id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
242,600 | wal-e/wal-e | wal_e/worker/pg/psql_worker.py | PgBackupStatements.run_start_backup | def run_start_backup(cls):
"""
Connects to a server and attempts to start a hot backup
Yields the WAL information in a dictionary for bookkeeping and
recording.
"""
def handler(popen):
assert popen.returncode != 0
raise UserException('Could not s... | python | def run_start_backup(cls):
def handler(popen):
assert popen.returncode != 0
raise UserException('Could not start hot backup')
# The difficulty of getting a timezone-stamped, UTC,
# ISO-formatted datetime is downright embarrassing.
#
# See http://bugs.pyth... | [
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242,601 | wal-e/wal-e | wal_e/worker/pg/psql_worker.py | PgBackupStatements.run_stop_backup | def run_stop_backup(cls):
"""
Stop a hot backup, if it was running, or error
Return the last WAL file name and position that is required to
gain consistency on the captured heap.
"""
def handler(popen):
assert popen.returncode != 0
raise UserExce... | python | def run_stop_backup(cls):
def handler(popen):
assert popen.returncode != 0
raise UserException('Could not stop hot backup')
return cls._dict_transform(psql_csv_run(
"SELECT file_name, "
" lpad(file_offset::text, 8, '0') AS file_offset "
... | [
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242,602 | wal-e/wal-e | wal_e/blobstore/s3/calling_format.py | _is_ipv4_like | def _is_ipv4_like(s):
"""Find if a string superficially looks like an IPv4 address.
AWS documentation plays it fast and loose with this; in other
regions, it seems like even non-valid IPv4 addresses (in
particular, ones that possess decimal numbers out of range for
IPv4) are rejected.
"""
p... | python | def _is_ipv4_like(s):
parts = s.split('.')
if len(parts) != 4:
return False
for part in parts:
try:
int(part)
except ValueError:
return False
return True | [
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242,603 | wal-e/wal-e | wal_e/blobstore/s3/calling_format.py | _is_mostly_subdomain_compatible | def _is_mostly_subdomain_compatible(bucket_name):
"""Returns True if SubdomainCallingFormat can be used...mostly
This checks to make sure that putting aside certificate validation
issues that a bucket_name is able to use the
SubdomainCallingFormat.
"""
return (bucket_name.lower() == bucket_name... | python | def _is_mostly_subdomain_compatible(bucket_name):
return (bucket_name.lower() == bucket_name and
len(bucket_name) >= 3 and
len(bucket_name) <= 63 and
'_' not in bucket_name and
'..' not in bucket_name and
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242,604 | wal-e/wal-e | wal_e/blobstore/s3/calling_format.py | _connect_secureish | def _connect_secureish(*args, **kwargs):
"""Connect using the safest available options.
This turns on encryption (works in all supported boto versions)
and certificate validation (in the subset of supported boto
versions that can handle certificate validation, namely, those
after 2.6.0).
Versi... | python | def _connect_secureish(*args, **kwargs):
if tuple(int(x) for x in boto.__version__.split('.')) >= (2, 6, 0):
kwargs['validate_certs'] = True
kwargs['is_secure'] = True
auth_region_name = kwargs.pop('auth_region_name', None)
conn = connection.S3Connection(*args, **kwargs)
if auth_region_na... | [
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242,605 | wal-e/wal-e | wal_e/blobstore/s3/calling_format.py | from_store_name | def from_store_name(bucket_name, region=None):
"""Construct a CallingInfo value from a bucket name.
This is useful to encapsulate the ugliness of setting up S3
connections, especially with regions and TLS certificates are
involved.
"""
# Late-bind `region` for the sake of tests that inject the
... | python | def from_store_name(bucket_name, region=None):
# Late-bind `region` for the sake of tests that inject the
# AWS_REGION environment variable.
if region is None:
region = os.getenv('AWS_REGION')
mostly_ok = _is_mostly_subdomain_compatible(bucket_name)
if not mostly_ok:
return Calling... | [
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242,606 | wal-e/wal-e | wal_e/blobstore/s3/calling_format.py | CallingInfo.connect | def connect(self, creds):
"""Return a boto S3Connection set up with great care.
This includes TLS settings, calling format selection, and
region detection.
The credentials are applied by the caller because in many
cases (instance-profile IAM) it is possible for those
cr... | python | def connect(self, creds):
def _conn_help(*args, **kwargs):
return _connect_secureish(
*args,
provider=creds,
calling_format=self.calling_format(),
auth_region_name=self.region,
**kwargs)
# If WALE_S3_ENDPOINT is... | [
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242,607 | wal-e/wal-e | wal_e/blobstore/file/calling_format.py | remove_empty_dirs | def remove_empty_dirs(path):
""" removes empty dirs under a given path """
for root, dirs, files in os.walk(path):
for d in dirs:
dir_path = os.path.join(root, d)
if not os.listdir(dir_path):
os.rmdir(dir_path) | python | def remove_empty_dirs(path):
for root, dirs, files in os.walk(path):
for d in dirs:
dir_path = os.path.join(root, d)
if not os.listdir(dir_path):
os.rmdir(dir_path) | [
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242,608 | wal-e/wal-e | wal_e/blobstore/file/calling_format.py | ensure_dir_exists | def ensure_dir_exists(path):
""" create a directory if required """
dir_path = os.path.dirname(path)
if not os.path.exists(dir_path):
os.makedirs(dir_path) | python | def ensure_dir_exists(path):
dir_path = os.path.dirname(path)
if not os.path.exists(dir_path):
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242,609 | wal-e/wal-e | wal_e/cmd.py | external_program_check | def external_program_check(
to_check=frozenset([PSQL_BIN, LZOP_BIN, PV_BIN])):
"""
Validates the existence and basic working-ness of other programs
Implemented because it is easy to get confusing error output when
one does not install a dependency because of the fork-worker model
that is both n... | python | def external_program_check(
to_check=frozenset([PSQL_BIN, LZOP_BIN, PV_BIN])):
could_not_run = []
error_msgs = []
def psql_err_handler(popen):
assert popen.returncode != 0
error_msgs.append(textwrap.fill(
'Could not get a connection to the database: '
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242,610 | wal-e/wal-e | wal_e/cmd.py | parse_boolean_envvar | def parse_boolean_envvar(val):
"""Parse a boolean environment variable."""
if not val or val.lower() in {'false', '0'}:
return False
elif val.lower() in {'true', '1'}:
return True
else:
raise ValueError('Invalid boolean environment variable: %s' % val) | python | def parse_boolean_envvar(val):
if not val or val.lower() in {'false', '0'}:
return False
elif val.lower() in {'true', '1'}:
return True
else:
raise ValueError('Invalid boolean environment variable: %s' % val) | [
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242,611 | wal-e/wal-e | wal_e/cmd.py | _config_hint_generate | def _config_hint_generate(optname, both_env_and_param):
"""Generate HINT language for missing configuration"""
env = optname.replace('-', '_').upper()
if both_env_and_param:
option = '--' + optname.lower()
return ('Pass "{0}" or set the environment variable "{1}".'
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242,612 | wal-e/wal-e | wal_e/cmd.py | render_subcommand | def render_subcommand(args):
"""Render a subcommand for human-centric viewing"""
if args.subcommand == 'delete':
return 'delete ' + args.delete_subcommand
if args.subcommand in ('wal-prefetch', 'wal-push', 'wal-fetch'):
return None
return args.subcommand | python | def render_subcommand(args):
if args.subcommand == 'delete':
return 'delete ' + args.delete_subcommand
if args.subcommand in ('wal-prefetch', 'wal-push', 'wal-fetch'):
return None
return args.subcommand | [
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242,613 | wal-e/wal-e | wal_e/worker/worker_util.py | do_lzop_put | def do_lzop_put(creds, url, local_path, gpg_key):
"""
Compress and upload a given local path.
:type url: string
:param url: A (s3|wabs)://bucket/key style URL that is the destination
:type local_path: string
:param local_path: a path to a file to be compressed
"""
assert url.endswith(... | python | def do_lzop_put(creds, url, local_path, gpg_key):
assert url.endswith('.lzo')
blobstore = get_blobstore(storage.StorageLayout(url))
with tempfile.NamedTemporaryFile(
mode='r+b', buffering=pipebuf.PIPE_BUF_BYTES) as tf:
with pipeline.get_upload_pipeline(
open(local_path, ... | [
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242,614 | wal-e/wal-e | wal_e/worker/worker_util.py | do_lzop_get | def do_lzop_get(creds, url, path, decrypt, do_retry=True):
"""
Get and decompress an S3 or WABS URL
This streams the content directly to lzop; the compressed version
is never stored on disk.
"""
blobstore = get_blobstore(storage.StorageLayout(url))
return blobstore.do_lzop_get(creds, url, ... | python | def do_lzop_get(creds, url, path, decrypt, do_retry=True):
blobstore = get_blobstore(storage.StorageLayout(url))
return blobstore.do_lzop_get(creds, url, path, decrypt, do_retry=do_retry) | [
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] | 027263860e72a403bc0e1497bb3e67523138e7a2 | https://github.com/wal-e/wal-e/blob/027263860e72a403bc0e1497bb3e67523138e7a2/wal_e/worker/worker_util.py#L49-L58 |
242,615 | wal-e/wal-e | wal_e/worker/base.py | _BackupList.find_all | def find_all(self, query):
"""A procedure to assist in finding or detailing specific backups
Currently supports:
* a backup name (base_number_number)
* the psuedo-name LATEST, which finds the backup with the most
recent modification date
"""
match = re.matc... | python | def find_all(self, query):
match = re.match(storage.BASE_BACKUP_REGEXP, query)
if match is not None:
for backup in iter(self):
if backup.name == query:
yield backup
elif query == 'LATEST':
all_backups = list(iter(self))
if... | [
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242,616 | wal-e/wal-e | wal_e/worker/base.py | _DeleteFromContext._delete_wals_before | def _delete_wals_before(self, segment_info):
"""
Delete all WAL files before segment_info.
Doesn't delete any base-backup data.
"""
wal_key_depth = self.layout.wal_directory().count('/') + 1
for key in self._backup_list(prefix=self.layout.wal_directory()):
ke... | python | def _delete_wals_before(self, segment_info):
wal_key_depth = self.layout.wal_directory().count('/') + 1
for key in self._backup_list(prefix=self.layout.wal_directory()):
key_name = self.layout.key_name(key)
bucket = self._container_name(key)
url = '{scm}://{bucket}/{n... | [
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242,617 | wal-e/wal-e | wal_e/worker/base.py | _DeleteFromContext.delete_everything | def delete_everything(self):
"""Delete everything in a storage layout
Named provocatively for a reason: can (and in fact intended
to) cause irrecoverable loss of data. This can be used to:
* Completely obliterate data from old WAL-E versions
(i.e. layout.VERSION is an obsole... | python | def delete_everything(self):
for k in self._backup_list(prefix=self.layout.basebackups()):
self._maybe_delete_key(k, 'part of a base backup')
for k in self._backup_list(prefix=self.layout.wal_directory()):
self._maybe_delete_key(k, 'part of wal logs')
if self.deleter:
... | [
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242,618 | wal-e/wal-e | wal_e/worker/base.py | _DeleteFromContext.delete_before | def delete_before(self, segment_info):
"""
Delete all base backups and WAL before a given segment
This is the most commonly-used deletion operator; to delete
old backups and WAL.
"""
# This will delete all base backup data before segment_info.
self._delete_base... | python | def delete_before(self, segment_info):
# This will delete all base backup data before segment_info.
self._delete_base_backups_before(segment_info)
# This will delete all WAL segments before segment_info.
self._delete_wals_before(segment_info)
if self.deleter:
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242,619 | wal-e/wal-e | wal_e/worker/base.py | _DeleteFromContext.delete_with_retention | def delete_with_retention(self, num_to_retain):
"""
Retain the num_to_retain most recent backups and delete all data
before them.
"""
base_backup_sentinel_depth = self.layout.basebackups().count('/') + 1
# Sweep over base backup files, collecting sentinel files from
... | python | def delete_with_retention(self, num_to_retain):
base_backup_sentinel_depth = self.layout.basebackups().count('/') + 1
# Sweep over base backup files, collecting sentinel files from
# completed backups.
completed_basebackups = []
for key in self._backup_list(prefix=self.layout.ba... | [
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242,620 | wal-e/wal-e | wal_e/blobstore/swift/calling_format.py | connect | def connect(creds):
"""
Construct a connection value from a container
"""
return swiftclient.Connection(
authurl=creds.authurl,
user=creds.user,
key=creds.password,
auth_version=creds.auth_version,
tenant_name=creds.tenant_name,
os_options={
"r... | python | def connect(creds):
return swiftclient.Connection(
authurl=creds.authurl,
user=creds.user,
key=creds.password,
auth_version=creds.auth_version,
tenant_name=creds.tenant_name,
os_options={
"region_name": creds.region,
"endpoint_type": creds.endp... | [
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242,621 | wal-e/wal-e | wal_e/blobstore/gs/calling_format.py | connect | def connect(creds, max_retries=100):
"""Construct a connection value to Google Storage API
The credentials are retrieved using get_credentials that checks
the environment for the correct values.
"""
credentials, project = google.auth.default()
return RetryClient(max_retries=max_retries, projec... | python | def connect(creds, max_retries=100):
credentials, project = google.auth.default()
return RetryClient(max_retries=max_retries, project=project,
credentials=credentials) | [
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242,622 | wal-e/wal-e | wal_e/retries.py | retry | def retry(exception_processor=generic_exception_processor, max_retries=100):
"""
Generic retry decorator
Tries to call the decorated function. Should no exception be
raised, the value is simply returned, otherwise, call an
exception_processor function with the exception (type, value,
traceback... | python | def retry(exception_processor=generic_exception_processor, max_retries=100):
max_retries = int(os.getenv('WALE_RETRIES', max_retries))
def yield_new_function_from(f):
def shim(*args, **kwargs):
exc_processor_cxt = None
retries = 0
while True:
# Avoid... | [
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242,623 | wal-e/wal-e | wal_e/worker/upload_pool.py | TarUploadPool._start | def _start(self, tpart):
"""Start upload and accout for resource consumption."""
g = gevent.Greenlet(self.uploader, tpart)
g.link(self._finish)
# Account for concurrency_burden before starting the greenlet
# to avoid racing against .join.
self.concurrency_burden += 1
... | python | def _start(self, tpart):
g = gevent.Greenlet(self.uploader, tpart)
g.link(self._finish)
# Account for concurrency_burden before starting the greenlet
# to avoid racing against .join.
self.concurrency_burden += 1
self.member_burden += len(tpart)
g.start() | [
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242,624 | wal-e/wal-e | wal_e/worker/upload_pool.py | TarUploadPool._finish | def _finish(self, g):
"""Called on completion of an upload greenlet.
Takes care to forward Exceptions or, if there is no error, the
finished TarPartition value across a channel.
"""
assert g.ready()
if g.successful():
finished_tpart = g.get()
sel... | python | def _finish(self, g):
assert g.ready()
if g.successful():
finished_tpart = g.get()
self.wait_change.put(finished_tpart)
else:
self.wait_change.put(g.exception) | [
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242,625 | wal-e/wal-e | wal_e/worker/upload_pool.py | TarUploadPool._wait | def _wait(self):
"""Block until an upload finishes
Raise an exception if that tar volume failed with an error.
"""
val = self.wait_change.get()
if isinstance(val, Exception):
# Don't other uncharging, because execution is going to stop
raise val
... | python | def _wait(self):
val = self.wait_change.get()
if isinstance(val, Exception):
# Don't other uncharging, because execution is going to stop
raise val
else:
# Uncharge for resources.
self.member_burden -= len(val)
self.concurrency_burden ... | [
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242,626 | wal-e/wal-e | wal_e/worker/upload_pool.py | TarUploadPool.put | def put(self, tpart):
"""Upload a tar volume
Blocks if there is too much work outstanding already, and
raise errors of previously submitted greenlets that die
unexpectedly.
"""
if self.closed:
raise UserCritical(msg='attempt to upload tar after closing',
... | python | def put(self, tpart):
if self.closed:
raise UserCritical(msg='attempt to upload tar after closing',
hint='report a bug')
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too_many = (
self.concurrency_burden + 1 > self.max_concurrency
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242,627 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | close_filenos | def close_filenos(preserve):
""" Close unprotected file descriptors
Close all open file descriptors that are not in preserve.
If ulimit -nofile is "unlimited", all is defined filenos <= 4096,
else all is <= the output of resource.getrlimit().
:param preserve: set with protected files
:type pr... | python | def close_filenos(preserve):
maxfd = resource.getrlimit(resource.RLIMIT_NOFILE)[1]
if maxfd == resource.RLIM_INFINITY:
maxfd = 4096
for fileno in range(maxfd):
if fileno not in preserve:
try:
os.close(fileno)
except OSError as err:
if n... | [
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242,628 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | default_signal_map | def default_signal_map():
""" Create the default signal map for this system.
:return: dict
"""
name_map = {
'SIGTSTP': None,
'SIGTTIN': None,
'SIGTTOU': None,
'SIGTERM': 'terminate'}
signal_map = {}
for name, target in list(name_map.items()):
if hasattr(s... | python | def default_signal_map():
name_map = {
'SIGTSTP': None,
'SIGTTIN': None,
'SIGTTOU': None,
'SIGTERM': 'terminate'}
signal_map = {}
for name, target in list(name_map.items()):
if hasattr(signal, name):
signal_map[getattr(signal, name)] = target
return si... | [
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242,629 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | parent_is_inet | def parent_is_inet():
""" Check if parent is inet
Check if our parent seems ot be a superserver, aka inetd/xinetd.
This is done by checking if sys.__stdin__ is a network socket.
:return: bool
"""
result = False
sock = socket.fromfd(
sys.__stdin__.fileno(),
socket.AF_INET,
... | python | def parent_is_inet():
result = False
sock = socket.fromfd(
sys.__stdin__.fileno(),
socket.AF_INET,
socket.SOCK_RAW)
try:
sock.getsockopt(socket.SOL_SOCKET, socket.SO_TYPE)
result = True
except (OSError, socket.error) as err:
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242,630 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | redirect_stream | def redirect_stream(system, target):
""" Redirect Unix streams
If None, redirect Stream to /dev/null, else redirect to target.
:param system: ether sys.stdin, sys.stdout, or sys.stderr
:type system: file object
:param target: File like object, or None
:type target: None, File Object
:ret... | python | def redirect_stream(system, target):
if target is None:
target_fd = os.open(os.devnull, os.O_RDWR)
else:
target_fd = target.fileno()
try:
os.dup2(target_fd, system.fileno())
except OSError as err:
raise DaemonError('Could not redirect {0} to {1}: {2}'
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242,631 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | DaemonContext._get_signal_handler | def _get_signal_handler(self, handler):
""" get the callback function for handler
If the handler is None, returns signal.SIG_IGN.
If the handler is a string, return the matching attribute of this
instance if possible.
Else return the handler itself.
:param handler:
... | python | def _get_signal_handler(self, handler):
if not handler:
result = signal.SIG_IGN
elif isinstance(handler, string_types):
result = getattr(self, handler)
else:
result = handler
return result | [
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242,632 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | DaemonContext._files_preserve | def _files_preserve(self):
""" create a set of protected files
create a set of files, based on self.files_preserve and
self.stdin, self,stdout and self.stderr, that should not get
closed while daemonizing.
:return: set
"""
result = set()
files = [] if no... | python | def _files_preserve(self):
result = set()
files = [] if not self.files_preserve else self.files_preserve
files.extend([self.stdin, self.stdout, self.stderr])
for item in files:
if hasattr(item, 'fileno'):
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242,633 | wal-e/wal-e | wal_e/pep3143daemon/daemon.py | DaemonContext.working_directory | def working_directory(self):
""" The working_directory property
:return: str
"""
if self.chroot_directory and not \
self._working_directory.startswith(self.chroot_directory):
return self.chroot_directory + self._working_directory
else:
ret... | python | def working_directory(self):
if self.chroot_directory and not \
self._working_directory.startswith(self.chroot_directory):
return self.chroot_directory + self._working_directory
else:
return self._working_directory | [
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242,634 | treyhunner/django-simple-history | simple_history/__init__.py | register | def register(
model,
app=None,
manager_name="history",
records_class=None,
table_name=None,
**records_config
):
"""
Create historical model for `model` and attach history manager to `model`.
Keyword arguments:
app -- App to install historical model into (defaults to model.__modu... | python | def register(
model,
app=None,
manager_name="history",
records_class=None,
table_name=None,
**records_config
):
from . import models
if records_class is None:
records_class = models.HistoricalRecords
records = records_class(**records_config)
records.manager_name = manag... | [
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manager_name -- class attribute name to use for historical manager
records_class -- class to use for history relation (defaults to
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242,635 | treyhunner/django-simple-history | simple_history/admin.py | SimpleHistoryAdmin.get_urls | def get_urls(self):
"""Returns the additional urls used by the Reversion admin."""
urls = super(SimpleHistoryAdmin, self).get_urls()
admin_site = self.admin_site
opts = self.model._meta
info = opts.app_label, opts.model_name
history_urls = [
url(
... | python | def get_urls(self):
urls = super(SimpleHistoryAdmin, self).get_urls()
admin_site = self.admin_site
opts = self.model._meta
info = opts.app_label, opts.model_name
history_urls = [
url(
"^([^/]+)/history/([^/]+)/$",
admin_site.admin_view(... | [
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242,636 | treyhunner/django-simple-history | simple_history/admin.py | SimpleHistoryAdmin.save_model | def save_model(self, request, obj, form, change):
"""Set special model attribute to user for reference after save"""
obj._history_user = request.user
super(SimpleHistoryAdmin, self).save_model(request, obj, form, change) | python | def save_model(self, request, obj, form, change):
obj._history_user = request.user
super(SimpleHistoryAdmin, self).save_model(request, obj, form, change) | [
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242,637 | treyhunner/django-simple-history | simple_history/management/commands/populate_history.py | Command._bulk_history_create | def _bulk_history_create(self, model, batch_size):
"""Save a copy of all instances to the historical model.
:param model: Model you want to bulk create
:param batch_size: number of models to create at once.
:return:
"""
instances = []
history = utils.get_history... | python | def _bulk_history_create(self, model, batch_size):
instances = []
history = utils.get_history_manager_for_model(model)
if self.verbosity >= 1:
self.stdout.write(
"Starting bulk creating history models for {} instances {}-{}".format(
model, 0, batch... | [
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242,638 | treyhunner/django-simple-history | simple_history/models.py | transform_field | def transform_field(field):
"""Customize field appropriately for use in historical model"""
field.name = field.attname
if isinstance(field, models.AutoField):
field.__class__ = models.IntegerField
elif isinstance(field, models.FileField):
# Don't copy file, just path.
field.__cl... | python | def transform_field(field):
field.name = field.attname
if isinstance(field, models.AutoField):
field.__class__ = models.IntegerField
elif isinstance(field, models.FileField):
# Don't copy file, just path.
field.__class__ = models.TextField
# Historical instance shouldn't change... | [
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242,639 | treyhunner/django-simple-history | simple_history/models.py | HistoricalRecords.create_history_model | def create_history_model(self, model, inherited):
"""
Creates a historical model to associate with the model provided.
"""
attrs = {
"__module__": self.module,
"_history_excluded_fields": self.excluded_fields,
}
app_module = "%s.models" % model._m... | python | def create_history_model(self, model, inherited):
attrs = {
"__module__": self.module,
"_history_excluded_fields": self.excluded_fields,
}
app_module = "%s.models" % model._meta.app_label
if inherited:
# inherited use models module
attrs[... | [
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242,640 | treyhunner/django-simple-history | simple_history/models.py | HistoricalRecords.copy_fields | def copy_fields(self, model):
"""
Creates copies of the model's original fields, returning
a dictionary mapping field name to copied field object.
"""
fields = {}
for field in self.fields_included(model):
field = copy.copy(field)
field.remote_field... | python | def copy_fields(self, model):
fields = {}
for field in self.fields_included(model):
field = copy.copy(field)
field.remote_field = copy.copy(field.remote_field)
if isinstance(field, OrderWrt):
# OrderWrt is a proxy field, switch to a plain IntegerField
... | [
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242,641 | treyhunner/django-simple-history | simple_history/models.py | HistoricalRecords.get_extra_fields | def get_extra_fields(self, model, fields):
"""Return dict of extra fields added to the historical record model"""
def revert_url(self):
"""URL for this change in the default admin site."""
opts = model._meta
app_label, model_name = opts.app_label, opts.model_name
... | python | def get_extra_fields(self, model, fields):
def revert_url(self):
"""URL for this change in the default admin site."""
opts = model._meta
app_label, model_name = opts.app_label, opts.model_name
return reverse(
"%s:%s_%s_simple_history" % (admin.site... | [
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242,642 | treyhunner/django-simple-history | simple_history/models.py | HistoricalRecords.get_meta_options | def get_meta_options(self, model):
"""
Returns a dictionary of fields that will be added to
the Meta inner class of the historical record model.
"""
meta_fields = {
"ordering": ("-history_date", "-history_id"),
"get_latest_by": "history_date",
}
... | python | def get_meta_options(self, model):
meta_fields = {
"ordering": ("-history_date", "-history_id"),
"get_latest_by": "history_date",
}
if self.user_set_verbose_name:
name = self.user_set_verbose_name
else:
name = format_lazy("historical {}", s... | [
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242,643 | treyhunner/django-simple-history | simple_history/models.py | HistoricalRecords.get_history_user | def get_history_user(self, instance):
"""Get the modifying user from instance or middleware."""
try:
return instance._history_user
except AttributeError:
request = None
try:
if self.thread.request.user.is_authenticated:
requ... | python | def get_history_user(self, instance):
try:
return instance._history_user
except AttributeError:
request = None
try:
if self.thread.request.user.is_authenticated:
request = self.thread.request
except AttributeError:
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242,644 | treyhunner/django-simple-history | simple_history/manager.py | HistoryManager.most_recent | def most_recent(self):
"""
Returns the most recent copy of the instance available in the history.
"""
if not self.instance:
raise TypeError(
"Can't use most_recent() without a {} instance.".format(
self.model._meta.object_name
... | python | def most_recent(self):
if not self.instance:
raise TypeError(
"Can't use most_recent() without a {} instance.".format(
self.model._meta.object_name
)
)
tmp = []
excluded_fields = getattr(self.model, "_history_excluded_fi... | [
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242,645 | treyhunner/django-simple-history | simple_history/manager.py | HistoryManager.as_of | def as_of(self, date):
"""Get a snapshot as of a specific date.
Returns an instance, or an iterable of the instances, of the
original model with all the attributes set according to what
was present on the object on the date provided.
"""
if not self.instance:
... | python | def as_of(self, date):
if not self.instance:
return self._as_of_set(date)
queryset = self.get_queryset().filter(history_date__lte=date)
try:
history_obj = queryset[0]
except IndexError:
raise self.instance.DoesNotExist(
"%s had not yet ... | [
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242,646 | treyhunner/django-simple-history | simple_history/manager.py | HistoryManager.bulk_history_create | def bulk_history_create(self, objs, batch_size=None):
"""Bulk create the history for the objects specified by objs"""
historical_instances = [
self.model(
history_date=getattr(instance, "_history_date", now()),
history_user=getattr(instance, "_history_user", ... | python | def bulk_history_create(self, objs, batch_size=None):
historical_instances = [
self.model(
history_date=getattr(instance, "_history_date", now()),
history_user=getattr(instance, "_history_user", None),
history_change_reason=getattr(instance, "changeRea... | [
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242,647 | treyhunner/django-simple-history | simple_history/utils.py | get_history_manager_for_model | def get_history_manager_for_model(model):
"""Return the history manager for a given app model."""
try:
manager_name = model._meta.simple_history_manager_attribute
except AttributeError:
raise NotHistoricalModelError(
"Cannot find a historical model for {model}.".format(model=mode... | python | def get_history_manager_for_model(model):
try:
manager_name = model._meta.simple_history_manager_attribute
except AttributeError:
raise NotHistoricalModelError(
"Cannot find a historical model for {model}.".format(model=model)
)
return getattr(model, manager_name) | [
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242,648 | sony/nnabla | python/src/nnabla/parameter.py | pop_parameter | def pop_parameter(key):
'''Remove and get parameter by key.
Args:
key(str): Key of parameter.
Returns: ~nnabla.Variable
Parameter if key found, otherwise None.
'''
names = key.split('/')
if len(names) > 1:
with parameter_scope(names[0]):
return pop_paramete... | python | def pop_parameter(key):
'''Remove and get parameter by key.
Args:
key(str): Key of parameter.
Returns: ~nnabla.Variable
Parameter if key found, otherwise None.
'''
names = key.split('/')
if len(names) > 1:
with parameter_scope(names[0]):
return pop_paramete... | [
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242,649 | sony/nnabla | python/src/nnabla/parameter.py | get_parameter_or_create | def get_parameter_or_create(name, shape=None, initializer=None, need_grad=True,
as_need_grad=None):
"""
Returns an existing parameter variable with the provided name.
If a variable with the provided name does not exist,
a new variable with the provided name is returned.
... | python | def get_parameter_or_create(name, shape=None, initializer=None, need_grad=True,
as_need_grad=None):
names = name.split('/')
if len(names) > 1:
with parameter_scope(names[0]):
return get_parameter_or_create('/'.join(names[1:]), shape, initializer, need_grad, as_nee... | [
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242,650 | sony/nnabla | python/src/nnabla/parameter.py | get_parameters | def get_parameters(params=None, path='', grad_only=True):
"""Get parameter Variables under the current parameter scope.
Args:
params (dict): Internal use. User doesn't set it manually.
path (str): Internal use. User doesn't set it manually.
grad_only (bool): Retrieve all parameters und... | python | def get_parameters(params=None, path='', grad_only=True):
global current_scope
if params is None:
params = OrderedDict()
for k, v in iteritems(current_scope):
if isinstance(v, dict):
with parameter_scope(k):
params = get_parameters(
params, '/'... | [
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242,651 | sony/nnabla | python/src/nnabla/ext_utils.py | import_extension_module | def import_extension_module(ext_name):
'''
Import an extension module by name.
The extension modules are installed under the `nnabla_ext` package as
namespace packages. All extension modules provide a unified set of APIs.
Args:
ext_name(str): Extension name. e.g. 'cpu', 'cuda', 'cudnn' etc... | python | def import_extension_module(ext_name):
'''
Import an extension module by name.
The extension modules are installed under the `nnabla_ext` package as
namespace packages. All extension modules provide a unified set of APIs.
Args:
ext_name(str): Extension name. e.g. 'cpu', 'cuda', 'cudnn' etc... | [
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242,652 | sony/nnabla | python/src/nnabla/ext_utils.py | list_extensions | def list_extensions():
'''
List up available extensions.
Note:
It may not work on some platforms/environments since it depends
on the directory structure of the namespace packages.
Returns: list of str
Names of available extensions.
'''
import nnabla_ext.cpu
from o... | python | def list_extensions():
'''
List up available extensions.
Note:
It may not work on some platforms/environments since it depends
on the directory structure of the namespace packages.
Returns: list of str
Names of available extensions.
'''
import nnabla_ext.cpu
from o... | [
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Note:
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Returns: list of str
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242,653 | sony/nnabla | python/src/nnabla/utils/image_utils/pil_utils.py | imsave | def imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True):
"""
Save image by pillow module.
Currently, pillow supports only uint8 to save.
Args:
path (str): output filename
img (numpy.ndarray): Image array to save. Image shape is considered as (height, width, channel)... | python | def imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True):
img = _imsave_before(img, channel_first, auto_scale)
if img.dtype == np.uint16 or as_uint16:
raise ValueError("Pillow only supports uint8 image to save. Cast img to uint8."
"If you want to save image ... | [
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242,654 | sony/nnabla | python/src/nnabla/utils/nnp_graph.py | NnpLoader.get_network | def get_network(self, name, batch_size=None, callback=None):
'''Create a variable graph given network by name
Returns: NnpNetwork
'''
network_proto = nnabla_pb2.Network()
network_proto.CopyFrom(self.network_dict[name])
return NnpNetwork(network_proto, self._params, bat... | python | def get_network(self, name, batch_size=None, callback=None):
'''Create a variable graph given network by name
Returns: NnpNetwork
'''
network_proto = nnabla_pb2.Network()
network_proto.CopyFrom(self.network_dict[name])
return NnpNetwork(network_proto, self._params, bat... | [
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242,655 | sony/nnabla | python/src/nnabla/utils/converter/onnx/importer.py | set_function_name | def set_function_name(func, node_name, base_name, func_counter):
"""Set a sufficient name for the function"""
# NNabla requires each function to have a unique name
# so we generate one here.
func.name, count = generate_function_name(func.type, base_name, node_name,
... | python | def set_function_name(func, node_name, base_name, func_counter):
# NNabla requires each function to have a unique name
# so we generate one here.
func.name, count = generate_function_name(func.type, base_name, node_name,
func_counter)
update_function_counter... | [
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242,656 | sony/nnabla | python/src/nnabla/utils/converter/onnx/importer.py | generate_transpose | def generate_transpose(node_name, in_name, out_name, axes, base_name, func_counter):
"""Generate a Transpose operator to transpose the specified buffer.
"""
trans = nnabla_pb2.Function()
trans.type = "Transpose"
set_function_name(trans, node_name, base_name, func_counter)
trans.input.extend([in_... | python | def generate_transpose(node_name, in_name, out_name, axes, base_name, func_counter):
trans = nnabla_pb2.Function()
trans.type = "Transpose"
set_function_name(trans, node_name, base_name, func_counter)
trans.input.extend([in_name])
trans.output.extend([out_name])
tp = trans.transpose_param
tp... | [
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242,657 | sony/nnabla | python/src/nnabla/utils/converter/onnx/importer.py | generate_broadcast_to | def generate_broadcast_to(node_name, x, y, out_name, axis, base_name, func_counter):
"""Generate a BroadcastTo operator to brodcast specified buffer"""
bt = nnabla_pb2.Function()
bt.type = "BroadcastTo"
set_function_name(bt, node_name, base_name, func_counter)
bt.input.extend([x, y])
bt.output.e... | python | def generate_broadcast_to(node_name, x, y, out_name, axis, base_name, func_counter):
bt = nnabla_pb2.Function()
bt.type = "BroadcastTo"
set_function_name(bt, node_name, base_name, func_counter)
bt.input.extend([x, y])
bt.output.extend([out_name])
btp = bt.broadcast_to_param
btp.axis = axis
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242,658 | sony/nnabla | python/src/nnabla/utils/converter/onnx/importer.py | convert_parameter_shape | def convert_parameter_shape(pb):
"""Convert the shape of some parameters so they fit NNabla's requirements.
We do this as a post conversion because in the future we may be able to
delete the whole conversion if NNabla's code gets changed"""
if len(pb.network) != 1:
raise ValueError(
... | python | def convert_parameter_shape(pb):
if len(pb.network) != 1:
raise ValueError(
"NNP with more then a single network is currently not supported")
net = pb.network[0]
batch_norm_constants = []
for f in net.function:
if f.type == "BatchNormalization":
# BatchNormalizati... | [
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242,659 | sony/nnabla | python/src/nnabla/utils/converter/onnx/importer.py | add_tensor_as_parameter | def add_tensor_as_parameter(pb, tensor):
"""Add given tensor as a parameter"""
p = pb.parameter.add()
p.variable_name = tensor.name
p.shape.dim.extend(tensor.dims)
if tensor.data_type == TensorProto.FLOAT:
# convert raw bytestream to floating points
if tensor.raw_data:
p.... | python | def add_tensor_as_parameter(pb, tensor):
p = pb.parameter.add()
p.variable_name = tensor.name
p.shape.dim.extend(tensor.dims)
if tensor.data_type == TensorProto.FLOAT:
# convert raw bytestream to floating points
if tensor.raw_data:
p.data.extend(np.fromstring(tensor.raw_data,... | [
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242,660 | sony/nnabla | python/src/nnabla/utils/converter/onnx/importer.py | OnnxImporter.BroadcastOperator | def BroadcastOperator(self, func_name, func_list, n):
"""Converts a broadcasting operator to a composite with BroadcastTo"""
broadcasting = False
broadcast_axis = -1
func = self.generate_default_function(func_name, n)
for attr in n.attribute:
if attr.name == "axis":
... | python | def BroadcastOperator(self, func_name, func_list, n):
broadcasting = False
broadcast_axis = -1
func = self.generate_default_function(func_name, n)
for attr in n.attribute:
if attr.name == "axis":
if attr.type != AttributeProto.INT:
raise Va... | [
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242,661 | sony/nnabla | python/src/nnabla/utils/image_utils/pypng_utils.py | imread | def imread(path, grayscale=False, size=None, interpolate="bilinear",
channel_first=False, as_uint16=False, num_channels=-1):
"""
Read image by pypng module.
Args:
path (str or 'file object'): File path or object to read.
grayscale (bool):
size (tupple of int):
... | python | def imread(path, grayscale=False, size=None, interpolate="bilinear",
channel_first=False, as_uint16=False, num_channels=-1):
_imread_before(grayscale, num_channels)
f = path if hasattr(path, "read") else open(path, "rb")
r = png.Reader(file=f)
width, height, pixels, metadata = r.asDirect()
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242,662 | sony/nnabla | python/src/nnabla/utils/image_utils/pypng_utils.py | imsave | def imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True):
"""
Save image by pypng module.
Args:
path (str): output filename
img (numpy.ndarray): Image array to save. Image shape is considered as (height, width, channel) by default.
channel_first:
This... | python | def imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True):
img = _imsave_before(img, channel_first, auto_scale)
if auto_scale:
img = upscale_pixel_intensity(img, as_uint16)
img = check_type_and_cast_if_necessary(img, as_uint16)
bitdepth = 8 if img.dtype == np.uint8 else 16
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242,663 | sony/nnabla | python/src/nnabla/context.py | context_scope | def context_scope(ctx):
"""
Context as Python context.
.. code-block:: python
import nnabla as nn
import nnabla.functions as F
x = nn.Variable([2, 3 ,4])
ctx = nnabla_ext.cuda.context('0')
with context_scope(ctx):
# Inside with scope, the specified conte... | python | def context_scope(ctx):
global current_ctx
global context_level
context_level += 1
prev_context = current_ctx
current_ctx = ctx
try:
yield
finally:
context_level -= 1
current_ctx = prev_context | [
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import nnabla.functions as F
x = nn.Variable([2, 3 ,4])
ctx = nnabla_ext.cuda.context('0')
with context_scope(ctx):
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242,664 | sony/nnabla | python/src/nnabla/utils/converter/onnx/exporter.py | generate_scalar_constant | def generate_scalar_constant(output_name, tensor_name, scalar):
"""Convert a scalar value to a Constant buffer.
This is mainly used for xxScalar operators."""
t = onnx.helper.make_tensor(tensor_name,
data_type=TensorProto.FLOAT,
dims=[1], vals=... | python | def generate_scalar_constant(output_name, tensor_name, scalar):
t = onnx.helper.make_tensor(tensor_name,
data_type=TensorProto.FLOAT,
dims=[1], vals=[scalar])
c = onnx.helper.make_node("Constant",
[],
... | [
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242,665 | sony/nnabla | python/src/nnabla/utils/converter/onnx/exporter.py | replace_negative_size_with_batch_size | def replace_negative_size_with_batch_size(shape, batch_size):
"""Replace all dimensions with negative values to batch size"""
sl = []
for d in shape.dim:
if d < 0:
# Negative size means batch size
sl.append(batch_size)
else:
sl.append(d)
out_shape = nn... | python | def replace_negative_size_with_batch_size(shape, batch_size):
sl = []
for d in shape.dim:
if d < 0:
# Negative size means batch size
sl.append(batch_size)
else:
sl.append(d)
out_shape = nnabla_pb2.Shape()
out_shape.dim.extend(sl)
return out_shape | [
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242,666 | sony/nnabla | python/src/nnabla/utils/converter/onnx/exporter.py | OnnxExporter.BinarySigmoid | def BinarySigmoid(self, func):
'''
Currently, caffe2 does not support this function.
'''
n = onnx.helper.make_node(
'HardSigmoid',
func.input,
func.output,
alpha=1.0,
beta=0.0
)
return [n] | python | def BinarySigmoid(self, func):
'''
Currently, caffe2 does not support this function.
'''
n = onnx.helper.make_node(
'HardSigmoid',
func.input,
func.output,
alpha=1.0,
beta=0.0
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return [n] | [
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242,667 | sony/nnabla | python/src/nnabla/experimental/graph_converters/sequential.py | SequentialConverter.convert | def convert(self, vroot, entry_variables):
"""Convert a given graph.
Convert a given graph using the `converters` in the order of the registeration, i.e., sequentially.
Args:
vroot (:obj:`Variable`): NNabla Variable
entry_variables (:obj:`Variable`): Entry variable from... | python | def convert(self, vroot, entry_variables):
for converter in self.converters:
vroot = converter.convert(vroot, entry_variables)
return vroot | [
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242,668 | sony/nnabla | python/src/nnabla/initializer.py | calc_normal_std_he_forward | def calc_normal_std_he_forward(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by He et al.
.. math::
\sigma = \sqrt{\frac{2}{NK}}
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
outmaps (int): Map size of an output Variable, :math:`M`.... | python | def calc_normal_std_he_forward(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by He et al.
.. math::
\sigma = \sqrt{\frac{2}{NK}}
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
outmaps (int): Map size of an output Variable, :math:`M`.... | [
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242,669 | sony/nnabla | python/src/nnabla/initializer.py | calc_normal_std_glorot | def calc_normal_std_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by Glorot et al.
.. math::
\sigma = \sqrt{\frac{2}{NK + M}}
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
outmaps (int): Map size of an output Variable, :math:... | python | def calc_normal_std_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the standard deviation proposed by Glorot et al.
.. math::
\sigma = \sqrt{\frac{2}{NK + M}}
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
outmaps (int): Map size of an output Variable, :math:... | [
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242,670 | sony/nnabla | python/src/nnabla/initializer.py | calc_uniform_lim_glorot | def calc_uniform_lim_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the lower bound and the upper bound of the uniform distribution proposed by Glorot et al.
.. math::
b &= \sqrt{\frac{6}{NK + M}}\\
a &= -b
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
... | python | def calc_uniform_lim_glorot(inmaps, outmaps, kernel=(1, 1)):
r"""Calculates the lower bound and the upper bound of the uniform distribution proposed by Glorot et al.
.. math::
b &= \sqrt{\frac{6}{NK + M}}\\
a &= -b
Args:
inmaps (int): Map size of an input Variable, :math:`N`.
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242,671 | sony/nnabla | python/src/nnabla/utils/save.py | _get_unique_function_name | def _get_unique_function_name(function_type, functions):
'''Get a unique function name.
Args:
function_type(str): Name of Function. Ex) Convolution, Affine
functions(OrderedDict of (str, Function)
Returns: str
A unique function name
'''
function_name = function_name_base = ... | python | def _get_unique_function_name(function_type, functions):
'''Get a unique function name.
Args:
function_type(str): Name of Function. Ex) Convolution, Affine
functions(OrderedDict of (str, Function)
Returns: str
A unique function name
'''
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242,672 | sony/nnabla | python/src/nnabla/utils/save.py | _get_unique_variable_name | def _get_unique_variable_name(vname, variables):
'''Get a unique variable name.
Args:
vname(str): A candidate name.
variable(OrderedDict of str and Variable)
Returns: str
A unique variable name
'''
count = 2
vname_base = vname
while vname in variables:
vname... | python | def _get_unique_variable_name(vname, variables):
'''Get a unique variable name.
Args:
vname(str): A candidate name.
variable(OrderedDict of str and Variable)
Returns: str
A unique variable name
'''
count = 2
vname_base = vname
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242,673 | sony/nnabla | python/src/nnabla/functions.py | sum | def sum(x, axis=None, keepdims=False):
"""Reduction along axes with sum operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which the sum is
calculated. Passing the default value `None` will reduce all dimensions.
keepdims ... | python | def sum(x, axis=None, keepdims=False):
from .function_bases import sum as sum_base
if axis is None:
axis = range(x.ndim)
elif not hasattr(axis, '__iter__'):
axis = [axis]
return sum_base(x, axis, keepdims) | [
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242,674 | sony/nnabla | python/src/nnabla/functions.py | mean | def mean(x, axis=None, keepdims=False):
"""Reduction along axes with mean operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which mean is
calculated. Passing the default value `None` will reduce all dimensions.
keepdims (... | python | def mean(x, axis=None, keepdims=False):
from .function_bases import mean as mean_base
if axis is None:
axis = range(x.ndim)
elif not hasattr(axis, '__iter__'):
axis = [axis]
return mean_base(x, axis, keepdims) | [
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242,675 | sony/nnabla | python/src/nnabla/functions.py | prod | def prod(x, axis=None, keepdims=False):
"""Reduction along axes with product operation.
Args:
x (Variable): An input variable.
axis (None, int or tuple of ints): Axis or axes along which product is
calculated. Passing the default value `None` will reduce all dimensions.
keep... | python | def prod(x, axis=None, keepdims=False):
from .function_bases import prod as prod_base
if axis is None:
axis = range(x.ndim)
elif not hasattr(axis, '__iter__'):
axis = [axis]
return prod_base(x, axis, keepdims) | [
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242,676 | sony/nnabla | python/src/nnabla/functions.py | reduce | def reduce(x, op='sum'):
"""Reduction function with given operation.
Args:
x (Variable): An input.
op (str): 'sum' or 'mean'.
Note:
This is deprecated. Use ``mean`` or ``sum`` instead.
"""
import warnings
warnings.warn(
"Deprecated API. Use ``sum`` or ``mean`` ... | python | def reduce(x, op='sum'):
import warnings
warnings.warn(
"Deprecated API. Use ``sum`` or ``mean`` instead.", DeprecationWarning)
from .function_bases import reduce_sum, reduce_mean
if op == 'sum':
return reduce_sum(x)
elif op == 'mean':
return reduce_mean(x)
raise ValueErr... | [
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242,677 | sony/nnabla | python/src/nnabla/functions.py | split | def split(x, axis=0):
"""
Split arrays at the specified axis.
It returns a number corresponding the size of the given
axis (i.e ``x.shape[axis]``) of :obj:`~nnabla.Variable` s.
Args:
x(~nnabla.Variable): N-D array
axis(int): Axis
Returns: A :obj:`tuple` of :obj:`~nnabla.Variab... | python | def split(x, axis=0):
from .function_bases import split as split_base
return split_base(x, axis, x.shape[axis]) | [
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Args:
x(~nnabla.Variable): N-D array
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242,678 | sony/nnabla | python/src/nnabla/functions.py | batch_normalization | def batch_normalization(x, beta, gamma, mean, variance, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, n_outputs=None):
r"""
Batch normalization.
.. math::
\begin{eqnarray}
\mu &=& \frac{1}{M} \sum x_i \\
\sigma^2 &=& \frac{1}{M} \sum \left(x_i - \mu\ri... | python | def batch_normalization(x, beta, gamma, mean, variance, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, n_outputs=None):
r"""
Batch normalization.
.. math::
\begin{eqnarray}
\mu &=& \frac{1}{M} \sum x_i \\
\sigma^2 &=& \frac{1}{M} \sum \left(x_i - \mu\ri... | [
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Batch normalization.
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242,679 | sony/nnabla | python/src/nnabla/functions.py | fixed_point_quantize | def fixed_point_quantize(x, sign=True, n=8, delta=2**-4, quantize=True, ste_fine_grained=True, outputs=None):
r"""Fixed Point Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
n (int): Bit width used. Note th... | python | def fixed_point_quantize(x, sign=True, n=8, delta=2**-4, quantize=True, ste_fine_grained=True, outputs=None):
r"""Fixed Point Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
n (int): Bit width used. Note th... | [
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242,680 | sony/nnabla | python/src/nnabla/functions.py | pow2_quantize | def pow2_quantize(x, sign=True, with_zero=True, n=8, m=1, quantize=True, ste_fine_grained=True, outputs=None):
r"""Pow2 Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
with_zero (bool): Indicate using zero ... | python | def pow2_quantize(x, sign=True, with_zero=True, n=8, m=1, quantize=True, ste_fine_grained=True, outputs=None):
r"""Pow2 Quantize
Args:
x (Variable): An input variable.
sign (bool): Indicate the signed number or the unsigned number. Default is true.
with_zero (bool): Indicate using zero ... | [
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242,681 | sony/nnabla | python/src/nnabla/functions.py | clip_by_value | def clip_by_value(x, min, max):
r"""Clip inputs by values.
.. math::
y = \begin{cases}
max & (x > max) \\
x & (otherwise) \\
min & (x < min)
\end{cases}.
Args:
x (Variable): An input variable.
min (Variable): A min variab... | python | def clip_by_value(x, min, max):
r"""Clip inputs by values.
.. math::
y = \begin{cases}
max & (x > max) \\
x & (otherwise) \\
min & (x < min)
\end{cases}.
Args:
x (Variable): An input variable.
min (Variable): A min variab... | [
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242,682 | sony/nnabla | python/src/nnabla/functions.py | interpolate | def interpolate(x, scale=None, output_size=None, mode='linear', align_corners=None):
'''
Resize an ND array with interpolation.
Scaling factors for spatial dimensions are determined by either
``scale`` or ``output_size``.
``nd = len(scale)`` or ``nd = len(output_size)`` determines the number of
... | python | def interpolate(x, scale=None, output_size=None, mode='linear', align_corners=None):
'''
Resize an ND array with interpolation.
Scaling factors for spatial dimensions are determined by either
``scale`` or ``output_size``.
``nd = len(scale)`` or ``nd = len(output_size)`` determines the number of
... | [
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242,683 | sony/nnabla | python/src/nnabla/functions.py | sort | def sort(x, axis=-1, reverse=False, with_index=False, only_index=False):
"""Sorts the elements of `x` along a given `axis` in ascending order
by value. A negative `axis` counts from the last dimension of `x`,
so the default of -1 sorts along the last dimension. If `reverse`
is True, then the elements ar... | python | def sort(x, axis=-1, reverse=False, with_index=False, only_index=False):
from .function_bases import sort as sort_base
n_outputs = 2 if with_index and not only_index else 1
return sort_base(x, axis, reverse, with_index, only_index, n_outputs) | [
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242,684 | sony/nnabla | python/src/nnabla/utils/download.py | download | def download(url, output_file=None, open_file=True, allow_overwrite=False):
'''Download a file from URL.
Args:
url (str): URL.
output_file (str, optional): If given, the downloaded file is written to the given path.
open_file (bool): If True, it returns an opened file stream of the down... | python | def download(url, output_file=None, open_file=True, allow_overwrite=False):
'''Download a file from URL.
Args:
url (str): URL.
output_file (str, optional): If given, the downloaded file is written to the given path.
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242,685 | sony/nnabla | python/src/nnabla/utils/image_utils/cv2_utils.py | imread | def imread(path, grayscale=False, size=None, interpolate="bilinear",
channel_first=False, as_uint16=False, num_channels=-1):
"""
Read image by cv2 module.
Args:
path (str or 'file object'): File path or object to read.
grayscale (bool):
size (tupple of int):
(... | python | def imread(path, grayscale=False, size=None, interpolate="bilinear",
channel_first=False, as_uint16=False, num_channels=-1):
_imread_before(grayscale, num_channels)
r_mode = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_UNCHANGED
img = _imread_helper(path, r_mode)
if as_uint16 and img.d... | [
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242,686 | sony/nnabla | python/src/nnabla/utils/learning_rate_scheduler.py | PolynomialScheduler.get_learning_rate | def get_learning_rate(self, iter):
'''
Get learning rate with polymomial decay based on current iteration.
Args:
iter (int): current iteration (starting with 0).
Returns:
float: Learning rate
'''
return self.init_lr * ((1.0 - iter * 1.0 / self.ma... | python | def get_learning_rate(self, iter):
'''
Get learning rate with polymomial decay based on current iteration.
Args:
iter (int): current iteration (starting with 0).
Returns:
float: Learning rate
'''
return self.init_lr * ((1.0 - iter * 1.0 / self.ma... | [
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242,687 | sony/nnabla | python/src/nnabla/utils/learning_rate_scheduler.py | CosineScheduler.get_learning_rate | def get_learning_rate(self, iter):
'''
Get learning rate with cosine decay based on current iteration.
Args:
iter (int): Current iteration (starting with 0).
Returns:
float: Learning rate
'''
return self.init_lr * ((math.cos(iter * 1.0 / (self.ma... | python | def get_learning_rate(self, iter):
'''
Get learning rate with cosine decay based on current iteration.
Args:
iter (int): Current iteration (starting with 0).
Returns:
float: Learning rate
'''
return self.init_lr * ((math.cos(iter * 1.0 / (self.ma... | [
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242,688 | sony/nnabla | python/src/nnabla/parametric_functions.py | affine | def affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
apply_w=None, apply_b=None):
"""
The affine layer, also known as the fully connected layer. Computes
.. math::
{\\mathbf y} = {\\mathbf A} {\... | python | def affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
apply_w=None, apply_b=None):
if not hasattr(n_outmaps, '__iter__'):
n_outmaps = [n_outmaps]
n_outmaps = list(n_outmaps)
n_outmap = int(np.prod... | [
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where :math:`{\\mathbf x}, {\\mathbf y}` are the inputs and outputs respectively,
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242,689 | sony/nnabla | python/src/nnabla/parametric_functions.py | binary_weight_affine | def binary_weight_affine(inp, n_outmaps,
base_axis=1, quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
"""Binary Weight Affine, multiplier-less inner-product with a scale factor.
... | python | def binary_weight_affine(inp, n_outmaps,
base_axis=1, quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
if not hasattr(n_outmaps, '__iter__'):
n_outmaps = [n_outmaps]
n... | [
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Binary Weight Affine is the affine function, but the inner product
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.. math::
y_j = \\frac{1}{\\|\\mathbf{w}_j\\|_{\\ell_1}} \sum_{i} sign(w_{ji}) x_i
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242,690 | sony/nnabla | python/src/nnabla/parametric_functions.py | inq_affine | def inq_affine(inp, n_outmaps, base_axis=1, num_bits=4,
inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
"""Incremental Network Quantization Affine Layer
During training... | python | def inq_affine(inp, n_outmaps, base_axis=1, num_bits=4,
inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True):
if not hasattr(n_outmaps, '__iter__'):
n_outmaps = [n_outmaps... | [
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242,691 | sony/nnabla | python/src/nnabla/parametric_functions.py | binary_connect_convolution | def binary_connect_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
base_axis=1, fix_parameters=False,... | python | def binary_connect_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
quantize_zero_to=1.0,
w_init=None, wb_init=None, b_init=None,
base_axis=1, fix_parameters=False,... | [
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Binary Connect Convolution is the convolution function,
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The input-output relation of this function is as follows:
.. math::
y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} sign(w_{n, m,... | [
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242,692 | sony/nnabla | python/src/nnabla/parametric_functions.py | inq_convolution | def inq_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
num_bits=4, inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=Non... | python | def inq_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
num_bits=4, inq_iterations=(), selection_algorithm='random',
seed=-1, w_init=None, i_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=Non... | [
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242,693 | sony/nnabla | python/src/nnabla/parametric_functions.py | depthwise_convolution | def depthwise_convolution(inp, kernel, pad=None, stride=None, dilation=None,
multiplier=1, w_init=None, b_init=None, base_axis=1,
fix_parameters=False, rng=None, with_bias=True):
"""
N-D Depthwise Convolution with a bias term.
Reference:
- F. Chollet... | python | def depthwise_convolution(inp, kernel, pad=None, stride=None, dilation=None,
multiplier=1, w_init=None, b_init=None, base_axis=1,
fix_parameters=False, rng=None, with_bias=True):
if w_init is None:
w_init = UniformInitializer(
calc_uniform_lim_... | [
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... | N-D Depthwise Convolution with a bias term.
Reference:
- F. Chollet: Chollet, Francois. "Xception: Deep Learning with Depthwise Separable Convolutions. https://arxiv.org/abs/1610.02357
Args:
inp (~nnabla.Variable): N-D array.
kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. F... | [
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242,694 | sony/nnabla | python/src/nnabla/parametric_functions.py | batch_normalization | def batch_normalization(inp, axes=[1], decay_rate=0.9, eps=1e-5,
batch_stat=True, output_stat=False, fix_parameters=False,
param_init=None):
"""
Batch normalization layer.
.. math::
\\begin{array}{lcl}
\\mu &=& \\frac{1}{M} \\sum x_i\\\\
... | python | def batch_normalization(inp, axes=[1], decay_rate=0.9, eps=1e-5,
batch_stat=True, output_stat=False, fix_parameters=False,
param_init=None):
shape_stat = [1 for _ in inp.shape]
for i in range(len(axes)):
shape_stat[axes[i]] = inp.shape[axes[i]]
if par... | [
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.. math::
\\begin{array}{lcl}
\\mu &=& \\frac{1}{M} \\sum x_i\\\\
\\sigma^2 &=& \\frac{1}{M} \\sum \\left(x_i - \\mu\\right)^2\\\\
\\hat{x}_i &=& \\frac{x_i - \\mu}{\\sqrt{\\sigma^2 + \\epsilon }}\\\\
y_i &= & \\hat{x}_i \\gamma + \\beta.
... | [
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242,695 | sony/nnabla | python/src/nnabla/parametric_functions.py | mean_subtraction | def mean_subtraction(inp, base_axis=1, update_running_mean=True, fix_parameters=False):
"""
Mean subtraction layer.
It subtracts the mean of the elements of the input array,
and normalizes it to :math:`0`. Preprocessing arrays with this function has the effect of improving accuracy
in various tasks... | python | def mean_subtraction(inp, base_axis=1, update_running_mean=True, fix_parameters=False):
assert len(inp.shape) >= base_axis
shape = inp.shape[base_axis:]
mean = get_parameter_or_create(
"mean", shape, ConstantInitializer(0), False)
t = get_parameter_or_create(
"t", (1, ), ConstantInitiali... | [
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It subtracts the mean of the elements of the input array,
and normalizes it to :math:`0`. Preprocessing arrays with this function has the effect of improving accuracy
in various tasks such as image classification.
At training time, this function is defined as
.. math::
... | [
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242,696 | sony/nnabla | python/src/nnabla/parametric_functions.py | prelu | def prelu(inp, base_axis=1, shared=True, fix_parameters=False):
"""
Parametrized Rectified Linear Unit function defined as
.. math::
y_i = \max(0, x_i) + w_i \min(0, -x_i)
where negative slope :math:`w` is learned and can vary across channels (an
axis specified with base_axis). Weights are... | python | def prelu(inp, base_axis=1, shared=True, fix_parameters=False):
shape = tuple() if shared else (inp.shape[base_axis],)
w = get_parameter_or_create("slope", shape,
ConstantInitializer(-1), True, not fix_parameters)
return F.prelu(inp, w, base_axis) | [
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242,697 | sony/nnabla | python/src/nnabla/parametric_functions.py | fixed_point_quantized_affine | def fixed_point_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, n_w=8, delta_w=2**-4, ... | python | def fixed_point_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, n_w=8, delta_w=2**-4, ... | [
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Fixed-Point Quantized Affine is the affine function,
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The input-output relation of this function is as follows:
.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
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242,698 | sony/nnabla | python/src/nnabla/parametric_functions.py | fixed_point_quantized_convolution | def fixed_point_quantized_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
... | python | def fixed_point_quantized_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None, with_bias=True,
... | [
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Fixed-Point Quantized Convolution is the convolution function,
except the definition of the inner product is modified.
The input-output relation of this function is as follows:
.. math::
y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i,... | [
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242,699 | sony/nnabla | python/src/nnabla/parametric_functions.py | pow2_quantized_affine | def pow2_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, with_zero_w=False, n_w=8, m_w=2, ste_fine_grained_w=True,... | python | def pow2_quantized_affine(inp, n_outmaps,
base_axis=1,
w_init=None, b_init=None,
fix_parameters=False, rng=None, with_bias=True,
quantize_w=True, sign_w=True, with_zero_w=False, n_w=8, m_w=2, ste_fine_grained_w=True,... | [
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Pow2 Quantized Affine is the affine function,
except the definition of the inner product is modified.
The input-output relation of this function is as follows:
.. math::
y_j = \sum_{i} Q(w_{ji}) x_i,
where :math:`Q(w_{ji})` is the power-of-2 quantization function.
... | [
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] | aaf3d33b7cbb38f2a03aa754178ba8f7c8481320 | https://github.com/sony/nnabla/blob/aaf3d33b7cbb38f2a03aa754178ba8f7c8481320/python/src/nnabla/parametric_functions.py#L2026-L2132 |
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