
|
|
return value
|
||
|
|
|
||
|
|
if closure is not None:
|
||
|
|
closure = tuple(ensure_value_to_cell(_) for _ in closure)
|
||
|
|
try:
|
||
|
|
raw_code = codecs.decode(code.encode("ascii"), "base64")
|
||
|
|
except (UnicodeEncodeError, binascii.Error):
|
||
|
|
raw_code = code.encode("raw_unicode_escape")
|
||
|
|
code = marshal.loads(raw_code)
|
||
|
|
if globs is None:
|
||
|
|
globs = globals()
|
||
|
|
return python_types.FunctionType(
|
||
|
|
code, globs, name=code.co_name, argdefs=defaults, closure=closure
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def has_arg(fn, name, accept_all=False):
|
||
|
|
"""Checks if a callable accepts a given keyword argument.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
fn: Callable to inspect.
|
||
|
|
name: Check if `fn` can be called with `name` as a keyword argument.
|
||
|
|
accept_all: What to return if there is no parameter called `name` but
|
||
|
|
the function accepts a `**kwargs` argument.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
bool, whether `fn` accepts a `name` keyword argument.
|
||
|
|
"""
|
||
|
|
arg_spec = tf_inspect.getfullargspec(fn)
|
||
|
|
if accept_all and arg_spec.varkw is not None:
|
||
|
|
return True
|
||
|
|
return name in arg_spec.args or name in arg_spec.kwonlyargs
|
||
|
|
|
||
|
|
|
||
|
|
@keras_export("keras.utils.Progbar")
|
||
|
|
class Progbar:
|
||
|
|
"""Displays a progress bar.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
target: Total number of steps expected, None if unknown.
|
||
|
|
width: Progress bar width on screen.
|
||
|
|
verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
|
||
|
|
stateful_metrics: Iterable of string names of metrics that should *not*
|
||
|
|
be averaged over time. Metrics in this list will be displayed as-is.
|
||
|
|
All others will be averaged by the progbar before display.
|
||
|
|
interval: Minimum visual progress update interval (in seconds).
|
||
|
|
unit_name: Display name for step counts (usually "step" or "sample").
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
target,
|
||
|
|
width=30,
|
||
|
|
verbose=1,
|
||
|
|
interval=0.05,
|
||
|
|
stateful_metrics=None,
|
||
|
|
unit_name="step",
|
||
|
|
):
|
||
|
|
self.target = target
|
||
|
|
self.width = width
|
||
|
|
self.verbose = verbose
|
||
|
|
self.interval = interval
|
||
|
|
self.unit_name = unit_name
|
||
|
|
if stateful_metrics:
|
||
|
|
self.stateful_metrics = set(stateful_metrics)
|
||
|
|
else:
|
||
|
|
self.stateful_metrics = set()
|
||
|
|
|
||
|
|
self._dynamic_display = (
|
||
|
|
(hasattr(sys.stdout, "isatty") and sys.stdout.isatty())
|
||
|
|
or "ipykernel" in sys.modules
|
||
|
|
or "posix" in sys.modules
|
||
|
|
or "PYCHARM_HOSTED" in os.environ
|
||
|
|
)
|
||
|
|
self._total_width = 0
|
||
|
|
self._seen_so_far = 0
|
||
|
|
# We use a dict + list to avoid garbage collection
|
||
|
|
# issues found in OrderedDict
|
||
|
|
self._values = {}
|
||
|
|
self._values_order = []
|
||
|
|
self._start = time.time()
|
||
|
|
self._last_update = 0
|
||
|
|
self._time_at_epoch_start = self._start
|
||
|
|
self._time_at_epoch_end = None
|
||
|
|
self._time_after_first_step = None
|
||
|
|
|
||
|
|
def update(self, current, values=None, finalize=None):
|
||
|
|
"""Updates the progress bar.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
current: Index of current step.
|
||
|
|
values: List of tuples: `(name, value_for_last_step)`. If `name` is
|
||
|
|
in `stateful_metrics`, `value_for_last_step` will be displayed
|
||
|
|
as-is. Else, an average of the metric over time will be
|
||
|
|
displayed.
|
||
|
|
finalize: Whether this is the last update for the progress bar. If
|
||
|
|
`None`, defaults to `current >= self.target`.
|
||
|
|
"""
|
||
|
|
if finalize is None:
|
||
|
|
if self.target is None:
|
||
|
|
finalize = False
|
||
|
|
else:
|
||
|
|
finalize = current >= self.target
|
||
|
|
|
||
|
|
values = values or []
|
||
|
|
for k, v in values:
|
||
|
|
if k not in self._values_order:
|
||
|
|
self._values_order.append(k)
|
||
|
|
if k not in self.stateful_metrics:
|
||
|
|
# In the case that progress bar doesn't have a target value in
|
||
|
|
# the first epoch, both on_batch_end and on_epoch_end will be
|
||
|
|
# called, which will cause 'current' and 'self._seen_so_far' to
|
||
|
|
# have the same value. Force the minimal value to 1 here,
|
||
|
|
# otherwise stateful_metric will be 0s.
|
||
|
|
value_base = max(current - self._seen_so_far, 1)
|
||
|
|
if k not in self._values:
|
||
|
|
self._values[k] = [v * value_base, value_base]
|
||
|
|
else:
|
||
|
|
self._values[k][0] += v * value_base
|
||
|
|
self._values[k][1] += value_base
|
||
|
|
else:
|
||
|
|
# Stateful metrics output a numeric value. This representation
|
||
|
|
# means "take an average from a single value" but keeps the
|
||
|
|
# numeric formatting.
|
||
|
|
self._values[k] = [v, 1]
|
||
|
|
self._seen_so_far = current
|
||
|
|
|
||
|
|
message = ""
|
||
|
|
now = time.time()
|
||
|
|
info = f" - {now - self._start:.0f}s"
|
||
|
|
if current == self.target:
|
||
|
|
self._time_at_epoch_end = now
|
||
|
|
if self.verbose == 1:
|
||
|
|
if now - self._last_update < self.interval and not finalize:
|
||
|
|
return
|
||
|
|
|
||
|
|
prev_total_width = self._total_width
|
||
|
|
if self._dynamic_display:
|
||
|
|
message += "\b" * prev_total_width
|
||
|
|
message += "\r"
|
||
|
|
else:
|
||
|
|
message += "\n"
|
||
|
|
|
||
|
|
if self.target is not None:
|
||
|
|
numdigits = int(np.log10(self.target)) + 1
|
||
|
|
bar = ("%" + str(numdigits) + "d/%d [") % (current, self.target)
|
||
|
|
prog = float(current) / self.target
|
||
|
|
prog_width = int(self.width * prog)
|
||
|
|
if prog_width > 0:
|
||
|
|
bar += "=" * (prog_width - 1)
|
||
|
|
if current < self.target:
|
||
|
|
bar += ">"
|
||
|
|
else:
|
||
|
|
bar += "="
|
||
|
|
bar += "." * (self.width - prog_width)
|
||
|
|
bar += "]"
|
||
|
|
else:
|
||
|
|
bar = "%7d/Unknown" % current
|
||
|
|
|
||
|
|
self._total_width = len(bar)
|
||
|
|
message += bar
|
||
|
|
|
||
|
|
time_per_unit = self._estimate_step_duration(current, now)
|
||
|
|
|
||
|
|
if self.target is None or finalize:
|
||
|
|
info += self._format_time(time_per_unit, self.unit_name)
|
||
|
|
else:
|
||
|
|
eta = time_per_unit * (self.target - current)
|
||
|
|
if eta > 3600:
|
||
|
|
eta_format = "%d:%02d:%02d" % (
|
||
|
|
eta // 3600,
|
||
|
|
(eta % 3600) // 60,
|
||
|
|
eta % 60,
|
||
|
|
)
|
||
|
|
elif eta > 60:
|
||
|
|
eta_format = "%d:%02d" % (eta // 60, eta % 60)
|
||
|
|
else:
|
||
|
|
eta_format = "%ds" % eta
|
||
|
|
|
||
|
|
info = f" - ETA: {eta_format}"
|
||
|
|
|
||
|
|
for k in self._values_order:
|
||
|
|
info += f" - {k}:"
|
||
|
|
if isinstance(self._values[k], list):
|
||
|
|
avg = np.mean(
|
||
|
|
self._values[k][0] / max(1, self._values[k][1])
|
||
|
|
)
|
||
|
|
if abs(avg) > 1e-3:
|
||
|
|
info += f" {avg:.4f}"
|
||
|
|
else:
|
||
|
|
info += f" {avg:.4e}"
|
||
|
|
else:
|
||
|
|
info += f" {self._values[k]}"
|
||
|
|
|
||
|
|
self._total_width += len(info)
|
||
|
|
if prev_total_width > self._total_width:
|
||
|
|
info += " " * (prev_total_width - self._total_width)
|
||
|
|
|
||
|
|
if finalize:
|
||
|
|
info += "\n"
|
||
|
|
|
||
|
|
message += info
|
||
|
|
io_utils.print_msg(message, line_break=False)
|
||
|
|
message = ""
|
||
|
|
|
||
|
|
elif self.verbose == 2:
|
||
|
|
if finalize:
|
||
|
|
numdigits = int(np.log10(self.target)) + 1
|
||
|
|
count = ("%" + str(numdigits) + "d/%d") % (current, self.target)
|
||
|
|
info = count + info
|
||
|
|
for k in self._values_order:
|
||
|
|
info += f" - {k}:"
|
||
|
|
avg = np.mean(
|
||
|
|
self._values[k][0] / max(1, self._values[k][1])
|
||
|
|
)
|
||
|
|
if avg > 1e-3:
|
||
|
|
info += f" {avg:.4f}"
|
||
|
|
else:
|
||
|
|
info += f" {avg:.4e}"
|
||
|
|
if self._time_at_epoch_end:
|
||
|
|
time_per_epoch = (
|
||
|
|
self._time_at_epoch_end - self._time_at_epoch_start
|
||
|
|
)
|
||
|
|
avg_time_per_step = time_per_epoch / self.target
|
||
|
|
self._time_at_epoch_start = now
|
||
|
|
self._time_at_epoch_end = None
|
||
|
|
info += " -" + self._format_time(time_per_epoch, "epoch")
|
||
|
|
info += " -" + self._format_time(
|
||
|
|
avg_time_per_step, self.unit_name
|
||
|
|
)
|
||
|
|
info += "\n"
|
||
|
|
message += info
|
||
|
|
io_utils.print_msg(message, line_break=False)
|
||
|
|
message = ""
|
||
|
|
|
||
|
|
self._last_update = now
|
||
|
|
|
||
|
|
def add(self, n, values=None):
|
||
|
|
self.update(self._seen_so_far + n, values)
|
||
|
|
|
||
|
|
def _format_time(self, time_per_unit, unit_name):
|
||
|
|
"""format a given duration to display to the user.
|
||
|
|
|
||
|
|
Given the duration, this function formats it in either milliseconds
|
||
|
|
or seconds and displays the unit (i.e. ms/step or s/epoch)
|
||
|
|
Args:
|
||
|
|
time_per_unit: the duration to display
|
||
|
|
unit_name: the name of the unit to display
|
||
|
|
Returns:
|
||
|
|
a string with the correctly formatted duration and units
|
||
|
|
"""
|
||
|
|
formatted = ""
|
||
|
|
if time_per_unit >= 1 or time_per_unit == 0:
|
||
|
|
formatted += f" {time_per_unit:.0f}s/{unit_name}"
|
||
|
|
elif time_per_unit >= 1e-3:
|
||
|
|
formatted += f" {time_per_unit * 1000.0:.0f}ms/{unit_name}"
|
||
|
|
else:
|
||
|
|
formatted += f" {time_per_unit * 1000000.0:.0f}us/{unit_name}"
|
||
|
|
return formatted
|
||
|
|
|
||
|
|
def _estimate_step_duration(self, current, now):
|
||
|
|
"""Estimate the duration of a single step.
|
||
|
|
|
||
|
|
Given the step number `current` and the corresponding time `now` this
|
||
|
|
function returns an estimate for how long a single step takes. If this
|
||
|
|
is called before one step has been completed (i.e. `current == 0`) then
|
||
|
|
zero is given as an estimate. The duration estimate ignores the duration
|
||
|
|
of the (assumed to be non-representative) first step for estimates when
|
||
|
|
more steps are available (i.e. `current>1`).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
current: Index of current step.
|
||
|
|
now: The current time.
|
||
|
|
|
||
|
|
Returns: Estimate of the duration of a single step.
|
||
|
|
"""
|
||
|
|
if current:
|
||
|
|
# there are a few special scenarios here:
|
||
|
|
# 1) somebody is calling the progress bar without ever supplying
|
||
|
|
# step 1
|
||
|
|
# 2) somebody is calling the progress bar and supplies step one
|
||
|
|
# multiple times, e.g. as part of a finalizing call
|
||
|
|
# in these cases, we just fall back to the simple calculation
|
||
|
|
if self._time_after_first_step is not None and current > 1:
|
||
|
|
time_per_unit = (now - self._time_after_first_step) / (
|
||
|
|
current - 1
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
time_per_unit = (now - self._start) / current
|
||
|
|
|
||
|
|
if current == 1:
|
||
|
|
self._time_after_first_step = now
|
||
|
|
return time_per_unit
|
||
|
|
else:
|
||
|
|
return 0
|
||
|
|
|
||
|
|
def _update_stateful_metrics(self, stateful_metrics):
|
||
|
|
self.stateful_metrics = self.stateful_metrics.union(stateful_metrics)
|
||
|
|
|
||
|
|
|
||
|
|
def make_batches(size, batch_size):
|
||
|
|
"""Returns a list of batch indices (tuples of indices).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
size: Integer, total size of the data to slice into batches.
|
||
|
|
batch_size: Integer, batch size.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A list of tuples of array indices.
|
||
|
|
"""
|
||
|
|
num_batches = int(np.ceil(size / float(batch_size)))
|
||
|
|
return [
|
||
|
|
(i * batch_size, min(size, (i + 1) * batch_size))
|
||
|
|
for i in range(0, num_batches)
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def slice_arrays(arrays, start=None, stop=None):
|
||
|
|
"""Slice an array or list of arrays.
|
||
|
|
|
||
|
|
This takes an array-like, or a list of
|
||
|
|
array-likes, and outputs:
|
||
|
|
- arrays[start:stop] if `arrays` is an array-like
|
||
|
|
- [x[start:stop] for x in arrays] if `arrays` is a list
|
||
|
|
|
||
|
|
Can also work on list/array of indices: `slice_arrays(x, indices)`
|
||
|
|
|
||
|
|
Args:
|
||
|
|
arrays: Single array or list of arrays.
|
||
|
|
start: can be an integer index (start index) or a list/array of indices
|
||
|
|
stop: integer (stop index); should be None if `start` was a list.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A slice of the array(s).
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: If the value of start is a list and stop is not None.
|
||
|
|
"""
|
||
|
|
if arrays is None:
|
||
|
|
return [None]
|
||
|
|
if isinstance(start, list) and stop is not None:
|
||
|
|
raise ValueError(
|
||
|
|
"The stop argument has to be None if the value of start "
|
||
|
|
f"is a list. Received start={start}, stop={stop}"
|
||
|
|
)
|
||
|
|
elif isinstance(arrays, list):
|
||
|
|
if hasattr(start, "__len__"):
|
||
|
|
# hdf5 datasets only support list objects as indices
|
||
|
|
if hasattr(start, "shape"):
|
||
|
|
start = start.tolist()
|
||
|
|
return [None if x is None else x[start] for x in arrays]
|
||
|
|
return [
|
||
|
|
None
|
||
|
|
if x is None
|
||
|
|
else None
|
||
|
|
if not hasattr(x, "__getitem__")
|
||
|
|
else x[start:stop]
|
||
|
|
for x in arrays
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
if hasattr(start, "__len__"):
|
||
|
|
if hasattr(start, "shape"):
|
||
|
|
start = start.tolist()
|
||
|
|
return arrays[start]
|
||
|
|
if hasattr(start, "__getitem__"):
|
||
|
|
return arrays[start:stop]
|
||
|
|
return [None]
|
||
|
|
|
||
|
|
|
||
|
|
def to_list(x):
|
||
|
|
"""Normalizes a list/tensor into a list.
|
||
|
|
|
||
|
|
If a tensor is passed, we return
|
||
|
|
a list of size 1 containing the tensor.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: target object to be normalized.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A list.
|
||
|
|
"""
|
||
|
|
if isinstance(x, list):
|
||
|
|
return x
|
||
|
|
return [x]
|
||
|
|
|
||
|
|
|
||
|
|
def to_snake_case(name):
|
||
|
|
intermediate = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name)
|
||
|
|
insecure = re.sub("([a-z])([A-Z])", r"\1_\2", intermediate).lower()
|
||
|
|
# If the class is private the name starts with "_" which is not secure
|
||
|
|
# for creating scopes. We prefix the name with "private" in this case.
|
||
|
|
if insecure[0] != "_":
|
||
|
|
return insecure
|
||
|
|
return "private" + insecure
|
||
|
|
|
||
|
|
|
||
|
|
def is_all_none(structure):
|
||
|
|
iterable = tf.nest.flatten(structure)
|
||
|
|
# We cannot use Python's `any` because the iterable may return Tensors.
|
||
|
|
for element in iterable:
|
||
|
|
if element is not None:
|
||
|
|
return False
|
||
|
|
return True
|
||
|
|
|
||
|
|
|
||
|
|
def check_for_unexpected_keys(name, input_dict, expected_values):
|
||
|
|
unknown = set(input_dict.keys()).difference(expected_values)
|
||
|
|
if unknown:
|
||
|
|
raise ValueError(
|
||
|
|
f"Unknown entries in {name} dictionary: {list(unknown)}. "
|
||
|
|
f"Only expected following keys: {expected_values}"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def validate_kwargs(
|
||
|
|
kwargs, allowed_kwargs, error_message="Keyword argument not understood:"
|
||
|
|
):
|
||
|
|
"""Checks that all keyword arguments are in the set of allowed keys."""
|
||
|
|
for kwarg in kwargs:
|
||
|
|
if kwarg not in allowed_kwargs:
|
||
|
|
raise TypeError(error_message, kwarg)
|
||
|
|
|
||
|
|
|
||
|
|
def default(method):
|
||
|
|
"""Decorates a method to detect overrides in subclasses."""
|
||
|
|
method._is_default = True
|
||
|
|
return method
|
||
|
|
|
||
|
|
|
||
|
|
def is_default(method):
|
||
|
|
"""Check if a method is decorated with the `default` wrapper."""
|
||
|
|
return getattr(method, "_is_default", False)
|
||
|
|
|
||
|
|
|
||
|
|
def populate_dict_with_module_objects(target_dict, modules, obj_filter):
|
||
|
|
for module in modules:
|
||
|
|
for name in dir(module):
|
||
|
|
obj = getattr(module, name)
|
||
|
|
if obj_filter(obj):
|
||
|
|
target_dict[name] = obj
|
||
|
|
|
||
|
|
|
||
|
|
class LazyLoader(python_types.ModuleType):
|
||
|
|
"""Lazily import a module, mainly to avoid pulling in large dependencies."""
|
||
|
|
|
||
|
|
def __init__(self, local_name, parent_module_globals, name):
|
||
|
|
self._local_name = local_name
|
||
|
|
self._parent_module_globals = parent_module_globals
|
||
|
|
super().__init__(name)
|
||
|
|
|
||
|
|
def _load(self):
|
||
|
|
"""Load the module and insert it into the parent's globals."""
|
||
|
|
# Import the target module and insert it into the parent's namespace
|
||
|
|
module = importlib.import_module(self.__name__)
|
||
|
|
self._parent_module_globals[self._local_name] = module
|
||
|
|
# Update this object's dict so that if someone keeps a reference to the
|
||
|
|
# LazyLoader, lookups are efficient (__getattr__ is only called on
|
||
|
|
# lookups that fail).
|
||
|
|
self.__dict__.update(module.__dict__)
|
||
|
|
return module
|
||
|
|
|
||
|
|
def __getattr__(self, item):
|
||
|
|
module = self._load()
|
||
|
|
return getattr(module, item)
|