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 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
30,100 | iterative/dvc | dvc/analytics.py | Analytics.load | def load(path):
"""Loads analytics report from json file specified by path.
Args:
path (str): path to json file with analytics report.
"""
with open(path, "r") as fobj:
analytics = Analytics(info=json.load(fobj))
os.unlink(path)
return analytics | python | def load(path):
"""Loads analytics report from json file specified by path.
Args:
path (str): path to json file with analytics report.
"""
with open(path, "r") as fobj:
analytics = Analytics(info=json.load(fobj))
os.unlink(path)
return analytics | [
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30,101 | iterative/dvc | dvc/analytics.py | Analytics.collect | def collect(self):
"""Collect analytics report."""
from dvc.scm import SCM
from dvc.utils import is_binary
from dvc.repo import Repo
from dvc.exceptions import NotDvcRepoError
self.info[self.PARAM_DVC_VERSION] = __version__
self.info[self.PARAM_IS_BINARY] = is_bi... | python | def collect(self):
"""Collect analytics report."""
from dvc.scm import SCM
from dvc.utils import is_binary
from dvc.repo import Repo
from dvc.exceptions import NotDvcRepoError
self.info[self.PARAM_DVC_VERSION] = __version__
self.info[self.PARAM_IS_BINARY] = is_bi... | [
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30,102 | iterative/dvc | dvc/analytics.py | Analytics.collect_cmd | def collect_cmd(self, args, ret):
"""Collect analytics info from a CLI command."""
from dvc.command.daemon import CmdDaemonAnalytics
assert isinstance(ret, int) or ret is None
if ret is not None:
self.info[self.PARAM_CMD_RETURN_CODE] = ret
if args is not None and h... | python | def collect_cmd(self, args, ret):
"""Collect analytics info from a CLI command."""
from dvc.command.daemon import CmdDaemonAnalytics
assert isinstance(ret, int) or ret is None
if ret is not None:
self.info[self.PARAM_CMD_RETURN_CODE] = ret
if args is not None and h... | [
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30,103 | iterative/dvc | dvc/analytics.py | Analytics.dump | def dump(self):
"""Save analytics report to a temporary file.
Returns:
str: path to the temporary file that contains the analytics report.
"""
import tempfile
with tempfile.NamedTemporaryFile(delete=False, mode="w") as fobj:
json.dump(self.info, fobj)
... | python | def dump(self):
"""Save analytics report to a temporary file.
Returns:
str: path to the temporary file that contains the analytics report.
"""
import tempfile
with tempfile.NamedTemporaryFile(delete=False, mode="w") as fobj:
json.dump(self.info, fobj)
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30,104 | iterative/dvc | dvc/analytics.py | Analytics.send_cmd | def send_cmd(cmd, args, ret):
"""Collect and send analytics for CLI command.
Args:
args (list): parsed args for the CLI command.
ret (int): return value of the CLI command.
"""
from dvc.daemon import daemon
if not Analytics._is_enabled(cmd):
... | python | def send_cmd(cmd, args, ret):
"""Collect and send analytics for CLI command.
Args:
args (list): parsed args for the CLI command.
ret (int): return value of the CLI command.
"""
from dvc.daemon import daemon
if not Analytics._is_enabled(cmd):
... | [
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30,105 | iterative/dvc | dvc/analytics.py | Analytics.send | def send(self):
"""Collect and send analytics."""
import requests
if not self._is_enabled():
return
self.collect()
logger.debug("Sending analytics: {}".format(self.info))
try:
requests.post(self.URL, json=self.info, timeout=self.TIMEOUT_POST)
... | python | def send(self):
"""Collect and send analytics."""
import requests
if not self._is_enabled():
return
self.collect()
logger.debug("Sending analytics: {}".format(self.info))
try:
requests.post(self.URL, json=self.info, timeout=self.TIMEOUT_POST)
... | [
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30,106 | iterative/dvc | dvc/data_cloud.py | DataCloud.push | def push(self, targets, jobs=None, remote=None, show_checksums=False):
"""Push data items in a cloud-agnostic way.
Args:
targets (list): list of targets to push to the cloud.
jobs (int): number of jobs that can be running simultaneously.
remote (dvc.remote.base.Remot... | python | def push(self, targets, jobs=None, remote=None, show_checksums=False):
"""Push data items in a cloud-agnostic way.
Args:
targets (list): list of targets to push to the cloud.
jobs (int): number of jobs that can be running simultaneously.
remote (dvc.remote.base.Remot... | [
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Args:
targets (list): list of targets to push to the cloud.
jobs (int): number of jobs that can be running simultaneously.
remote (dvc.remote.base.RemoteBase): optional remote to push to.
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30,107 | iterative/dvc | dvc/data_cloud.py | DataCloud.status | def status(self, targets, jobs=None, remote=None, show_checksums=False):
"""Check status of data items in a cloud-agnostic way.
Args:
targets (list): list of targets to check status for.
jobs (int): number of jobs that can be running simultaneously.
remote (dvc.remot... | python | def status(self, targets, jobs=None, remote=None, show_checksums=False):
"""Check status of data items in a cloud-agnostic way.
Args:
targets (list): list of targets to check status for.
jobs (int): number of jobs that can be running simultaneously.
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30,108 | iterative/dvc | dvc/repo/brancher.py | brancher | def brancher( # noqa: E302
self, branches=None, all_branches=False, tags=None, all_tags=False
):
"""Generator that iterates over specified revisions.
Args:
branches (list): a list of branches to iterate over.
all_branches (bool): iterate over all available branches.
tags (list): a ... | python | def brancher( # noqa: E302
self, branches=None, all_branches=False, tags=None, all_tags=False
):
"""Generator that iterates over specified revisions.
Args:
branches (list): a list of branches to iterate over.
all_branches (bool): iterate over all available branches.
tags (list): a ... | [
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... | [
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30,109 | iterative/dvc | dvc/state.py | State.load | def load(self):
"""Loads state database."""
retries = 1
while True:
assert self.database is None
assert self.cursor is None
assert self.inserts == 0
empty = not os.path.exists(self.state_file)
self.database = sqlite3.connect(self.state_... | python | def load(self):
"""Loads state database."""
retries = 1
while True:
assert self.database is None
assert self.cursor is None
assert self.inserts == 0
empty = not os.path.exists(self.state_file)
self.database = sqlite3.connect(self.state_... | [
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30,110 | iterative/dvc | dvc/state.py | State.dump | def dump(self):
"""Saves state database."""
assert self.database is not None
cmd = "SELECT count from {} WHERE rowid={}"
self._execute(cmd.format(self.STATE_INFO_TABLE, self.STATE_INFO_ROW))
ret = self._fetchall()
assert len(ret) == 1
assert len(ret[0]) == 1
... | python | def dump(self):
"""Saves state database."""
assert self.database is not None
cmd = "SELECT count from {} WHERE rowid={}"
self._execute(cmd.format(self.STATE_INFO_TABLE, self.STATE_INFO_ROW))
ret = self._fetchall()
assert len(ret) == 1
assert len(ret[0]) == 1
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30,111 | iterative/dvc | dvc/state.py | State.save | def save(self, path_info, checksum):
"""Save checksum for the specified path info.
Args:
path_info (dict): path_info to save checksum for.
checksum (str): checksum to save.
"""
assert path_info["scheme"] == "local"
assert checksum is not None
pat... | python | def save(self, path_info, checksum):
"""Save checksum for the specified path info.
Args:
path_info (dict): path_info to save checksum for.
checksum (str): checksum to save.
"""
assert path_info["scheme"] == "local"
assert checksum is not None
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30,112 | iterative/dvc | dvc/state.py | State.get | def get(self, path_info):
"""Gets the checksum for the specified path info. Checksum will be
retrieved from the state database if available.
Args:
path_info (dict): path info to get the checksum for.
Returns:
str or None: checksum for the specified path info or ... | python | def get(self, path_info):
"""Gets the checksum for the specified path info. Checksum will be
retrieved from the state database if available.
Args:
path_info (dict): path info to get the checksum for.
Returns:
str or None: checksum for the specified path info or ... | [
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30,113 | iterative/dvc | dvc/state.py | State.save_link | def save_link(self, path_info):
"""Adds the specified path to the list of links created by dvc. This
list is later used on `dvc checkout` to cleanup old links.
Args:
path_info (dict): path info to add to the list of links.
"""
assert path_info["scheme"] == "local"
... | python | def save_link(self, path_info):
"""Adds the specified path to the list of links created by dvc. This
list is later used on `dvc checkout` to cleanup old links.
Args:
path_info (dict): path info to add to the list of links.
"""
assert path_info["scheme"] == "local"
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30,114 | iterative/dvc | dvc/state.py | State.remove_unused_links | def remove_unused_links(self, used):
"""Removes all saved links except the ones that are used.
Args:
used (list): list of used links that should not be removed.
"""
unused = []
self._execute("SELECT * FROM {}".format(self.LINK_STATE_TABLE))
for row in self.c... | python | def remove_unused_links(self, used):
"""Removes all saved links except the ones that are used.
Args:
used (list): list of used links that should not be removed.
"""
unused = []
self._execute("SELECT * FROM {}".format(self.LINK_STATE_TABLE))
for row in self.c... | [
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30,115 | iterative/dvc | dvc/lock.py | Lock.lock | def lock(self):
"""Acquire lock for dvc repo."""
try:
self._do_lock()
return
except LockError:
time.sleep(self.TIMEOUT)
self._do_lock() | python | def lock(self):
"""Acquire lock for dvc repo."""
try:
self._do_lock()
return
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30,116 | PySimpleGUI/PySimpleGUI | PySimpleGUI27.py | TkScrollableFrame.set_scrollregion | def set_scrollregion(self, event=None):
""" Set the scroll region on the canvas"""
self.canvas.configure(scrollregion=self.canvas.bbox('all')) | python | def set_scrollregion(self, event=None):
""" Set the scroll region on the canvas"""
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30,117 | PySimpleGUI/PySimpleGUI | PySimpleGUI27.py | TKCalendar._show_selection | def _show_selection(self, text, bbox):
"""Configure canvas for a new selection."""
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textw = self._font.measure(text)
canvas = self._canvas
canvas.configure(width=width, height=height)
canvas.coords(canvas.text, width - textw, height / 2 - 1)
... | python | def _show_selection(self, text, bbox):
"""Configure canvas for a new selection."""
x, y, width, height = bbox
textw = self._font.measure(text)
canvas = self._canvas
canvas.configure(width=width, height=height)
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30,118 | PySimpleGUI/PySimpleGUI | PySimpleGUI27.py | TKCalendar._prev_month | def _prev_month(self):
"""Updated calendar to show the previous month."""
self._canvas.place_forget()
self._date = self._date - self.timedelta(days=1)
self._date = self.datetime(self._date.year, self._date.month, 1)
self._build_calendar() | python | def _prev_month(self):
"""Updated calendar to show the previous month."""
self._canvas.place_forget()
self._date = self._date - self.timedelta(days=1)
self._date = self.datetime(self._date.year, self._date.month, 1)
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30,119 | PySimpleGUI/PySimpleGUI | PySimpleGUI27.py | TKCalendar._next_month | def _next_month(self):
"""Update calendar to show the next month."""
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self._date = self._date + self.timedelta(
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self._date = self.datetime(self._d... | python | def _next_month(self):
"""Update calendar to show the next month."""
self._canvas.place_forget()
year, month = self._date.year, self._date.month
self._date = self._date + self.timedelta(
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30,120 | PySimpleGUI/PySimpleGUI | PySimpleGUI27.py | TKCalendar.selection | def selection(self):
"""Return a datetime representing the current selected date."""
if not self._selection:
return None
year, month = self._date.year, self._date.month
return self.datetime(year, month, int(self._selection[0])) | python | def selection(self):
"""Return a datetime representing the current selected date."""
if not self._selection:
return None
year, month = self._date.year, self._date.month
return self.datetime(year, month, int(self._selection[0])) | [
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30,121 | PySimpleGUI/PySimpleGUI | PySimpleGUI27.py | Window.AddRow | def AddRow(self, *args):
''' Parms are a variable number of Elements '''
NumRows = len(self.Rows) # number of existing rows is our row number
CurrentRowNumber = NumRows # this row's number
CurrentRow = [] # start with a blank row and build up
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''' Parms are a variable number of Elements '''
NumRows = len(self.Rows) # number of existing rows is our row number
CurrentRowNumber = NumRows # this row's number
CurrentRow = [] # start with a blank row and build up
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30,122 | PySimpleGUI/PySimpleGUI | DemoPrograms/Demo_Uno_Card_Game.py | Card.setColor | def setColor(self, color):
'''Sets Card's color and escape code.'''
if color == 'blue':
self.color = 'blue'
self.colorCode = self.colors['blue']
self.colorCodeDark = self.colors['dblue']
elif color == 'red':
self.color = 'red'
self.colo... | python | def setColor(self, color):
'''Sets Card's color and escape code.'''
if color == 'blue':
self.color = 'blue'
self.colorCode = self.colors['blue']
self.colorCodeDark = self.colors['dblue']
elif color == 'red':
self.color = 'red'
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30,123 | PySimpleGUI/PySimpleGUI | DemoPrograms/Demo_Img_Viewer.py | get_img_data | def get_img_data(f, maxsize = (1200, 850), first = False):
"""Generate image data using PIL
"""
img = Image.open(f)
img.thumbnail(maxsize)
if first: # tkinter is inactive the first time
bio = io.BytesIO()
img.save(bio, format = "PNG")
del img
retur... | python | def get_img_data(f, maxsize = (1200, 850), first = False):
"""Generate image data using PIL
"""
img = Image.open(f)
img.thumbnail(maxsize)
if first: # tkinter is inactive the first time
bio = io.BytesIO()
img.save(bio, format = "PNG")
del img
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30,124 | PySimpleGUI/PySimpleGUI | DemoPrograms/Demo_Matplotlib_Ping_Graph.py | quiet_ping | def quiet_ping(hostname, timeout=WAIT_TIMEOUT, count=NUM_PACKETS,
packet_size=PACKET_SIZE, path_finder=False):
"""
Same as verbose_ping, but the results are returned as tuple
"""
myStats = MyStats() # Reset the stats
mySeqNumber = 0 # Starting value
try:
destIP = socket.g... | python | def quiet_ping(hostname, timeout=WAIT_TIMEOUT, count=NUM_PACKETS,
packet_size=PACKET_SIZE, path_finder=False):
"""
Same as verbose_ping, but the results are returned as tuple
"""
myStats = MyStats() # Reset the stats
mySeqNumber = 0 # Starting value
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destIP = socket.g... | [
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30,125 | PySimpleGUI/PySimpleGUI | DemoPrograms/Demo_DOC_Viewer_PIL.py | get_page | def get_page(pno, zoom = False, max_size = None, first = False):
"""Return a PNG image for a document page number.
"""
dlist = dlist_tab[pno] # get display list of page number
if not dlist: # create if not yet there
dlist_tab[pno] = doc[pno].getDisplayList()
dlist = dlist_ta... | python | def get_page(pno, zoom = False, max_size = None, first = False):
"""Return a PNG image for a document page number.
"""
dlist = dlist_tab[pno] # get display list of page number
if not dlist: # create if not yet there
dlist_tab[pno] = doc[pno].getDisplayList()
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30,126 | PySimpleGUI/PySimpleGUI | DemoPrograms/Demo_Conways_Game_of_Life.py | GameOfLife.play | def play(self):
""" Play Conway's Game of Life. """
# Write the initial configuration to file.
self.t = 1 # Current time level
while self.t <= self.T: # Evolve!
# print( "At time level %d" % t)
# Loop over each cell of the grid and apply Conway's rules.
... | python | def play(self):
""" Play Conway's Game of Life. """
# Write the initial configuration to file.
self.t = 1 # Current time level
while self.t <= self.T: # Evolve!
# print( "At time level %d" % t)
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30,127 | PySimpleGUI/PySimpleGUI | DemoPrograms/Demo_Desktop_Widget_psutil_Dashboard.py | human_size | def human_size(bytes, units=[' bytes','KB','MB','GB','TB', 'PB', 'EB']):
""" Returns a human readable string reprentation of bytes"""
return str(bytes) + units[0] if bytes < 1024 else human_size(bytes>>10, units[1:]) | python | def human_size(bytes, units=[' bytes','KB','MB','GB','TB', 'PB', 'EB']):
""" Returns a human readable string reprentation of bytes"""
return str(bytes) + units[0] if bytes < 1024 else human_size(bytes>>10, units[1:]) | [
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30,128 | PySimpleGUI/PySimpleGUI | PySimpleGUIWeb/Demo Programs/widgets_overview_app.py | MyApp.list_view_on_selected | def list_view_on_selected(self, widget, selected_item_key):
""" The selection event of the listView, returns a key of the clicked event.
You can retrieve the item rapidly
"""
self.lbl.set_text('List selection: ' + self.listView.children[selected_item_key].get_text()) | python | def list_view_on_selected(self, widget, selected_item_key):
""" The selection event of the listView, returns a key of the clicked event.
You can retrieve the item rapidly
"""
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30,129 | PySimpleGUI/PySimpleGUI | DemoPrograms/ping.py | receive_one_ping | def receive_one_ping(mySocket, myID, timeout):
"""
Receive the ping from the socket. Timeout = in ms
"""
timeLeft = timeout/1000
while True: # Loop while waiting for packet or timeout
startedSelect = default_timer()
whatReady = select.select([mySocket], [], [], timeLeft)
how... | python | def receive_one_ping(mySocket, myID, timeout):
"""
Receive the ping from the socket. Timeout = in ms
"""
timeLeft = timeout/1000
while True: # Loop while waiting for packet or timeout
startedSelect = default_timer()
whatReady = select.select([mySocket], [], [], timeLeft)
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30,130 | tensorflow/hub | tensorflow_hub/module_spec.py | ModuleSpec.export | def export(self, path, _sentinel=None, # pylint: disable=invalid-name
checkpoint_path=None, name_transform_fn=None):
"""Exports a ModuleSpec with weights taken from a checkpoint.
This is an helper to export modules directly from a ModuleSpec
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30,131 | tensorflow/hub | tensorflow_hub/module_spec.py | ModuleSpec.get_attached_message | def get_attached_message(self, key, message_type, tags=None, required=False):
"""Returns the message attached to the module under the given key, or None.
Module publishers can attach protocol messages to modules at creation time
to provide module consumers with additional information, e.g., on module
u... | python | def get_attached_message(self, key, message_type, tags=None, required=False):
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30,132 | tensorflow/hub | examples/image_retraining/retrain.py | create_image_lists | def create_image_lists(image_dir, testing_percentage, validation_percentage):
"""Builds a list of training images from the file system.
Analyzes the sub folders in the image directory, splits them into stable
training, testing, and validation sets, and returns a data structure
describing the lists of images fo... | python | def create_image_lists(image_dir, testing_percentage, validation_percentage):
"""Builds a list of training images from the file system.
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30,133 | tensorflow/hub | examples/image_retraining/retrain.py | get_image_path | def get_image_path(image_lists, label_name, index, image_dir, category):
"""Returns a path to an image for a label at the given index.
Args:
image_lists: OrderedDict of training images for each label.
label_name: Label string we want to get an image for.
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"""Returns a path to an image for a label at the given index.
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30,134 | tensorflow/hub | examples/image_retraining/retrain.py | get_bottleneck_path | def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
category, module_name):
"""Returns a path to a bottleneck file for a label at the given index.
Args:
image_lists: OrderedDict of training images for each label.
label_name: Label string we want to get an image f... | python | def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
category, module_name):
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30,135 | tensorflow/hub | examples/image_retraining/retrain.py | create_module_graph | def create_module_graph(module_spec):
"""Creates a graph and loads Hub Module into it.
Args:
module_spec: the hub.ModuleSpec for the image module being used.
Returns:
graph: the tf.Graph that was created.
bottleneck_tensor: the bottleneck values output by the module.
resized_input_tensor: the in... | python | def create_module_graph(module_spec):
"""Creates a graph and loads Hub Module into it.
Args:
module_spec: the hub.ModuleSpec for the image module being used.
Returns:
graph: the tf.Graph that was created.
bottleneck_tensor: the bottleneck values output by the module.
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30,136 | tensorflow/hub | examples/image_retraining/retrain.py | run_bottleneck_on_image | def run_bottleneck_on_image(sess, image_data, image_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor):
"""Runs inference on an image to extract the 'bottleneck' summary layer.
Args:
sess: Current active TensorFlow Session.
im... | python | def run_bottleneck_on_image(sess, image_data, image_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor):
"""Runs inference on an image to extract the 'bottleneck' summary layer.
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sess: Current active TensorFlow Session.
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30,137 | tensorflow/hub | examples/image_retraining/retrain.py | create_bottleneck_file | def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
image_dir, category, sess, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor):
"""Create a single bottleneck file."""
tf.logging.... | python | def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
image_dir, category, sess, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor):
"""Create a single bottleneck file."""
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30,138 | tensorflow/hub | examples/image_retraining/retrain.py | get_or_create_bottleneck | def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
category, bottleneck_dir, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor, module_name):
"""Retrieves or calculates bottl... | python | def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
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decoded_image_tensor, resized_input_tensor,
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30,139 | tensorflow/hub | examples/image_retraining/retrain.py | cache_bottlenecks | def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
jpeg_data_tensor, decoded_image_tensor,
resized_input_tensor, bottleneck_tensor, module_name):
"""Ensures all the training, testing, and validation bottlenecks are cached.
Because we're likely to read th... | python | def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
jpeg_data_tensor, decoded_image_tensor,
resized_input_tensor, bottleneck_tensor, module_name):
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30,140 | tensorflow/hub | examples/image_retraining/retrain.py | get_random_cached_bottlenecks | def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
bottleneck_dir, image_dir, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor, module_name):
"""Retrieves bottlene... | python | def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
bottleneck_dir, image_dir, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor, module_name):
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30,141 | tensorflow/hub | examples/image_retraining/retrain.py | get_random_distorted_bottlenecks | def get_random_distorted_bottlenecks(
sess, image_lists, how_many, category, image_dir, input_jpeg_tensor,
distorted_image, resized_input_tensor, bottleneck_tensor):
"""Retrieves bottleneck values for training images, after distortions.
If we're training with distortions like crops, scales, or flips, we ha... | python | def get_random_distorted_bottlenecks(
sess, image_lists, how_many, category, image_dir, input_jpeg_tensor,
distorted_image, resized_input_tensor, bottleneck_tensor):
"""Retrieves bottleneck values for training images, after distortions.
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30,142 | tensorflow/hub | examples/image_retraining/retrain.py | add_input_distortions | def add_input_distortions(flip_left_right, random_crop, random_scale,
random_brightness, module_spec):
"""Creates the operations to apply the specified distortions.
During training it can help to improve the results if we run the images
through simple distortions like crops, scales, and... | python | def add_input_distortions(flip_left_right, random_crop, random_scale,
random_brightness, module_spec):
"""Creates the operations to apply the specified distortions.
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30,143 | tensorflow/hub | examples/image_retraining/retrain.py | add_final_retrain_ops | def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
quantize_layer, is_training):
"""Adds a new softmax and fully-connected layer for training and eval.
We need to retrain the top layer to identify our new classes, so this function
adds the right operations to t... | python | def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
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"""Adds a new softmax and fully-connected layer for training and eval.
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30,144 | tensorflow/hub | examples/image_retraining/retrain.py | add_evaluation_step | def add_evaluation_step(result_tensor, ground_truth_tensor):
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Args:
result_tensor: The new final node that produces results.
ground_truth_tensor: The node we feed ground truth data
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Tuple of (evaluation step,... | python | def add_evaluation_step(result_tensor, ground_truth_tensor):
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result_tensor: The new final node that produces results.
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30,145 | tensorflow/hub | examples/image_retraining/retrain.py | run_final_eval | def run_final_eval(train_session, module_spec, class_count, image_lists,
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"""Runs a final evaluation on an eval graph using the test data set.
Args:
train_session: Session for the train graph ... | python | def run_final_eval(train_session, module_spec, class_count, image_lists,
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30,146 | tensorflow/hub | examples/image_retraining/retrain.py | build_eval_session | def build_eval_session(module_spec, class_count):
"""Builds an restored eval session without train operations for exporting.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: Number of classes
Returns:
Eval session containing the restored eval graph.
The bottlen... | python | def build_eval_session(module_spec, class_count):
"""Builds an restored eval session without train operations for exporting.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: Number of classes
Returns:
Eval session containing the restored eval graph.
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30,147 | tensorflow/hub | examples/image_retraining/retrain.py | save_graph_to_file | def save_graph_to_file(graph_file_name, module_spec, class_count):
"""Saves an graph to file, creating a valid quantized one if necessary."""
sess, _, _, _, _, _ = build_eval_session(module_spec, class_count)
graph = sess.graph
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph.... | python | def save_graph_to_file(graph_file_name, module_spec, class_count):
"""Saves an graph to file, creating a valid quantized one if necessary."""
sess, _, _, _, _, _ = build_eval_session(module_spec, class_count)
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30,148 | tensorflow/hub | examples/image_retraining/retrain.py | add_jpeg_decoding | def add_jpeg_decoding(module_spec):
"""Adds operations that perform JPEG decoding and resizing to the graph..
Args:
module_spec: The hub.ModuleSpec for the image module being used.
Returns:
Tensors for the node to feed JPEG data into, and the output of the
preprocessing steps.
"""
input_height... | python | def add_jpeg_decoding(module_spec):
"""Adds operations that perform JPEG decoding and resizing to the graph..
Args:
module_spec: The hub.ModuleSpec for the image module being used.
Returns:
Tensors for the node to feed JPEG data into, and the output of the
preprocessing steps.
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30,149 | tensorflow/hub | examples/image_retraining/retrain.py | export_model | def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
"""
# The SavedModel should... | python | def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving.
Args:
module_spec: The hub.ModuleSpec for the image module being used.
class_count: The number of classes.
saved_model_dir: Directory in which to save exported model and variables.
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30,150 | tensorflow/hub | examples/image_retraining/retrain.py | logging_level_verbosity | def logging_level_verbosity(logging_verbosity):
"""Converts logging_level into TensorFlow logging verbosity value
Args:
logging_level: String value representing logging level: 'DEBUG', 'INFO',
'WARN', 'ERROR', 'FATAL'
"""
name_to_level = {
'FATAL': tf.logging.FATAL,
'ERROR': tf.logging.ERROR,
... | python | def logging_level_verbosity(logging_verbosity):
"""Converts logging_level into TensorFlow logging verbosity value
Args:
logging_level: String value representing logging level: 'DEBUG', 'INFO',
'WARN', 'ERROR', 'FATAL'
"""
name_to_level = {
'FATAL': tf.logging.FATAL,
'ERROR': tf.logging.ERROR,
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30,151 | tensorflow/hub | tensorflow_hub/image_util.py | get_image_module_info | def get_image_module_info(module_or_spec, required=False):
"""Returns the module's attached ImageModuleInfo message, or None."""
return module_or_spec.get_attached_message(
IMAGE_MODULE_INFO_KEY, ImageModuleInfo, required=required) | python | def get_image_module_info(module_or_spec, required=False):
"""Returns the module's attached ImageModuleInfo message, or None."""
return module_or_spec.get_attached_message(
IMAGE_MODULE_INFO_KEY, ImageModuleInfo, required=required) | [
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30,152 | tensorflow/hub | tensorflow_hub/image_util.py | get_num_image_channels | def get_num_image_channels(module_or_spec, signature=None, input_name=None):
"""Returns expected num_channels dimensions of an image input.
This is for advanced users only who expect to handle modules with
image inputs that might not have the 3 usual RGB channels.
Args:
module_or_spec: a Module or ModuleS... | python | def get_num_image_channels(module_or_spec, signature=None, input_name=None):
"""Returns expected num_channels dimensions of an image input.
This is for advanced users only who expect to handle modules with
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30,153 | tensorflow/hub | tensorflow_hub/tensor_info.py | _parse_tensor_info_proto | def _parse_tensor_info_proto(tensor_info):
"""Returns a ParsedTensorInfo instance from a TensorInfo proto."""
encoding = tensor_info.WhichOneof("encoding")
dtype = tf.DType(tensor_info.dtype)
shape = tf.TensorShape(tensor_info.tensor_shape)
if encoding == "name":
return ParsedTensorInfo(dtype=dtype, shape... | python | def _parse_tensor_info_proto(tensor_info):
"""Returns a ParsedTensorInfo instance from a TensorInfo proto."""
encoding = tensor_info.WhichOneof("encoding")
dtype = tf.DType(tensor_info.dtype)
shape = tf.TensorShape(tensor_info.tensor_shape)
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30,154 | tensorflow/hub | tensorflow_hub/tensor_info.py | _is_sparse | def _is_sparse(x):
"""Returns whether x is a SparseTensor or a parsed sparse tensor info."""
return (
isinstance(x, (tf.SparseTensor, tf_v1.SparseTensorValue)) or
(hasattr(x, "is_sparse") and x.is_sparse)) | python | def _is_sparse(x):
"""Returns whether x is a SparseTensor or a parsed sparse tensor info."""
return (
isinstance(x, (tf.SparseTensor, tf_v1.SparseTensorValue)) or
(hasattr(x, "is_sparse") and x.is_sparse)) | [
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30,155 | tensorflow/hub | tensorflow_hub/tensor_info.py | _convert_to_compatible_tensor | def _convert_to_compatible_tensor(value, target, error_prefix):
"""Converts `value` into a tensor that can be feed into `tensor_info`.
Args:
value: A value to convert into Tensor or SparseTensor.
target: An object returned by `parse_tensor_info_map`.
error_prefix: A string to prefix on raised TypeError... | python | def _convert_to_compatible_tensor(value, target, error_prefix):
"""Converts `value` into a tensor that can be feed into `tensor_info`.
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value: A value to convert into Tensor or SparseTensor.
target: An object returned by `parse_tensor_info_map`.
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30,156 | tensorflow/hub | tensorflow_hub/tensor_info.py | convert_dict_to_compatible_tensor | def convert_dict_to_compatible_tensor(values, targets):
"""Converts dict `values` in tensors that are compatible with `targets`.
Args:
values: A dict to objects to convert with same keys as `targets`.
targets: A dict returned by `parse_tensor_info_map`.
Returns:
A map with the same keys as `values` ... | python | def convert_dict_to_compatible_tensor(values, targets):
"""Converts dict `values` in tensors that are compatible with `targets`.
Args:
values: A dict to objects to convert with same keys as `targets`.
targets: A dict returned by `parse_tensor_info_map`.
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30,157 | tensorflow/hub | tensorflow_hub/tensor_info.py | build_input_map | def build_input_map(protomap, inputs):
"""Builds a map to feed tensors in `protomap` using `inputs`.
Args:
protomap: A proto map<string,TensorInfo>.
inputs: A map with same keys as `protomap` of Tensors and SparseTensors.
Returns:
A map from nodes refered by TensorInfo protos to corresponding input
... | python | def build_input_map(protomap, inputs):
"""Builds a map to feed tensors in `protomap` using `inputs`.
Args:
protomap: A proto map<string,TensorInfo>.
inputs: A map with same keys as `protomap` of Tensors and SparseTensors.
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A map from nodes refered by TensorInfo protos to corresponding input
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30,158 | tensorflow/hub | tensorflow_hub/tensor_info.py | build_output_map | def build_output_map(protomap, get_tensor_by_name):
"""Builds a map of tensors from `protomap` using `get_tensor_by_name`.
Args:
protomap: A proto map<string,TensorInfo>.
get_tensor_by_name: A lambda that receives a tensor name and returns a
Tensor instance.
Returns:
A map from string to Tenso... | python | def build_output_map(protomap, get_tensor_by_name):
"""Builds a map of tensors from `protomap` using `get_tensor_by_name`.
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protomap: A proto map<string,TensorInfo>.
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30,159 | tensorflow/hub | examples/text_embeddings/export.py | parse_line | def parse_line(line):
"""Parses a line of a text embedding file.
Args:
line: (str) One line of the text embedding file.
Returns:
A token string and its embedding vector in floats.
"""
columns = line.split()
token = columns.pop(0)
values = [float(column) for column in columns]
return token, val... | python | def parse_line(line):
"""Parses a line of a text embedding file.
Args:
line: (str) One line of the text embedding file.
Returns:
A token string and its embedding vector in floats.
"""
columns = line.split()
token = columns.pop(0)
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30,160 | tensorflow/hub | examples/text_embeddings/export.py | load | def load(file_path, parse_line_fn):
"""Loads a text embedding into memory as a numpy matrix.
Args:
file_path: Path to the text embedding file.
parse_line_fn: callback function to parse each file line.
Returns:
A tuple of (list of vocabulary tokens, numpy matrix of embedding vectors).
Raises:
... | python | def load(file_path, parse_line_fn):
"""Loads a text embedding into memory as a numpy matrix.
Args:
file_path: Path to the text embedding file.
parse_line_fn: callback function to parse each file line.
Returns:
A tuple of (list of vocabulary tokens, numpy matrix of embedding vectors).
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30,161 | tensorflow/hub | examples/text_embeddings/export.py | make_module_spec | def make_module_spec(vocabulary_file, vocab_size, embeddings_dim,
num_oov_buckets, preprocess_text):
"""Makes a module spec to simply perform token to embedding lookups.
Input of this module is a 1-D list of string tokens. For T tokens input and
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30,162 | tensorflow/hub | examples/text_embeddings/export.py | export | def export(export_path, vocabulary, embeddings, num_oov_buckets,
preprocess_text):
"""Exports a TF-Hub module that performs embedding lookups.
Args:
export_path: Location to export the module.
vocabulary: List of the N tokens in the vocabulary.
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preprocess_text):
"""Exports a TF-Hub module that performs embedding lookups.
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export_path: Location to export the module.
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30,163 | tensorflow/hub | examples/text_embeddings/export.py | maybe_append_oov_vectors | def maybe_append_oov_vectors(embeddings, num_oov_buckets):
"""Adds zero vectors for oov buckets if num_oov_buckets > 0.
Since we are assigning zero vectors, adding more that one oov bucket is only
meaningful if we perform fine-tuning.
Args:
embeddings: Embeddings to extend.
num_oov_buckets: Number of ... | python | def maybe_append_oov_vectors(embeddings, num_oov_buckets):
"""Adds zero vectors for oov buckets if num_oov_buckets > 0.
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30,164 | tensorflow/hub | tensorflow_hub/estimator.py | register_module_for_export | def register_module_for_export(module, export_name):
"""Register a Module to be exported under `export_name`.
This function registers `module` to be exported by `LatestModuleExporter`
under a subdirectory named `export_name`.
Note that `export_name` must be unique for each module exported from the
current ... | python | def register_module_for_export(module, export_name):
"""Register a Module to be exported under `export_name`.
This function registers `module` to be exported by `LatestModuleExporter`
under a subdirectory named `export_name`.
Note that `export_name` must be unique for each module exported from the
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30,165 | tensorflow/hub | tensorflow_hub/estimator.py | _make_estimator_serving_session | def _make_estimator_serving_session(estimator, serving_input_fn,
checkpoint_path):
"""Returns a session constructed using `estimator` and `serving_input_fn`.
The Estimator API does not provide an API to construct a graph and session,
making it necessary for this function to re... | python | def _make_estimator_serving_session(estimator, serving_input_fn,
checkpoint_path):
"""Returns a session constructed using `estimator` and `serving_input_fn`.
The Estimator API does not provide an API to construct a graph and session,
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30,166 | tensorflow/hub | tensorflow_hub/native_module.py | create_module_spec | def create_module_spec(module_fn, tags_and_args=None, drop_collections=None):
"""Creates a ModuleSpec from a function that builds the module's graph.
The `module_fn` is called on a new graph (not the current one) to build the
graph of the module and define its signatures via `hub.add_signature()`.
Example:
... | python | def create_module_spec(module_fn, tags_and_args=None, drop_collections=None):
"""Creates a ModuleSpec from a function that builds the module's graph.
The `module_fn` is called on a new graph (not the current one) to build the
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30,167 | tensorflow/hub | tensorflow_hub/native_module.py | add_signature | def add_signature(name=None, inputs=None, outputs=None):
"""Adds a signature to the module definition.
NOTE: This must be called within a `module_fn` that is defining a Module.
Args:
name: Signature name as a string. If omitted, it is interpreted as 'default'
and is the signature used when `Module.__c... | python | def add_signature(name=None, inputs=None, outputs=None):
"""Adds a signature to the module definition.
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Args:
name: Signature name as a string. If omitted, it is interpreted as 'default'
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30,168 | tensorflow/hub | tensorflow_hub/native_module.py | attach_message | def attach_message(key, message):
"""Adds an attached message to the module definition.
NOTE: This must be called within a `module_fn` that is defining a Module.
See ModuleSpec.get_attached_message() for an introduction to attached messages
and the API for module consumers.
To define a new type of attached... | python | def attach_message(key, message):
"""Adds an attached message to the module definition.
NOTE: This must be called within a `module_fn` that is defining a Module.
See ModuleSpec.get_attached_message() for an introduction to attached messages
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30,169 | tensorflow/hub | tensorflow_hub/native_module.py | list_registered_stateful_ops_without_inputs | def list_registered_stateful_ops_without_inputs():
"""Returns set of registered stateful ops that do not expect inputs.
This list is used to identify the ops to be included in the state-graph and
that are subsequently fed into the apply-graphs.
Returns:
A set of strings.
"""
return set([
name
... | python | def list_registered_stateful_ops_without_inputs():
"""Returns set of registered stateful ops that do not expect inputs.
This list is used to identify the ops to be included in the state-graph and
that are subsequently fed into the apply-graphs.
Returns:
A set of strings.
"""
return set([
name
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30,170 | tensorflow/hub | tensorflow_hub/native_module.py | get_state_map | def get_state_map(meta_graph, state_ops, unsupported_state_ops,
get_tensor_by_name):
"""Returns a map from tensor names to tensors that hold the state."""
state_map = {}
for node in meta_graph.graph_def.node:
if node.op in state_ops:
tensor_name = node.name + ":0"
tensor = get_te... | python | def get_state_map(meta_graph, state_ops, unsupported_state_ops,
get_tensor_by_name):
"""Returns a map from tensor names to tensors that hold the state."""
state_map = {}
for node in meta_graph.graph_def.node:
if node.op in state_ops:
tensor_name = node.name + ":0"
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30,171 | tensorflow/hub | tensorflow_hub/native_module.py | replace_apply_state | def replace_apply_state(meta_graph, state_ops, feed_map):
"""Replaces state ops with non state Placeholder ops for the apply graph."""
for node in meta_graph.graph_def.node:
keys_to_purge = []
tensor_name = node.name + ":0"
# Verify that the node is a state op and that its due to be rewired
# in the... | python | def replace_apply_state(meta_graph, state_ops, feed_map):
"""Replaces state ops with non state Placeholder ops for the apply graph."""
for node in meta_graph.graph_def.node:
keys_to_purge = []
tensor_name = node.name + ":0"
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30,172 | tensorflow/hub | tensorflow_hub/native_module.py | _extract_variable_parts | def _extract_variable_parts(variable_key, variable):
"""Matches a variable to individual parts.
Args:
variable_key: String identifier of the variable in the module scope.
variable: Variable tensor.
Returns:
partitioned: Whether the variable is partitioned.
name: Name of the variable up to the pa... | python | def _extract_variable_parts(variable_key, variable):
"""Matches a variable to individual parts.
Args:
variable_key: String identifier of the variable in the module scope.
variable: Variable tensor.
Returns:
partitioned: Whether the variable is partitioned.
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30,173 | tensorflow/hub | tensorflow_hub/native_module.py | recover_partitioned_variable_map | def recover_partitioned_variable_map(var_node_map):
"""Builds a proper variable map if it contains PartitionedVariables.
Args:
var_node_map: A map to tf.Variables. PartitionedVariables show up in this
map as N entries with keys "<var_name>/part_n".
Returns:
A map to tf.Variables or to list of tf.V... | python | def recover_partitioned_variable_map(var_node_map):
"""Builds a proper variable map if it contains PartitionedVariables.
Args:
var_node_map: A map to tf.Variables. PartitionedVariables show up in this
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30,174 | tensorflow/hub | tensorflow_hub/native_module.py | check_unique_tags | def check_unique_tags(tag_list):
"""Checks that tag list contains each set of tags only once."""
frozen_tags_seen = set()
for tags in tag_list:
frozen_tags = frozenset(tags)
if frozen_tags in frozen_tags_seen:
raise ValueError("Tags %r used repeatedly" % tags)
frozen_tags_seen.add(frozen_tags) | python | def check_unique_tags(tag_list):
"""Checks that tag list contains each set of tags only once."""
frozen_tags_seen = set()
for tags in tag_list:
frozen_tags = frozenset(tags)
if frozen_tags in frozen_tags_seen:
raise ValueError("Tags %r used repeatedly" % tags)
frozen_tags_seen.add(frozen_tags) | [
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30,175 | tensorflow/hub | tensorflow_hub/native_module.py | check_collections_are_supported | def check_collections_are_supported(saved_model_handler, supported):
"""Checks that SavedModelHandler only uses supported collections."""
for meta_graph in saved_model_handler.meta_graphs:
used_collection_keys = set(meta_graph.collection_def.keys())
unsupported = used_collection_keys - supported
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"""Checks that SavedModelHandler only uses supported collections."""
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used_collection_keys = set(meta_graph.collection_def.keys())
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30,176 | tensorflow/hub | tensorflow_hub/native_module.py | register_ops_if_needed | def register_ops_if_needed(graph_ops):
"""Register graph ops absent in op_def_registry, if present in c++ registry.
Args:
graph_ops: set with graph op names to register.
Raises:
RuntimeError: if `graph_ops` contains ops that are not in either python or
c++ registry.
"""
missing_ops = graph_ops... | python | def register_ops_if_needed(graph_ops):
"""Register graph ops absent in op_def_registry, if present in c++ registry.
Args:
graph_ops: set with graph op names to register.
Raises:
RuntimeError: if `graph_ops` contains ops that are not in either python or
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"""
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30,177 | tensorflow/hub | tensorflow_hub/native_module.py | fix_colocation_after_import | def fix_colocation_after_import(input_map, absolute_import_scope):
"""Fixes colocation attributes after import according to input_map.
This function is meant to be called after importing a GraphDef, in order
to rewrite colocate_with constrains analogous to how inputs to ops
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30,178 | tensorflow/hub | tensorflow_hub/native_module.py | _build_colocation_attr_map | def _build_colocation_attr_map(input_map, absolute_import_scope):
"""Returns a dict mapping from pre-import to post-import colocation attrs.
Args:
input_map: as for fix_colocation_after_import.
absolute_import_scope: as for fix_colocation_after_import.
Returns:
A dict that maps bytes `"loc:@" + abso... | python | def _build_colocation_attr_map(input_map, absolute_import_scope):
"""Returns a dict mapping from pre-import to post-import colocation attrs.
Args:
input_map: as for fix_colocation_after_import.
absolute_import_scope: as for fix_colocation_after_import.
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30,179 | tensorflow/hub | tensorflow_hub/native_module.py | _apply_colocation_attr_map | def _apply_colocation_attr_map(colocation_attr_map, absolute_import_scope):
"""Rewrites colocation constraints in the current default graph.
Nodes in `absolute_import_scope` get their "_class" attr lists rewritten
according to `colocation_attr_map`: each entry that matches a key gets
replaced by the associated... | python | def _apply_colocation_attr_map(colocation_attr_map, absolute_import_scope):
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30,180 | tensorflow/hub | tensorflow_hub/native_module.py | find_state_op_colocation_error | def find_state_op_colocation_error(graph, reported_tags=None):
"""Returns error message for colocation of state ops, or None if ok."""
state_op_types = list_registered_stateful_ops_without_inputs()
state_op_map = {op.name: op for op in graph.get_operations()
if op.type in state_op_types}
for o... | python | def find_state_op_colocation_error(graph, reported_tags=None):
"""Returns error message for colocation of state ops, or None if ok."""
state_op_types = list_registered_stateful_ops_without_inputs()
state_op_map = {op.name: op for op in graph.get_operations()
if op.type in state_op_types}
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30,181 | tensorflow/hub | tensorflow_hub/native_module.py | find_signature_input_colocation_error | def find_signature_input_colocation_error(signature_name, inputs):
"""Returns error message for colocation of signature inputs, or None if ok."""
for input_name, tensor in inputs.items():
expected_colocation_groups = [tf.compat.as_bytes("loc:@" + tensor.op.name)]
if tensor.op.colocation_groups() != expected... | python | def find_signature_input_colocation_error(signature_name, inputs):
"""Returns error message for colocation of signature inputs, or None if ok."""
for input_name, tensor in inputs.items():
expected_colocation_groups = [tf.compat.as_bytes("loc:@" + tensor.op.name)]
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30,182 | tensorflow/hub | tensorflow_hub/native_module.py | find_signature_inputs_from_multivalued_ops | def find_signature_inputs_from_multivalued_ops(inputs):
"""Returns error message for module inputs from ops with multiple outputs."""
dense_inputs = [] # List of (str, Tensor), with SparseTensors decomposed.
for name, tensor in sorted(inputs.items()):
if isinstance(tensor, tf.SparseTensor):
dense_input... | python | def find_signature_inputs_from_multivalued_ops(inputs):
"""Returns error message for module inputs from ops with multiple outputs."""
dense_inputs = [] # List of (str, Tensor), with SparseTensors decomposed.
for name, tensor in sorted(inputs.items()):
if isinstance(tensor, tf.SparseTensor):
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30,183 | tensorflow/hub | tensorflow_hub/native_module.py | _ModuleImpl._create_state_graph | def _create_state_graph(self, name):
"""Creates the graph nodes that hold the state of the Module.
Args:
name: name scope to create the state graph in.
Returns:
A tuple consisting of:
variables_tensor_map: a map from tensor names in the original graph def
to the created Varia... | python | def _create_state_graph(self, name):
"""Creates the graph nodes that hold the state of the Module.
Args:
name: name scope to create the state graph in.
Returns:
A tuple consisting of:
variables_tensor_map: a map from tensor names in the original graph def
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30,184 | tensorflow/hub | tensorflow_hub/native_module.py | _ModuleImpl.create_apply_graph | def create_apply_graph(self, signature, input_tensors, name):
"""See `ModuleImpl.create_apply_graph`."""
signature_def = self._meta_graph.signature_def.get(signature)
meta_graph = meta_graph_pb2.MetaGraphDef()
meta_graph.CopyFrom(self._meta_graph)
apply_graph = tf_v1.get_default_graph()
infeed_m... | python | def create_apply_graph(self, signature, input_tensors, name):
"""See `ModuleImpl.create_apply_graph`."""
signature_def = self._meta_graph.signature_def.get(signature)
meta_graph = meta_graph_pb2.MetaGraphDef()
meta_graph.CopyFrom(self._meta_graph)
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30,185 | tensorflow/hub | tensorflow_hub/native_module.py | _ModuleImpl.export | def export(self, path, session):
"""See `Module.export`."""
def variables_saver(variables_path):
if self._saver:
self._saver.save(
session, variables_path,
write_meta_graph=False,
write_state=False)
self._spec._export(path, variables_saver) | python | def export(self, path, session):
"""See `Module.export`."""
def variables_saver(variables_path):
if self._saver:
self._saver.save(
session, variables_path,
write_meta_graph=False,
write_state=False)
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30,186 | tensorflow/hub | tensorflow_hub/native_module.py | _ConsistentValue.Set | def Set(self, value, context=None):
"""Receives a value for the object and some context on its source."""
if self.has_error: return
if self.value is None:
self.value = value
self._context["old_value"] = value
self._context.update({"old_" + k: v for k, v in context.items()})
elif self.v... | python | def Set(self, value, context=None):
"""Receives a value for the object and some context on its source."""
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self.value = value
self._context["old_value"] = value
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30,187 | tensorflow/hub | tensorflow_hub/native_module.py | _ConsistentValue.GetConsistentValueOrRaise | def GetConsistentValueOrRaise(self, error_format, context=None):
"""Gets consistent value or raises ValueError with formatted contexts."""
if self.has_error:
full_context = dict(self._context)
if context: full_context.update(context)
raise ValueError(error_format.format(**full_context))
re... | python | def GetConsistentValueOrRaise(self, error_format, context=None):
"""Gets consistent value or raises ValueError with formatted contexts."""
if self.has_error:
full_context = dict(self._context)
if context: full_context.update(context)
raise ValueError(error_format.format(**full_context))
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30,188 | tensorflow/hub | tensorflow_hub/compressed_module_resolver.py | _module_dir | def _module_dir(handle):
"""Returns the directory where to cache the module."""
cache_dir = resolver.tfhub_cache_dir(use_temp=True)
return resolver.create_local_module_dir(
cache_dir,
hashlib.sha1(handle.encode("utf8")).hexdigest()) | python | def _module_dir(handle):
"""Returns the directory where to cache the module."""
cache_dir = resolver.tfhub_cache_dir(use_temp=True)
return resolver.create_local_module_dir(
cache_dir,
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30,189 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | get_variables_path | def get_variables_path(export_dir):
"""Returns the path for storing variables checkpoints."""
return os.path.join(
tf.compat.as_bytes(export_dir),
tf.compat.as_bytes(tf_v1.saved_model.constants.VARIABLES_DIRECTORY),
tf.compat.as_bytes(tf_v1.saved_model.constants.VARIABLES_FILENAME)) | python | def get_variables_path(export_dir):
"""Returns the path for storing variables checkpoints."""
return os.path.join(
tf.compat.as_bytes(export_dir),
tf.compat.as_bytes(tf_v1.saved_model.constants.VARIABLES_DIRECTORY),
tf.compat.as_bytes(tf_v1.saved_model.constants.VARIABLES_FILENAME)) | [
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30,190 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | add_signature | def add_signature(key, inputs, outputs):
"""Adds a signature to current graph.
Args:
key: Signature key as a string.
inputs: Signature inputs as a map from string to Tensor or SparseTensor.
outputs: Signature outputs as a map from string to Tensor or SparseTensor.
(Recall that a Variable is not a... | python | def add_signature(key, inputs, outputs):
"""Adds a signature to current graph.
Args:
key: Signature key as a string.
inputs: Signature inputs as a map from string to Tensor or SparseTensor.
outputs: Signature outputs as a map from string to Tensor or SparseTensor.
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30,191 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | _export_signatures | def _export_signatures(meta_graph):
"""Exports signatures from current graph into a MetaGraphDef."""
named_signatures = tf_v1.get_collection(_SIGNATURE_COLLECTION)
if not named_signatures:
raise ValueError("No signatures present. Please call hub.add_signature(...)"
"at least once in the m... | python | def _export_signatures(meta_graph):
"""Exports signatures from current graph into a MetaGraphDef."""
named_signatures = tf_v1.get_collection(_SIGNATURE_COLLECTION)
if not named_signatures:
raise ValueError("No signatures present. Please call hub.add_signature(...)"
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30,192 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | attach_bytes | def attach_bytes(key, the_bytes):
"""Adds a ModuleAttachment to the current graph.
Args:
key: A string with the unique key of the attachment.
the_bytes: A bytes object with the serialized attachment.
"""
tf_v1.add_to_collection(
_ATTACHMENT_COLLECTION_INTERNAL,
module_attachment_pb2.ModuleA... | python | def attach_bytes(key, the_bytes):
"""Adds a ModuleAttachment to the current graph.
Args:
key: A string with the unique key of the attachment.
the_bytes: A bytes object with the serialized attachment.
"""
tf_v1.add_to_collection(
_ATTACHMENT_COLLECTION_INTERNAL,
module_attachment_pb2.ModuleA... | [
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30,193 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | _export_module_attachments | def _export_module_attachments(meta_graph):
"""Exports ModuleAttachments from the current tf.Graph into `meta_graph`."""
added_attachments = tf_v1.get_collection(_ATTACHMENT_COLLECTION_INTERNAL)
if not added_attachments: return # Don't touch `meta_graph`.
unique_attachments = collections.OrderedDict( # Avoid ... | python | def _export_module_attachments(meta_graph):
"""Exports ModuleAttachments from the current tf.Graph into `meta_graph`."""
added_attachments = tf_v1.get_collection(_ATTACHMENT_COLLECTION_INTERNAL)
if not added_attachments: return # Don't touch `meta_graph`.
unique_attachments = collections.OrderedDict( # Avoid ... | [
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30,194 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | get_attached_bytes_map | def get_attached_bytes_map(meta_graph):
"""Returns the dict of ModuleAttachments stored in `meta_graph`.
Args:
meta_graph: A MetaGraphDef, as built by SavedModelHandler.add_graph_copy()
from some graph.
Returns:
A dict, containing the `(key, bytes)` items passed to `attach_bytes()`
when the gr... | python | def get_attached_bytes_map(meta_graph):
"""Returns the dict of ModuleAttachments stored in `meta_graph`.
Args:
meta_graph: A MetaGraphDef, as built by SavedModelHandler.add_graph_copy()
from some graph.
Returns:
A dict, containing the `(key, bytes)` items passed to `attach_bytes()`
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30,195 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | _check_asset_node_def | def _check_asset_node_def(node_def):
"""Raises TypeError if `node_def` does not match the expectations."""
if node_def.op != "Const":
raise TypeError("Asset node must be of type constant.")
if tf.as_dtype(node_def.attr["dtype"].type) != tf.string:
raise TypeError("Asset node must be of dtype string.")
i... | python | def _check_asset_node_def(node_def):
"""Raises TypeError if `node_def` does not match the expectations."""
if node_def.op != "Const":
raise TypeError("Asset node must be of type constant.")
if tf.as_dtype(node_def.attr["dtype"].type) != tf.string:
raise TypeError("Asset node must be of dtype string.")
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30,196 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | _merge_assets_key_collection | def _merge_assets_key_collection(saved_model_proto, path):
"""Merges the ASSETS_KEY collection into the GraphDefs in saved_model_proto.
Removes the ASSETS_KEY collection from the GraphDefs in the SavedModel and
modifies nodes with the assets filenames to point to the assets in `path`.
After this transformation... | python | def _merge_assets_key_collection(saved_model_proto, path):
"""Merges the ASSETS_KEY collection into the GraphDefs in saved_model_proto.
Removes the ASSETS_KEY collection from the GraphDefs in the SavedModel and
modifies nodes with the assets filenames to point to the assets in `path`.
After this transformation... | [
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Removes the ASSETS_KEY collection from the GraphDefs in the SavedModel and
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After this transformation, the SavedModel GraphDefs can be used without
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30,197 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | _make_assets_key_collection | def _make_assets_key_collection(saved_model_proto, export_path):
"""Creates an ASSETS_KEY collection in the GraphDefs in saved_model_proto.
Adds an ASSETS_KEY collection to the GraphDefs in the SavedModel and returns
a map from original asset filename to filename when exporting the SavedModel
to `export_path`.... | python | def _make_assets_key_collection(saved_model_proto, export_path):
"""Creates an ASSETS_KEY collection in the GraphDefs in saved_model_proto.
Adds an ASSETS_KEY collection to the GraphDefs in the SavedModel and returns
a map from original asset filename to filename when exporting the SavedModel
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30,198 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | _parse_saved_model | def _parse_saved_model(path):
"""Reads the savedmodel.pb file containing `SavedModel`."""
# Based on tensorflow/python/saved_model/loader.py implementation.
path_to_pb = _get_saved_model_proto_path(path)
file_content = tf_v1.gfile.Open(path_to_pb, "rb").read()
saved_model = saved_model_pb2.SavedModel()
try:... | python | def _parse_saved_model(path):
"""Reads the savedmodel.pb file containing `SavedModel`."""
# Based on tensorflow/python/saved_model/loader.py implementation.
path_to_pb = _get_saved_model_proto_path(path)
file_content = tf_v1.gfile.Open(path_to_pb, "rb").read()
saved_model = saved_model_pb2.SavedModel()
try:... | [
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30,199 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | load | def load(path):
"""Creates a SavedModelHandler from a SavedModel in `path`."""
proto = _parse_saved_model(path)
_merge_assets_key_collection(proto, path)
handler = SavedModelHandler()
handler._proto = proto # pylint: disable=protected-access
return handler | python | def load(path):
"""Creates a SavedModelHandler from a SavedModel in `path`."""
proto = _parse_saved_model(path)
_merge_assets_key_collection(proto, path)
handler = SavedModelHandler()
handler._proto = proto # pylint: disable=protected-access
return handler | [
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