repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
chainer | chainer-master/chainer/training/extensions/warmup_shift.py | from __future__ import division
from chainer.training import extension
class WarmupShift(extension.Extension):
"""Trainer extension to gradually initialize an optimizer attribute.
This extension changes an optimizer attribute evenly at the
beginning of one training.
For example, suppose that this ... | 2,174 | 35.864407 | 77 | py |
chainer | chainer-master/chainer/training/extensions/print_report.py | import os
import sys
from chainer.training import extension
from chainer.training.extensions import log_report as log_report_module
from chainer.training.extensions import util
class PrintReport(extension.Extension):
"""Trainer extension to print the accumulated results.
This extension uses the log accumul... | 2,967 | 32.727273 | 77 | py |
chainer | chainer-master/chainer/training/extensions/fail_on_nonnumber.py | from chainer.training import extension
class FailOnNonNumber(extension.Extension):
"""Trainer extension to raise RuntimeError if parameters contain NaN or Inf.
Although parameters including non-number such as NaN and Inf are
unnecessary in most cases, :class:`~chainer.training.Trainer` will continue
... | 1,008 | 41.041667 | 80 | py |
chainer | chainer-master/chainer/training/extensions/computational_graph.py | import os
import subprocess
from chainer import computational_graph
from chainer import configuration
from chainer.training import extension
from chainer.utils import argument
from chainer import variable
def is_return_code_zero(args):
"""Return `True` if the return code of the given command
is zero.
Al... | 5,787 | 36.584416 | 79 | py |
chainer | chainer-master/chainer/training/extensions/variable_statistics_plot.py | from __future__ import division
import os
import warnings
import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.training import extension
from chainer.training import trigger as trigger_module
from chainer.utils import argument
_available = None
def _try... | 13,511 | 35.617886 | 79 | py |
chainer | chainer-master/chainer/training/extensions/evaluator.py | import copy
import datetime
import warnings
import six
from chainer import backend
from chainer import configuration
from chainer.dataset import convert
from chainer.dataset import iterator as iterator_module
from chainer import function
from chainer import iterators
from chainer import link
from chainer import repor... | 11,956 | 38.075163 | 79 | py |
chainer | chainer-master/chainer/training/extensions/polynomial_shift.py | from __future__ import division
import numpy
from chainer.training import extension
class PolynomialShift(extension.Extension):
"""Trainer extension to polynomially shift an optimizer attribute.
This extension polynomially decreases the specified attribute of the
optimizer. The typical use case is a p... | 3,354 | 34.315789 | 79 | py |
chainer | chainer-master/chainer/training/extensions/micro_average.py | from chainer import reporter
from chainer.training import extension
from chainer.training import util
class MicroAverage(extension.Extension):
"""Calculates micro-average ratio.
Give :math:`N` batches and values :math:`\\{n_1, \\dots, n_N\\}` and
:math:`\\{d_1, \\dots, d_N\\}`, this extension calculates... | 3,192 | 33.706522 | 78 | py |
chainer | chainer-master/chainer/training/extensions/util.py | import collections
import os
import sys
import time
if os.name == 'nt':
import ctypes
_STD_OUTPUT_HANDLE = -11
class _COORD(ctypes.Structure):
_fields_ = [('X', ctypes.c_short), ('Y', ctypes.c_short)]
class _SMALL_RECT(ctypes.Structure):
_fields_ = [('Left', ctypes.c_short), ('Top', c... | 3,869 | 32.947368 | 77 | py |
chainer | chainer-master/chainer/training/extensions/variable_unchain.py | import six
from chainer import configuration
from chainer.training import extension
from chainer import variable
class unchain_variables(extension.Extension):
"""Trainer extension to unchain all comptational graphs.
This extenstion unchains all comptational graphs after all extensions are
run to release... | 1,139 | 30.666667 | 79 | py |
chainer | chainer-master/chainer/training/extensions/value_observation.py | from chainer.training import extension
def observe_value(observation_key, target_func):
"""Returns a trainer extension to continuously record a value.
Args:
observation_key (str): Key of observation to record.
target_func (function): Function that returns the value to record.
It m... | 1,504 | 32.444444 | 79 | py |
chainer | chainer-master/chainer/training/extensions/parameter_statistics.py | import numpy
import six
import chainer
from chainer import backend
from chainer import reporter
from chainer.training import extension
from chainer.training import trigger as trigger_module
_default_statistics = {
'mean': lambda x: backend.get_array_module(x).mean(x),
'std': lambda x: backend.get_array_modul... | 7,507 | 41.179775 | 79 | py |
chainer | chainer-master/chainer/training/extensions/__init__.py | # import classes and functions
from chainer.training.extensions._snapshot import snapshot # NOQA
from chainer.training.extensions._snapshot import snapshot_object # NOQA
from chainer.training.extensions.computational_graph import DumpGraph # NOQA
from chainer.training.extensions.evaluator import Evaluator # NOQA
fr... | 1,820 | 66.444444 | 95 | py |
chainer | chainer-master/chainer/training/extensions/step_shift.py | from __future__ import division
import numpy
from chainer.training import extension
class StepShift(extension.Extension):
"""Trainer extension to shift an optimizer attribute in "steps".
This extension multiplies the specified attribute of the optimizer in
"steps". The typical use case is to scale the... | 3,214 | 35.534091 | 79 | py |
chainer | chainer-master/chainer/training/extensions/log_report.py | import json
import os
import shutil
import warnings
import six
from chainer import reporter
from chainer import serializer as serializer_module
from chainer.training import extension
from chainer.training import trigger as trigger_module
from chainer import utils
from chainer.utils import argument
class LogReport(e... | 5,706 | 37.560811 | 79 | py |
chainer | chainer-master/chainer/training/extensions/plot_report.py | import json
from os import path
import warnings
import numpy
import six
from chainer import reporter
from chainer import serializer as serializer_module
from chainer.training import extension
from chainer.training import trigger as trigger_module
from chainer.utils import argument
_available = None
def _try_impor... | 7,114 | 33.877451 | 79 | py |
chainer | chainer-master/chainer/training/triggers/interval_trigger.py | import warnings
class IntervalTrigger(object):
"""Trigger based on a fixed interval.
This trigger accepts iterations divided by a given interval. There are two
ways to specify the interval: per iterations and epochs. `Iteration` means
the number of updates, while `epoch` means the number of sweeps o... | 4,434 | 34.766129 | 79 | py |
chainer | chainer-master/chainer/training/triggers/minmax_value_trigger.py | from chainer import reporter
from chainer.training import util
class BestValueTrigger(object):
"""Trigger invoked when specific value becomes best.
Args:
key (str): Key of value.
compare (callable): Compare function which takes current best value and
new value and returns whether... | 3,888 | 34.036036 | 79 | py |
chainer | chainer-master/chainer/training/triggers/manual_schedule_trigger.py | import warnings
class ManualScheduleTrigger(object):
"""Trigger invoked at specified point(s) of iterations or epochs.
This trigger accepts iterations or epochs indicated by given point(s).
There are two ways to specify the point(s): iteration and epoch.
``iteration`` means the number of updates, wh... | 5,231 | 37.470588 | 79 | py |
chainer | chainer-master/chainer/training/triggers/once_trigger.py | import warnings
class OnceTrigger(object):
"""Trigger based on the starting point of the iteration.
This trigger accepts only once at starting point of the iteration. There
are two ways to specify the starting point: only starting point in whole
iteration or called again when training resumed.
... | 1,517 | 31.297872 | 77 | py |
chainer | chainer-master/chainer/training/triggers/__init__.py | # import classes and functions
from chainer.training.triggers.early_stopping_trigger import EarlyStoppingTrigger # NOQA
from chainer.training.triggers.interval_trigger import IntervalTrigger # NOQA
from chainer.training.triggers.manual_schedule_trigger import ManualScheduleTrigger # NOQA
from chainer.training.trigge... | 684 | 67.5 | 91 | py |
chainer | chainer-master/chainer/training/triggers/early_stopping_trigger.py | import operator
import warnings
from chainer import reporter
from chainer.training import util
from chainer.utils import argument
class EarlyStoppingTrigger(object):
"""__init__(\
self, check_trigger=(1, 'epoch'), monitor='main/loss', \
patience=3, mode='auto', verbose=False, \
max_trigge... | 5,200 | 32.127389 | 79 | py |
chainer | chainer-master/chainer/training/triggers/time_trigger.py | class TimeTrigger(object):
"""Trigger based on a fixed time interval.
This trigger accepts iterations with a given interval time.
Args:
period (float): Interval time. It is given in seconds.
"""
def __init__(self, period):
self._period = period
self._next_time = self._pe... | 622 | 23.92 | 66 | py |
chainer | chainer-master/chainer/backends/intel64.py | from __future__ import absolute_import
import numpy
import chainer
from chainer import _backend
from chainer.backends import _cpu
from chainer.configuration import config
_ideep_version = None
_error = None
try:
import ideep4py as ideep # NOQA
from ideep4py import mdarray # type: ignore # NOQA
_ideep... | 5,920 | 30.833333 | 80 | py |
chainer | chainer-master/chainer/backends/cuda.py | """Device, context and memory management on CuPy.
.. note::
The package ``chainer.cuda`` has been renamed to
:mod:`chainer.backends.cuda` as of v4.0.0, but the previous module path
``chainer.cuda`` is also available.
Chainer uses `CuPy <https://cupy.chainer.org/>`_ (with very thin wrapper)
to exploit the spe... | 26,329 | 30.915152 | 80 | py |
chainer | chainer-master/chainer/backends/_chainerx.py | import numpy
import chainer
from chainer import _backend
from chainer.backends import _cpu
from chainer.backends import cuda
from chainer.backends import intel64
import chainerx
class ChainerxDevice(_backend.Device):
"""Device for ChainerX backend"""
xp = chainerx
supported_array_types = (chainerx.ndar... | 6,701 | 32.343284 | 78 | py |
chainer | chainer-master/chainer/backends/__init__.py | from chainer.backends import cuda # NOQA
from chainer.backends import intel64 # NOQA
# TODO(niboshi): Refactor registration of backend modules for functions like
# chainer.get_device().
| 190 | 26.285714 | 76 | py |
chainer | chainer-master/chainer/backends/_cpu.py | import numpy
from chainer import _backend
# TODO(kmaehashi): `from chainer.backends import cuda` causes circular imports.
# Surprisingly, `import chianer.backends` works as a workaround to avoid, but
# we should fix circular dependencies themselves around `chainer.backends.*`.
import chainer.backends
import chainerx
... | 1,783 | 27.774194 | 79 | py |
chainer | chainer-master/chainer/initializers/uniform.py | import numpy
from chainer import backend
from chainer import initializer
from chainer.utils import argument
# Original code forked from MIT licensed keras project
# https://github.com/fchollet/keras/blob/master/keras/initializations.py
class Uniform(initializer.Initializer):
"""Initializes array with a scaled ... | 5,202 | 33.230263 | 76 | py |
chainer | chainer-master/chainer/initializers/normal.py | import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import initializer
from chainer.utils import argument
# Original code forked from MIT licensed keras project
# https://github.com/fchollet/keras/blob/master/keras/initializations.py
class Normal(initializer.Initializer):
""... | 6,197 | 34.016949 | 78 | py |
chainer | chainer-master/chainer/initializers/constant.py | import numpy
import chainer
from chainer import backend
from chainer import initializer
from chainer import types # NOQA
class Identity(initializer.Initializer):
"""Initializes array with the identity matrix.
It initializes the given array with the constant
multiple of the identity matrix.
Note th... | 2,918 | 25.779817 | 79 | py |
chainer | chainer-master/chainer/initializers/sampling.py | import numpy
from chainer.backends import cuda
from chainer import initializer
# Original code from Berkeley FCN
# https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/surgery.py
def _get_linear_filter(size, ndim, upsampling=True):
"""Make a 2D and 3D linear kernel suitable for up/downsampling"""
... | 3,704 | 30.939655 | 77 | py |
chainer | chainer-master/chainer/initializers/orthogonal.py | import numpy
from chainer import backend
from chainer import initializer
from chainer import utils
from chainer.utils import argument
_orthogonal_constraints = { # (assert emb., assert proj.)
'auto': (False, False),
'projection': (False, True),
'embedding': (True, False),
'basis': (True, True),
}
... | 4,361 | 38.297297 | 78 | py |
chainer | chainer-master/chainer/initializers/__init__.py | import typing as tp # NOQA
import numpy
import chainer
from chainer import backend
from chainer.backends import _chainerx # NOQA
# import class and function
from chainer.initializers.constant import Constant
from chainer.initializers.constant import Identity # NOQA
from chainer.initializers.constant import NaN #... | 3,923 | 37.851485 | 79 | py |
chainer | chainer-master/chainer/link_hooks/spectral_normalization.py | import chainer
from chainer import backend
from chainer import configuration
import chainer.functions as F
from chainer import link_hook
import chainer.links as L
from chainer import variable
import chainerx
def l2normalize(xp, v, eps):
"""Normalize a vector by its L2 norm.
Args:
xp (numpy or cupy):
... | 11,603 | 38.604096 | 79 | py |
chainer | chainer-master/chainer/link_hooks/timer.py | import collections
import os
import sys
import time
import numpy
from chainer.backends import cuda
from chainer import link_hook
# Select the best-resolution timer function
try:
_get_time = time.perf_counter
except AttributeError:
if os.name == 'nt':
_get_time = time.clock
else:
_get_tim... | 5,855 | 32.849711 | 79 | py |
chainer | chainer-master/chainer/link_hooks/__init__.py | from chainer.link_hooks.spectral_normalization import SpectralNormalization # NOQA
from chainer.link_hooks.timer import TimerHook # NOQA
from chainer.link_hooks.weight_standardization import WeightStandardization # NOQA
| 223 | 55 | 83 | py |
chainer | chainer-master/chainer/link_hooks/weight_standardization.py | import chainer
from chainer.functions.normalization import group_normalization
from chainer import link_hook
class WeightStandardization(link_hook.LinkHook):
"""Weight Standardization (WS) link hook implementation.
This hook standardizes a weight by *weight statistics*.
This link hook implements a WS wh... | 3,678 | 40.806818 | 79 | py |
chainer | chainer-master/chainer/graph_optimizations/static_graph_utilities.py | import inspect
import chainer
def static_code(*dec_args, **dec_kwargs):
"""Decorator to mark a function for inclusion in the static schedule.
This decorator is used to mark a function or method to be included
in a static schedule. There are multiple types of static schedules, such
as "forward pass s... | 10,620 | 43.439331 | 77 | py |
chainer | chainer-master/chainer/graph_optimizations/__init__.py |
from chainer.graph_optimizations.static_graph_utilities import static_code # NOQA
| 84 | 27.333333 | 82 | py |
chainer | chainer-master/chainer/graph_optimizations/static_graph.py | import sys
import weakref
import numpy as np
import chainer
from chainer.backends import cuda
import chainer.function_node
def _is_xp(x):
return isinstance(x, np.ndarray) or isinstance(x, cuda.ndarray)
class ScheduleInfo(object):
"""A callable wrapper for a function in the static schedule.
Args:
... | 67,103 | 44.617947 | 79 | py |
chainer | chainer-master/chainer/datasets/text_dataset.py | import io
import sys
import threading
import six
from chainer.dataset import dataset_mixin
class TextDataset(dataset_mixin.DatasetMixin):
"""Dataset of a line-oriented text file.
This dataset reads each line of text file(s) on every call of the
:meth:`__getitem__` operator.
Positions of line bound... | 6,272 | 35.260116 | 78 | py |
chainer | chainer-master/chainer/datasets/kuzushiji_mnist.py | import os
import numpy
import chainer
from chainer.dataset import download
from chainer.datasets._mnist_helper import make_npz
from chainer.datasets._mnist_helper import preprocess_mnist
_kuzushiji_mnist_labels = [('o', u'\u304A'), ('ki', u'\u304D'),
('su', u'\u3059'), ('tsu', u'\u3064'),... | 4,102 | 40.444444 | 79 | py |
chainer | chainer-master/chainer/datasets/concatenated_dataset.py | from chainer.dataset import dataset_mixin
class ConcatenatedDataset(dataset_mixin.DatasetMixin):
"""Dataset which concatenates some base datasets.
This dataset wraps some base datasets and works as a concatenated dataset.
For example, if a base dataset with 10 samples and
another base dataset with 2... | 939 | 27.484848 | 78 | py |
chainer | chainer-master/chainer/datasets/image_dataset.py | import bisect
import io
import os
import threading
import zipfile
import numpy
try:
from PIL import Image
available = True
except ImportError as e:
available = False
_import_error = e
import six
import chainer
from chainer.dataset import dataset_mixin
def _read_image_as_array(path, dtype):
f = I... | 11,769 | 36.845659 | 79 | py |
chainer | chainer-master/chainer/datasets/fashion_mnist.py | import os
import numpy
import chainer
from chainer.dataset import download
from chainer.datasets._mnist_helper import make_npz
from chainer.datasets._mnist_helper import preprocess_mnist
_fashion_mnist_labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Snea... | 3,771 | 38.705263 | 79 | py |
chainer | chainer-master/chainer/datasets/tuple_dataset.py | import six
class TupleDataset(object):
"""Dataset of tuples from multiple equal-length datasets.
A ``TupleDataset`` combines multiple equal-length datasets into a single
dataset of tuples. The ``i``-th tuple contains the ``i``-th example from
each of the argument datasets, in the same order that the... | 2,024 | 37.942308 | 79 | py |
chainer | chainer-master/chainer/datasets/dict_dataset.py | import six
class DictDataset(object):
"""Dataset of a dictionary of datasets.
It combines multiple datasets into one dataset. Each example is represented
by a dictionary mapping a key to an example of the corresponding dataset.
Args:
datasets: Underlying datasets. The keys are used as the k... | 1,334 | 30.785714 | 79 | py |
chainer | chainer-master/chainer/datasets/transform_dataset.py | from chainer.dataset import dataset_mixin
class TransformDataset(dataset_mixin.DatasetMixin):
"""Dataset that indexes the base dataset and transforms the data.
This dataset wraps the base dataset by modifying the behavior of the base
dataset's :meth:`__getitem__`. Arrays returned by :meth:`__getitem__` ... | 2,158 | 39.735849 | 78 | py |
chainer | chainer-master/chainer/datasets/__init__.py | # import classes and functions
from chainer.datasets.cifar import get_cifar10 # NOQA
from chainer.datasets.cifar import get_cifar100 # NOQA
from chainer.datasets.concatenated_dataset import ConcatenatedDataset # NOQA
from chainer.datasets.dict_dataset import DictDataset # NOQA
from chainer.datasets.fashion_mnist im... | 2,144 | 64 | 85 | py |
chainer | chainer-master/chainer/datasets/svhn.py | import os
import numpy
try:
from scipy import io
_scipy_available = True
except Exception as e:
_error = e
_scipy_available = False
import chainer
from chainer.dataset import download
from chainer.datasets import tuple_dataset
def get_svhn(withlabel=True, scale=1., dtype=None, label_dtype=numpy.int3... | 3,857 | 32.258621 | 79 | py |
chainer | chainer-master/chainer/datasets/_mnist_helper.py | import gzip
import struct
import numpy
import six
from chainer.dataset import download
from chainer.datasets import tuple_dataset
def make_npz(path, urls):
x_url, y_url = urls
x_path = download.cached_download(x_url)
y_path = download.cached_download(y_url)
with gzip.open(x_path, 'rb') as fx, gzip.... | 1,614 | 27.839286 | 76 | py |
chainer | chainer-master/chainer/datasets/cifar.py | import os
import sys
import tarfile
import numpy
import six.moves.cPickle as pickle
import chainer
from chainer.dataset import download
from chainer.datasets import tuple_dataset
def get_cifar10(withlabel=True, ndim=3, scale=1., dtype=None):
"""Gets the CIFAR-10 dataset.
`CIFAR-10 <https://www.cs.toronto.e... | 6,772 | 39.076923 | 79 | py |
chainer | chainer-master/chainer/datasets/ptb.py | import os
import numpy
from chainer.dataset import download
def get_ptb_words():
"""Gets the Penn Tree Bank dataset as long word sequences.
`Penn Tree Bank <https://catalog.ldc.upenn.edu/LDC99T42>`_
is originally a corpus of English sentences with linguistic structure
annotations. This function use... | 3,359 | 30.111111 | 98 | py |
chainer | chainer-master/chainer/datasets/mnist.py | import os
import numpy
import chainer
from chainer.dataset import download
from chainer.datasets._mnist_helper import make_npz
from chainer.datasets._mnist_helper import preprocess_mnist
def get_mnist(withlabel=True, ndim=1, scale=1., dtype=None,
label_dtype=numpy.int32, rgb_format=False):
"""Gets... | 3,215 | 40.766234 | 79 | py |
chainer | chainer-master/chainer/datasets/pickle_dataset.py | import io
import multiprocessing.util
import threading
import six
import six.moves.cPickle as pickle
from chainer.dataset import dataset_mixin
class PickleDatasetWriter(object):
"""Writer class that makes PickleDataset.
To make :class:`PickleDataset`, a user needs to prepare data using
:class:`PickleD... | 6,689 | 25.338583 | 79 | py |
chainer | chainer-master/chainer/datasets/sub_dataset.py | import numpy
import six
import warnings
from chainer.dataset import dataset_mixin
class SubDataset(dataset_mixin.DatasetMixin):
"""Subset of a base dataset.
SubDataset defines a subset of a given base dataset. The subset is defined
as an interval of indexes, optionally with a given permutation.
If ... | 10,676 | 38.988764 | 79 | py |
chainer | chainer-master/chainer/exporters/__init__.py | from chainer.exporters import caffe # NOQA
| 44 | 21.5 | 43 | py |
chainer | chainer-master/chainer/exporters/caffe.py | import collections
import heapq
import os
import numpy
import six
import chainer
from chainer import function
from chainer import function_node
from chainer.links.caffe.protobuf3 import caffe_pb2 as caffe_pb
from chainer import variable
_function_types = (function.Function, function_node.FunctionNode)
def _add_bl... | 18,050 | 35.763747 | 79 | py |
chainer | chainer-master/chainer/optimizer_hooks/gradient_noise.py | import numpy
import chainer
from chainer import cuda
def exponential_decay_noise(xp, shape, dtype, hook, opt):
"""Time-dependent annealed Gaussian noise function from the paper:
`Adding Gradient Noise Improves Learning for Very Deep Networks
<https://arxiv.org/pdf/1511.06807>`_.
"""
std = numpy.... | 2,840 | 33.228916 | 79 | py |
chainer | chainer-master/chainer/optimizer_hooks/weight_decay.py | import chainer
from chainer import cuda
class WeightDecay(object):
"""Optimizer/UpdateRule hook function for weight decay regularization.
This hook function adds a scaled parameter to the corresponding gradient.
It can be used as a regularization.
Args:
rate (float): Coefficient for the wei... | 1,897 | 34.148148 | 79 | py |
chainer | chainer-master/chainer/optimizer_hooks/lasso.py | import chainer
from chainer import cuda
class Lasso(object):
"""Optimizer/UpdateRule hook function for Lasso regularization.
This hook function adds a scaled parameter to the sign of each weight.
It can be used as a regularization.
Args:
rate (float): Coefficient for the weight decay.
A... | 1,773 | 33.784314 | 79 | py |
chainer | chainer-master/chainer/optimizer_hooks/__init__.py | from chainer.optimizer_hooks.gradient_clipping import GradientClipping # NOQA
from chainer.optimizer_hooks.gradient_hard_clipping import GradientHardClipping # NOQA
from chainer.optimizer_hooks.gradient_lars import GradientLARS # NOQA
from chainer.optimizer_hooks.gradient_noise import GradientNoise # NOQA
from chai... | 436 | 61.428571 | 87 | py |
chainer | chainer-master/chainer/optimizer_hooks/gradient_lars.py | import chainer
from chainer import backend
class GradientLARS(object):
"""Optimizer/UpdateRule hook function for layer wise adaptive rate scaling.
See: `Large Batch Training of Convolutional Networks
<https://arxiv.org/abs/1708.03888>`_.
See: `Convergence Analysis of Gradient Descent Algorithms
... | 4,461 | 38.839286 | 79 | py |
chainer | chainer-master/chainer/optimizer_hooks/gradient_hard_clipping.py | import chainer
from chainer import backend
class GradientHardClipping(object):
"""Optimizer/UpdateRule hook function for gradient clipping.
This hook function clips all gradient arrays to be within a lower and upper
bound.
Args:
lower_bound (float): The lower bound of the gradient value.
... | 2,360 | 38.35 | 79 | py |
chainer | chainer-master/chainer/optimizer_hooks/gradient_clipping.py | import collections
import numpy
import six
import chainer
from chainer import backend
def _sum_sqnorm_grads(params):
# Calculates sum of squares of gradients.
# Returns a tuple of the sum and the device of the sum.
# The device will be `None` in multi-device case.
# If the inputs are on a single d... | 3,363 | 30.439252 | 79 | py |
chainer | chainer-master/chainer/iterators/dali_iterator.py | from __future__ import division
from chainer.dataset import iterator
from chainer import utils
class DaliIterator(iterator.Iterator):
"""(Experimental) Iterator for DALI pipeline.
Args:
pipeline: DALI pipeline.
repeat (bool): If ``True``, it infinitely loops over the dataset.
Ot... | 2,926 | 28.867347 | 75 | py |
chainer | chainer-master/chainer/iterators/_statemachine.py | import collections
import numpy
IteratorState = collections.namedtuple('IteratorState', (
'current_position', 'epoch', 'is_new_epoch', 'order'))
def iterator_statemachine(state, batch_size, repeat, order_sampler,
dataset_len):
i, epoch, _, order = state
if not repeat and epoc... | 1,564 | 26.45614 | 71 | py |
chainer | chainer-master/chainer/iterators/multithread_iterator.py | from __future__ import division
from multiprocessing import pool
import numpy
from chainer.dataset import iterator
from chainer.iterators import _statemachine
from chainer.iterators.order_samplers import ShuffleOrderSampler
class MultithreadIterator(iterator.Iterator):
"""Dataset iterator that loads examples i... | 6,202 | 32.52973 | 78 | py |
chainer | chainer-master/chainer/iterators/multiprocess_iterator.py | from __future__ import division
import datetime
import multiprocessing
from multiprocessing import sharedctypes # type: ignore
import signal
import sys
import threading
import warnings
import numpy
import six
from chainer.dataset import iterator
from chainer.iterators import _statemachine
from chainer.iterators.orde... | 22,878 | 33.98318 | 79 | py |
chainer | chainer-master/chainer/iterators/__init__.py | # import classes and functions
from chainer.iterators.multiprocess_iterator import MultiprocessIterator # NOQA
from chainer.iterators.multithread_iterator import MultithreadIterator # NOQA
from chainer.iterators.serial_iterator import SerialIterator # NOQA
from chainer.iterators.dali_iterator import DaliIterator #... | 466 | 45.7 | 80 | py |
chainer | chainer-master/chainer/iterators/serial_iterator.py | from __future__ import division
import numpy
from chainer.dataset import iterator
from chainer.iterators import _statemachine
from chainer.iterators.order_samplers import ShuffleOrderSampler
class SerialIterator(iterator.Iterator):
"""Dataset iterator that serially reads the examples.
This is a simple imp... | 5,543 | 35.473684 | 78 | py |
chainer | chainer-master/chainer/iterators/order_samplers.py | import numpy
class OrderSampler(object):
"""Base class of all order samplers.
Every order sampler subclass has to provide a method
:meth:`__call__`.
This method is called by an iterator before a new epoch,
and it should return a new index order for the next epoch.
"""
def __call__(self... | 1,793 | 28.9 | 78 | py |
chainer | chainer-master/chainer/optimizers/smorms3.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class SMORMS3Hyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of Simon Funk's SMORMS3.
This is only... | 3,641 | 29.605042 | 86 | py |
chainer | chainer-master/chainer/optimizers/momentum_sgd.py | import chainer
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class MomentumSGDHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of classical moment... | 3,423 | 29.571429 | 90 | py |
chainer | chainer-master/chainer/optimizers/sgd.py | from chainer.backends import cuda
from chainer.backends import intel64
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class SGDHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of vanilla stochastic gradient descent.
... | 2,194 | 25.768293 | 84 | py |
chainer | chainer-master/chainer/optimizers/corrected_momentum_sgd.py | import chainer
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class CorrectedMomentumSGDHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of correct... | 4,407 | 32.393939 | 99 | py |
chainer | chainer-master/chainer/optimizers/msvag.py | from __future__ import division
import numpy
import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class MSVAGHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of M-SVAG.
... | 6,356 | 31.433673 | 84 | py |
chainer | chainer-master/chainer/optimizers/ada_grad.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class AdaGradHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of AdaGrad.
This is only for PEP 544 ... | 2,899 | 26.619048 | 86 | py |
chainer | chainer-master/chainer/optimizers/adam.py | from __future__ import division
import math
import warnings
import numpy
import chainer
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class AdamHyperparameter(tpe.Protoco... | 22,884 | 38.321306 | 83 | py |
chainer | chainer-master/chainer/optimizers/__init__.py | # import classes and functions
from chainer.optimizers.ada_delta import AdaDelta # NOQA
from chainer.optimizers.ada_grad import AdaGrad # NOQA
from chainer.optimizers.adam import Adam # NOQA
from chainer.optimizers.adam import AdamW # NOQA
from chainer.optimizers.adam import AMSGrad # NOQA
from chainer.optimizers.... | 887 | 51.235294 | 82 | py |
chainer | chainer-master/chainer/optimizers/ada_delta.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class AdaDeltaHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of Zeiler's ADADELTA.
This is only f... | 3,514 | 29.301724 | 87 | py |
chainer | chainer-master/chainer/optimizers/rmsprop_graves.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class RMSpropGravesHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of Alex Graves's RMSprop.
This ... | 4,709 | 32.642857 | 92 | py |
chainer | chainer-master/chainer/optimizers/rmsprop.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class RMSpropHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of RMSprop.
This is only for PEP 544 ... | 5,381 | 33.948052 | 86 | py |
chainer | chainer-master/chainer/optimizers/nesterov_ag.py | import chainer
from chainer.backends import cuda
from chainer import optimizer
from chainer import types
if types.TYPE_CHECKING:
import typing_extensions as tpe
class NesterovAGHyperparameter(tpe.Protocol):
"""Protocol class for hyperparameter of Nesterov's Accelerated Gradient.
This is only... | 3,234 | 28.953704 | 89 | py |
chainer | chainer-master/chainer/function_hooks/cuda_profile.py | from chainer.backends import cuda
from chainer import function_hook
class CUDAProfileHook(function_hook.FunctionHook):
name = 'CUDAProfileHook'
def __init__(self):
cuda.check_cuda_available()
if not cuda.cupy.cuda.nvtx_enabled:
raise RuntimeError('nvtx is required for CUDAProfile... | 780 | 30.24 | 70 | py |
chainer | chainer-master/chainer/function_hooks/timer.py | import os
import sys
import time
import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import function_hook
# Select the best-resolution timer function
try:
_get_time = time.perf_counter
except AttributeError:
if os.name == 'nt':
_get_time = time.clock
else:
... | 6,045 | 33.947977 | 79 | py |
chainer | chainer-master/chainer/function_hooks/cupy_memory_profile.py | import collections
import sys
import typing as tp # NOQA
from chainer.backends import cuda
from chainer import function_hook
try:
MemoryHook = cuda.cupy.cuda.memory_hook.MemoryHook # type: tp.Any # to handle https://github.com/python/mypy/issues/2477 # NOQA
memory_hook_available = True
except Exception as ... | 8,573 | 38.694444 | 132 | py |
chainer | chainer-master/chainer/function_hooks/debug_print.py | import sys
import warnings
from chainer import backend
from chainer import function_hook
from chainer import variable
class PrintHook(function_hook.FunctionHook):
"""Function hook that prints debug information.
This function hook outputs the debug information of input arguments of
``forward`` and ``back... | 3,334 | 35.648352 | 77 | py |
chainer | chainer-master/chainer/function_hooks/__init__.py | # import classes and functions
from chainer.function_hooks.cuda_profile import CUDAProfileHook # NOQA
from chainer.function_hooks.cupy_memory_profile import CupyMemoryProfileHook # NOQA
from chainer.function_hooks.debug_print import PrintHook # NOQA
from chainer.function_hooks.timer import TimerHook # NOQA
| 312 | 51.166667 | 84 | py |
chainer | chainer-master/chainer/utils/error.py | def _format_array_props(arrays):
# Formats array shapes and dtypes for error messages.
assert isinstance(arrays, (list, tuple))
return ', '.join([
None if arr is None
else '{}:{}'.format(arr.shape, arr.dtype.name)
for arr in arrays])
| 271 | 29.222222 | 57 | py |
chainer | chainer-master/chainer/utils/type_check.py | import contextlib
import functools
import operator
import sys
import threading
import numpy
import six
import chainer
from chainer.backends import cuda
_thread_local = threading.local()
@contextlib.contextmanager
def get_function_check_context(f):
try:
default = _thread_local.current_function
exce... | 18,493 | 27.063733 | 85 | py |
chainer | chainer-master/chainer/utils/_collections.py | import collections
import weakref
import six
if six.PY3:
OrderedDict = collections.OrderedDict
else:
# Reference counting cannot free keys in old `collections.OrderedDict`,
# where a doubly linked list is used to maintain the order.
class OrderedDict(object):
"""Dictionary that remembers inse... | 994 | 25.184211 | 76 | py |
chainer | chainer-master/chainer/utils/nondeterministic.py | import warnings
from chainer import configuration
def nondeterministic(f_name):
"""Function to warn non-deterministic functions
If `config.warn_nondeterministic` is True, this function will give a
warning that this functions contains a non-deterministic function, such
as atomicAdd.
"""
if co... | 526 | 30 | 75 | py |
chainer | chainer-master/chainer/utils/conv_nd.py | import itertools
import numpy
import six
from chainer.backends import cuda
from chainer.utils.conv import get_conv_outsize
from chainer.utils import conv_nd_kernel
def as_tuple(x, n):
if hasattr(x, '__getitem__'):
assert len(x) == n
return tuple(x)
return (x,) * n
def im2col_nd_cpu(img, ks... | 5,382 | 36.643357 | 78 | py |
chainer | chainer-master/chainer/utils/array.py | import warnings
import numpy
import six
import chainer
from chainer.backends import cuda
def as_vec(x):
warnings.warn(
'chainer.utils.array.as_vec is deprecated. Please refer to '
'numpy.ravel or other array backend functions to flatten ndarrays.',
DeprecationWarning)
if x.ndim == 1:... | 1,688 | 24.590909 | 78 | py |
chainer | chainer-master/chainer/utils/sparse.py | import numpy
import chainer
from chainer import backend
from chainer import cuda
def _add_at(add_at, x, row, col, data):
assert data.size > 0
last_nz = data.size - (data != 0)[::-1].argmax()
add_at(x, (row[:last_nz], col[:last_nz]), data[:last_nz])
class CooMatrix(object):
"""A sparse matrix in CO... | 7,680 | 34.725581 | 79 | py |
chainer | chainer-master/chainer/utils/argument.py | import inspect
def check_unexpected_kwargs(kwargs, **unexpected):
for key, message in unexpected.items():
if key in kwargs:
raise ValueError(message)
def parse_kwargs(kwargs, *name_and_values, **unexpected):
values = [kwargs.pop(name, default_value)
for name, default_value ... | 773 | 28.769231 | 68 | py |
chainer | chainer-master/chainer/utils/precision.py | import functools
import numpy
def _fp16_mixed_precision_helper(fn):
"""Decorator to perform computation in FP32 for FP16 inputs/outputs
Decorator to perform forward computation in FP32 for FP16 inputs,
returning outputs casted back to FP16. Do nothing for FP32 and FP64
inputs.
"""
@functools... | 1,091 | 27 | 71 | py |
chainer | chainer-master/chainer/utils/walker_alias.py | import numpy
import chainer
from chainer.backends import cuda
from chainer import device_resident
class WalkerAlias(device_resident.DeviceResident):
"""Implementation of Walker's alias method.
This method generates a random sample from given probabilities
:math:`p_1, \\dots, p_n` in :math:`O(1)` time.
... | 3,976 | 32.70339 | 78 | py |
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