PatrickHaller/the-stack-python-1M · Datasets at Hugging Face

142c57102f42f9d4e92a394fc2208b8994abf6b8

155

py

Python

2020/inner_functions.py

srikanteswartalluri/pyutils

bf8d56ac9e9b0786861c08ef32eae49b021f20a3

[ "0BSD" ]

null

2020/inner_functions.py

srikanteswartalluri/pyutils

bf8d56ac9e9b0786861c08ef32eae49b021f20a3

[ "0BSD" ]

null

2020/inner_functions.py

srikanteswartalluri/pyutils

bf8d56ac9e9b0786861c08ef32eae49b021f20a3

[ "0BSD" ]

null

def outer_util(l): print(l) def stay_tuned(): print("I am inside inner") stay_tuned() print("I am back to outer") outer_util([1, 3, 4])

17.222222

32

0.606452

29120306298a7f9571aa5d053f13e1ce72ca2ac2

2,623

py

Python

helper/markov_equiprob.py

cphysics/simulation

6fc2056c77a021105a6851809e2bacdcc0148ba3

[ "MIT" ]

3

2020-12-02T19:34:02.000Z

2022-03-17T03:12:07.000Z

helper/markov_equiprob.py

cphysics/simulation

6fc2056c77a021105a6851809e2bacdcc0148ba3

[ "MIT" ]

null

helper/markov_equiprob.py

cphysics/simulation

6fc2056c77a021105a6851809e2bacdcc0148ba3

[ "MIT" ]

17

2020-09-17T12:32:46.000Z

2021-11-06T03:25:15.000Z

import random def markov_disks_box(L, delta, sigma): condition = True while condition == True: a = random.choice(L) b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)] min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a) box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma if not (box_cond or min_dist < 4.0 * sigma ** 2): a[:] = b condition = False break return L #inputs of the markov_disks_box function: #initial positions of the disks to startup the Markov-chain L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]] delta = 0.1 sigma = 0.15 #radius n_steps = 1000000 del_xy = 0.05 #"uncertainty" #Define the "marked" configurations: conf_a = ((0.30, 0.30), (0.30, 0.70), (0.70, 0.30), (0.70,0.70)) conf_b = ((0.20, 0.20), (0.20, 0.80), (0.75, 0.25), (0.75,0.75)) conf_c = ((0.30, 0.20), (0.30, 0.80), (0.70, 0.20), (0.70,0.70)) configurations = [conf_a, conf_b, conf_c] #list the configurations hits = {conf_a: 0, conf_b: 0, conf_c: 0} #initialise the number of times each marked configuration occurs for run in range(n_steps): x_vec = direct_disks_box(4, sigma) #generates a random sample by direct sampling for conf in configurations: #run a loop iterating over the given 3 configurations #condition that a randomly generated configuration L is the same as a, b or c up to uncertainty of del_xy condition_hit = True for b in conf: #run a loop iterating over the 4 disk coordinates in a specific configuration #If the max(x distance and y distance between a disk in L and a disk in conf_a,b,c) #is less than the given uncertainty del_xy, then we treat the two disks as in the same location. #Note that the "any two disks" condition is realised by minimising over all 4 disks in a #randomly sampled configuration L. condition_b = min(max(abs(a[0] - b[0]), abs(a[1] - b[1])) for a in x_vec) < del_xy #The following logical variable is 1 only if there exists 4 disk pairs are within del_xy range. #If at least any one of the disks does not have a pair within del_xy, then it is 0. condition_hit *= condition_b #multiplies condition_b's (for all 4 disks) #If the current L and a, b or c are the same up to uncertainty del_xy, then increase: if condition_hit: hits[conf] += 1 for conf in configurations: print conf, hits[conf] #Print the configurations and the number of times they occured.

50.442308

109

0.636294

3142651428d6f3dd3ae78cd1d5976777efd6ce0e

1,680

py

Python

argus/migrations/0005_auto_20170609_2353.py

dehu4ka/lna

f5ee176bdb5c7507b76fba5ae651ce333b71c3db

[ "MIT" ]

null

argus/migrations/0005_auto_20170609_2353.py

dehu4ka/lna

f5ee176bdb5c7507b76fba5ae651ce333b71c3db

[ "MIT" ]

null

argus/migrations/0005_auto_20170609_2353.py

dehu4ka/lna

f5ee176bdb5c7507b76fba5ae651ce333b71c3db

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2017-06-09 18:53 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('argus', '0004_auto_20170609_2345'), ] operations = [ migrations.AlterField( model_name='argusadsl', name='address', field=models.CharField(default='N/A', max_length=512), ), migrations.AlterField( model_name='argusadsl', name='city', field=models.CharField(default='N/A', max_length=512), ), migrations.AlterField( model_name='argusadsl', name='fio', field=models.CharField(default='N/A', max_length=512), ), migrations.AlterField( model_name='argusadsl', name='hostname', field=models.CharField(default='None', max_length=512), ), migrations.AlterField( model_name='argusadsl', name='inet_login', field=models.CharField(db_index=True, max_length=512), ), migrations.AlterField( model_name='argusadsl', name='iptv_login', field=models.CharField(db_index=True, max_length=512), ), migrations.AlterField( model_name='argusadsl', name='room', field=models.CharField(default='N/A', max_length=512), ), migrations.AlterField( model_name='argusadsl', name='xdsl_slot', field=models.CharField(default='N/A', max_length=512), ), ]

30

67

0.560119

ff40206d99347bd096dcc0e0a0c784795cd7422a

26,320

py

Python

databrowse/support/renderer_support.py

limatix/Databrowse

af33bc6cca930e59acc3762beeec2409d8fd8634

[ "BSD-3-Clause" ]

3

2016-09-20T07:04:09.000Z

2018-07-17T17:31:21.000Z

databrowse/support/renderer_support.py

limatix/Databrowse

af33bc6cca930e59acc3762beeec2409d8fd8634

[ "BSD-3-Clause" ]

19

2016-10-25T07:05:28.000Z

2018-08-07T23:18:16.000Z

databrowse/support/renderer_support.py

limatix/Databrowse

af33bc6cca930e59acc3762beeec2409d8fd8634

[ "BSD-3-Clause" ]

2

2016-10-28T00:12:42.000Z

2016-10-28T00:18:03.000Z

#!/usr/bin/env python ############################################################################### ## Databrowse: An Extensible Data Management Platform ## ## Copyright (C) 2012-2016 Iowa State University Research Foundation, Inc. ## ## All rights reserved. ## ## ## ## Redistribution and use in source and binary forms, with or without ## ## modification, are permitted provided that the following conditions are ## ## met: ## ## 1. Redistributions of source code must retain the above copyright ## ## notice, this list of conditions and the following disclaimer. ## ## 2. Redistributions in binary form must reproduce the above copyright ## ## notice, this list of conditions and the following disclaimer in the ## ## documentation and/or other materials provided with the distribution. ## ## 3. Neither the name of the copyright holder nor the names of its ## ## contributors may be used to endorse or promote products derived from ## ## this software without specific prior written permission. ## ## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ## ## "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED ## ## TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A ## ## PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER ## ## OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, ## ## EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, ## ## PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR ## ## PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF ## ## LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING ## ## NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS ## ## SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ## ## ## ## This material is based on work supported by the Air Force Research ## ## Laboratory under Contract #FA8650-10-D-5210, Task Order #023 and ## ## performed at Iowa State University. ## ## ## ## DISTRIBUTION A. Approved for public release: distribution unlimited; ## ## 19 Aug 2016; 88ABW-2016-4051. ## ############################################################################### """ support/renderer_support.py - Encapsulation Class for Renderer Plugins """ from lxml import etree from errno import EEXIST from stat import * import sys import os import string import random import copy import fnmatch class renderer_class(object): """ Renderer Plugin Support - Encapsulation Class for Renderer Plugins """ _relpath = None _fullpath = None _web_support = None _handler_support = None _caller = None _handlers = None _content_mode = None _style_mode = None _dynamic_style = None _default_content_mode = "title" _default_style_mode = "list" _default_recursion_depth = 2 _disable_load_style = False nsmap = {} class RendererException(Exception): pass def __init__(self, relpath, fullpath, web_support, handler_support, caller, handlers, content_mode=None, style_mode=None, recursion_depth=None): """ Default Initialization Function """ # Set all of our argument variables #print "In RendererException.__init__".center(100, '=') #print "Setting Class Variables" self._relpath = relpath self._fullpath = fullpath self._web_support = web_support self._handler_support = handler_support self._caller = caller self._handlers = handlers if content_mode is None: self._content_mode = self._default_content_mode pass else: self._content_mode = content_mode pass if style_mode is None: self._style_mode = self._default_style_mode pass else: self._style_mode = style_mode pass if recursion_depth is None: self._recursion_depth = self._default_recursion_depth pass else: self._recursion_depth = recursion_depth pass #print "Class Variables Set - Here's a Summary" #print "self._relpath = " + repr(self._relpath) #print "self._fullpath = " + repr(self._fullpath) #print "self._web_support = " + repr(self._web_support) #print "self._web_support.req.filename = " + repr(self._web_support.req.filename) #print "self._web_support.req.dirname = " + repr(self._web_support.req.dirname) #print "self._web_support.req.unparsed_uri = " + repr(self._web_support.req.unparsed_uri) #print "self._handler_support = " + repr(self._handler_support) #print "self._caller = " + repr(self._caller) #print "self._content_mode = " + repr(self._content_mode) #print "self._style_mode = " + repr(self._style_mode) #print "self._recursion_depth = " + repr(self._recursion_depth) self.nsmap = {} self.nsmap['db'] = 'http://thermal.cnde.iastate.edu/databrowse' # Try to Load Style if not self._disable_load_style: #print "About to call self.loadStyle()" self.loadStyle() #print "About to call etree.register_namespace" self.nsmap[self._namespace_local] = self._namespace_uri #etree.register_namespace(self._namespace_local, self._namespace_uri) pass pass def getContent(self): ''' Default getContent ''' return None def getSize(self, fullpath=None): """ Get Size of A File - Returns size of current file if none specified """ if fullpath is None: fullpath = self._fullpath pass st = os.stat(fullpath) return st[ST_SIZE] def getUserFriendlySize(self, fullpath=None, mode="alternative", custom=None): return self.ConvertUserFriendlySize(self.getSize(fullpath), mode, custom) def ConvertUserFriendlySize(self, bytes, mode="alternative", custom=None, rounding=None): """Human-readable file size. """ if custom is not None: formatstrings = custom pass elif mode == "traditional": formatstrings = [ (1024 ** 5, 'P'), (1024 ** 4, 'T'), (1024 ** 3, 'G'), (1024 ** 2, 'M'), (1024 ** 1, 'K'), (1024 ** 0, 'B'), ] elif mode == "alternative": formatstrings = [ (1024 ** 5, ' PB'), (1024 ** 4, ' TB'), (1024 ** 3, ' GB'), (1024 ** 2, ' MB'), (1024 ** 1, ' KB'), (1024 ** 0, (' byte', ' bytes')), ] elif mode == "bitrate": formatstrings = [ (1024 ** 5, ' Pbps'), (1024 ** 4, ' Tbps'), (1024 ** 3, ' Gbps'), (1024 ** 2, ' Mbps'), (1024 ** 1, ' Kbps'), (1024 ** 0, ' bps'), ] elif mode == "frequency": formatstrings = [ (1000 ** 5, ' PHz'), (1000 ** 4, ' THz'), (1000 ** 3, ' GHz'), (1000 ** 2, ' MHz'), (1000 ** 1, ' KHz'), (1000 ** 0, ' Hz'), ] elif mode == "time": formatstrings = [ (4 ** 4, (' week', ' weeks')), (7 ** 3, (' day', ' days')), (24 ** 2, (' hr', ' hrs')), (60 ** 1, ' min'), (60 ** 0, ' sec'), ] elif mode == "verbose": formatstrings = [ (1024 ** 5, (' petabyte', ' petabytes')), (1024 ** 4, (' terabyte', ' terabytes')), (1024 ** 3, (' gigabyte', ' gigabytes')), (1024 ** 2, (' megabyte', ' megabytes')), (1024 ** 1, (' kilobyte', ' kilobytes')), (1024 ** 0, (' byte', ' bytes')), ] elif mode == "iec": formatstrings = [ (1024 ** 5, 'Pi'), (1024 ** 4, 'Ti'), (1024 ** 3, 'Gi'), (1024 ** 2, 'Mi'), (1024 ** 1, 'Ki'), (1024 ** 0, ''), ] elif mode == "si": formatstrings = [ (1000 ** 5, 'P'), (1000 ** 4, 'T'), (1000 ** 3, 'G'), (1000 ** 2, 'M'), (1000 ** 1, 'K'), (1000 ** 0, 'B'), ] else: formatstrings = [ (1024 ** 5, ' PB'), (1024 ** 4, ' TB'), (1024 ** 3, ' GB'), (1024 ** 2, ' MB'), (1024 ** 1, ' KB'), (1024 ** 0, (' byte', ' bytes')), ] for factor, suffix in formatstrings: if bytes >= factor: break amount = float(bytes/factor) if isinstance(suffix, tuple): singular, multiple = suffix if amount == 1: suffix = singular else: suffix = multiple if rounding is not None: amount = round(amount, rounding) return str(amount) + suffix def ConvertUserFriendlyPermissions(self, p): ts = { 0o0140000: 'ssocket', 0o0120000: 'llink', 0o0100000: '-file', 0o0060000: 'bblock', 0o0040000: 'ddir', 0o0020000: 'cchar', 0o0010000: 'pfifo' } t = p & 0o0170000 permstr = ts[t][0] if t in ts else 'u' permstr += 'r' if p & 0x0100 else '-' permstr += 'w' if p & 0x0080 else '-' permstr += 's' if p & 0x0800 else 'x' if p & 0x0040 else 'S' if p & 0x0800 else '-' permstr += 'r' if p & 0x0020 else '-' permstr += 'w' if p & 0x0010 else '-' permstr += 's' if p & 0x0400 else 'x' if p & 0x0008 else 'S' if p & 0x0400 else '-' permstr += 'r' if p & 0x0004 else '-' permstr += 'w' if p & 0x0002 else '-' permstr += 's' if p & 0x0200 else 'x' if p & 0x0001 else 'S' if p & 0x0200 else '-' return permstr def isRaw(self): #print "isRaw being called" if self._content_mode == "raw": return True else: return False def isGit(self): if self._web_support.req.agent.startswith("git"): return True else: return False def getStyleMode(self): #print "getStyleMode being called" return self._style_mode def getContentMode(self): #print "getContentMode being called" return self._content_mode def getURL(self, relpath, **kwargs): """ Return Full URL to a Relative Path """ #print "getURL being called" # We need to tack in handler if handler is overridden if self._handlers[-1] != self.__class__.__name__ and "handler" not in kwargs: kwargs["handler"] = self.__class__.__name__ pass elif "handler" in kwargs and kwargs["handler"] is None: del kwargs["handler"] pass # Add flag for hidden files if needed if "showhiddenfiles" in self._web_support.req.form and "showhiddenfiles" not in kwargs: kwargs["showhiddenfiles"] = "" elif "showhiddenfiles" in kwargs and kwargs["showhiddenfiles"] is None: del kwargs["showhiddenfiles"] # Build the URL if self._web_support.seo_urls is True: url = self._web_support.siteurl + relpath if len(kwargs) > 0: url = url + '?' z = 1 pass for i in kwargs: if z == 1: url = url + i + '=' + str(kwargs[i]) z = 2 pass else: url = url + '&' + i + '=' + str(kwargs[i]) pass pass pass else: url = self._web_support.siteurl + '/?path=' + relpath for i in kwargs: url = url + '&' + i + '=' + str(kwargs[i]) pass pass return url def getURLToParent(self, relpath, **kwargs): #print "getURLToParent being called" if relpath == "/": return self.getURL(relpath, **kwargs) pass else: relpath = os.path.normpath(relpath + '/../') return self.getURL(relpath, **kwargs) pass pass def getDirectoryList(self, fullpath, sort=None, order="asc"): """ Build a Sorted List of Files with Appropriate Files Removed """ #print "getDirectoryList being called" (hiddenlist, shownlist) = self._handler_support.GetHiddenFileList() reallist = os.listdir(fullpath) if "showhiddenfiles" in self._web_support.req.form: returnlist = reallist else: removelist = copy.copy(reallist) for item in hiddenlist: removelist = [n for n in removelist if not fnmatch.fnmatch(n, item[1])] pass addlist = [] for item in shownlist: addlist = [n for n in reallist if fnmatch.fnmatch(n, item[1])] pass returnlist = list(set(removelist + addlist)) exec("returnlist.sort(%s%s)" % ("reverse=True" if order == "desc" else "reverse=False", ",key=%s" % sort if sort is not None else ",key=str.lower")) returndirlist = [f for f in returnlist if os.path.isdir(os.path.join(fullpath, f))] returnfilelist = [f for f in returnlist if os.path.isfile(os.path.join(fullpath, f))] returnlist = returndirlist returnlist.extend(returnfilelist) return returnlist pass class CacheFileHandler(file): """ Overrride File Close Class to Reassign Timestamp """ timestamp = None def __init__(self, filename, mode='r', timestamp=None): self.timestamp = timestamp super(renderer_class.CacheFileHandler, self).__init__(filename, mode) def close(self): super(renderer_class.CacheFileHandler, self).close() if self.mode not in ['r', 'rb'] and self.timestamp is not None: st = os.stat(self.name) atime = st[ST_ATIME] os.utime(self.name, (atime, self.timestamp)) else: pass pass def getCacheFileHandler(self, mode='r', tag=None, extension=None): """ Return File Handler For Cache File """ filename = self.getCacheFileName(tag, extension) st = os.stat(self._fullpath) timestamp = st[ST_MTIME] if mode not in ['r', 'rb']: self.PrepareCacheDir() if not os.access(filename, os.W_OK) and os.path.exists(filename): raise self.RendererException("Unable to Open Cache File for Writing: " + filename) else: if not os.access(filename, os.R_OK): raise self.RendererException("Unable to Open Cache File for Reading: " + filename) return self.CacheFileHandler(filename, mode, timestamp) def PrepareCacheDir(self): cachedirname = self.getCacheDirName() if not os.path.exists(cachedirname): try: os.makedirs(cachedirname) except OSError as err: if err.errno == EEXIST: # Handle the Race Condition pass else: raise pass def CacheFileExists(self, tag=None, extension=None): """ Return Boolean after Verifying the Existance of a Cache File """ if "ignorecache" in self._web_support.req.form: return False filename = self.getCacheFileName(tag, extension) if os.access(filename, os.R_OK) and os.path.exists(filename): basestat = os.stat(self._fullpath) cachestat = os.stat(filename) if basestat[ST_MTIME] > cachestat[ST_MTIME]: return False else: return True else: return False def getCacheDirName(self): return os.path.abspath(os.path.dirname(self._fullpath) + "/.databrowse/cache/" + self.__class__.__name__ + "/") def getCacheFileName(self, tag=None, extension=None): """ Get the Name of a Cache File Given a Tag and Extension """ basefilename = os.path.splitext(os.path.basename(self._fullpath)) basedirname = self.getCacheDirName() filename = basefilename[0] if tag is not None: filename = filename + "_" + tag if extension is not None: filename = filename + "." + extension else: filename = filename + basefilename[1] return os.path.join(basedirname, filename) def loadMenu(self): """ Load Menu Items for all current handlers """ newmenu = etree.Element('{http://thermal.cnde.iastate.edu/databrowse}navbar') isDirectory = os.path.isdir(self._fullpath) for handler in reversed(self._handlers): dirlist = [os.path.splitext(item)[0][4:] for item in os.listdir(os.path.abspath(os.path.dirname(sys.modules['databrowse.plugins.' + handler].__file__) + '/')) if item.lower().startswith("dbs_")] additionalitems = [] if isDirectory: if os.path.exists(os.path.join(self._fullpath, '.databrowse', 'stylesheets', handler)): additionalitems = [os.path.splitext(item)[0][4:] for item in os.listdir(os.path.join(self._fullpath, '.databrowse', 'stylesheets', handler)) if item.lower().startswith("dbs_")] else: if os.path.exists(os.path.join(os.path.dirname(self._fullpath), '.databrowse', 'stylesheets', handler)): additionalitems = [os.path.splitext(item)[0][4:] for item in os.listdir(os.path.join(os.path.dirname(self._fullpath), '.databrowse', 'stylesheets', handler)) if item.lower().startswith("dbs_")] dirlist = dirlist + additionalitems navelem = etree.SubElement(newmenu, "{http://thermal.cnde.iastate.edu/databrowse}navelem") title = etree.SubElement(navelem, "{http://www.w3.org/1999/xhtml}a") title.text = " ".join([i[0].title()+i[1:] for i in handler[3:].split("_")]) navitems = etree.SubElement(navelem, "{http://thermal.cnde.iastate.edu/databrowse}navdir", alwaysopen="true") for item in dirlist: if item not in self._handler_support.hiddenstylesheets: if not isDirectory and item not in self._handler_support.directorystylesheets: link = self.getURL(self._relpath, handler=handler, style_mode=item) if self._style_mode == item and self.__class__.__name__ == handler: itemelem = etree.SubElement(navitems, "{http://thermal.cnde.iastate.edu/databrowse}navelem", selected="true") else: itemelem = etree.SubElement(navitems, "{http://thermal.cnde.iastate.edu/databrowse}navelem") menuitem = etree.SubElement(itemelem, "{http://www.w3.org/1999/xhtml}a", href=link) menuitem.text = " ".join([i[0].title()+i[1:] for i in item.split("_")]) pass elif isDirectory: link = self.getURL(self._relpath, handler=handler, style_mode=item) if self._style_mode == item and self.__class__.__name__ == handler: itemelem = etree.SubElement(navitems, "{http://thermal.cnde.iastate.edu/databrowse}navelem", selected="true") else: itemelem = etree.SubElement(navitems, "{http://thermal.cnde.iastate.edu/databrowse}navelem") menuitem = etree.SubElement(itemelem, "{http://www.w3.org/1999/xhtml}a", href=link) menuitem.text = " ".join([i[0].title()+i[1:] for i in item.split("_")]) pass else: pass pass self._web_support.menu.AddMenu(newmenu) pass def loadStyle(self): """ Safe Function Wrapper To Prevent Errors When Stylesheet Doesn't Exist """ #print "loadStyle being called" try: #print "About to call loadStyleFunction" self.loadStyleFunction() pass except self.RendererException: #print "loadStyleFunction failed with error" if self._caller in self._handler_support.directoryplugins: pass elif self._style_mode == self._default_style_mode: raise else: self._style_mode = self._default_style_mode self.loadStyleFunction() pass pass def loadStyleFunction(self): """ Look In Standard Places For the Appropriate Static Stylesheet """ # Get Variables Containing Search Locations Ready #print "In loadStyleFunction" #print "Path = " + self._fullpath #print "Plugin = " + self.__class__.__name__ custompath = os.path.abspath((self._fullpath if os.path.isdir(self._fullpath) else os.path.dirname(self._fullpath)) + '/.databrowse/stylesheets/' + self.__class__.__name__ + '/dbs_' + self._style_mode + '.xml') defaultpath = os.path.abspath(os.path.dirname(sys.modules['databrowse.plugins.' + self.__class__.__name__].__file__) + '/dbs_' + self._style_mode + '.xml') #print "Custom Search Path = " + custompath #print "Default Search Path = " + defaultpath # Look for Custom Stylesheets in a .databrowse folder relative to the current path filename = custompath if os.path.exists(custompath) else None #print "Looking For Custom File === Filename is now " + repr(filename) # If we find one, see if its overriding the standard stylesheet and set a flag to remind us later override = False if filename is not None: override = True if (os.path.exists(defaultpath) or hasattr(self, '_style_' + self._style_mode)) else False pass #print "Checking for Default Stylesheets === Override is now " + repr(override) # Let's first check if we have already loaded the standard stylesheets if filename is None: #print "Filename is still empty so let's see if we have loaded the default already" if self._web_support.style.IsStyleLoaded(self._namespace_uri) and override != True: #print "We have loaded already === IsStyleLoaded is %s and override is %s" % (repr(self._web_support.style.IsStyleLoaded(self._namespace_uri)), repr(override)) return else: # If not, let's look for normal stylesheets #print "Not loaded already === IsStyleLoaded is %s and override is %s" % (repr(self._web_support.style.IsStyleLoaded(self._namespace_uri)), repr(override)) filename = defaultpath if os.path.exists(defaultpath) else None pass # Let's check for a stylesheet in the current file if filename is None: #print "Filename is still none = looking for variable" if hasattr(self, '_style_' + self._style_mode): stylestring = getattr(self, '_style_' + self._style_mode) pass else: # Unable to Find Stylesheet Anywhere - Return Error #print "Unable to find stylesheet" raise self.RendererException("Unable To Locate Stylesheet for Style Mode %s in %s" % (self._style_mode, self.__class__.__name__)) else: # Lets load up whatever stylesheet we found f = open(filename, 'r') stylestring = f.read() f.close() pass #print "Stylesheet Loaded Successfully:" #print stylestring # If we set the flag earlier, we need to change the namespace if override is True: #print "Override is True = Lets Modify Our Stylesheet" randomid = ''.join(random.choice(string.ascii_uppercase + string.digits) for x in range(10)) #print "Random ID is " + randomid newnamespace = self._namespace_uri + randomid newlocalns = self._namespace_local + randomid #print "New namespace is " + newnamespace newnamedtemplates = self.__class__.__name__ + '-' + randomid + '-' #print "Named templates are now prefixed " + newnamedtemplates stylestring = stylestring.replace(self._namespace_uri, newnamespace) stylestring = stylestring.replace(self._namespace_local + ":", newlocalns + ":") stylestring = stylestring.replace("xmlns:" + self._namespace_local, "xmlns:" + newlocalns) #print "Namespace Changed:" #print stylestring stylestring = stylestring.replace(self.__class__.__name__ + '-', newnamedtemplates) #print "Named Templates Updated:" #print stylestring self._namespace_uri = newnamespace self._namespace_local = newlocalns pass #print "Adding Style" self._web_support.style.AddStyle(self._namespace_uri, stylestring) pass

44.309764

213

0.549582

18607162db83cf7bf76d8c3099f747d3be47138e

49,059

py

Python

predictor.py

Min-Sheng/medicaldetectiontoolkit

2b24a87a0e3dfa9ccbcda4a35c82437f3ff11d22

[ "Apache-2.0" ]

null

predictor.py

Min-Sheng/medicaldetectiontoolkit

2b24a87a0e3dfa9ccbcda4a35c82437f3ff11d22

[ "Apache-2.0" ]

null

predictor.py

Min-Sheng/medicaldetectiontoolkit

2b24a87a0e3dfa9ccbcda4a35c82437f3ff11d22

[ "Apache-2.0" ]

1

2022-02-09T13:24:09.000Z

#!/usr/bin/env python # Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ). # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import os import numpy as np import torch from scipy.stats import norm from collections import OrderedDict from multiprocessing import Pool import pickle from copy import deepcopy import pandas as pd import utils.exp_utils as utils from plotting import plot_batch_prediction class Predictor: """ Prediction pipeline: - receives a patched patient image (n_patches, c, y, x, (z)) from patient data loader. - forwards patches through model in chunks of batch_size. (method: batch_tiling_forward) - unmolds predictions (boxes and segmentations) to original patient coordinates. (method: spatial_tiling_forward) Ensembling (mode == 'test'): - for inference, forwards 4 mirrored versions of image to through model and unmolds predictions afterwards accordingly (method: data_aug_forward) - for inference, loads multiple parameter-sets of the trained model corresponding to different epochs. for each parameter-set loops over entire test set, runs prediction pipeline for each patient. (method: predict_test_set) Consolidation of predictions: - consolidates a patient's predictions (boxes, segmentations) collected over patches, data_aug- and temporal ensembling, performs clustering and weighted averaging (external function: apply_wbc_to_patient) to obtain consistent outptus. - for 2D networks, consolidates box predictions to 3D cubes via clustering (adaption of non-maximum surpression). (external function: merge_2D_to_3D_preds_per_patient) Ground truth handling: - dissmisses any ground truth boxes returned by the model (happens in validation mode, patch-based groundtruth) - if provided by data loader, adds 3D ground truth to the final predictions to be passed to the evaluator. """ def __init__(self, cf, net, logger, mode): self.cf = cf self.logger = logger # mode is 'val' for patient-based validation/monitoring and 'test' for inference. self.mode = mode # model instance. In validation mode, contains parameters of current epoch. self.net = net # rank of current epoch loaded (for temporal averaging). this info is added to each prediction, # for correct weighting during consolidation. self.rank_ix = '0' # number of ensembled models. used to calculate the number of expected predictions per position # during consolidation of predictions. Default is 1 (no ensembling, e.g. in validation). self.n_ens = 1 if self.mode == 'test': try: self.epoch_ranking = np.load(os.path.join(self.cf.fold_dir, 'epoch_ranking.npy'))[:cf.test_n_epochs] except: raise RuntimeError('no epoch ranking file in fold directory. ' 'seems like you are trying to run testing without prior training...') self.n_ens = cf.test_n_epochs if self.cf.test_aug: self.n_ens *= 4 self.example_plot_dir = os.path.join(cf.test_dir, "example_plots") os.makedirs(self.example_plot_dir, exist_ok=True) def predict_patient(self, batch): """ predicts one patient. called either directly via loop over validation set in exec.py (mode=='val') or from self.predict_test_set (mode=='test). in val mode: adds 3D ground truth info to predictions and runs consolidation and 2Dto3D merging of predictions. in test mode: returns raw predictions (ground truth addition, consolidation, 2D to 3D merging are done in self.predict_test_set, because patient predictions across several epochs might be needed to be collected first, in case of temporal ensembling). :return. results_dict: stores the results for one patient. dictionary with keys: - 'boxes': list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions (if not merged to 3D), and a dummy batch dimension of 1 for 3D predictions. - 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z)) - losses (only in validation mode) """ #self.logger.info('\revaluating patient {} for fold {} '.format(batch['pid'], self.cf.fold)) print('\revaluating patient {} for fold {} '.format(batch['pid'], self.cf.fold), end="", flush=True) # True if patient is provided in patches and predictions need to be tiled. self.patched_patient = 'patch_crop_coords' in batch.keys() # forward batch through prediction pipeline. print(batch['data'].shape) results_dict = self.data_aug_forward(batch) if self.mode == 'val': for b in range(batch['patient_bb_target'].shape[0]): for t in range(len(batch['patient_bb_target'][b])): results_dict['boxes'][b].append({'box_coords': batch['patient_bb_target'][b][t], 'box_label': batch['patient_roi_labels'][b][t], 'box_type': 'gt'}) if self.patched_patient: wcs_input = [results_dict['boxes'], 'dummy_pid', self.cf.class_dict, self.cf.wcs_iou, self.n_ens] results_dict['boxes'] = apply_wbc_to_patient(wcs_input)[0] if self.cf.merge_2D_to_3D_preds: merge_dims_inputs = [results_dict['boxes'], 'dummy_pid', self.cf.class_dict, self.cf.merge_3D_iou] results_dict['boxes'] = merge_2D_to_3D_preds_per_patient(merge_dims_inputs)[0] return results_dict def predict_test_set(self, batch_gen, return_results=True): """ wrapper around test method, which loads multiple (or one) epoch parameters (temporal ensembling), loops through the test set and collects predictions per patient. Also flattens the results per patient and epoch and adds optional ground truth boxes for evaluation. Saves out the raw result list for later analysis and optionally consolidates and returns predictions immediately. :return: (optionally) list_of_results_per_patient: list over patient results. each entry is a dict with keys: - 'boxes': list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions (if not merged to 3D), and a dummy batch dimension of 1 for 3D predictions. - 'seg_preds': not implemented yet. todo for evaluation of instance/semantic segmentation. """ dict_of_patient_results = OrderedDict() # get paths of all parameter sets to be loaded for temporal ensembling. (or just one for no temp. ensembling). weight_paths = [os.path.join(self.cf.fold_dir, '{}_best_checkpoint'.format(epoch), 'params.pth') for epoch in self.epoch_ranking] n_test_plots = min(batch_gen['n_test'], 1) for rank_ix, weight_path in enumerate(weight_paths): self.logger.info(('tmp ensembling over rank_ix:{} epoch:{}'.format(rank_ix, weight_path))) self.net.load_state_dict(torch.load(weight_path)) self.net.eval() self.rank_ix = str(rank_ix) # get string of current rank for unique patch ids. plot_batches = np.random.choice(np.arange(batch_gen['n_test']), size=n_test_plots, replace=False) with torch.no_grad(): for i in range(batch_gen['n_test']): batch = next(batch_gen['test']) # store batch info in patient entry of results dict. if rank_ix == 0: dict_of_patient_results[batch['pid']] = {} dict_of_patient_results[batch['pid']]['results_dicts'] = [] dict_of_patient_results[batch['pid']]['patient_bb_target'] = batch['patient_bb_target'] dict_of_patient_results[batch['pid']]['patient_roi_labels'] = batch['patient_roi_labels'] # call prediction pipeline and store results in dict. results_dict = self.predict_patient(batch) dict_of_patient_results[batch['pid']]['results_dicts'].append({"boxes": results_dict['boxes']}) if i in plot_batches and not self.patched_patient: # view qualitative results of random test case # plotting for patched patients is too expensive, thus not done. Change at will. try: out_file = os.path.join(self.example_plot_dir, 'batch_example_test_{}_rank_{}.png'.format(self.cf.fold, rank_ix)) results_for_plotting = deepcopy(results_dict) # seg preds of test augs are included separately. for viewing, only show aug 0 (merging # would need multiple changes, incl in every model). if results_for_plotting["seg_preds"].shape[1] > 1: results_for_plotting["seg_preds"] = results_dict['seg_preds'][:, [0]] for bix in range(batch["seg"].shape[0]): # batch dim should be 1 for tix in range(len(batch['bb_target'][bix])): results_for_plotting['boxes'][bix].append({'box_coords': batch['bb_target'][bix][tix], 'box_label': batch['class_target'][bix][tix], 'box_type': 'gt'}) utils.split_off_process(plot_batch_prediction, batch, results_for_plotting, self.cf, outfile=out_file, suptitle="Test plot:\nunmerged TTA overlayed.") except Exception as e: self.logger.info("WARNING: error in plotting example test batch: {}".format(e)) self.logger.info('finished predicting test set. starting post-processing of predictions.') results_per_patient = [] # loop over patients again to flatten results across epoch predictions. # if provided, add ground truth boxes for evaluation. for pid, p_dict in dict_of_patient_results.items(): tmp_ens_list = p_dict['results_dicts'] results_dict = {} # collect all boxes/seg_preds of same batch_instance over temporal instances. b_size = len(tmp_ens_list[0]["boxes"]) results_dict['boxes'] = [[item for rank_dict in tmp_ens_list for item in rank_dict["boxes"][batch_instance]] for batch_instance in range(b_size)] # TODO return for instance segmentation: # results_dict['seg_preds'] = np.mean(results_dict['seg_preds'], 1)[:, None] # results_dict['seg_preds'] = np.array([[item for d in tmp_ens_list for item in d['seg_preds'][batch_instance]] # for batch_instance in range(len(tmp_ens_list[0]['boxes']))]) # add 3D ground truth boxes for evaluation. for b in range(p_dict['patient_bb_target'].shape[0]): for t in range(len(p_dict['patient_bb_target'][b])): results_dict['boxes'][b].append({'box_coords': p_dict['patient_bb_target'][b][t], 'box_label': p_dict['patient_roi_labels'][b][t], 'box_type': 'gt'}) results_per_patient.append([results_dict, pid]) # save out raw predictions. out_string = 'raw_pred_boxes_hold_out_list' if self.cf.hold_out_test_set else 'raw_pred_boxes_list' with open(os.path.join(self.cf.fold_dir, '{}.pickle'.format(out_string)), 'wb') as handle: pickle.dump(results_per_patient, handle) if return_results: final_patient_box_results = [(res_dict["boxes"], pid) for res_dict, pid in results_per_patient] # consolidate predictions. self.logger.info('applying wcs to test set predictions with iou = {} and n_ens = {}.'.format( self.cf.wcs_iou, self.n_ens)) pool = Pool(processes=6) mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.wcs_iou, self.n_ens] for ii in final_patient_box_results] final_patient_box_results = pool.map(apply_wbc_to_patient, mp_inputs, chunksize=1) pool.close() pool.join() # merge 2D boxes to 3D cubes. (if model predicts 2D but evaluation is run in 3D) if self.cf.merge_2D_to_3D_preds: self.logger.info('applying 2Dto3D merging to test set predictions with iou = {}.'.format(self.cf.merge_3D_iou)) pool = Pool(processes=6) mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.merge_3D_iou] for ii in final_patient_box_results] final_patient_box_results = pool.map(merge_2D_to_3D_preds_per_patient, mp_inputs, chunksize=1) pool.close() pool.join() # final_patient_box_results holds [avg_boxes, pid] if wbc for ix in range(len(results_per_patient)): assert results_per_patient[ix][1] == final_patient_box_results[ix][1], "should be same pid" results_per_patient[ix][0]["boxes"] = final_patient_box_results[ix][0] return results_per_patient def load_saved_predictions(self, apply_wbc=False): """ loads raw predictions saved by self.predict_test_set. consolidates and merges 2D boxes to 3D cubes for evaluation. (if model predicts 2D but evaluation is run in 3D) :return: (optionally) results_list: list over patient results. each entry is a dict with keys: - 'boxes': list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions (if not merged to 3D), and a dummy batch dimension of 1 for 3D predictions. - 'seg_preds': not implemented yet. todo for evaluation of instance/semantic segmentation. """ # load predictions for a single test-set fold. results_file = 'raw_pred_boxes_hold_out_list.pickle' if self.cf.hold_out_test_set else 'raw_pred_boxes_list.pickle' if not self.cf.hold_out_test_set or not self.cf.ensemble_folds: with open(os.path.join(self.cf.fold_dir, results_file), 'rb') as handle: results_list = pickle.load(handle) box_results_list = [(res_dict["boxes"], pid) for res_dict, pid in results_list] da_factor = 4 if self.cf.test_aug else 1 n_ens = self.cf.test_n_epochs * da_factor self.logger.info('loaded raw test set predictions with n_patients = {} and n_ens = {}'.format( len(results_list), n_ens)) # if hold out test set was perdicted, aggregate predictions of all trained models # corresponding to all CV-folds and flatten them. else: self.logger.info("loading saved predictions of hold-out test set and ensembling over folds.") fold_dirs = sorted([os.path.join(self.cf.exp_dir, f) for f in os.listdir(self.cf.exp_dir) if os.path.isdir(os.path.join(self.cf.exp_dir, f)) and f.startswith("fold")]) results_list = [] folds_loaded = 0 for fold in range(self.cf.n_cv_splits): fold_dir = os.path.join(self.cf.exp_dir, 'fold_{}'.format(fold)) if fold_dir in fold_dirs: with open(os.path.join(fold_dir, results_file), 'rb') as handle: fold_list = pickle.load(handle) results_list += fold_list folds_loaded += 1 else: self.logger.info("Skipping fold {} since no saved predictions found.".format(fold)) box_results_list = [] for res_dict, pid in results_list: #without filtering gt out: box_results_list.append((res_dict['boxes'], pid)) da_factor = 4 if self.cf.test_aug else 1 n_ens = self.cf.test_n_epochs * da_factor * folds_loaded # consolidate predictions. if apply_wbc: self.logger.info('applying wcs to test set predictions with iou = {} and n_ens = {}.'.format( self.cf.wcs_iou, n_ens)) pool = Pool(processes=6) mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.wcs_iou, n_ens] for ii in box_results_list] box_results_list = pool.map(apply_wbc_to_patient, mp_inputs, chunksize=1) pool.close() pool.join() # merge 2D box predictions to 3D cubes (if model predicts 2D but evaluation is run in 3D) if self.cf.merge_2D_to_3D_preds: self.logger.info( 'applying 2Dto3D merging to test set predictions with iou = {}.'.format(self.cf.merge_3D_iou)) pool = Pool(processes=6) mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.merge_3D_iou] for ii in box_results_list] box_results_list = pool.map(merge_2D_to_3D_preds_per_patient, mp_inputs, chunksize=1) pool.close() pool.join() for ix in range(len(results_list)): assert np.all( results_list[ix][1] == box_results_list[ix][1]), "pid mismatch between loaded and aggregated results" results_list[ix][0]["boxes"] = box_results_list[ix][0] return results_list # holds (results_dict, pid) def data_aug_forward(self, batch): """ in val_mode: passes batch through to spatial_tiling method without data_aug. in test_mode: if cf.test_aug is set in configs, createst 4 mirrored versions of the input image, passes all of them to the next processing step (spatial_tiling method) and re-transforms returned predictions to original image version. :return. results_dict: stores the results for one patient. dictionary with keys: - 'boxes': list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions, and a dummy batch dimension of 1 for 3D predictions. - 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z)) - losses (only in validation mode) """ patch_crops = batch['patch_crop_coords'] if self.patched_patient else None results_list = [self.spatial_tiling_forward(batch, patch_crops)] org_img_shape = batch['original_img_shape'] if self.mode == 'test' and self.cf.test_aug: if self.patched_patient: # apply mirror transformations to patch-crop coordinates, for correct tiling in spatial_tiling method. mirrored_patch_crops = get_mirrored_patch_crops(patch_crops, batch['original_img_shape']) else: mirrored_patch_crops = [None] * 3 img = np.copy(batch['data']) # first mirroring: y-axis. batch['data'] = np.flip(img, axis=2).copy() chunk_dict = self.spatial_tiling_forward(batch, mirrored_patch_crops[0], n_aug='1') # re-transform coordinates. for ix in range(len(chunk_dict['boxes'])): for boxix in range(len(chunk_dict['boxes'][ix])): coords = chunk_dict['boxes'][ix][boxix]['box_coords'].copy() coords[0] = org_img_shape[2] - chunk_dict['boxes'][ix][boxix]['box_coords'][2] coords[2] = org_img_shape[2] - chunk_dict['boxes'][ix][boxix]['box_coords'][0] assert coords[2] >= coords[0], [coords, chunk_dict['boxes'][ix][boxix]['box_coords'].copy()] assert coords[3] >= coords[1], [coords, chunk_dict['boxes'][ix][boxix]['box_coords'].copy()] chunk_dict['boxes'][ix][boxix]['box_coords'] = coords # re-transform segmentation predictions. chunk_dict['seg_preds'] = np.flip(chunk_dict['seg_preds'], axis=2) results_list.append(chunk_dict) # second mirroring: x-axis. batch['data'] = np.flip(img, axis=3).copy() chunk_dict = self.spatial_tiling_forward(batch, mirrored_patch_crops[1], n_aug='2') # re-transform coordinates. for ix in range(len(chunk_dict['boxes'])): for boxix in range(len(chunk_dict['boxes'][ix])): coords = chunk_dict['boxes'][ix][boxix]['box_coords'].copy() coords[1] = org_img_shape[3] - chunk_dict['boxes'][ix][boxix]['box_coords'][3] coords[3] = org_img_shape[3] - chunk_dict['boxes'][ix][boxix]['box_coords'][1] assert coords[2] >= coords[0], [coords, chunk_dict['boxes'][ix][boxix]['box_coords'].copy()] assert coords[3] >= coords[1], [coords, chunk_dict['boxes'][ix][boxix]['box_coords'].copy()] chunk_dict['boxes'][ix][boxix]['box_coords'] = coords # re-transform segmentation predictions. chunk_dict['seg_preds'] = np.flip(chunk_dict['seg_preds'], axis=3) results_list.append(chunk_dict) # third mirroring: y-axis and x-axis. batch['data'] = np.flip(np.flip(img, axis=2), axis=3).copy() chunk_dict = self.spatial_tiling_forward(batch, mirrored_patch_crops[2], n_aug='3') # re-transform coordinates. for ix in range(len(chunk_dict['boxes'])): for boxix in range(len(chunk_dict['boxes'][ix])): coords = chunk_dict['boxes'][ix][boxix]['box_coords'].copy() coords[0] = org_img_shape[2] - chunk_dict['boxes'][ix][boxix]['box_coords'][2] coords[2] = org_img_shape[2] - chunk_dict['boxes'][ix][boxix]['box_coords'][0] coords[1] = org_img_shape[3] - chunk_dict['boxes'][ix][boxix]['box_coords'][3] coords[3] = org_img_shape[3] - chunk_dict['boxes'][ix][boxix]['box_coords'][1] assert coords[2] >= coords[0], [coords, chunk_dict['boxes'][ix][boxix]['box_coords'].copy()] assert coords[3] >= coords[1], [coords, chunk_dict['boxes'][ix][boxix]['box_coords'].copy()] chunk_dict['boxes'][ix][boxix]['box_coords'] = coords # re-transform segmentation predictions. chunk_dict['seg_preds'] = np.flip(np.flip(chunk_dict['seg_preds'], axis=2), axis=3).copy() results_list.append(chunk_dict) batch['data'] = img # aggregate all boxes/seg_preds per batch element from data_aug predictions. results_dict = {} results_dict['boxes'] = [[item for d in results_list for item in d['boxes'][batch_instance]] for batch_instance in range(org_img_shape[0])] results_dict['seg_preds'] = np.array([[item for d in results_list for item in d['seg_preds'][batch_instance]] for batch_instance in range(org_img_shape[0])]) if self.mode == 'val': try: results_dict['torch_loss'] = results_list[0]['torch_loss'] results_dict['class_loss'] = results_list[0]['class_loss'] except KeyError: pass return results_dict def spatial_tiling_forward(self, batch, patch_crops=None, n_aug='0'): """ forwards batch to batch_tiling_forward method and receives and returns a dictionary with results. if patch-based prediction, the results received from batch_tiling_forward will be on a per-patch-basis. this method uses the provided patch_crops to re-transform all predictions to whole-image coordinates. Patch-origin information of all box-predictions will be needed for consolidation, hence it is stored as 'patch_id', which is a unique string for each patch (also takes current data aug and temporal epoch instances into account). all box predictions get additional information about the amount overlapping patches at the respective position (used for consolidation). :return. results_dict: stores the results for one patient. dictionary with keys: - 'boxes': list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions, and a dummy batch dimension of 1 for 3D predictions. - 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z)) - losses (only in validation mode) """ if patch_crops is not None: patches_dict = self.batch_tiling_forward(batch) results_dict = {'boxes': [[] for _ in range(batch['original_img_shape'][0])]} # instanciate segemntation output array. Will contain averages over patch predictions. out_seg_preds = np.zeros(batch['original_img_shape'], dtype=np.float16)[:, 0][:, None] # counts patch instances per pixel-position. patch_overlap_map = np.zeros_like(out_seg_preds, dtype='uint8') #unmold segmentation outputs. loop over patches. for pix, pc in enumerate(patch_crops): if self.cf.dim == 3: out_seg_preds[:, :, pc[0]:pc[1], pc[2]:pc[3], pc[4]:pc[5]] += patches_dict['seg_preds'][pix][None] patch_overlap_map[:, :, pc[0]:pc[1], pc[2]:pc[3], pc[4]:pc[5]] += 1 else: out_seg_preds[pc[4]:pc[5], :, pc[0]:pc[1], pc[2]:pc[3], ] += patches_dict['seg_preds'][pix] patch_overlap_map[pc[4]:pc[5], :, pc[0]:pc[1], pc[2]:pc[3], ] += 1 # take mean in overlapping areas. out_seg_preds[patch_overlap_map > 0] /= patch_overlap_map[patch_overlap_map > 0] results_dict['seg_preds'] = out_seg_preds # unmold box outputs. loop over patches. for pix, pc in enumerate(patch_crops): patch_boxes = patches_dict['boxes'][pix] for box in patch_boxes: # add unique patch id for consolidation of predictions. box['patch_id'] = self.rank_ix + '_' + n_aug + '_' + str(pix) # boxes from the edges of a patch have a lower prediction quality, than the ones at patch-centers. # hence they will be downweighted for consolidation, using the 'box_patch_center_factor', which is # obtained by a normal distribution over positions in the patch and average over spatial dimensions. # Also the info 'box_n_overlaps' is stored for consolidation, which depicts the amount over # overlapping patches at the box's position. c = box['box_coords'] box_centers = [(c[ii] + c[ii + 2]) / 2 for ii in range(2)] if self.cf.dim == 3: box_centers.append((c[4] + c[5]) / 2) box['box_patch_center_factor'] = np.mean( [norm.pdf(bc, loc=pc, scale=pc * 0.8) * np.sqrt(2 * np.pi) * pc * 0.8 for bc, pc in zip(box_centers, np.array(self.cf.patch_size) / 2)]) if self.cf.dim == 3: c += np.array([pc[0], pc[2], pc[0], pc[2], pc[4], pc[4]]) int_c = [int(np.floor(ii)) if ix%2 == 0 else int(np.ceil(ii)) for ix, ii in enumerate(c)] box['box_n_overlaps'] = np.mean(patch_overlap_map[:, :, int_c[1]:int_c[3], int_c[0]:int_c[2], int_c[4]:int_c[5]]) results_dict['boxes'][0].append(box) else: c += np.array([pc[0], pc[2], pc[0], pc[2]]) int_c = [int(np.floor(ii)) if ix % 2 == 0 else int(np.ceil(ii)) for ix, ii in enumerate(c)] box['box_n_overlaps'] = np.mean(patch_overlap_map[pc[4], :, int_c[1]:int_c[3], int_c[0]:int_c[2]]) results_dict['boxes'][pc[4]].append(box) if self.mode == 'val': try: results_dict['torch_loss'] = patches_dict['torch_loss'] results_dict['class_loss'] = patches_dict['class_loss'] except KeyError: pass # if predictions are not patch-based: # add patch-origin info to boxes (entire image is the same patch with overlap=1) and return results. else: results_dict = self.batch_tiling_forward(batch) for b in results_dict['boxes']: for box in b: box['box_patch_center_factor'] = 1 box['box_n_overlaps'] = 1 box['patch_id'] = self.rank_ix + '_' + n_aug return results_dict def batch_tiling_forward(self, batch): """ calls the actual network forward method. in patch-based prediction, the batch dimension might be overladed with n_patches >> batch_size, which would exceed gpu memory. In this case, batches are processed in chunks of batch_size. validation mode calls the train method to monitor losses (returned ground truth objects are discarded). test mode calls the test forward method, no ground truth required / involved. :return. results_dict: stores the results for one patient. dictionary with keys: - 'boxes': list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions, and a dummy batch dimension of 1 for 3D predictions. - 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z)) - losses (only in validation mode) """ #self.logger.info('forwarding (patched) patient with shape: {}'.format(batch['data'].shape)) img = batch['data'] if img.shape[0] <= self.cf.batch_size: if self.mode == 'val': # call training method to monitor losses results_dict = self.net.train_forward(batch, is_validation=True) # discard returned ground-truth boxes (also training info boxes). results_dict['boxes'] = [[box for box in b if box['box_type'] == 'det'] for b in results_dict['boxes']] else: results_dict = self.net.test_forward(batch, return_masks=self.cf.return_masks_in_test) else: split_ixs = np.split(np.arange(img.shape[0]), np.arange(img.shape[0])[::self.cf.batch_size]) chunk_dicts = [] for chunk_ixs in split_ixs[1:]: # first split is elements before 0, so empty b = {k: batch[k][chunk_ixs] for k in batch.keys() if (isinstance(batch[k], np.ndarray) and batch[k].shape[0] == img.shape[0])} if self.mode == 'val': chunk_dicts += [self.net.train_forward(b, is_validation=True)] else: chunk_dicts += [self.net.test_forward(b, return_masks=self.cf.return_masks_in_test)] results_dict = {} # flatten out batch elements from chunks ([chunk, chunk] -> [b, b, b, b, ...]) results_dict['boxes'] = [item for d in chunk_dicts for item in d['boxes']] results_dict['seg_preds'] = np.array([item for d in chunk_dicts for item in d['seg_preds']]) if self.mode == 'val': try: # estimate metrics by mean over batch_chunks. Most similar to training metrics. results_dict['torch_loss'] = torch.mean(torch.cat([d['torch_loss'] for d in chunk_dicts])) results_dict['class_loss'] = np.mean([d['class_loss'] for d in chunk_dicts]) except KeyError: # losses are not necessarily monitored pass # discard returned ground-truth boxes (also training info boxes). results_dict['boxes'] = [[box for box in b if box['box_type'] == 'det'] for b in results_dict['boxes']] return results_dict def apply_wbc_to_patient(inputs): """ wrapper around prediction box consolidation: weighted cluster scoring (wcs). processes a single patient. loops over batch elements in patient results (1 in 3D, slices in 2D) and foreground classes, aggregates and stores results in new list. :return. patient_results_list: list over batch elements. each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions, and a dummy batch dimension of 1 for 3D predictions. :return. pid: string. patient id. """ in_patient_results_list, pid, class_dict, wcs_iou, n_ens = inputs out_patient_results_list = [[] for _ in range(len(in_patient_results_list))] for bix, b in enumerate(in_patient_results_list): for cl in list(class_dict.keys()): boxes = [(ix, box) for ix, box in enumerate(b) if (box['box_type'] == 'det' and box['box_pred_class_id'] == cl)] box_coords = np.array([b[1]['box_coords'] for b in boxes]) box_scores = np.array([b[1]['box_score'] for b in boxes]) box_center_factor = np.array([b[1]['box_patch_center_factor'] for b in boxes]) box_n_overlaps = np.array([b[1]['box_n_overlaps'] for b in boxes]) box_patch_id = np.array([b[1]['patch_id'] for b in boxes]) if 0 not in box_scores.shape: keep_scores, keep_coords = weighted_box_clustering( np.concatenate((box_coords, box_scores[:, None], box_center_factor[:, None], box_n_overlaps[:, None]), axis=1), box_patch_id, wcs_iou, n_ens) for boxix in range(len(keep_scores)): out_patient_results_list[bix].append({'box_type': 'det', 'box_coords': keep_coords[boxix], 'box_score': keep_scores[boxix], 'box_pred_class_id': cl}) # add gt boxes back to new output list. out_patient_results_list[bix].extend([box for box in b if box['box_type'] == 'gt']) return [out_patient_results_list, pid] def merge_2D_to_3D_preds_per_patient(inputs): """ wrapper around 2Dto3D merging operation. Processes a single patient. Takes 2D patient results (slices in batch dimension) and returns 3D patient results (dummy batch dimension of 1). Applies an adaption of Non-Maximum Surpression (Detailed methodology is described in nms_2to3D). :return. results_dict_boxes: list over batch elements (1 in 3D). each element is a list over boxes, where each box is one dictionary: [[box_0, ...], [box_n,...]]. :return. pid: string. patient id. """ in_patient_results_list, pid, class_dict, merge_3D_iou = inputs out_patient_results_list = [] for cl in list(class_dict.keys()): boxes, slice_ids = [], [] # collect box predictions over batch dimension (slices) and store slice info as slice_ids. for bix, b in enumerate(in_patient_results_list): det_boxes = [(ix, box) for ix, box in enumerate(b) if (box['box_type'] == 'det' and box['box_pred_class_id'] == cl)] boxes += det_boxes slice_ids += [bix] * len(det_boxes) box_coords = np.array([b[1]['box_coords'] for b in boxes]) box_scores = np.array([b[1]['box_score'] for b in boxes]) slice_ids = np.array(slice_ids) if 0 not in box_scores.shape: keep_ix, keep_z = nms_2to3D( np.concatenate((box_coords, box_scores[:, None], slice_ids[:, None]), axis=1), merge_3D_iou) else: keep_ix, keep_z = [], [] # store kept predictions in new results list and add corresponding z-dimension info to coordinates. for kix, kz in zip(keep_ix, keep_z): out_patient_results_list.append({'box_type': 'det', 'box_coords': list(box_coords[kix]) + kz, 'box_score': box_scores[kix], 'box_pred_class_id': cl}) gt_boxes = [box for b in in_patient_results_list for box in b if box['box_type'] == 'gt'] if len(gt_boxes) > 0: assert np.all([len(box["box_coords"]) == 6 for box in gt_boxes]), "expanded preds to 3D but GT is 2D." out_patient_results_list += gt_boxes # add dummy batch dimension 1 for 3D. return [[out_patient_results_list], pid] def weighted_box_clustering(dets, box_patch_id, thresh, n_ens): """ consolidates overlapping predictions resulting from patch overlaps, test data augmentations and temporal ensembling. clusters predictions together with iou > thresh (like in NMS). Output score and coordinate for one cluster are the average weighted by individual patch center factors (how trustworthy is this candidate measured by how centered its position the patch is) and the size of the corresponding box. The number of expected predictions at a position is n_data_aug * n_temp_ens * n_overlaps_at_position (1 prediction per unique patch). Missing predictions at a cluster position are defined as the number of unique patches in the cluster, which did not contribute any predict any boxes. :param dets: (n_dets, (y1, x1, y2, x2, (z1), (z2), scores, box_pc_facts, box_n_ovs) :param thresh: threshold for iou_matching. :param n_ens: number of models, that are ensembled. (-> number of expected predicitions per position) :return: keep_scores: (n_keep) new scores of boxes to be kept. :return: keep_coords: (n_keep, (y1, x1, y2, x2, (z1), (z2)) new coordinates of boxes to be kept. """ dim = 2 if dets.shape[1] == 7 else 3 y1 = dets[:, 0] x1 = dets[:, 1] y2 = dets[:, 2] x2 = dets[:, 3] scores = dets[:, -3] box_pc_facts = dets[:, -2] box_n_ovs = dets[:, -1] areas = (y2 - y1 + 1) * (x2 - x1 + 1) if dim == 3: z1 = dets[:, 4] z2 = dets[:, 5] areas *= (z2 - z1 + 1) # order is the sorted index. maps order to index o[1] = 24 (rank1, ix 24) order = scores.argsort()[::-1] keep = [] keep_scores = [] keep_coords = [] while order.size > 0: i = order[0] # higehst scoring element xx1 = np.maximum(x1[i], x1[order]) yy1 = np.maximum(y1[i], y1[order]) xx2 = np.minimum(x2[i], x2[order]) yy2 = np.minimum(y2[i], y2[order]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h if dim == 3: zz1 = np.maximum(z1[i], z1[order]) zz2 = np.minimum(z2[i], z2[order]) d = np.maximum(0.0, zz2 - zz1 + 1) inter *= d # overall between currently highest scoring box and all boxes. ovr = inter / (areas[i] + areas[order] - inter) # get all the predictions that match the current box to build one cluster. matches = np.argwhere(ovr > thresh) match_n_ovs = box_n_ovs[order[matches]] match_pc_facts = box_pc_facts[order[matches]] match_patch_id = box_patch_id[order[matches]] match_ov_facts = ovr[matches] match_areas = areas[order[matches]] match_scores = scores[order[matches]] # weight all socres in cluster by patch factors, and size. match_score_weights = match_ov_facts * match_areas * match_pc_facts match_scores *= match_score_weights # for the weigted average, scores have to be divided by the number of total expected preds at the position # of the current cluster. 1 Prediction per patch is expected. therefore, the number of ensembled models is # multiplied by the mean overlaps of patches at this position (boxes of the cluster might partly be # in areas of different overlaps). n_expected_preds = n_ens * np.mean(match_n_ovs) # the number of missing predictions is obtained as the number of patches, # which did not contribute any prediction to the current cluster. n_missing_preds = np.max((0, n_expected_preds - np.unique(match_patch_id).shape[0])) # missing preds are given the mean weighting # (expected prediction is the mean over all predictions in cluster). denom = np.sum(match_score_weights) + n_missing_preds * np.mean(match_score_weights) # compute weighted average score for the cluster avg_score = np.sum(match_scores) / denom # compute weighted average of coordinates for the cluster. now only take existing # predictions into account. avg_coords = [np.sum(y1[order[matches]] * match_scores) / np.sum(match_scores), np.sum(x1[order[matches]] * match_scores) / np.sum(match_scores), np.sum(y2[order[matches]] * match_scores) / np.sum(match_scores), np.sum(x2[order[matches]] * match_scores) / np.sum(match_scores)] if dim == 3: avg_coords.append(np.sum(z1[order[matches]] * match_scores) / np.sum(match_scores)) avg_coords.append(np.sum(z2[order[matches]] * match_scores) / np.sum(match_scores)) # some clusters might have very low scores due to high amounts of missing predictions. # filter out the with a conservative threshold, to speed up evaluation. if avg_score > 0.01: keep_scores.append(avg_score) keep_coords.append(avg_coords) # get index of all elements that were not matched and discard all others. inds = np.where(ovr <= thresh)[0] order = order[inds] return keep_scores, keep_coords def nms_2to3D(dets, thresh): """ Merges 2D boxes to 3D cubes. Therefore, boxes of all slices are projected into one slices. An adaptation of Non-maximum surpression is applied, where clusters are found (like in NMS) with an extra constrained, that surpressed boxes have to have 'connected' z-coordinates w.r.t the core slice (cluster center, highest scoring box). 'connected' z-coordinates are determined as the z-coordinates with predictions until the first coordinate, where no prediction was found. example: a cluster of predictions was found overlap > iou thresh in xy (like NMS). The z-coordinate of the highest scoring box is 50. Other predictions have 23, 46, 48, 49, 51, 52, 53, 56, 57. Only the coordinates connected with 50 are clustered to one cube: 48, 49, 51, 52, 53. (46 not because nothing was found in 47, so 47 is a 'hole', which interrupts the connection). Only the boxes corresponding to these coordinates are surpressed. All others are kept for building of further clusters. This algorithm works better with a certain min_confidence of predictions, because low confidence (e.g. noisy/cluttery) predictions can break the relatively strong assumption of defining cubes' z-boundaries at the first 'hole' in the cluster. :param dets: (n_detections, (y1, x1, y2, x2, scores, slice_id) :param thresh: iou matchin threshold (like in NMS). :return: keep: (n_keep) 1D tensor of indices to be kept. :return: keep_z: (n_keep, [z1, z2]) z-coordinates to be added to boxes, which are kept in order to form cubes. """ y1 = dets[:, 0] x1 = dets[:, 1] y2 = dets[:, 2] x2 = dets[:, 3] scores = dets[:, -2] slice_id = dets[:, -1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) order = scores.argsort()[::-1] keep = [] keep_z = [] while order.size > 0: # order is the sorted index. maps order to index o[1] = 24 (rank1, ix 24) i = order[0] # pop higehst scoring element xx1 = np.maximum(x1[i], x1[order]) yy1 = np.maximum(y1[i], y1[order]) xx2 = np.minimum(x2[i], x2[order]) yy2 = np.minimum(y2[i], y2[order]) w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (areas[i] + areas[order] - inter) matches = np.argwhere(ovr > thresh) # get all the elements that match the current box and have a lower score slice_ids = slice_id[order[matches]] core_slice = slice_id[int(i)] upper_wholes = [ii for ii in np.arange(core_slice, np.max(slice_ids)) if ii not in slice_ids] lower_wholes = [ii for ii in np.arange(np.min(slice_ids), core_slice) if ii not in slice_ids] max_valid_slice_id = np.min(upper_wholes) if len(upper_wholes) > 0 else np.max(slice_ids) min_valid_slice_id = np.max(lower_wholes) if len(lower_wholes) > 0 else np.min(slice_ids) z_matches = matches[(slice_ids <= max_valid_slice_id) & (slice_ids >= min_valid_slice_id)] z1 = np.min(slice_id[order[z_matches]]) - 1 z2 = np.max(slice_id[order[z_matches]]) + 1 keep.append(i) keep_z.append([z1, z2]) order = np.delete(order, z_matches, axis=0) return keep, keep_z def get_mirrored_patch_crops(patch_crops, org_img_shape): """ apply 3 mirrror transformations (x-axis, y-axis, x&y-axis) to given patch crop coordinates and return the transformed coordinates. Handles 2D and 3D coordinates. :param patch_crops: list of crops: each element is a list of coordinates for given crop [[y1, x1, ...], [y1, x1, ..]] :param org_img_shape: shape of patient volume used as world coordinates. :return: list of mirrored patch crops: lenght=3. each element is a list of transformed patch crops. """ mirrored_patch_crops = [] # y-axis transform. mirrored_patch_crops.append([[org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], ii[2], ii[3]] if len(ii) == 4 else [org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], ii[2], ii[3], ii[4], ii[5]] for ii in patch_crops]) # x-axis transform. mirrored_patch_crops.append([[ii[0], ii[1], org_img_shape[3] - ii[3], org_img_shape[3] - ii[2]] if len(ii) == 4 else [ii[0], ii[1], org_img_shape[3] - ii[3], org_img_shape[3] - ii[2], ii[4], ii[5]] for ii in patch_crops]) # y-axis and x-axis transform. mirrored_patch_crops.append([[org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], org_img_shape[3] - ii[3], org_img_shape[3] - ii[2]] if len(ii) == 4 else [org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], org_img_shape[3] - ii[3], org_img_shape[3] - ii[2], ii[4], ii[5]] for ii in patch_crops]) return mirrored_patch_crops

55.371332

137

0.602805

038b2e5473d1a26776a50eb83498478505d6268d

3,850

py

Python

tests/test_handler_hausdorff_distance.py

tatuanb/monai_V1

41e492b61c78bb3c303f38b03fe9fdc74a3c2e96

[ "Apache-2.0" ]

1

2021-05-06T15:08:26.000Z

tests/test_handler_hausdorff_distance.py

catherine1996cn/MONAI

ff9bbfa82763de46cbac75553e340633e3d84ecb

[ "Apache-2.0" ]

2

2020-11-13T23:15:00.000Z

2020-11-16T14:54:08.000Z

tests/test_handler_hausdorff_distance.py

catherine1996cn/MONAI

ff9bbfa82763de46cbac75553e340633e3d84ecb

[ "Apache-2.0" ]

1

2021-11-18T22:37:40.000Z

# Copyright 2020 - 2021 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from typing import Tuple import numpy as np import torch from ignite.engine import Engine from monai.handlers import HausdorffDistance def create_spherical_seg_3d( radius: float = 20.0, centre: Tuple[int, int, int] = (49, 49, 49), im_shape: Tuple[int, int, int] = (99, 99, 99) ) -> np.ndarray: """ Return a 3D image with a sphere inside. Voxel values will be 1 inside the sphere, and 0 elsewhere. Args: radius: radius of sphere (in terms of number of voxels, can be partial) centre: location of sphere centre. im_shape: shape of image to create See also: :py:meth:`~create_test_image_3d` """ # Create image image = np.zeros(im_shape, dtype=np.int32) spy, spx, spz = np.ogrid[ -centre[0] : im_shape[0] - centre[0], -centre[1] : im_shape[1] - centre[1], -centre[2] : im_shape[2] - centre[2] ] circle = (spx * spx + spy * spy + spz * spz) <= radius * radius image[circle] = 1 image[~circle] = 0 return image sampler_sphere = torch.Tensor(create_spherical_seg_3d(radius=20, centre=(20, 20, 20))).unsqueeze(0).unsqueeze(0) # test input a list of channel-first tensor sampler_sphere_gt = [torch.Tensor(create_spherical_seg_3d(radius=20, centre=(10, 20, 20))).unsqueeze(0)] sampler_sphere_zeros = torch.zeros_like(sampler_sphere) TEST_SAMPLE_1 = [sampler_sphere, sampler_sphere_gt] TEST_SAMPLE_2 = [sampler_sphere_gt, sampler_sphere_gt] TEST_SAMPLE_3 = [sampler_sphere_zeros, sampler_sphere_gt] TEST_SAMPLE_4 = [sampler_sphere_zeros, sampler_sphere_zeros] class TestHandlerHausdorffDistance(unittest.TestCase): # TODO test multi node Hausdorff Distance def test_compute(self): hd_metric = HausdorffDistance(include_background=True) def _val_func(engine, batch): pass engine = Engine(_val_func) hd_metric.attach(engine, "hausdorff_distance") y_pred, y = TEST_SAMPLE_1 hd_metric.update([y_pred, y]) self.assertEqual(hd_metric.compute(), 10) y_pred, y = TEST_SAMPLE_2 hd_metric.update([y_pred, y]) self.assertEqual(hd_metric.compute(), 5) y_pred, y = TEST_SAMPLE_3 hd_metric.update([y_pred, y]) self.assertEqual(hd_metric.compute(), float("inf")) y_pred, y = TEST_SAMPLE_4 hd_metric.update([y_pred, y]) self.assertEqual(hd_metric.compute(), float("inf")) def test_shape_mismatch(self): hd_metric = HausdorffDistance(include_background=True) with self.assertRaises((AssertionError, ValueError)): y_pred = TEST_SAMPLE_1[0] y = torch.ones((1, 1, 10, 10, 10)) hd_metric.update([y_pred, y]) def test_reduction(self): hd_metric = HausdorffDistance(include_background=True, reduction="mean_channel") def _val_func(engine, batch): pass engine = Engine(_val_func) hd_metric.attach(engine, "hausdorff_distance") y_pred, y = TEST_SAMPLE_1 hd_metric.update([y_pred, y]) y_pred, y = TEST_SAMPLE_2 hd_metric.update([y_pred, y]) torch.testing.assert_allclose(hd_metric.compute().float(), torch.tensor([10.0, 0.0])) if __name__ == "__main__": unittest.main()

35

120

0.683377

7fe8369b76005cc6046340ffec7dc890dd67e097

5,625

py

Python

StarCipher/StarCipher.py

starhound/StarCipher

f07faccfb6389b78494a5f87b8f3c4443f526d02

[ "MIT" ]

3

2019-09-16T21:02:42.000Z

2021-01-24T17:08:23.000Z

StarCipher/StarCipher.py

starhound/StarCipher

f07faccfb6389b78494a5f87b8f3c4443f526d02

[ "MIT" ]

null

StarCipher/StarCipher.py

starhound/StarCipher

f07faccfb6389b78494a5f87b8f3c4443f526d02

[ "MIT" ]

2

2019-09-16T08:56:12.000Z

2021-01-15T04:45:46.000Z

# StarCipher (Written and Maintained by Wesley Reid - http://starhound.com) import py.pycrypt import pyperclip import secretpy.cmdecorators as md from secretpy import Atbash from secretpy import Trifid from secretpy import CryptMachine from secretpy import Zigzag from secretpy import alphabet from py.pycrypt import reverse_cipher from py.pycrypt import rot13_cipher out = '' SYMBOLS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' MAX_KEY_SIZE = 24 def getType(): while True: print("""\nSelect cipher type: (1) Ceasar (2) Zig-Zag (3) Trifid (4) Reverse (5) Atbash (6) Rot13 """) mode = input().lower() if mode in 'ceasar 1 reverse 2 rot13 3 xor 4 affine 5 viginere 6'.split(): return mode else: print('Please select value 1 through 6 for a cipher type.') def getMode(): while True: print('Do you wish to (e)ncrypt or (d)ecrypt a message?') mode = input().lower() if mode in 'encrypt e decrypt d'.split(): return mode else: print('Enter either "encrypt" or "e" or "decrypt" or "d".') def getMessage(): print('\nEnter your message:') return input() def getKey(): key = 0 while True: print('\nEnter the key number (1-%s)' % (MAX_KEY_SIZE)) key = int(input()) if (key >= 1 and key <= MAX_KEY_SIZE): return key def hasNumbers(inputString): return any(char.isdigit() for char in inputString) #Some ciphers from secretpy expect string/letter based keys def getKeyString(): while True: print('\nEnter the key value (String Form):') key = input() if hasNumbers(key): print('\nKey must be a string value.') else: if len(key) > 0: return key else: print("\nPlease enter a key with at least one letter length.") def ceasarTranslatedMessage(mode, message, key): if mode[0] == 'd': key = -key translated = '' for symbol in message: symbolIndex = SYMBOLS.find(symbol) if symbolIndex == -1: # Symbol not found in SYMBOLS. translated += symbol else: symbolIndex += key if symbolIndex >= len(SYMBOLS): symbolIndex -= len(SYMBOLS) elif symbolIndex < 0: symbolIndex += len(SYMBOLS) translated += SYMBOLS[symbolIndex] return translated #Completed def ceasarCipher(): mode = getMode() message = getMessage() key = getKey() print('\nYour translated text is:') out = ceasarTranslatedMessage(mode, message, key) print(out) return out #Completed def zigzagCipher(): mode = getMode() message = getMessage() key = getKey() cipher = Zigzag() print('\nYour translated text is:') if mode == 'e' or mode == 'encrypt': out = cipher.encrypt(message, key) print(out) else: out = cipher.decrypt(message, key, SYMBOLS) print(out) return out #Complete def reverseCipher(): mode = getMode() message = getMessage() print('\nYour translated text is:') if mode == 'e' or mode == 'encrypt': out = py.pycrypt.reverse_cipher(message) print(out) else: out = reverse_cipher(message) print(out) return out #Completed def trifidCipher(): mode = getMode() message = getMessage() key = getKey() machine = CryptMachine(Trifid(), key) print('\nYour translated text is:') if mode == 'e' or mode == 'encrypt': out = machine.encrypt(message) print(out) else: out = machine.decrypt(message) print(out) return out #TODO: key input def atbashCipher(): mode = getMode() message = getMessage() cm = CryptMachine(Atbash()) cm = md.NoSpaces(md.UpperCase(cm)) print('\nYour translated text is:') if mode == 'e' or mode == 'encrypt': out = cm.encrypt(message) print(out) else: out = cm.decrypt(message) print(out) return out #Completed def rot13Cipher(): mode = getMode() message = getMessage() print('\nYour translated text is:') out = '' if mode == 'e' or mode == 'encrypt': out = py.pycrypt.rot13_cipher(message) print(out) else: out = rot13_cipher(message) print(out) return out def copyPrompt(message): print("\nCopy output to clipboard? (y/n)") mode = input().lower() if mode == 'y' or mode == 'yes': pyperclip.copy(message) spam = pyperclip.paste() print('Text copied to clipboard.') def determineType(): type = getType() if type == '1': out = ceasarCipher() if type == '2': out = zigzagCipher() if type == '3': out = trifidCipher() if type == '4': out = reverseCipher() if type == '5': out = atbashCipher() if type == '6': out = rot13Cipher() if out: copyPrompt(out) def main(): print("\nWelcome to the StarCipher v1.0") determineType() restart = input("\nDo you want to restart the program? (y/n) > ") if str(restart) == str("y") or str(restart) == str('yes'): print('\nRestarting...') main() else: print("\nThe program will be closed.") main()

27.043269

83

0.558933

8c41e02808229de35fe9c889149f42676a2821ee

972

py

Python

setup.py

altoyield/python-beanieclient

448b8dd328054eaf32dd7d0bdff700e603b5c27d

[ "Apache-2.0" ]

null

setup.py

altoyield/python-beanieclient

448b8dd328054eaf32dd7d0bdff700e603b5c27d

[ "Apache-2.0" ]

null

setup.py

altoyield/python-beanieclient

448b8dd328054eaf32dd7d0bdff700e603b5c27d

[ "Apache-2.0" ]

null

# coding: utf-8 """ Beanie ERP API An API specification for interacting with the Beanie ERP system # noqa: E501 OpenAPI spec version: 0.8 Contact: dev@bean.ie Generated by: https://github.com/swagger-api/swagger-codegen.git """ from setuptools import setup, find_packages # noqa: H301 NAME = "beanie-api" VERSION = "0.8.1" # To install the library, run the following # # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = ["urllib3 >= 1.15", "six >= 1.10", "certifi", "python-dateutil"] setup( name=NAME, version=VERSION, description="Beanie ERP API", author_email="dev@bean.ie", url="https://bean.ie/beanie-api", keywords=["Swagger", "Beanie ERP API"], install_requires=REQUIRES, packages=find_packages(), include_package_data=True, long_description="""\ An API specification for interacting with the Beanie ERP system # noqa: E501 """ )

23.707317

81

0.680041

6d623370b4b071249f1e204969c9e6122c713aad

5,772

py

Python

lib/utils/filelock.py

modulus-sa/ganeti

592c0e945cc2c7b0013f813ea8c9d8ec0d5bab98

[ "BSD-2-Clause" ]

396

2015-01-22T11:44:32.000Z

2022-03-31T14:14:29.000Z

lib/utils/filelock.py

modulus-sa/ganeti

592c0e945cc2c7b0013f813ea8c9d8ec0d5bab98

[ "BSD-2-Clause" ]

1,550

2015-04-05T09:53:50.000Z

2022-03-28T17:42:20.000Z

lib/utils/filelock.py

modulus-sa/ganeti

592c0e945cc2c7b0013f813ea8c9d8ec0d5bab98

[ "BSD-2-Clause" ]

119

2015-01-06T21:37:15.000Z

2022-03-07T06:36:26.000Z

# # # Copyright (C) 2006, 2007, 2010, 2011 Google Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED # TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """Utility functions for file-based locks. """ import fcntl import errno import os import logging from ganeti import errors from ganeti.utils import retry def LockFile(fd): """Locks a file using POSIX locks. @type fd: int @param fd: the file descriptor we need to lock """ try: fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) except IOError as err: if err.errno == errno.EAGAIN: raise errors.LockError("File already locked") raise class FileLock(object): """Utility class for file locks. """ def __init__(self, fd, filename): """Constructor for FileLock. @type fd: file @param fd: File object @type filename: str @param filename: Path of the file opened at I{fd} """ self.fd = fd self.filename = filename @classmethod def Open(cls, filename): """Creates and opens a file to be used as a file-based lock. @type filename: string @param filename: path to the file to be locked """ # Using "os.open" is necessary to allow both opening existing file # read/write and creating if not existing. Vanilla "open" will truncate an # existing file -or- allow creating if not existing. _flags = os.O_RDWR | os.O_CREAT return cls(os.fdopen(os.open(filename, _flags, 0o664), "w+"), filename) def __del__(self): self.Close() def Close(self): """Close the file and release the lock. """ if hasattr(self, "fd") and self.fd: self.fd.close() self.fd = None def _flock(self, flag, blocking, timeout, errmsg): """Wrapper for fcntl.flock. @type flag: int @param flag: operation flag @type blocking: bool @param blocking: whether the operation should be done in blocking mode. @type timeout: None or float @param timeout: for how long the operation should be retried (implies non-blocking mode). @type errmsg: string @param errmsg: error message in case operation fails. """ assert self.fd, "Lock was closed" assert timeout is None or timeout >= 0, \ "If specified, timeout must be positive" assert not (flag & fcntl.LOCK_NB), "LOCK_NB must not be set" # When a timeout is used, LOCK_NB must always be set if not (timeout is None and blocking): flag |= fcntl.LOCK_NB if timeout is None: self._Lock(self.fd, flag, timeout) else: try: retry.Retry(self._Lock, (0.1, 1.2, 1.0), timeout, args=(self.fd, flag, timeout)) except retry.RetryTimeout: raise errors.LockError(errmsg) @staticmethod def _Lock(fd, flag, timeout): try: fcntl.flock(fd, flag) except IOError as err: if timeout is not None and err.errno == errno.EAGAIN: raise retry.RetryAgain() logging.exception("fcntl.flock failed") raise def Exclusive(self, blocking=False, timeout=None): """Locks the file in exclusive mode. @type blocking: boolean @param blocking: whether to block and wait until we can lock the file or return immediately @type timeout: int or None @param timeout: if not None, the duration to wait for the lock (in blocking mode) """ self._flock(fcntl.LOCK_EX, blocking, timeout, "Failed to lock %s in exclusive mode" % self.filename) def Shared(self, blocking=False, timeout=None): """Locks the file in shared mode. @type blocking: boolean @param blocking: whether to block and wait until we can lock the file or return immediately @type timeout: int or None @param timeout: if not None, the duration to wait for the lock (in blocking mode) """ self._flock(fcntl.LOCK_SH, blocking, timeout, "Failed to lock %s in shared mode" % self.filename) def Unlock(self, blocking=True, timeout=None): """Unlocks the file. According to C{flock(2)}, unlocking can also be a nonblocking operation:: To make a non-blocking request, include LOCK_NB with any of the above operations. @type blocking: boolean @param blocking: whether to block and wait until we can lock the file or return immediately @type timeout: int or None @param timeout: if not None, the duration to wait for the lock (in blocking mode) """ self._flock(fcntl.LOCK_UN, blocking, timeout, "Failed to unlock %s" % self.filename)

30.539683

78

0.682259

0dee57a434e4bbccce19541392a8863fb61bd3cf

2,516

py

Python

pylib/serialcomm.py

jheidel/ece4760-lab5

c1ab262e40c22ee77e54067fe6dbfa2b7ee78c89

[ "MIT" ]

4

2016-02-05T08:04:17.000Z

2021-08-30T15:44:46.000Z

pylib/serialcomm.py

jheidel/ece4760-lab5

c1ab262e40c22ee77e54067fe6dbfa2b7ee78c89

[ "MIT" ]

null

pylib/serialcomm.py

jheidel/ece4760-lab5

c1ab262e40c22ee77e54067fe6dbfa2b7ee78c89

[ "MIT" ]

4

2016-02-05T08:04:18.000Z

2019-06-04T15:48:49.000Z

from threading import Thread, Event, Condition from multiprocessing import Process from ctypes import * from pylib.log import with_logging from time import sleep, time try: clib = CDLL("clib/clib.so") except: print "C shared library missing!" print "Did you forget to \"make\"?" sys.exit(1) @with_logging class SerialComm(Thread): def __init__(self, port): Thread.__init__(self) self.log.info("Starting serial communications on port %s" % port) ret = clib.init_serial(port) if ret != 0: raise Exception("SERIAL LIBRARY RETURNED %d" % ret) clib.scanner_init() self.kill = Event() self.c = Condition() self.frame = None self.set_pps(800) #default PPS def set_point(self, x, y, blank): """ Set point for the laser (x,y) with blank indicating whether the laser is blanked or not """ #self.log.info("Setting point %d, %d" % (x,y)) ret = clib.serial_new_point(x, y, blank) if ret != 0: raise Exception("SERIAL LIBRARY RETURNED %d" % ret) #Conversion from ILDA point space to laser coordinates def set_ilda_point(self, x, y, blank): def map_pt(p): #return (p + 2**15) / 16 return int((((math.asin((float(p) / 2**15)) / math.asin(1.0)) + 1) / 2) * 4096) self.set_point(map_pt(x), map_pt(y), blank) def stop(self): clib.scanner_stop() sleep(2 * 1.0 / self.pps) self.set_point(0,0,True) def set_frame(self, frame): """ Sets a new ilda frame for the laser projector to scan through None indicates that scanning should stop and the laser should be blanked """ if frame is not None: if frame != self.frame: self.frame = frame #self.log.debug("Frame has %d points" % frame.get_length()) clib.new_point_set(frame.get_length()) for i,pt in enumerate(frame.get_mapped_points()): clib.set_point_by_index(i, pt["x"], pt["y"], pt["blank"]) clib.activate_point_set() else: self.set_point(0,0,True) def set_pps(self, pps): """ Sets a new scanning speed for the ilda frame """ self.pps = pps self.log.info("Settings laser to %d PPS" % pps) clib.scanner_set_pps(pps) def run(self): pass

30.313253

91

0.564388

a506d292c8099e0edfa24a839f59adbb50a38f79

951

py

Python

docs/examples/compute/profitbricks/create_volume.py

zimventures/libcloud

be0765df384f1baccde24539156119856cb96816

[ "Apache-2.0" ]

1,435

2015-01-07T05:32:51.000Z

2022-03-25T19:39:34.000Z

docs/examples/compute/profitbricks/create_volume.py

zimventures/libcloud

be0765df384f1baccde24539156119856cb96816

[ "Apache-2.0" ]

1,158

2015-01-04T18:08:42.000Z

2022-03-24T14:34:57.000Z

docs/examples/compute/profitbricks/create_volume.py

zimventures/libcloud

be0765df384f1baccde24539156119856cb96816

[ "Apache-2.0" ]

832

2015-01-05T09:20:21.000Z

2022-03-24T19:22:19.000Z

import os from libcloud.compute.types import Provider from libcloud.compute.providers import get_driver cls = get_driver(Provider.PROFIT_BRICKS) # Get ProfitBricks credentials from environment variables pb_username = os.environ.get('PROFITBRICKS_USERNAME') pb_password = os.environ.get('PROFITBRICKS_PASSWORD') driver = cls(pb_username, pb_password) datacenters = driver.ex_list_datacenters() location = driver.ex_describe_location(ex_location_id='us/las') datacenter = [dc for dc in datacenters if dc.extra['location'] == location.id] images = driver.list_images(image_type='HDD') image = [img for img in images if img.extra['location'] == location.id][0] # Create a new SSD volume. Set `ex_type='HDD'` to create a HDD volume. ssd_volume = driver.create_volume( name='Example SSD volume', size=100, image=image, ex_type='SSD', ex_datacenter=datacenter[0], ex_password='PuTSoMeSTRONGPaSsWoRdHeRe2017' ) print(ssd_volume)

31.7

78

0.768665

13d858dfd7b4a7ec6acb817bfb6f091a9a221a22

12,375

py

Python

nltk/tag/perceptron.py

RedShiftCompany/nltk

3bf1254da2567418e6168021a2b9f29bec582ab3

[ "Apache-2.0" ]

null

nltk/tag/perceptron.py

RedShiftCompany/nltk

3bf1254da2567418e6168021a2b9f29bec582ab3

[ "Apache-2.0" ]

null

nltk/tag/perceptron.py

RedShiftCompany/nltk

3bf1254da2567418e6168021a2b9f29bec582ab3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # This module is a port of the Textblob Averaged Perceptron Tagger # Author: Matthew Honnibal <honnibal+gh@gmail.com>, # Long Duong <longdt219@gmail.com> (NLTK port) # URL: <https://github.com/sloria/textblob-aptagger> # <http://nltk.org/> # Copyright 2013 Matthew Honnibal # NLTK modifications Copyright 2015 The NLTK Project # # This module is provided under the terms of the MIT License. import random from collections import defaultdict import pickle import logging from nltk.tag.api import TaggerI from nltk.data import find, load try: import numpy as np except ImportError: pass PICKLE = "averaged_perceptron_tagger.pickle" class AveragedPerceptron(object): """An averaged perceptron, as implemented by Matthew Honnibal. See more implementation details here: https://explosion.ai/blog/part-of-speech-pos-tagger-in-python """ def __init__(self): # Each feature gets its own weight vector, so weights is a dict-of-dicts self.weights = {} self.classes = set() # The accumulated values, for the averaging. These will be keyed by # feature/clas tuples self._totals = defaultdict(int) # The last time the feature was changed, for the averaging. Also # keyed by feature/clas tuples # (tstamps is short for timestamps) self._tstamps = defaultdict(int) # Number of instances seen self.i = 0 def _softmax(self, scores): s = np.fromiter(scores.values(), dtype=float) exps = np.exp(s) return exps / np.sum(exps) def predict(self, features, return_conf=False): """Dot-product the features and current weights and return the best label.""" scores = defaultdict(float) for feat, value in features.items(): if feat not in self.weights or value == 0: continue weights = self.weights[feat] for label, weight in weights.items(): scores[label] += value * weight # Do a secondary alphabetic sort, for stability best_label = max(self.classes, key=lambda label: (scores[label], label)) # compute the confidence conf = max(self._softmax(scores)) if return_conf == True else None return best_label, conf def update(self, truth, guess, features): """Update the feature weights.""" def upd_feat(c, f, w, v): param = (f, c) self._totals[param] += (self.i - self._tstamps[param]) * w self._tstamps[param] = self.i self.weights[f][c] = w + v self.i += 1 if truth == guess: return None for f in features: weights = self.weights.setdefault(f, {}) upd_feat(truth, f, weights.get(truth, 0.0), 1.0) upd_feat(guess, f, weights.get(guess, 0.0), -1.0) def average_weights(self): """Average weights from all iterations.""" for feat, weights in self.weights.items(): new_feat_weights = {} for clas, weight in weights.items(): param = (feat, clas) total = self._totals[param] total += (self.i - self._tstamps[param]) * weight averaged = round(total / self.i, 3) if averaged: new_feat_weights[clas] = averaged self.weights[feat] = new_feat_weights def save(self, path): """Save the pickled model weights.""" with open(path, "wb") as fout: return pickle.dump(dict(self.weights), fout) def load(self, path): """Load the pickled model weights.""" self.weights = load(path) class PerceptronTagger(TaggerI): """ Greedy Averaged Perceptron tagger, as implemented by Matthew Honnibal. See more implementation details here: https://explosion.ai/blog/part-of-speech-pos-tagger-in-python >>> from nltk.tag.perceptron import PerceptronTagger Train the model >>> tagger = PerceptronTagger(load=False) >>> tagger.train([[('today','NN'),('is','VBZ'),('good','JJ'),('day','NN')], ... [('yes','NNS'),('it','PRP'),('beautiful','JJ')]]) >>> tagger.tag(['today','is','a','beautiful','day']) [('today', 'NN'), ('is', 'PRP'), ('a', 'PRP'), ('beautiful', 'JJ'), ('day', 'NN')] Use the pretrain model (the default constructor) >>> pretrain = PerceptronTagger() >>> pretrain.tag('The quick brown fox jumps over the lazy dog'.split()) [('The', 'DT'), ('quick', 'JJ'), ('brown', 'NN'), ('fox', 'NN'), ('jumps', 'VBZ'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dog', 'NN')] >>> pretrain.tag("The red cat".split()) [('The', 'DT'), ('red', 'JJ'), ('cat', 'NN')] """ START = ["-START-", "-START2-"] END = ["-END-", "-END2-"] def __init__(self, load=True): """ :param load: Load the pickled model upon instantiation. """ self.model = AveragedPerceptron() self.tagdict = {} self.classes = set() if load: AP_MODEL_LOC = "file:" + str( find("taggers/averaged_perceptron_tagger/" + PICKLE) ) self.load(AP_MODEL_LOC) def tag(self, tokens, return_conf=False, use_tagdict=True): """ Tag tokenized sentences. :params tokens: list of word :type tokens: list(str) """ prev, prev2 = self.START output = [] context = self.START + [self.normalize(w) for w in tokens] + self.END for i, word in enumerate(tokens): tag, conf = ( (self.tagdict.get(word), 1.0) if use_tagdict == True else (None, None) ) if not tag: features = self._get_features(i, word, context, prev, prev2) tag, conf = self.model.predict(features, return_conf) output.append((word, tag, conf) if return_conf == True else (word, tag)) prev2 = prev prev = tag return output def train(self, sentences, save_loc=None, nr_iter=5): """Train a model from sentences, and save it at ``save_loc``. ``nr_iter`` controls the number of Perceptron training iterations. :param sentences: A list or iterator of sentences, where each sentence is a list of (words, tags) tuples. :param save_loc: If not ``None``, saves a pickled model in this location. :param nr_iter: Number of training iterations. """ # We'd like to allow ``sentences`` to be either a list or an iterator, # the latter being especially important for a large training dataset. # Because ``self._make_tagdict(sentences)`` runs regardless, we make # it populate ``self._sentences`` (a list) with all the sentences. # This saves the overheard of just iterating through ``sentences`` to # get the list by ``sentences = list(sentences)``. self._sentences = list() # to be populated by self._make_tagdict... self._make_tagdict(sentences) self.model.classes = self.classes for iter_ in range(nr_iter): c = 0 n = 0 for sentence in self._sentences: words, tags = zip(*sentence) prev, prev2 = self.START context = self.START + [self.normalize(w) for w in words] + self.END for i, word in enumerate(words): guess = self.tagdict.get(word) if not guess: feats = self._get_features(i, word, context, prev, prev2) guess, _ = self.model.predict(feats) self.model.update(tags[i], guess, feats) prev2 = prev prev = guess c += guess == tags[i] n += 1 random.shuffle(self._sentences) logging.info("Iter {0}: {1}/{2}={3}".format(iter_, c, n, _pc(c, n))) # We don't need the training sentences anymore, and we don't want to # waste space on them when we pickle the trained tagger. self._sentences = None self.model.average_weights() # Pickle as a binary file if save_loc is not None: with open(save_loc, "wb") as fout: # changed protocol from -1 to 2 to make pickling Python 2 compatible pickle.dump((self.model.weights, self.tagdict, self.classes), fout, 2) def load(self, loc): """ :param loc: Load a pickled model at location. :type loc: str """ self.model.weights, self.tagdict, self.classes = load(loc) self.model.classes = self.classes def normalize(self, word): """ Normalization used in pre-processing. - All words are lower cased - Groups of digits of length 4 are represented as !YEAR; - Other digits are represented as !DIGITS :rtype: str """ if "-" in word and word[0] != "-": return "!HYPHEN" elif word.isdigit() and len(word) == 4: return "!YEAR" elif word[0].isdigit(): return "!DIGITS" else: return word.lower() def _get_features(self, i, word, context, prev, prev2): """Map tokens into a feature representation, implemented as a {hashable: int} dict. If the features change, a new model must be trained. """ def add(name, *args): features[" ".join((name,) + tuple(args))] += 1 i += len(self.START) features = defaultdict(int) # It's useful to have a constant feature, which acts sort of like a prior add("bias") add("i suffix", word[-3:]) add("i pref1", word[0]) add("i-1 tag", prev) add("i-2 tag", prev2) add("i tag+i-2 tag", prev, prev2) add("i word", context[i]) add("i-1 tag+i word", prev, context[i]) add("i-1 word", context[i - 1]) add("i-1 suffix", context[i - 1][-3:]) add("i-2 word", context[i - 2]) add("i+1 word", context[i + 1]) add("i+1 suffix", context[i + 1][-3:]) add("i+2 word", context[i + 2]) return features def _make_tagdict(self, sentences): """ Make a tag dictionary for single-tag words. :param sentences: A list of list of (word, tag) tuples. """ counts = defaultdict(lambda: defaultdict(int)) for sentence in sentences: self._sentences.append(sentence) for word, tag in sentence: counts[word][tag] += 1 self.classes.add(tag) freq_thresh = 20 ambiguity_thresh = 0.97 for word, tag_freqs in counts.items(): tag, mode = max(tag_freqs.items(), key=lambda item: item[1]) n = sum(tag_freqs.values()) # Don't add rare words to the tag dictionary # Only add quite unambiguous words if n >= freq_thresh and (mode / n) >= ambiguity_thresh: self.tagdict[word] = tag def _pc(n, d): return (n / d) * 100 def _load_data_conll_format(filename): print("Read from file: ", filename) with open(filename, "rb") as fin: sentences = [] sentence = [] for line in fin.readlines(): line = line.strip() # print line if len(line) == 0: sentences.append(sentence) sentence = [] continue tokens = line.split("\t") word = tokens[1] tag = tokens[4] sentence.append((word, tag)) return sentences def _get_pretrain_model(): # Train and test on English part of ConLL data (WSJ part of Penn Treebank) # Train: section 2-11 # Test : section 23 tagger = PerceptronTagger() training = _load_data_conll_format("english_ptb_train.conll") testing = _load_data_conll_format("english_ptb_test.conll") print("Size of training and testing (sentence)", len(training), len(testing)) # Train and save the model tagger.train(training, PICKLE) print("Accuracy : ", tagger.evaluate(testing)) if __name__ == "__main__": # _get_pretrain_model() pass

35.458453

148

0.567919

b26744e4deea27c27acaa3c66eb2e9e1aaeac771

40,432

py

Python

legtool/tabs/gait_tab.py

jpieper/legtool

ab3946051bd16817b61d3073ce7be8bd27af90d0

[ "Apache-2.0" ]

10

2015-09-23T19:28:06.000Z

2021-04-27T02:32:27.000Z

legtool/tabs/gait_tab.py

jpieper/legtool

ab3946051bd16817b61d3073ce7be8bd27af90d0

[ "Apache-2.0" ]

null

legtool/tabs/gait_tab.py

jpieper/legtool

ab3946051bd16817b61d3073ce7be8bd27af90d0

[ "Apache-2.0" ]

9

2015-10-16T07:26:18.000Z

2021-01-13T07:18:35.000Z

# Copyright 2014 Josh Pieper, jjp@pobox.com. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools import numpy import time import sys import trollius as asyncio from trollius import From, Task import PySide.QtCore as QtCore import PySide.QtGui as QtGui from ..tf import tf from ..servo import selector from ..gait import ripple from . import convexhull from . import settings from .common import BoolContext from . import graphics_scene PLAYBACK_TIMEOUT_MS = 40 class GaitGeometryDisplay(object): FRAME_ROBOT, FRAME_WORLD, FRAME_BODY = range(3) PROJECTION_XY, PROJECTION_YZ, PROJECTION_XZ = range(3) def __init__(self, ui): self.graphics_scene = QtGui.QGraphicsScene() self.graphics_view = ui.gaitGeometryView self.graphics_view.setScene(self.graphics_scene) self.graphics_view.setTransform(QtGui.QTransform().scale(1, -1)) self.config = None self.state = None self.frame = self.FRAME_ROBOT self.projection = self.PROJECTION_XY self.scale = 300.0 self.axes_item = graphics_scene.AxesItem(true_scale=True, grid_skip=2) self.axes_item.x_scale = self.scale self.axes_item.y_scale = self.scale self.axes_item.x_suffix = 'mm' self.axes_item.y_suffix = 'mm' self.graphics_scene.addItem(self.axes_item) self.support_poly = QtGui.QGraphicsPolygonItem() self.support_poly.setPen(QtGui.QColor(0, 0, 0, 0)) self.support_poly.setBrush(QtGui.QBrush(QtGui.QColor(0, 128, 0, 15))) self.graphics_scene.addItem(self.support_poly) self.items = [] def resize(self): self.graphics_view.fitInView(-self.scale, -self.scale, 2 * self.scale, 2 * self.scale, QtCore.Qt.KeepAspectRatio) def set_view(self, frame, projection, scale): self.frame = frame self.projection = projection self.scale = scale self.axes_item.x_scale = scale self.axes_item.y_scale = scale if self.config is not None and self.state is not None: self.set_state(self.state) self.axes_item.update() def set_gait_config(self, config): assert config is not None self.config = config for item in self.items: self.graphics_scene.removeItem(item) self.items = [] # Things to render: # * Body position # * Body CoG # * Shoulder positions # * Leg positions # * Stability polygon # # Nice to have: Coordinate axes and scale labels. body_poly = QtGui.QPolygonF([ QtCore.QPointF(-10.0, -10.0), QtCore.QPointF(-10.0, 10.0), QtCore.QPointF(10.0, 10.0), QtCore.QPointF(10.0, -10.0)]) self.body = self.graphics_scene.addPolygon( body_poly, QtGui.QPen(QtCore.Qt.black), QtGui.QBrush(QtCore.Qt.red)) self.body.setFlags(QtGui.QGraphicsItem.ItemIgnoresTransformations) self.items.append(self.body) self.cog = self.graphics_scene.addEllipse( -5, -5, 10, 10, QtGui.QPen(QtCore.Qt.black), QtGui.QBrush(QtCore.Qt.yellow)) self.cog.setFlags(QtGui.QGraphicsItem.ItemIgnoresTransformations) self.items.append(self.cog) self.shoulders = {} self.legs = {} shoulder_poly = QtGui.QPolygonF([ QtCore.QPointF(-10, 0), QtCore.QPointF(0, 10), QtCore.QPointF(10, 0), QtCore.QPointF(0, -10)]) for leg_num, leg in config.mechanical.leg_config.iteritems(): this_shoulder = self.graphics_scene.addPolygon( shoulder_poly, QtGui.QPen(QtCore.Qt.black), QtGui.QBrush(QtCore.Qt.blue)) this_shoulder.setFlags( QtGui.QGraphicsItem.ItemIgnoresTransformations) self.shoulders[leg_num] = this_shoulder self.items.append(this_shoulder) this_leg = self.graphics_scene.addEllipse( -10, -10, 20, 20, QtGui.QPen(QtCore.Qt.black), QtGui.QBrush(QtCore.Qt.green)) this_leg.setFlags(QtGui.QGraphicsItem.ItemIgnoresTransformations) self.legs[leg_num] = this_leg self.items.append(this_leg) def _project(self, point, frame): target_frame = None if self.frame == self.FRAME_ROBOT: target_frame = self.state.robot_frame elif self.frame == self.FRAME_WORLD: target_frame = self.state.world_frame elif self.frame == self.FRAME_BODY: target_frame = self.state.body_frame target_point = target_frame.map_from_frame(frame, point) if self.projection == self.PROJECTION_XY: return (target_point.x, target_point.y) elif self.projection == self.PROJECTION_YZ: return (target_point.y, target_point.z) elif self.projection == self.PROJECTION_XZ: return (target_point.x, target_point.z) def set_state(self, state): assert self.config is not None self.state = state stance_points = [] self.body.setPos(*self._project(tf.Point3D(), state.body_frame)) self.cog.setPos(*self._project(tf.Point3D(), state.cog_frame)) for leg_num, shoulder in self.shoulders.iteritems(): if leg_num not in state.legs: continue shoulder.setPos(*self._project(tf.Point3D(), state.legs[leg_num].shoulder_frame)) for leg_num, leg_item in self.legs.iteritems(): if leg_num not in state.legs: continue leg = state.legs[leg_num] point = self._project(leg.point, leg.frame) leg_item.setPos(*point) shoulder_point = leg.shoulder_frame.map_from_frame( leg.frame, leg.point) ik_result = leg.leg_ik.do_ik(shoulder_point) if ik_result is None: color = QtCore.Qt.red elif leg.mode == ripple.STANCE: color = QtCore.Qt.green stance_points.append(point) elif leg.mode == ripple.SWING: color = QtCore.Qt.yellow else: assert False, 'unknown leg mode %d' % leg.mode leg_item.setBrush(QtGui.QBrush(color)) if len(stance_points) >= 3: self.support_poly.setVisible(True) hull = convexhull.convexHull(stance_points) poly = QtGui.QPolygonF([QtCore.QPointF(x, y) for x, y in hull]) self.support_poly.setPolygon(poly) else: self.support_poly.setVisible(False) self.resize() class GaitGraphDisplay(object): def __init__(self, ui): self.graphics_scene = QtGui.QGraphicsScene() self.graphics_view = ui.gaitGraphView self.graphics_view.setScene(self.graphics_scene) self.phase_line = None self.phase = 0.0 def resize(self): self.fit_in_view() def fit_in_view(self): self.graphics_view.fitInView(QtCore.QRectF(-0.1, 0, 1.1, 1)) def set_phase(self, phase): self.phase = phase if self.phase_line: pos = self.phase_line.pos() self.phase_line.setPos(phase, pos.y()) def set_gait_graph(self, graph): self.graphics_scene.clear() leg_numbers = sorted(graph.leg.keys()) count = len(leg_numbers) if count == 0: return self.graphics_scene.addRect(0., 0., 1., 1.) self.phase_line = self.graphics_scene.addLine(0., 0., 0., 1.0) y_offset = 0.0 y_size = 1.0 / count for leg_number in leg_numbers: label = self.graphics_scene.addSimpleText("%d" % leg_number) label.setPos(-0.08, y_offset + 0.1 * y_size) label.setFlag(QtGui.QGraphicsItem.ItemIgnoresTransformations) self.graphics_scene.addLine(0, y_offset, 1.0, y_offset) old_phase = 0.0 old_swing = False for phase, mode in graph.leg[leg_number].sequence: if mode == ripple.STANCE and old_swing: # Render a black bar for this swing phase. self.graphics_scene.addRect( old_phase, y_offset + 0.1 * y_size, phase - old_phase, 0.8 * y_size, QtGui.QPen(), QtGui.QBrush(QtCore.Qt.black)) old_phase = phase old_swing = True if mode == ripple.SWING else False y_offset += y_size self.fit_in_view() class CommandWidget(object): ATTR_NAMES = ['translate_x_mm_s', 'translate_y_mm_s', 'rotate_deg_s', 'body_x_mm', 'body_y_mm', 'body_z_mm', 'body_pitch_deg', 'body_roll_deg', 'body_yaw_deg',] ATTR_SUFFIXES = ['mm/s', 'mm/s', 'deg/s', 'mm', 'mm', 'mm', 'deg', 'deg', 'deg',] def __init__(self, ui, command, command_change_callback): self.ui = ui self.parent_command = command self.command_change_callback = command_change_callback self.scales = [ 400.0, 400.0, 50.0, 100.0, 100.0, 100.0, 45.0, 45.0, 45.0 ] self.update_lock = asyncio.Lock() self.config = None self.command = None self.graphics_scene = graphics_scene.GraphicsScene() self.graphics_scene.sceneMouseMoveEvent.connect( self.handle_mouse_move) self.graphics_scene.sceneMousePressEvent.connect( self.handle_mouse_press) self.graphics_view = self.ui.gaitCommandView self.graphics_view.setTransform(QtGui.QTransform().scale(1, -1)) self.graphics_view.setScene(self.graphics_scene) self.in_scale_changed = BoolContext() for combo in [self.ui.commandXCombo, self.ui.commandYCombo]: combo.currentIndexChanged.connect(self.handle_axis_change) for spin in [self.ui.commandXScaleSpin, self.ui.commandYScaleSpin]: spin.valueChanged.connect(self.handle_scale_change) self.axes_item = graphics_scene.AxesItem() self.graphics_scene.addItem(self.axes_item) self.grid_count = 10 self.usable_rects = {} for x in range(-self.grid_count + 1, self.grid_count): for y in range(-self.grid_count + 1, self.grid_count): self.usable_rects[(x, y)] = \ self.graphics_scene.addRect( (x - 0.5) / self.grid_count, (y - 0.5) / self.grid_count, 1.0 / self.grid_count, 1.0 / self.grid_count) for rect in self.usable_rects.itervalues(): rect.setPen(QtGui.QPen(QtCore.Qt.NoPen)) rect.setZValue(-20) def resize(self): self.fit_in_view() def fit_in_view(self): self.graphics_view.fitInView(QtCore.QRectF(-1, -1, 2, 2)) def read_settings(self, config): def set_combo(combo, name): settings.restore_combo(config, 'gaitconfig', combo, name) set_combo(self.ui.commandXCombo, 'command_x_axis') set_combo(self.ui.commandYCombo, 'command_y_axis') for index in range(len(self.scales)): name = 'command_axis_scale_%d' % index if config.has_option('gaitconfig', name): self.scales[index] = config.getfloat('gaitconfig', name) self.handle_axis_change() def write_settings(self, config): config.set('gaitconfig', 'command_x_axis', self.ui.commandXCombo.currentText()) config.set('gaitconfig', 'command_y_axis', self.ui.commandYCombo.currentText()) for index, value in enumerate(self.scales): config.set('gaitconfig', 'command_axis_scale_%d' % index, value) def handle_axis_change(self): with self.in_scale_changed: self.ui.commandXScaleSpin.setValue(self.x_scale()) self.ui.commandYScaleSpin.setValue(self.y_scale()) self.axes_item.x_scale = self.x_scale() self.axes_item.y_scale = self.y_scale() self.axes_item.x_suffix = self.ATTR_SUFFIXES[self.x_axis()] self.axes_item.y_suffix = self.ATTR_SUFFIXES[self.y_axis()] self.axes_item.update() if self.config is not None: self.update_allowable(self.config, self.command) def handle_scale_change(self, value): if self.in_scale_changed.value: return with self.in_scale_changed: if self.x_axis() == self.y_axis(): self.ui.commandXScaleSpin.setValue(value) self.ui.commandYScaleSpin.setValue(value) self.scales[self.x_axis()] = self.ui.commandXScaleSpin.value() self.scales[self.y_axis()] = self.ui.commandYScaleSpin.value() self.axes_item.x_scale = self.x_scale() self.axes_item.y_scale = self.y_scale() self.axes_item.update() if self.config is not None: self.update_allowable(self.config, self.command) def x_axis(self): return self.ui.commandXCombo.currentIndex() def y_axis(self): return self.ui.commandYCombo.currentIndex() def x_scale(self): return self.scales[self.x_axis()] def y_scale(self): return self.scales[self.y_axis()] def handle_mouse_move(self, cursor): x_value = cursor.x() * self.x_scale() y_value = cursor.y() * self.y_scale() if self.x_axis() == self.y_axis(): x_value = y_value = 0.5 * (x_value + y_value) setattr(self.parent_command, self.ATTR_NAMES[self.x_axis()], x_value) setattr(self.parent_command, self.ATTR_NAMES[self.y_axis()], y_value) self.command_change_callback() def handle_mouse_press(self, cursor): self.handle_mouse_move(cursor) def update_allowable(self, config, command): self.next_config = config.copy() self.next_command = command.copy() for (x, y), rect in self.usable_rects.iteritems(): old_brush = rect.brush() old_color = old_brush.color() rect.setBrush(QtGui.QBrush(QtGui.QColor( old_color.red(), old_color.green(), old_color.blue(), 64))) Task(self._really_update_allowable()) @asyncio.coroutine def _really_update_allowable(self): if self.update_lock.locked(): return yield From(self.update_lock.acquire()) try: while self.next_config is not None: yield From(self.do_update_allowable()) finally: self.update_lock.release() @asyncio.coroutine def do_update_allowable(self): self.config = self.next_config self.command = self.next_command my_gait = ripple.RippleGait(self.next_config) my_command = self.next_command.copy() self.next_config = None self.next_command = None next_wait = time.time() + 0.5 * 0.001 * PLAYBACK_TIMEOUT_MS for (x, y), rect in self.usable_rects.iteritems(): x_value = self.x_scale() * float(x) / self.grid_count y_value = self.y_scale() * float(y) / self.grid_count setattr(my_command, self.ATTR_NAMES[self.x_axis()], x_value) setattr(my_command, self.ATTR_NAMES[self.y_axis()], y_value) color = (0, 255, 0) try: my_gait.set_command(my_command) actual_command = my_gait.command actual_x_value = getattr( actual_command, self.ATTR_NAMES[self.x_axis()]) actual_y_value = getattr( actual_command, self.ATTR_NAMES[self.y_axis()]) if actual_x_value != x_value or actual_y_value != y_value: color = (255, 255, 0) except ripple.NotSupported: color = (255, 0, 0) rect.setBrush(QtGui.QBrush(QtGui.QColor(*color))) if time.time() > next_wait: yield From(asyncio.sleep(0.5 * 0.001 * PLAYBACK_TIMEOUT_MS)) if self.next_config is not None: return next_wait = time.time() + 0.5 * 0.001 * PLAYBACK_TIMEOUT_MS class GaitTab(object): (PLAYBACK_IDLE, PLAYBACK_SINGLE, PLAYBACK_REPEAT, PLAYBACK_SLOW_REPEAT) = range(4) def __init__(self, ui, ikconfig_tab, servo_tab): self.ui = ui self.ikconfig_tab = ikconfig_tab self.servo_tab = servo_tab self.current_command = None self.next_state = None self.playback_mode = self.PLAYBACK_IDLE self.in_gait_changed = BoolContext() self.in_number_changed = BoolContext() self.in_command_changed = BoolContext() self.ripple_config = ripple.RippleConfig() self.ripple = ripple.RippleGait(self.ripple_config) self.command = ripple.Command() self.command_widget = CommandWidget( ui, self.command, self.handle_widget_set_command) self.current_states = [] self.gait_graph_display = GaitGraphDisplay(self.ui) self.gait_geometry_display = GaitGeometryDisplay(self.ui) self.ui.gaitLegList.currentItemChanged.connect(self.handle_leg_change) for spin in [self.ui.mountingLegXSpin, self.ui.mountingLegYSpin, self.ui.mountingLegZSpin]: spin.valueChanged.connect(self.handle_leg_data_change) for spin in [self.ui.bodyCogXSpin, self.ui.bodyCogYSpin, self.ui.bodyCogZSpin, self.ui.idlePositionXSpin, self.ui.idlePositionYSpin, self.ui.idlePositionZSpin, self.ui.maxCycleTimeSpin, self.ui.liftHeightSpin, self.ui.swingPercentSpin, self.ui.positionMarginSpin, self.ui.bodyZOffsetSpin, self.ui.staticCenterSpin, self.ui.staticStableSpin, self.ui.staticMarginSpin]: spin.valueChanged.connect(self.handle_gait_config_change) self.ui.staticEnableCheck.toggled.connect( self.handle_gait_config_change) self.ui.legOrderEdit.editingFinished.connect( self.handle_leg_order_editing_finished) self.command_spins = [ (self.ui.commandXSpin, 'translate_x_mm_s'), (self.ui.commandYSpin, 'translate_y_mm_s'), (self.ui.commandRotSpin, 'rotate_deg_s'), (self.ui.commandBodyXSpin, 'body_x_mm'), (self.ui.commandBodyYSpin, 'body_y_mm'), (self.ui.commandBodyZSpin, 'body_z_mm'), (self.ui.commandPitchSpin, 'body_pitch_deg'), (self.ui.commandRollSpin, 'body_roll_deg'), (self.ui.commandYawSpin, 'body_yaw_deg'), ] for spin, _ in self.command_spins: spin.valueChanged.connect(self.handle_command_change) self.ui.commandResetButton.clicked.connect(self.handle_command_reset) for combo in [self.ui.geometryFrameCombo, self.ui.geometryProjectionCombo]: combo.currentIndexChanged.connect(self.handle_geometry_change) self.ui.geometryScaleSpin.valueChanged.connect( self.handle_geometry_change) self.ui.tabWidget.currentChanged.connect(self.handle_current_changed) self.phase_step = 2.0 / self.ui.playbackPhaseSlider.maximum() self.ui.playbackBeginCombo.currentIndexChanged.connect( self.handle_playback_config_change) self.ui.playbackPhaseSlider.valueChanged.connect( self.handle_playback_phase_change) for button, state in [ (self.ui.playbackSingleButton, self.PLAYBACK_SINGLE), (self.ui.playbackRepeatButton, self.PLAYBACK_REPEAT), (self.ui.playbackSlowRepeatButton, self.PLAYBACK_SLOW_REPEAT)]: button.toggled.connect( functools.partial(self.handle_playback_state_change, state)) self.playback_timer = QtCore.QTimer() self.playback_timer.timeout.connect(self.handle_playback_timer) def resizeEvent(self, event): if self.ui.tabWidget.currentIndex() == 2: self.gait_graph_display.resize() self.gait_geometry_display.resize() self.command_widget.resize() def get_float_configs(self): return [(self.ui.geometryScaleSpin, 'geometry_scale_mm'), (self.ui.commandXSpin, 'command_x_mm_s'), (self.ui.commandYSpin, 'command_y_mm_s'), (self.ui.commandRotSpin, 'command_rot_deg_s'), (self.ui.commandBodyXSpin, 'command_body_x_mm'), (self.ui.commandBodyYSpin, 'command_body_y_mm'), (self.ui.commandBodyZSpin, 'command_body_z_mm'), (self.ui.commandPitchSpin, 'command_body_pitch_deg'), (self.ui.commandRollSpin, 'command_body_roll_deg'), (self.ui.commandYawSpin, 'command_body_yaw_deg'), ] def string_to_leg_config(self, value): assert isinstance(value, str) fields = [float(x) for x in value.split(',')] assert len(fields) >= 3 result = ripple.LegConfig() result.mount_x_mm = fields[0] result.mount_y_mm = fields[1] result.mount_z_mm = fields[2] return result def leg_config_to_string(self, leg_config): assert isinstance(leg_config, ripple.LegConfig) return '%.2f,%.2f,%.2f' % ( leg_config.mount_x_mm, leg_config.mount_y_mm, leg_config.mount_z_mm) def read_settings(self, config): if not config.has_section('gaitconfig'): return class IkGetter(object): def __init__(self, parent): self.parent = parent def __getitem__(self, index): return self.parent.ikconfig_tab.get_leg_ik(index) self.ripple_config = \ ripple.RippleConfig.read_settings(config, 'gaitconfig', IkGetter(self)) with self.in_command_changed: for spin, name in self.get_float_configs(): if config.has_option('gaitconfig', name): spin.setValue(config.getfloat('gaitconfig', name)) def set_combo(combo, name): settings.restore_combo(config, 'gaitconfig', combo, name) set_combo(self.ui.geometryFrameCombo, 'geometry_frame') set_combo(self.ui.geometryProjectionCombo, 'geometry_projection') self.command_widget.read_settings(config) self.update_ui_from_config() self.handle_leg_change(self.ui.gaitLegList.currentItem()) self.handle_gait_config_change() self.handle_geometry_change() self.handle_command_change() def update_ui_from_config(self): with self.in_gait_changed: c = self.ripple_config m = c.mechanical legs = m.leg_config.values() l = legs[0] if len(legs) > 0 else ripple.LegConfig() spins = [ (self.ui.bodyCogXSpin, m.body_cog_x_mm), (self.ui.bodyCogYSpin, m.body_cog_y_mm), (self.ui.bodyCogZSpin, m.body_cog_z_mm), (self.ui.idlePositionXSpin, l.idle_x_mm), (self.ui.idlePositionYSpin, l.idle_y_mm), (self.ui.idlePositionZSpin, l.idle_z_mm), (self.ui.maxCycleTimeSpin, c.max_cycle_time_s), (self.ui.liftHeightSpin, c.lift_height_mm), (self.ui.swingPercentSpin, c.swing_percent), (self.ui.positionMarginSpin, c.position_margin_percent), (self.ui.bodyZOffsetSpin, c.body_z_offset_mm), (self.ui.staticCenterSpin, c.static_center_factor), (self.ui.staticStableSpin, c.static_stable_factor), (self.ui.staticMarginSpin, c.static_margin_mm), ] for spin, value in spins: spin.setValue(value) self.ui.staticEnableCheck.setChecked(c.statically_stable) self.ui.legOrderEdit.setText(c.str_leg_order(c.leg_order)) self.handle_leg_change(self.ui.gaitLegList.currentItem()) def write_settings(self, config): self.ripple_config.write_settings(config, 'gaitconfig') for spin, name in self.get_float_configs(): config.set('gaitconfig', name, spin.value()) config.set('gaitconfig', 'geometry_frame', self.ui.geometryFrameCombo.currentText()) config.set('gaitconfig', 'geometry_projection', self.ui.geometryProjectionCombo.currentText()) self.command_widget.write_settings(config) def stringify_leg_order(self, data): return ripple.RippleConfig.str_leg_order(data) def validate_leg_order(self, data): """Accept data that is human entered. Return a string containing a valid leg ordering, which will consist of a comma separate list of leg numbers, or tuples of leg numbers.""" entered_values = [] try: entered_values = ripple.RippleConfig.parse_leg_order(data) except: pass required_legs = self.ripple_config.mechanical.leg_config.keys() used_legs = {} actual_legs = [] for group in entered_values: if isinstance(group, int): x = group if x in required_legs and not x in used_legs: actual_legs.append(x) used_legs[x] = True else: next_tuple = () for x in group: if x in required_legs and not x in used_legs: next_tuple += (x,) used_legs[x] = True if len(next_tuple) > 1: actual_legs.append(next_tuple) elif len(next_tuple) == 1: actual_legs.append(next_tuple[0]) for x in required_legs: if not x in used_legs: actual_legs.append(x) return self.stringify_leg_order(actual_legs) def handle_current_changed(self, index=2): if index != 2: # Make sure we're not still playing. self.ui.playbackSingleButton.setChecked(False) self.ui.playbackRepeatButton.setChecked(False) self.ui.playbackSlowRepeatButton.setChecked(False) if self.servo_tab.controller: Task(self.servo_tab.controller.enable_power( selector.POWER_BRAKE)) return if self.servo_tab.controller: Task(self.servo_tab.controller.enable_power( selector.POWER_ENABLE)) # Update the leg list widget. available_legs = self.ikconfig_tab.get_all_legs() enabled_legs = set(self.ikconfig_tab.get_enabled_legs()) for leg_num in available_legs: leg_str = str(leg_num) if not self.ui.gaitLegList.findItems( leg_str, QtCore.Qt.MatchExactly): self.ui.gaitLegList.addItem(leg_str) items = self.ui.gaitLegList.findItems( leg_str, QtCore.Qt.MatchExactly) item = items[0] if leg_num in enabled_legs: item.setFlags(QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable) else: item.setFlags(0) self.handle_leg_change(self.ui.gaitLegList.currentItem()) # Make sure that our configuration is fully up to date. self.handle_gait_config_change() self.command_widget.fit_in_view() def handle_leg_change(self, current_item): widgets = [self.ui.mountingLegXSpin, self.ui.mountingLegYSpin, self.ui.mountingLegZSpin] if current_item is None: [x.setEnabled(False) for x in widgets] return [x.setEnabled(True) for x in widgets] with self.in_number_changed: number = int(current_item.text()) leg_config = self.ripple_config.mechanical.leg_config.get( number, ripple.LegConfig()) if leg_config.mount_x_mm is not None: self.ui.mountingLegXSpin.setValue(leg_config.mount_x_mm) if leg_config.mount_y_mm is not None: self.ui.mountingLegYSpin.setValue(leg_config.mount_y_mm) if leg_config.mount_z_mm is not None: self.ui.mountingLegZSpin.setValue(leg_config.mount_z_mm) self.handle_leg_data_change() def handle_leg_data_change(self): if self.in_number_changed.value: return number_item = self.ui.gaitLegList.currentItem() if number_item is None: return number = int(number_item.text()) leg_config = self.ripple_config.mechanical.leg_config.get( number, ripple.LegConfig()) leg_config.mount_x_mm = self.ui.mountingLegXSpin.value() leg_config.mount_y_mm = self.ui.mountingLegYSpin.value() leg_config.mount_z_mm = self.ui.mountingLegZSpin.value() self.ripple_config.mechanical.leg_config[number] = leg_config self.handle_gait_config_change() def handle_gait_config_change(self): if self.in_gait_changed.value: return # Put all of our GUI information into the RippleGait that # needs to be. mechanical = self.ripple_config.mechanical for item_num in range(self.ui.gaitLegList.count()): item = self.ui.gaitLegList.item(item_num) leg_number = int(item.text()) if not (item.flags() & QtCore.Qt.ItemIsEnabled): if leg_number in mechanical.leg_config: del mechanical.leg_config[leg_number] elif leg_number not in mechanical.leg_config: mechanical.leg_config[leg_number] = ripple.LegConfig() for leg_data in self.ripple_config.mechanical.leg_config.iteritems(): leg_number, leg_config = leg_data leg_config.idle_x_mm = self.ui.idlePositionXSpin.value() leg_config.idle_y_mm = self.ui.idlePositionYSpin.value() leg_config.idle_z_mm = self.ui.idlePositionZSpin.value() leg_config.leg_ik = self.ikconfig_tab.get_leg_ik(leg_number) self.ripple_config.mechanical.body_cog_x_mm = \ self.ui.bodyCogXSpin.value() self.ripple_config.mechanical.body_cog_y_mm = \ self.ui.bodyCogYSpin.value() self.ripple_config.mechanical.body_cog_z_mm = \ self.ui.bodyCogZSpin.value() self.ripple_config.max_cycle_time_s = self.ui.maxCycleTimeSpin.value() self.ripple_config.lift_height_mm = self.ui.liftHeightSpin.value() self.ripple_config.swing_percent = \ self.ui.swingPercentSpin.value() self.ripple_config.position_margin_percent = \ self.ui.positionMarginSpin.value() self.ripple_config.leg_order = \ ripple.RippleConfig.parse_leg_order( self.validate_leg_order(self.ui.legOrderEdit.text())) self.ripple_config.body_z_offset_mm = self.ui.bodyZOffsetSpin.value() self.ripple_config.statically_stable = \ self.ui.staticEnableCheck.isChecked() self.ripple_config.static_center_factor = \ self.ui.staticCenterSpin.value() self.ripple_config.static_stable_factor = \ self.ui.staticStableSpin.value() self.ripple_config.static_margin_mm = \ self.ui.staticMarginSpin.value() self.ripple = ripple.RippleGait(self.ripple_config) self.gait_geometry_display.set_gait_config(self.ripple_config) self.update_gait_graph() self.handle_playback_config_change() self.update_allowable_commands() def handle_leg_order_editing_finished(self): self.ui.legOrderEdit.setText(self.validate_leg_order( self.ui.legOrderEdit.text())) self.handle_gait_config_change() def update_gait_graph(self): self.gait_graph_display.set_gait_graph(self.ripple.get_gait_graph()) def get_start_state(self): begin_index = self.ui.playbackBeginCombo.currentIndex() this_ripple = ripple.RippleGait(self.ripple_config) if begin_index == 0: # Idle begin_state = this_ripple.get_idle_state() else: begin_state = this_ripple.get_idle_state() this_ripple.set_state(begin_state, self.command) for x in range(int(1.0 / self.phase_step)): begin_state = this_ripple.advance_phase(self.phase_step) # When setting a state, we are required to be exactly # zero. Verify that we are close enough to zero from a # numerical perspective, then force it to be exactly zero. assert abs(((begin_state.phase + 0.5) % 1.0) - 0.5) < 1e-4 begin_state.phase = 0.0 return begin_state def handle_playback_config_change(self): # Re-run the playback recording the state through an entire # phase. Then make sure that the graphic state is current for # the phase that is selected now. try: begin_state = self.get_start_state() begin_state = self.ripple.set_state(begin_state, self.command) except ripple.NotSupported: # guess we can't change anything self.ui.gaitOptionsBrowser.setText('command not possible') return self.current_states = ( [begin_state.copy()] + [self.ripple.advance_phase(self.phase_step).copy() for x in range(self.ui.playbackPhaseSlider.maximum())]) self.handle_playback_phase_change() options = self.ripple.options text = 'cycle_time: %.2fs\nservo_speed: %.1fdps' % ( options.cycle_time_s, options.servo_speed_dps) self.ui.gaitOptionsBrowser.setText(text) def handle_playback_phase_change(self): if self.playback_mode != self.PLAYBACK_IDLE: return self.update_phase(self.ui.playbackPhaseSlider.value() * self.phase_step) def update_phase(self, phase): # Update the current geometry rendering. state = self.current_states[int(phase / self.phase_step)] self.render_state(state) def render_state(self, state): # Render the phase line in the gait graph. self.gait_graph_display.set_phase(state.phase % 1.0) self.gait_geometry_display.set_state(state) if self.servo_tab.controller: try: command = state.command_dict() except ripple.NotSupported: return self.next_command = command if (self.current_command is not None and not self.current_command.done()): return self.current_command = Task(self.set_next_pose()) @asyncio.coroutine def set_next_pose(self): count = 0 while self.next_command is not None: count += 1 command, self.next_command = self.next_command, None yield From(self.servo_tab.controller.set_pose( command, pose_time=2 * 0.001 * PLAYBACK_TIMEOUT_MS)) def handle_geometry_change(self): frame = [GaitGeometryDisplay.FRAME_ROBOT, GaitGeometryDisplay.FRAME_WORLD, GaitGeometryDisplay.FRAME_BODY][ self.ui.geometryFrameCombo.currentIndex()] projection = [GaitGeometryDisplay.PROJECTION_XY, GaitGeometryDisplay.PROJECTION_YZ, GaitGeometryDisplay.PROJECTION_XZ][ self.ui.geometryProjectionCombo.currentIndex()] self.gait_geometry_display.set_view( frame, projection, self.ui.geometryScaleSpin.value()) def handle_command_change(self): if self.in_command_changed.value: return with self.in_command_changed: for spin, name in self.command_spins: setattr(self.command, name, spin.value()) self.update_command() self.update_allowable_commands() def update_allowable_commands(self): self.command_widget.update_allowable(self.ripple_config, self.command) def handle_command_reset(self): with self.in_command_changed: for spin, _ in self.command_spins: spin.setValue(0.0) self.handle_command_change() def handle_playback_state_change(self, state, checked): if not checked: # If nothing is checked, then stop playback. if (not self.ui.playbackSingleButton.isChecked() and not self.ui.playbackRepeatButton.isChecked() and not self.ui.playbackSlowRepeatButton.isChecked()): self.playback_timer.stop() self.playback_mode = self.PLAYBACK_IDLE self.handle_playback_config_change() return # Make sure everything else is unchecked. if state != self.PLAYBACK_SINGLE: self.ui.playbackSingleButton.setChecked(False) if state != self.PLAYBACK_REPEAT: self.ui.playbackRepeatButton.setChecked(False) if state != self.PLAYBACK_SLOW_REPEAT: self.ui.playbackSlowRepeatButton.setChecked(False) # Otherwise, start the appropriate playback mode. self.ripple.set_state(self.get_start_state(), self.command) self.playback_mode = state self.playback_timer.start(PLAYBACK_TIMEOUT_MS) def handle_playback_timer(self): if self.playback_mode == self.PLAYBACK_IDLE: print "WARNING: Playback timer fired when idle." return old_phase = self.ripple.state.phase advance = PLAYBACK_TIMEOUT_MS / 1000.0 if self.playback_mode == self.PLAYBACK_SLOW_REPEAT: advance *= 0.1 state = self.ripple.advance_time(advance) if (self.playback_mode == self.PLAYBACK_SINGLE and state.phase < 0.5 and old_phase > 0.5): self.ui.playbackSingleButton.setChecked(False) return self.render_state(state) def handle_widget_set_command(self): with self.in_command_changed: for spin, name in self.command_spins: spin.setValue(getattr(self.command, name)) self.update_command() def update_command(self): if self.playback_mode == self.PLAYBACK_IDLE: # If we're idle, we can just update the phase list right away. self.handle_playback_config_change() else: # Otherwise, just set the command on our gait and let # playback do its thing. self.ripple.set_command(self.command)

36.45807

80

0.608577

ca86916925e2940d7fc44bc91018986ceb5c1079

1,075

py

Python

cl_inn/contrib/sites/migrations/0003_set_site_domain_and_name.py

Ibrahem3amer/pos_api

af758680c583b85f87ed2fba985b5d2955ebdb23

[ "MIT" ]

null

cl_inn/contrib/sites/migrations/0003_set_site_domain_and_name.py

Ibrahem3amer/pos_api

af758680c583b85f87ed2fba985b5d2955ebdb23

[ "MIT" ]

null

cl_inn/contrib/sites/migrations/0003_set_site_domain_and_name.py

Ibrahem3amer/pos_api

af758680c583b85f87ed2fba985b5d2955ebdb23

[ "MIT" ]

null

""" To understand why this file is here, please read: http://cookiecutter-django.readthedocs.io/en/latest/faq.html#why-is-there-a-django-contrib-sites-directory-in-cookiecutter-django """ from django.conf import settings from django.db import migrations def update_site_forward(apps, schema_editor): """Set site domain and name.""" Site = apps.get_model('sites', 'Site') Site.objects.update_or_create( id=settings.SITE_ID, defaults={ 'domain': 'cloudinn.com', 'name': 'cl_inn' } ) def update_site_backward(apps, schema_editor): """Revert site domain and name to default.""" Site = apps.get_model('sites', 'Site') Site.objects.update_or_create( id=settings.SITE_ID, defaults={ 'domain': 'example.com', 'name': 'example.com' } ) class Migration(migrations.Migration): dependencies = [ ('sites', '0002_alter_domain_unique'), ] operations = [ migrations.RunPython(update_site_forward, update_site_backward), ]

25

129

0.64093

32696d7d6408a6de31313f7ff0436a8d0c94def5

269

py

Python

ur_operator/config.py

brennerm/uptimerobot-operator

25cdca5ec06e15d05ddd5ed4b13b20082fbb8889

[ "MIT" ]

47

2021-02-03T06:59:17.000Z

2022-03-30T03:44:43.000Z

ur_operator/config.py

brennerm/uptimerobot-operator

25cdca5ec06e15d05ddd5ed4b13b20082fbb8889

[ "MIT" ]

19

2021-02-02T23:26:56.000Z

2021-11-09T12:13:42.000Z

ur_operator/config.py

brennerm/uptimerobot-operator

25cdca5ec06e15d05ddd5ed4b13b20082fbb8889

[ "MIT" ]

2

2021-06-09T02:24:46.000Z

2021-08-15T06:45:20.000Z

import os class Config: @property def DISABLE_INGRESS_HANDLING(self): return os.getenv('URO_DISABLE_INGRESS_HANDLING', 'False').lower() in ['true', '1'] @property def UPTIMEROBOT_API_KEY(self): return os.environ['UPTIMEROBOT_API_KEY']

24.454545

90

0.687732

4b97a3f9934f8f57c8419fa3ca58eb92a8546959

824

py

Python

app/core/tests/test_commands.py

katalonac/recipe-app-api

e6347ec0aec0e1da1dc51e1c55e9d17b16a8d9f1

[ "MIT" ]

null

app/core/tests/test_commands.py

katalonac/recipe-app-api

e6347ec0aec0e1da1dc51e1c55e9d17b16a8d9f1

[ "MIT" ]

3

2020-06-06T01:28:06.000Z

2021-06-10T19:50:40.000Z

app/core/tests/test_commands.py

katalonac/recipe-app-api

e6347ec0aec0e1da1dc51e1c55e9d17b16a8d9f1

[ "MIT" ]

null

from unittest.mock import patch from django.core.management import call_command from django.db.utils import OperationalError from django.test import TestCase class CommandTests(TestCase): def test_wait_for_db_ready(self): """Test waiting for db when db is available""" with patch('django.db.utils.ConnectionHandler.__getitem__') as gi: gi.return_value = True call_command('wait_for_db') self.assertEqual(gi.call_count, 1) @patch('time.sleep', return_value=True) def test_wait_for_db(self, ts): """Test waiting for db""" with patch('django.db.utils.ConnectionHandler.__getitem__') as gi: gi.side_effect = [OperationalError] * 5 + [True] call_command('wait_for_db') self.assertEqual(gi.call_count, 6)

29.428571

74

0.674757

971ded76bed6277cca1a792de89daef4f37ea15a

9,605

py

Python

crichtonweb/release/widgets.py

bpluly/crichton

a2fa09c181ba1e44ee1aae7a57769e1778de7f3a

[ "Apache-2.0" ]

null

crichtonweb/release/widgets.py

bpluly/crichton

a2fa09c181ba1e44ee1aae7a57769e1778de7f3a

[ "Apache-2.0" ]

null

crichtonweb/release/widgets.py

bpluly/crichton

a2fa09c181ba1e44ee1aae7a57769e1778de7f3a

[ "Apache-2.0" ]

null

# Crichton, Admirable Source Configuration Management # Copyright 2012 British Broadcasting Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # import re # from django.forms.extras.widgets import SelectDateWidget from django.forms import DateInput from django.forms.widgets import Widget, Select, MultiWidget from django.utils.safestring import mark_safe __all__ = ('SelectTimeWidget', 'SplitSelectDateTimeWidget') # Attempt to match many time formats: # Example: "12:34:56 P.M." matches: # ('12', '34', ':56', '56', 'P.M.', 'P', '.', 'M', '.') # ('12', '34', ':56', '56', 'P.M.') # Note that the colon ":" before seconds is optional, but only if seconds are omitted time_pattern = r'(\d\d?):(\d\d)(:(\d\d))? *([aApP]\.?[mM]\.?)?$' RE_TIME = re.compile(time_pattern) # The following are just more readable ways to access re.matched groups: HOURS = 0 MINUTES = 1 SECONDS = 3 MERIDIEM = 4 class SelectTimeWidget(Widget): """ A Widget that splits time input into <select> elements. Allows form to show as 24hr: <hour>:<minute>:<second>, (default) or as 12hr: <hour>:<minute>:<second> <am|pm> Also allows user-defined increments for minutes/seconds Modified from: http://djangosnippets.org/snippets/1202/ """ hour_field = '%s_hour' minute_field = '%s_minute' second_field = '%s_second' meridiem_field = '%s_meridiem' twelve_hr = False # Default to 24hr. def __init__(self, attrs=None, hour_step=None, minute_step=None, second_step=None, twelve_hr=False, show_seconds=True): """ hour_step, minute_step, second_step are optional step values for for the range of values for the associated select element """ self.attrs = attrs or {} self.show_seconds = show_seconds if twelve_hr: self.twelve_hr = True # Do 12hr (rather than 24hr) self.meridiem_val = 'a.m.' # Default to Morning (A.M.) if hour_step and twelve_hr: self.hours = range(1,13,hour_step) elif hour_step: # 24hr, with stepping. self.hours = range(0,24,hour_step) elif twelve_hr: # 12hr, no stepping self.hours = range(1,13) else: # 24hr, no stepping self.hours = range(0,24) if minute_step: self.minutes = range(0,60,minute_step) else: self.minutes = range(0,60) if second_step: self.seconds = range(0,60,second_step) else: self.seconds = range(0,60) def render(self, name, value, attrs=None): try: # try to get time values from a datetime.time object (value) hour_val, minute_val, second_val = value.hour, value.minute, value.second if self.twelve_hr: if hour_val >= 12: self.meridiem_val = 'p.m.' else: self.meridiem_val = 'a.m.' except AttributeError: hour_val = minute_val = second_val = 0 if isinstance(value, basestring): match = RE_TIME.match(value) if match: time_groups = match.groups(); hour_val = int(time_groups[HOURS]) % 24 # force to range(0-24) minute_val = int(time_groups[MINUTES]) if time_groups[SECONDS] is None: second_val = 0 else: second_val = int(time_groups[SECONDS]) # check to see if meridiem was passed in if time_groups[MERIDIEM] is not None: self.meridiem_val = time_groups[MERIDIEM] else: # otherwise, set the meridiem based on the time if self.twelve_hr: if hour_val >= 12: self.meridiem_val = 'p.m.' else: self.meridiem_val = 'a.m.' else: self.meridiem_val = None # If we're doing a 12-hr clock, there will be a meridiem value, so make sure the # hours get printed correctly if self.twelve_hr and self.meridiem_val: if self.meridiem_val.lower().startswith('p') and hour_val > 12 and hour_val < 24: hour_val = hour_val % 12 elif hour_val == 0: hour_val = 12 output = [] if 'id' in self.attrs: id_ = self.attrs['id'] else: id_ = 'id_%s' % name # For times to get displayed correctly, the values MUST be converted to unicode # When Select builds a list of options, it checks against Unicode values hour_val = u"%.2d" % hour_val minute_val = u"%.2d" % minute_val second_val = u"%.2d" % second_val hour_choices = [("%.2d"%i, "%.2d"%i) for i in self.hours] local_attrs = self.build_attrs(id=self.hour_field % id_) select_html = Select(choices=hour_choices).render(self.hour_field % name, hour_val, local_attrs) output.append(select_html) minute_choices = [("%.2d"%i, "%.2d"%i) for i in self.minutes] local_attrs['id'] = self.minute_field % id_ select_html = Select(choices=minute_choices).render(self.minute_field % name, minute_val, local_attrs) output.append(select_html) if self.show_seconds: second_choices = [("%.2d"%i, "%.2d"%i) for i in self.seconds] local_attrs['id'] = self.second_field % id_ select_html = Select(choices=second_choices).render(self.second_field % name, second_val, local_attrs) output.append(select_html) if self.twelve_hr: # If we were given an initial value, make sure the correct meridiem gets selected. if self.meridiem_val is not None and self.meridiem_val.startswith('p'): meridiem_choices = [('p.m.','p.m.'), ('a.m.','a.m.')] else: meridiem_choices = [('a.m.','a.m.'), ('p.m.','p.m.')] local_attrs['id'] = local_attrs['id'] = self.meridiem_field % id_ select_html = Select(choices=meridiem_choices).render(self.meridiem_field % name, self.meridiem_val, local_attrs) output.append(select_html) return mark_safe(u'\n'.join(output)) def id_for_label(self, id_): return '%s_hour' % id_ id_for_label = classmethod(id_for_label) def value_from_datadict(self, data, files, name): # if there's not h:m:s data, assume zero: h = data.get(self.hour_field % name, '00') # hour m = data.get(self.minute_field % name, '00') # minute s = data.get(self.second_field % name, '00') # second if not h or not m or (self.show_seconds and not s): return data.get(name, None) meridiem = data.get(self.meridiem_field % name, None) #NOTE: if meridiem is None, assume 24-hr if meridiem is not None: if meridiem.lower().startswith('p') and int(h) != 12: h = '%.2d' % (int(h)+12)%24 elif meridiem.lower().startswith('a') and int(h) == 12: h = '00' return '%s:%s:%s' % (h, m, s) class SplitSelectDateTimeWidget(MultiWidget): """ MultiWidget = A widget that is composed of multiple widgets. This class combines SelectTimeWidget and SelectDateWidget so we have something like SpliteDateTimeWidget (in django.forms.widgets), but with Select elements. Modified from: http://djangosnippets.org/snippets/1206/ """ def __init__(self, attrs=None, date_format=None, hour_step=None, minute_step=None, second_step=None, twelve_hr=None, show_seconds=True, show_labels=True): """ pass all these parameters to their respective widget constructors...""" widgets = (DateInput(attrs=attrs, format=date_format), SelectTimeWidget(attrs=attrs, hour_step=hour_step, minute_step=minute_step, second_step=second_step, twelve_hr=twelve_hr, show_seconds=show_seconds)) super(SplitSelectDateTimeWidget, self).__init__(widgets, attrs) self.show_labels = show_labels def decompress(self, value): if value: return [value.date(), value.time().replace(microsecond=0)] return [None, None] def format_output(self, rendered_widgets): """ Given a list of rendered widgets (as strings), it inserts an HTML linebreak between them and optionally adds labels. Returns a string representing the HTML for the whole lot. """ if self.show_labels: return u"Date: %s<br/>Time: %s" % (rendered_widgets[0], rendered_widgets[1]) else: return u"<br/>".join(rendered_widgets) # def value_from_datadict(self, data, files, name): # values = super(SplitSelectDateTimeWidget, self).value_from_datadict(data, files, name) # return mark_safe(u' '.join(values))

41.76087

168

0.603019

8bb864f877aeaaa619c40ddfa528ca1daab64f14

1,664

py

Python

Ninja/Leetcode/482_License_Key_Formatting.py

cyandterry/Python-Study

b40e6c4db10da417e72247f61146f7570621106a

[ "MIT" ]

61

2015-02-03T20:25:55.000Z

2021-05-17T19:33:40.000Z

Ninja/Leetcode/482_License_Key_Formatting.py

cyandterry/Python-Study

b40e6c4db10da417e72247f61146f7570621106a

[ "MIT" ]

null

Ninja/Leetcode/482_License_Key_Formatting.py

cyandterry/Python-Study

b40e6c4db10da417e72247f61146f7570621106a

[ "MIT" ]

37

2015-02-04T07:12:52.000Z

2020-05-16T18:47:16.000Z

""" You are given a license key represented as a string S which consists only alphanumeric character and dashes. The string is separated into N+1 groups by N dashes. Given a number K, we would want to reformat the strings such that each group contains exactly K characters, except for the first group which could be shorter than K, but still must contain at least one character. Furthermore, there must be a dash inserted between two groups and all lowercase letters should be converted to uppercase. Given a non-empty string S and a number K, format the string according to the rules described above. Example 1: Input: S = "5F3Z-2e-9-w", K = 4 Output: "5F3Z-2E9W" Explanation: The string S has been split into two parts, each part has 4 characters. Note that the two extra dashes are not needed and can be removed. Example 2: Input: S = "2-5g-3-J", K = 2 Output: "2-5G-3J" Explanation: The string S has been split into three parts, each part has 2 characters except the first part as it could be shorter as mentioned above. Note: 1. The length of string S will not exceed 12,000, and K is a positive integer. 2. String S consists only of alphanumerical characters (a-z and/or A-Z and/or 0-9) and dashes(-). 3. String S is non-empty. """ class Solution: def licenseKeyFormatting(self, S: str, K: int) -> str: original = S.replace('-','').upper() reformat_list = [] length = len(original) for i in range(int(length/K) + 1): current = original[max(0, length-(i+1)*K):length-i*K] if current: reformat_list.insert(0, current) return '-'.join(reformat_list)

41.6

334

0.699519

5ba2d1883f0601ba1553e3955cb26406ca7949b7

2,658

py

Python

nltkma/test/unit/test_rte_classify.py

aydtmiri/nltk-ma

5d7dd01844ee063fc910a648948624b6a2dddaf9

[ "Apache-2.0" ]

null

nltkma/test/unit/test_rte_classify.py

aydtmiri/nltk-ma

5d7dd01844ee063fc910a648948624b6a2dddaf9

[ "Apache-2.0" ]

null

nltkma/test/unit/test_rte_classify.py

aydtmiri/nltk-ma

5d7dd01844ee063fc910a648948624b6a2dddaf9

[ "Apache-2.0" ]

null

import pytest from nltkma.corpus import rte as rte_corpus from nltkma.classify.rte_classify import RTEFeatureExtractor, rte_features, rte_classifier expected_from_rte_feature_extration = """ alwayson => True ne_hyp_extra => 0 ne_overlap => 1 neg_hyp => 0 neg_txt => 0 word_hyp_extra => 3 word_overlap => 3 alwayson => True ne_hyp_extra => 0 ne_overlap => 1 neg_hyp => 0 neg_txt => 0 word_hyp_extra => 2 word_overlap => 1 alwayson => True ne_hyp_extra => 1 ne_overlap => 1 neg_hyp => 0 neg_txt => 0 word_hyp_extra => 1 word_overlap => 2 alwayson => True ne_hyp_extra => 1 ne_overlap => 0 neg_hyp => 0 neg_txt => 0 word_hyp_extra => 6 word_overlap => 2 alwayson => True ne_hyp_extra => 1 ne_overlap => 0 neg_hyp => 0 neg_txt => 0 word_hyp_extra => 4 word_overlap => 0 alwayson => True ne_hyp_extra => 1 ne_overlap => 0 neg_hyp => 0 neg_txt => 0 word_hyp_extra => 3 word_overlap => 1 """ class TestRTEClassifier: # Test the feature extraction method. def test_rte_feature_extraction(self): pairs = rte_corpus.pairs(['rte1_dev.xml'])[:6] test_output = [ "%-15s => %s" % (key, rte_features(pair)[key]) for pair in pairs for key in sorted(rte_features(pair)) ] expected_output = expected_from_rte_feature_extration.strip().split('\n') # Remove null strings. expected_output = list(filter(None, expected_output)) assert test_output == expected_output # Test the RTEFeatureExtractor object. def test_feature_extractor_object(self): rtepair = rte_corpus.pairs(['rte3_dev.xml'])[33] extractor = RTEFeatureExtractor(rtepair) assert extractor.hyp_words == {'member', 'China', 'SCO.'} assert extractor.overlap('word') == set() assert extractor.overlap('ne') == {'China'} assert extractor.hyp_extra('word') == {'member'} # Test the RTE classifier training. def test_rte_classification_without_megam(self): # Use a sample size for unit testing, since we # don't need to fully train these classifiers clf = rte_classifier('IIS', sample_N=100) clf = rte_classifier('GIS', sample_N=100) @pytest.mark.skip("Skipping tests with dependencies on MEGAM") def test_rte_classification_with_megam(self): nltk.config_megam('/usr/local/bin/megam') clf = rte_classifier('megam', sample_N=100) clf = rte_classifier('BFGS', sample_N=100)

28.580645

90

0.624153

87950ece82941a89383034c41dc1552efac3a0a5

1,703

py

Python

schedule.py

bradgrantham/weather_fax

0ad4c9e54a405afa65357804da7e7677e56f5cbf

[ "Apache-2.0" ]

null

schedule.py

bradgrantham/weather_fax

0ad4c9e54a405afa65357804da7e7677e56f5cbf

[ "Apache-2.0" ]

null

schedule.py

bradgrantham/weather_fax

0ad4c9e54a405afa65357804da7e7677e56f5cbf

[ "Apache-2.0" ]

null

import urllib2 import bisect import time # http://tgftp.nws.noaa.gov/fax/hfreyes.txt stations = [ ["New Orleans, LA", "http://tgftp.nws.noaa.gov/fax/hfgulf.txt"], ["Pt. Reyes, CA", "http://tgftp.nws.noaa.gov/fax/hfreyes.txt"], ["Honolulu, HI", "http://tgftp.nws.noaa.gov/fax/hfhi.txt"], ["Kodiak, AK", "http://tgftp.nws.noaa.gov/fax/hfak.txt"], ["Boston, Massachusetts", "http://tgftp.nws.noaa.gov/fax/hfmarsh.txt"], ] for (name, url) in stations: stream = urllib2.urlopen(url) content = stream.read() lines = content.split("\r\n") last_time = 0 last_title = "" schedule = [] for l in lines: if len(l) >= 5 and l[4] == "/": start1 = l[0:4] start2 = l[5:9] title = l[11:50] if start1 != "----": t = int(start1[0:2]) * 100 + int(start1[2:4]) schedule.append((t, title)) if t > last_time: (last_time, last_title) = (t, title) if start2 != "----": t = int(start2[0:2]) * 100 + int(start2[2:4]) schedule.append((t, title)) if t > last_time: (last_time, last_title) = (t, title) schedule.append((0, last_title)) schedule.sort(key = lambda e : e[0]) gmt = time.gmtime() now = gmt[3] * 100 + gmt[4] which = bisect.bisect_left([t for (t, title) in schedule], now) print "%s:" % name print " current : %s started at %d" % (schedule[which - 1][1], schedule[which - 1][0]) print " next : %s starting at %d" % (schedule[which][1], schedule[which][0]) # for start, title in schedule: # print start, title

30.963636

93

0.526718

e8b72bb007d60498713fe9b445da437929f32bad

557

py

Python

export_models.py

eudaimoniatech/microNER

30ee51ee2360d8796844149bee6c47069d84c32a

[ "MIT" ]

null

export_models.py

eudaimoniatech/microNER

30ee51ee2360d8796844149bee6c47069d84c32a

[ "MIT" ]

null

export_models.py

eudaimoniatech/microNER

30ee51ee2360d8796844149bee6c47069d84c32a

[ "MIT" ]

2

2022-01-13T21:42:39.000Z

2022-01-14T10:01:45.000Z

# Since we were unable to reproduce the build of this Dockerfile, we had to export the models from the docker image uhhlt/microner:v0.1 on docker hub. import tensorflow as tf import scripts.ner as ner def export_model(filename): folder = "exported_models/" n = ner.NerModel(filename) n.model.save(folder+filename) mjson = n.model.to_json() f = open(folder+filename+".json", "w") f.write(mjson) f.close() export_model("conll.h5") export_model("germeval-conll.h5") export_model("germeval.h5") export_model("germeval-inner.h5")

27.85

150

0.721724

0560891d19d58b8d82714b22f25524cc155827a1

6,368

py

Python

src/main/python/apache/thermos/testing/runner.py

jeremyvdw/aurora

fa9d83a7ef3a96c522884089a471bbb0bef74c48

[ "Apache-2.0" ]

479

2015-03-27T22:59:49.000Z

2022-03-09T08:40:49.000Z

src/main/python/apache/thermos/testing/runner.py

jeremyvdw/aurora

fa9d83a7ef3a96c522884089a471bbb0bef74c48

[ "Apache-2.0" ]

69

2015-05-26T20:06:29.000Z

2020-01-13T19:18:59.000Z

src/main/python/apache/thermos/testing/runner.py

jeremyvdw/aurora

fa9d83a7ef3a96c522884089a471bbb0bef74c48

[ "Apache-2.0" ]

226

2015-03-27T20:02:59.000Z

2022-03-09T08:40:53.000Z

# # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function import atexit import errno import os import shutil import subprocess import sys import tempfile import time from thrift.TSerialization import deserialize as thrift_deserialize from twitter.common.contextutil import environment_as, temporary_file from apache.thermos.common.ckpt import CheckpointDispatcher from apache.thermos.common.path import TaskPath from apache.thermos.config.loader import ThermosTaskWrapper from gen.apache.thermos.ttypes import RunnerState class Runner(object): RUN_JOB_SCRIPT = """ # this is a hack to process wheel nspkg declarations import os, sys, site for path in sys.path: if path.endswith('.whl') and os.path.isdir(path): site.addsitedir(path) import os import random import sys from twitter.common import log from twitter.common.log.options import LogOptions from apache.thermos.config.loader import ThermosConfigLoader from apache.thermos.core.helper import TaskRunnerHelper from apache.thermos.core.runner import TaskRunner, TaskRunnerUniversalHandler from thrift.TSerialization import serialize as thrift_serialize random.seed(%(random_seed)d) log.init('runner_base') LogOptions.set_disk_log_level('DEBUG') task = ThermosConfigLoader.load_json('%(filename)s') task = task.tasks()[0].task success_rate=%(success_rate)d class AngryHandler(TaskRunnerUniversalHandler): def checkpoint(self, record): if not self._runner._recovery: if random.randint(0, 100) <= success_rate: super(AngryHandler, self).checkpoint(record) else: sys.exit(1) sandbox = os.path.join('%(sandbox)s', '%(task_id)s') args = %(extra_task_runner_args)r args['task_id'] = '%(task_id)s' args['universal_handler'] = AngryHandler runner = TaskRunner(task, '%(root)s', sandbox, **args) runner.run() with open('%(state_filename)s', 'w') as fp: fp.write(thrift_serialize(runner.state)) """ def __init__(self, task, success_rate=100, random_seed=31337, **extra_task_runner_args): """ task = Thermos task portmap = port map success_rate = success rate of writing checkpoint to disk """ self.task = task with temporary_file(cleanup=False) as fp: self.job_filename = fp.name fp.write(ThermosTaskWrapper(task).to_json()) self.state_filename = tempfile.mktemp() self.tempdir = tempfile.mkdtemp() self.task_id = '%s-runner-base' % int(time.time() * 1000000) self.sandbox = os.path.join(self.tempdir, 'sandbox') self.extra_task_runner_args = extra_task_runner_args self.cleaned = False self.pathspec = TaskPath(root=self.tempdir, task_id=self.task_id) self.script_filename = None self.success_rate = success_rate self.random_seed = random_seed self._run_count = 0 @property def pid(self): return self.po.pid @property def root(self): return self.tempdir def run(self): self._run_count += 1 atexit.register(self.cleanup) if self.script_filename: os.unlink(self.script_filename) with temporary_file(cleanup=False) as fp: self.script_filename = fp.name fp.write(self.RUN_JOB_SCRIPT % { 'filename': self.job_filename, 'sandbox': self.sandbox, 'root': self.tempdir, 'task_id': self.task_id, 'state_filename': self.state_filename, 'success_rate': self.success_rate, 'random_seed': self.random_seed + self._run_count, 'extra_task_runner_args': self.extra_task_runner_args, }) with environment_as(PYTHONPATH=os.pathsep.join(sys.path)): self.po = subprocess.Popen([sys.executable, self.script_filename], stdout=subprocess.PIPE, stderr=subprocess.PIPE) try: so, se = self.po.communicate() except OSError as e: if e.errno == errno.ECHILD: so = se = 'Killed' else: raise rc = self.po.returncode if rc != 0: if os.path.exists(self.job_filename): with open(self.job_filename) as fp: config = fp.read() else: config = 'Nonexistent!' if 'THERMOS_DEBUG' in os.environ: print("Runner failed!\n\n\nconfig:%s\n\n\nstdout:%s\n\n\nstderr:%s\n\n\n" % ( config, so, se)) try: with open(self.state_filename, 'r') as fp: self.state = thrift_deserialize(RunnerState(), fp.read()) except Exception as e: if 'THERMOS_DEBUG' in os.environ: print('Failed to load Runner state: %s' % e, file=sys.stderr) self.state = RunnerState() try: self.reconstructed_state = CheckpointDispatcher.from_file( self.pathspec.getpath('runner_checkpoint')) except Exception as e: print('Failed to replay checkpoint: %s' % e, file=sys.stderr) self.reconstructed_state = None self.initialized = True return rc def cleanup(self): if not self.cleaned: if hasattr(self, 'po'): try: self.po.kill() except Exception as e: print('Failed to kill runner: %s' % e, file=sys.stderr) pass os.unlink(self.job_filename) os.unlink(self.script_filename) if 'THERMOS_DEBUG' not in os.environ: shutil.rmtree(self.tempdir, ignore_errors=True) else: print('Logs saved in %s' % self.tempdir) self.cleaned = True class RunnerTestBase(object): @classmethod def extra_task_runner_args(cls): return dict(portmap=getattr(cls, 'portmap', {})) @classmethod def task(cls): raise NotImplementedError @classmethod def setup_class(cls): cls.runner = Runner(cls.task(), **cls.extra_task_runner_args()) cls.runner.run() cls.state = cls.runner.state @classmethod def teardown_class(cls): cls.runner.cleanup() def test_runner_state_reconstruction(self): assert self.state == self.runner.reconstructed_state

29.896714

90

0.696765

35226a09720f04f29e4bb7e8d62d263c2e23d78e

58,395

py

Python

test/engine/test_deprecations.py

daniel--/sqlalchemy

78e598e3a5b8df7419a600c291f90260e598c9b7

[ "MIT" ]

null

test/engine/test_deprecations.py

daniel--/sqlalchemy

78e598e3a5b8df7419a600c291f90260e598c9b7

[ "MIT" ]

null

test/engine/test_deprecations.py

daniel--/sqlalchemy

78e598e3a5b8df7419a600c291f90260e598c9b7

[ "MIT" ]

null

import re import time import sqlalchemy as tsa from sqlalchemy import column from sqlalchemy import create_engine from sqlalchemy import engine_from_config from sqlalchemy import event from sqlalchemy import ForeignKey from sqlalchemy import func from sqlalchemy import inspect from sqlalchemy import INT from sqlalchemy import Integer from sqlalchemy import literal from sqlalchemy import MetaData from sqlalchemy import pool from sqlalchemy import select from sqlalchemy import Sequence from sqlalchemy import String from sqlalchemy import testing from sqlalchemy import text from sqlalchemy import TypeDecorator from sqlalchemy import VARCHAR from sqlalchemy.engine.base import Engine from sqlalchemy.interfaces import ConnectionProxy from sqlalchemy.testing import assert_raises_message from sqlalchemy.testing import engines from sqlalchemy.testing import eq_ from sqlalchemy.testing import fixtures from sqlalchemy.testing.engines import testing_engine from sqlalchemy.testing.mock import call from sqlalchemy.testing.mock import Mock from sqlalchemy.testing.schema import Column from sqlalchemy.testing.schema import Table from sqlalchemy.testing.util import gc_collect from sqlalchemy.testing.util import lazy_gc from .test_parseconnect import mock_dbapi tlengine = None class SomeException(Exception): pass def _tlengine_deprecated(): return testing.expect_deprecated( "The 'threadlocal' engine strategy is deprecated" ) class TableNamesOrderByTest(fixtures.TestBase): @testing.provide_metadata def test_order_by_foreign_key(self): Table( "t1", self.metadata, Column("id", Integer, primary_key=True), test_needs_acid=True, ) Table( "t2", self.metadata, Column("id", Integer, primary_key=True), Column("t1id", Integer, ForeignKey("t1.id")), test_needs_acid=True, ) Table( "t3", self.metadata, Column("id", Integer, primary_key=True), Column("t2id", Integer, ForeignKey("t2.id")), test_needs_acid=True, ) self.metadata.create_all() insp = inspect(testing.db) with testing.expect_deprecated( "The get_table_names.order_by parameter is deprecated " ): tnames = insp.get_table_names(order_by="foreign_key") eq_(tnames, ["t1", "t2", "t3"]) class CreateEngineTest(fixtures.TestBase): def test_pool_threadlocal_from_config(self): dbapi = mock_dbapi config = { "sqlalchemy.url": "postgresql://scott:tiger@somehost/test", "sqlalchemy.pool_threadlocal": "false", } e = engine_from_config(config, module=dbapi, _initialize=False) eq_(e.pool._use_threadlocal, False) config = { "sqlalchemy.url": "postgresql://scott:tiger@somehost/test", "sqlalchemy.pool_threadlocal": "true", } with testing.expect_deprecated( "The Pool.use_threadlocal parameter is deprecated" ): e = engine_from_config(config, module=dbapi, _initialize=False) eq_(e.pool._use_threadlocal, True) class RecycleTest(fixtures.TestBase): __backend__ = True def test_basic(self): with testing.expect_deprecated( "The Pool.use_threadlocal parameter is deprecated" ): engine = engines.reconnecting_engine( options={"pool_threadlocal": True} ) with testing.expect_deprecated( r"The Engine.contextual_connect method is deprecated" ): conn = engine.contextual_connect() eq_(conn.execute(select([1])).scalar(), 1) conn.close() # set the pool recycle down to 1. # we aren't doing this inline with the # engine create since cx_oracle takes way # too long to create the 1st connection and don't # want to build a huge delay into this test. engine.pool._recycle = 1 # kill the DB connection engine.test_shutdown() # wait until past the recycle period time.sleep(2) # can connect, no exception with testing.expect_deprecated( r"The Engine.contextual_connect method is deprecated" ): conn = engine.contextual_connect() eq_(conn.execute(select([1])).scalar(), 1) conn.close() class TLTransactionTest(fixtures.TestBase): __requires__ = ("ad_hoc_engines",) __backend__ = True @classmethod def setup_class(cls): global users, metadata, tlengine with _tlengine_deprecated(): tlengine = testing_engine(options=dict(strategy="threadlocal")) metadata = MetaData() users = Table( "query_users", metadata, Column( "user_id", INT, Sequence("query_users_id_seq", optional=True), primary_key=True, ), Column("user_name", VARCHAR(20)), test_needs_acid=True, ) metadata.create_all(tlengine) def teardown(self): tlengine.execute(users.delete()).close() @classmethod def teardown_class(cls): tlengine.close() metadata.drop_all(tlengine) tlengine.dispose() def setup(self): # ensure tests start with engine closed tlengine.close() @testing.crashes( "oracle", "TNS error of unknown origin occurs on the buildbot." ) def test_rollback_no_trans(self): with _tlengine_deprecated(): tlengine = testing_engine(options=dict(strategy="threadlocal")) # shouldn't fail tlengine.rollback() tlengine.begin() tlengine.rollback() # shouldn't fail tlengine.rollback() def test_commit_no_trans(self): with _tlengine_deprecated(): tlengine = testing_engine(options=dict(strategy="threadlocal")) # shouldn't fail tlengine.commit() tlengine.begin() tlengine.rollback() # shouldn't fail tlengine.commit() def test_prepare_no_trans(self): with _tlengine_deprecated(): tlengine = testing_engine(options=dict(strategy="threadlocal")) # shouldn't fail tlengine.prepare() tlengine.begin() tlengine.rollback() # shouldn't fail tlengine.prepare() def test_connection_close(self): """test that when connections are closed for real, transactions are rolled back and disposed.""" c = tlengine.contextual_connect() c.begin() assert c.in_transaction() c.close() assert not c.in_transaction() def test_transaction_close(self): c = tlengine.contextual_connect() t = c.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") t2 = c.begin() tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.execute(users.insert(), user_id=4, user_name="user4") t2.close() result = c.execute("select * from query_users") assert len(result.fetchall()) == 4 t.close() external_connection = tlengine.connect() result = external_connection.execute("select * from query_users") try: assert len(result.fetchall()) == 0 finally: c.close() external_connection.close() def test_rollback(self): """test a basic rollback""" tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.rollback() external_connection = tlengine.connect() result = external_connection.execute("select * from query_users") try: assert len(result.fetchall()) == 0 finally: external_connection.close() def test_commit(self): """test a basic commit""" tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.commit() external_connection = tlengine.connect() result = external_connection.execute("select * from query_users") try: assert len(result.fetchall()) == 3 finally: external_connection.close() def test_with_interface(self): trans = tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") trans.commit() trans = tlengine.begin() tlengine.execute(users.insert(), user_id=3, user_name="user3") trans.__exit__(Exception, "fake", None) trans = tlengine.begin() tlengine.execute(users.insert(), user_id=4, user_name="user4") trans.__exit__(None, None, None) eq_( tlengine.execute( users.select().order_by(users.c.user_id) ).fetchall(), [(1, "user1"), (2, "user2"), (4, "user4")], ) def test_commits(self): connection = tlengine.connect() assert ( connection.execute("select count(*) from query_users").scalar() == 0 ) connection.close() connection = tlengine.contextual_connect() transaction = connection.begin() connection.execute(users.insert(), user_id=1, user_name="user1") transaction.commit() transaction = connection.begin() connection.execute(users.insert(), user_id=2, user_name="user2") connection.execute(users.insert(), user_id=3, user_name="user3") transaction.commit() transaction = connection.begin() result = connection.execute("select * from query_users") rows = result.fetchall() assert len(rows) == 3, "expected 3 got %d" % len(rows) transaction.commit() connection.close() def test_rollback_off_conn(self): # test that a TLTransaction opened off a TLConnection allows # that TLConnection to be aware of the transactional context conn = tlengine.contextual_connect() trans = conn.begin() conn.execute(users.insert(), user_id=1, user_name="user1") conn.execute(users.insert(), user_id=2, user_name="user2") conn.execute(users.insert(), user_id=3, user_name="user3") trans.rollback() external_connection = tlengine.connect() result = external_connection.execute("select * from query_users") try: assert len(result.fetchall()) == 0 finally: conn.close() external_connection.close() def test_morerollback_off_conn(self): # test that an existing TLConnection automatically takes place # in a TLTransaction opened on a second TLConnection conn = tlengine.contextual_connect() conn2 = tlengine.contextual_connect() trans = conn2.begin() conn.execute(users.insert(), user_id=1, user_name="user1") conn.execute(users.insert(), user_id=2, user_name="user2") conn.execute(users.insert(), user_id=3, user_name="user3") trans.rollback() external_connection = tlengine.connect() result = external_connection.execute("select * from query_users") try: assert len(result.fetchall()) == 0 finally: conn.close() conn2.close() external_connection.close() def test_commit_off_connection(self): conn = tlengine.contextual_connect() trans = conn.begin() conn.execute(users.insert(), user_id=1, user_name="user1") conn.execute(users.insert(), user_id=2, user_name="user2") conn.execute(users.insert(), user_id=3, user_name="user3") trans.commit() external_connection = tlengine.connect() result = external_connection.execute("select * from query_users") try: assert len(result.fetchall()) == 3 finally: conn.close() external_connection.close() def test_nesting_rollback(self): """tests nesting of transactions, rollback at the end""" external_connection = tlengine.connect() self.assert_( external_connection.connection is not tlengine.contextual_connect().connection ) tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.begin() tlengine.execute(users.insert(), user_id=4, user_name="user4") tlengine.execute(users.insert(), user_id=5, user_name="user5") tlengine.commit() tlengine.rollback() try: self.assert_( external_connection.scalar("select count(*) from query_users") == 0 ) finally: external_connection.close() def test_nesting_commit(self): """tests nesting of transactions, commit at the end.""" external_connection = tlengine.connect() self.assert_( external_connection.connection is not tlengine.contextual_connect().connection ) tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.begin() tlengine.execute(users.insert(), user_id=4, user_name="user4") tlengine.execute(users.insert(), user_id=5, user_name="user5") tlengine.commit() tlengine.commit() try: self.assert_( external_connection.scalar("select count(*) from query_users") == 5 ) finally: external_connection.close() def test_mixed_nesting(self): """tests nesting of transactions off the TLEngine directly inside of transactions off the connection from the TLEngine""" external_connection = tlengine.connect() self.assert_( external_connection.connection is not tlengine.contextual_connect().connection ) conn = tlengine.contextual_connect() trans = conn.begin() trans2 = conn.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.begin() tlengine.execute(users.insert(), user_id=4, user_name="user4") tlengine.begin() tlengine.execute(users.insert(), user_id=5, user_name="user5") tlengine.execute(users.insert(), user_id=6, user_name="user6") tlengine.execute(users.insert(), user_id=7, user_name="user7") tlengine.commit() tlengine.execute(users.insert(), user_id=8, user_name="user8") tlengine.commit() trans2.commit() trans.rollback() conn.close() try: self.assert_( external_connection.scalar("select count(*) from query_users") == 0 ) finally: external_connection.close() def test_more_mixed_nesting(self): """tests nesting of transactions off the connection from the TLEngine inside of transactions off the TLEngine directly.""" external_connection = tlengine.connect() self.assert_( external_connection.connection is not tlengine.contextual_connect().connection ) tlengine.begin() connection = tlengine.contextual_connect() connection.execute(users.insert(), user_id=1, user_name="user1") tlengine.begin() connection.execute(users.insert(), user_id=2, user_name="user2") connection.execute(users.insert(), user_id=3, user_name="user3") trans = connection.begin() connection.execute(users.insert(), user_id=4, user_name="user4") connection.execute(users.insert(), user_id=5, user_name="user5") trans.commit() tlengine.commit() tlengine.rollback() connection.close() try: self.assert_( external_connection.scalar("select count(*) from query_users") == 0 ) finally: external_connection.close() @testing.requires.savepoints def test_nested_subtransaction_rollback(self): tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.begin_nested() tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.rollback() tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.commit() tlengine.close() eq_( tlengine.execute( select([users.c.user_id]).order_by(users.c.user_id) ).fetchall(), [(1,), (3,)], ) tlengine.close() @testing.requires.savepoints @testing.crashes( "oracle+zxjdbc", "Errors out and causes subsequent tests to " "deadlock", ) def test_nested_subtransaction_commit(self): tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.begin_nested() tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.commit() tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.commit() tlengine.close() eq_( tlengine.execute( select([users.c.user_id]).order_by(users.c.user_id) ).fetchall(), [(1,), (2,), (3,)], ) tlengine.close() @testing.requires.savepoints def test_rollback_to_subtransaction(self): tlengine.begin() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.begin_nested() tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.begin() tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.rollback() tlengine.rollback() tlengine.execute(users.insert(), user_id=4, user_name="user4") tlengine.commit() tlengine.close() eq_( tlengine.execute( select([users.c.user_id]).order_by(users.c.user_id) ).fetchall(), [(1,), (4,)], ) tlengine.close() def test_connections(self): """tests that contextual_connect is threadlocal""" c1 = tlengine.contextual_connect() c2 = tlengine.contextual_connect() assert c1.connection is c2.connection c2.close() assert not c1.closed assert not tlengine.closed @testing.requires.independent_cursors def test_result_closing(self): """tests that contextual_connect is threadlocal""" r1 = tlengine.execute(select([1])) r2 = tlengine.execute(select([1])) row1 = r1.fetchone() row2 = r2.fetchone() r1.close() assert r2.connection is r1.connection assert not r2.connection.closed assert not tlengine.closed # close again, nothing happens since resultproxy calls close() # only once r1.close() assert r2.connection is r1.connection assert not r2.connection.closed assert not tlengine.closed r2.close() assert r2.connection.closed assert tlengine.closed @testing.crashes( "oracle+cx_oracle", "intermittent failures on the buildbot" ) def test_dispose(self): with _tlengine_deprecated(): eng = testing_engine(options=dict(strategy="threadlocal")) result = eng.execute(select([1])) eng.dispose() eng.execute(select([1])) @testing.requires.two_phase_transactions def test_two_phase_transaction(self): tlengine.begin_twophase() tlengine.execute(users.insert(), user_id=1, user_name="user1") tlengine.prepare() tlengine.commit() tlengine.begin_twophase() tlengine.execute(users.insert(), user_id=2, user_name="user2") tlengine.commit() tlengine.begin_twophase() tlengine.execute(users.insert(), user_id=3, user_name="user3") tlengine.rollback() tlengine.begin_twophase() tlengine.execute(users.insert(), user_id=4, user_name="user4") tlengine.prepare() tlengine.rollback() eq_( tlengine.execute( select([users.c.user_id]).order_by(users.c.user_id) ).fetchall(), [(1,), (2,)], ) class ConvenienceExecuteTest(fixtures.TablesTest): __backend__ = True @classmethod def define_tables(cls, metadata): cls.table = Table( "exec_test", metadata, Column("a", Integer), Column("b", Integer), test_needs_acid=True, ) def _trans_fn(self, is_transaction=False): def go(conn, x, value=None): if is_transaction: conn = conn.connection conn.execute(self.table.insert().values(a=x, b=value)) return go def _trans_rollback_fn(self, is_transaction=False): def go(conn, x, value=None): if is_transaction: conn = conn.connection conn.execute(self.table.insert().values(a=x, b=value)) raise SomeException("breakage") return go def _assert_no_data(self): eq_( testing.db.scalar( select([func.count("*")]).select_from(self.table) ), 0, ) def _assert_fn(self, x, value=None): eq_(testing.db.execute(self.table.select()).fetchall(), [(x, value)]) def test_transaction_tlocal_engine_ctx_commit(self): fn = self._trans_fn() with _tlengine_deprecated(): engine = engines.testing_engine( options=dict(strategy="threadlocal", pool=testing.db.pool) ) ctx = engine.begin() testing.run_as_contextmanager(ctx, fn, 5, value=8) self._assert_fn(5, value=8) def test_transaction_tlocal_engine_ctx_rollback(self): fn = self._trans_rollback_fn() with _tlengine_deprecated(): engine = engines.testing_engine( options=dict(strategy="threadlocal", pool=testing.db.pool) ) ctx = engine.begin() assert_raises_message( Exception, "breakage", testing.run_as_contextmanager, ctx, fn, 5, value=8, ) self._assert_no_data() def _proxy_execute_deprecated(): return ( testing.expect_deprecated("ConnectionProxy.execute is deprecated."), testing.expect_deprecated( "ConnectionProxy.cursor_execute is deprecated." ), ) class ProxyConnectionTest(fixtures.TestBase): """These are the same tests as EngineEventsTest, except using the deprecated ConnectionProxy interface. """ __requires__ = ("ad_hoc_engines",) __prefer_requires__ = ("two_phase_transactions",) @testing.uses_deprecated(r".*Use event.listen") @testing.fails_on("firebird", "Data type unknown") def test_proxy(self): stmts = [] cursor_stmts = [] class MyProxy(ConnectionProxy): def execute( self, conn, execute, clauseelement, *multiparams, **params ): stmts.append((str(clauseelement), params, multiparams)) return execute(clauseelement, *multiparams, **params) def cursor_execute( self, execute, cursor, statement, parameters, context, executemany, ): cursor_stmts.append((str(statement), parameters, None)) return execute(cursor, statement, parameters, context) def assert_stmts(expected, received): for stmt, params, posn in expected: if not received: assert False, "Nothing available for stmt: %s" % stmt while received: teststmt, testparams, testmultiparams = received.pop(0) teststmt = ( re.compile(r"[\n\t ]+", re.M) .sub(" ", teststmt) .strip() ) if teststmt.startswith(stmt) and ( testparams == params or testparams == posn ): break with testing.expect_deprecated( "ConnectionProxy.execute is deprecated.", "ConnectionProxy.cursor_execute is deprecated.", ): plain_engine = engines.testing_engine( options=dict(implicit_returning=False, proxy=MyProxy()) ) with testing.expect_deprecated( "ConnectionProxy.execute is deprecated.", "ConnectionProxy.cursor_execute is deprecated.", "The 'threadlocal' engine strategy is deprecated", ): tl_engine = engines.testing_engine( options=dict( implicit_returning=False, proxy=MyProxy(), strategy="threadlocal", ) ) for engine in (plain_engine, tl_engine): m = MetaData(engine) t1 = Table( "t1", m, Column("c1", Integer, primary_key=True), Column( "c2", String(50), default=func.lower("Foo"), primary_key=True, ), ) m.create_all() try: t1.insert().execute(c1=5, c2="some data") t1.insert().execute(c1=6) eq_( engine.execute("select * from t1").fetchall(), [(5, "some data"), (6, "foo")], ) finally: m.drop_all() engine.dispose() compiled = [ ("CREATE TABLE t1", {}, None), ( "INSERT INTO t1 (c1, c2)", {"c2": "some data", "c1": 5}, None, ), ("INSERT INTO t1 (c1, c2)", {"c1": 6}, None), ("select * from t1", {}, None), ("DROP TABLE t1", {}, None), ] cursor = [ ("CREATE TABLE t1", {}, ()), ( "INSERT INTO t1 (c1, c2)", {"c2": "some data", "c1": 5}, (5, "some data"), ), ("SELECT lower", {"lower_1": "Foo"}, ("Foo",)), ( "INSERT INTO t1 (c1, c2)", {"c2": "foo", "c1": 6}, (6, "foo"), ), ("select * from t1", {}, ()), ("DROP TABLE t1", {}, ()), ] assert_stmts(compiled, stmts) assert_stmts(cursor, cursor_stmts) @testing.uses_deprecated(r".*Use event.listen") def test_options(self): canary = [] class TrackProxy(ConnectionProxy): def __getattribute__(self, key): fn = object.__getattribute__(self, key) def go(*arg, **kw): canary.append(fn.__name__) return fn(*arg, **kw) return go with testing.expect_deprecated( *[ "ConnectionProxy.%s is deprecated" % name for name in [ "execute", "cursor_execute", "begin", "rollback", "commit", "savepoint", "rollback_savepoint", "release_savepoint", "begin_twophase", "prepare_twophase", "rollback_twophase", "commit_twophase", ] ] ): engine = engines.testing_engine(options={"proxy": TrackProxy()}) conn = engine.connect() c2 = conn.execution_options(foo="bar") eq_(c2._execution_options, {"foo": "bar"}) c2.execute(select([1])) c3 = c2.execution_options(bar="bat") eq_(c3._execution_options, {"foo": "bar", "bar": "bat"}) eq_(canary, ["execute", "cursor_execute"]) @testing.uses_deprecated(r".*Use event.listen") def test_transactional(self): canary = [] class TrackProxy(ConnectionProxy): def __getattribute__(self, key): fn = object.__getattribute__(self, key) def go(*arg, **kw): canary.append(fn.__name__) return fn(*arg, **kw) return go with testing.expect_deprecated( *[ "ConnectionProxy.%s is deprecated" % name for name in [ "execute", "cursor_execute", "begin", "rollback", "commit", "savepoint", "rollback_savepoint", "release_savepoint", "begin_twophase", "prepare_twophase", "rollback_twophase", "commit_twophase", ] ] ): engine = engines.testing_engine(options={"proxy": TrackProxy()}) conn = engine.connect() trans = conn.begin() conn.execute(select([1])) trans.rollback() trans = conn.begin() conn.execute(select([1])) trans.commit() eq_( canary, [ "begin", "execute", "cursor_execute", "rollback", "begin", "execute", "cursor_execute", "commit", ], ) @testing.uses_deprecated(r".*Use event.listen") @testing.requires.savepoints @testing.requires.two_phase_transactions def test_transactional_advanced(self): canary = [] class TrackProxy(ConnectionProxy): def __getattribute__(self, key): fn = object.__getattribute__(self, key) def go(*arg, **kw): canary.append(fn.__name__) return fn(*arg, **kw) return go with testing.expect_deprecated( *[ "ConnectionProxy.%s is deprecated" % name for name in [ "execute", "cursor_execute", "begin", "rollback", "commit", "savepoint", "rollback_savepoint", "release_savepoint", "begin_twophase", "prepare_twophase", "rollback_twophase", "commit_twophase", ] ] ): engine = engines.testing_engine(options={"proxy": TrackProxy()}) conn = engine.connect() trans = conn.begin() trans2 = conn.begin_nested() conn.execute(select([1])) trans2.rollback() trans2 = conn.begin_nested() conn.execute(select([1])) trans2.commit() trans.rollback() trans = conn.begin_twophase() conn.execute(select([1])) trans.prepare() trans.commit() canary = [t for t in canary if t not in ("cursor_execute", "execute")] eq_( canary, [ "begin", "savepoint", "rollback_savepoint", "savepoint", "release_savepoint", "rollback", "begin_twophase", "prepare_twophase", "commit_twophase", ], ) class HandleInvalidatedOnConnectTest(fixtures.TestBase): __requires__ = ("sqlite",) def setUp(self): e = create_engine("sqlite://") connection = Mock(get_server_version_info=Mock(return_value="5.0")) def connect(*args, **kwargs): return connection dbapi = Mock( sqlite_version_info=(99, 9, 9), version_info=(99, 9, 9), sqlite_version="99.9.9", paramstyle="named", connect=Mock(side_effect=connect), ) sqlite3 = e.dialect.dbapi dbapi.Error = (sqlite3.Error,) dbapi.ProgrammingError = sqlite3.ProgrammingError self.dbapi = dbapi self.ProgrammingError = sqlite3.ProgrammingError def test_dont_touch_non_dbapi_exception_on_contextual_connect(self): dbapi = self.dbapi dbapi.connect = Mock(side_effect=TypeError("I'm not a DBAPI error")) e = create_engine("sqlite://", module=dbapi) e.dialect.is_disconnect = is_disconnect = Mock() with testing.expect_deprecated( r"The Engine.contextual_connect method is deprecated" ): assert_raises_message( TypeError, "I'm not a DBAPI error", e.contextual_connect ) eq_(is_disconnect.call_count, 0) def test_invalidate_on_contextual_connect(self): """test that is_disconnect() is called during connect. interpretation of connection failures are not supported by every backend. """ dbapi = self.dbapi dbapi.connect = Mock( side_effect=self.ProgrammingError( "Cannot operate on a closed database." ) ) e = create_engine("sqlite://", module=dbapi) try: with testing.expect_deprecated( r"The Engine.contextual_connect method is deprecated" ): e.contextual_connect() assert False except tsa.exc.DBAPIError as de: assert de.connection_invalidated class HandleErrorTest(fixtures.TestBase): __requires__ = ("ad_hoc_engines",) __backend__ = True def tearDown(self): Engine.dispatch._clear() Engine._has_events = False def test_legacy_dbapi_error(self): engine = engines.testing_engine() canary = Mock() with testing.expect_deprecated( r"The ConnectionEvents.dbapi_error event is deprecated" ): event.listen(engine, "dbapi_error", canary) with engine.connect() as conn: try: conn.execute("SELECT FOO FROM I_DONT_EXIST") assert False except tsa.exc.DBAPIError as e: eq_(canary.mock_calls[0][1][5], e.orig) eq_(canary.mock_calls[0][1][2], "SELECT FOO FROM I_DONT_EXIST") def test_legacy_dbapi_error_no_ad_hoc_context(self): engine = engines.testing_engine() listener = Mock(return_value=None) with testing.expect_deprecated( r"The ConnectionEvents.dbapi_error event is deprecated" ): event.listen(engine, "dbapi_error", listener) nope = SomeException("nope") class MyType(TypeDecorator): impl = Integer def process_bind_param(self, value, dialect): raise nope with engine.connect() as conn: assert_raises_message( tsa.exc.StatementError, r"$.*SomeException$ " r"nope \[SQL\: u?'SELECT 1 ", conn.execute, select([1]).where(column("foo") == literal("bar", MyType())), ) # no legacy event eq_(listener.mock_calls, []) def test_legacy_dbapi_error_non_dbapi_error(self): engine = engines.testing_engine() listener = Mock(return_value=None) with testing.expect_deprecated( r"The ConnectionEvents.dbapi_error event is deprecated" ): event.listen(engine, "dbapi_error", listener) nope = TypeError("I'm not a DBAPI error") with engine.connect() as c: c.connection.cursor = Mock( return_value=Mock(execute=Mock(side_effect=nope)) ) assert_raises_message( TypeError, "I'm not a DBAPI error", c.execute, "select " ) # no legacy event eq_(listener.mock_calls, []) def MockDBAPI(): # noqa def cursor(): return Mock() def connect(*arg, **kw): def close(): conn.closed = True # mock seems like it might have an issue logging # call_count correctly under threading, not sure. # adding a side_effect for close seems to help. conn = Mock( cursor=Mock(side_effect=cursor), close=Mock(side_effect=close), closed=False, ) return conn def shutdown(value): if value: db.connect = Mock(side_effect=Exception("connect failed")) else: db.connect = Mock(side_effect=connect) db.is_shutdown = value db = Mock( connect=Mock(side_effect=connect), shutdown=shutdown, is_shutdown=False ) return db class PoolTestBase(fixtures.TestBase): def setup(self): pool.clear_managers() self._teardown_conns = [] def teardown(self): for ref in self._teardown_conns: conn = ref() if conn: conn.close() @classmethod def teardown_class(cls): pool.clear_managers() def _queuepool_fixture(self, **kw): dbapi, pool = self._queuepool_dbapi_fixture(**kw) return pool def _queuepool_dbapi_fixture(self, **kw): dbapi = MockDBAPI() return ( dbapi, pool.QueuePool(creator=lambda: dbapi.connect("foo.db"), **kw), ) class DeprecatedPoolListenerTest(PoolTestBase): @testing.requires.predictable_gc @testing.uses_deprecated( r".*Use the PoolEvents", r".*'listeners' argument .* is deprecated" ) def test_listeners(self): class InstrumentingListener(object): def __init__(self): if hasattr(self, "connect"): self.connect = self.inst_connect if hasattr(self, "first_connect"): self.first_connect = self.inst_first_connect if hasattr(self, "checkout"): self.checkout = self.inst_checkout if hasattr(self, "checkin"): self.checkin = self.inst_checkin self.clear() def clear(self): self.connected = [] self.first_connected = [] self.checked_out = [] self.checked_in = [] def assert_total(self, conn, fconn, cout, cin): eq_(len(self.connected), conn) eq_(len(self.first_connected), fconn) eq_(len(self.checked_out), cout) eq_(len(self.checked_in), cin) def assert_in(self, item, in_conn, in_fconn, in_cout, in_cin): eq_((item in self.connected), in_conn) eq_((item in self.first_connected), in_fconn) eq_((item in self.checked_out), in_cout) eq_((item in self.checked_in), in_cin) def inst_connect(self, con, record): print("connect(%s, %s)" % (con, record)) assert con is not None assert record is not None self.connected.append(con) def inst_first_connect(self, con, record): print("first_connect(%s, %s)" % (con, record)) assert con is not None assert record is not None self.first_connected.append(con) def inst_checkout(self, con, record, proxy): print("checkout(%s, %s, %s)" % (con, record, proxy)) assert con is not None assert record is not None assert proxy is not None self.checked_out.append(con) def inst_checkin(self, con, record): print("checkin(%s, %s)" % (con, record)) # con can be None if invalidated assert record is not None self.checked_in.append(con) class ListenAll(tsa.interfaces.PoolListener, InstrumentingListener): pass class ListenConnect(InstrumentingListener): def connect(self, con, record): pass class ListenFirstConnect(InstrumentingListener): def first_connect(self, con, record): pass class ListenCheckOut(InstrumentingListener): def checkout(self, con, record, proxy, num): pass class ListenCheckIn(InstrumentingListener): def checkin(self, con, record): pass def assert_listeners(p, total, conn, fconn, cout, cin): for instance in (p, p.recreate()): self.assert_(len(instance.dispatch.connect) == conn) self.assert_(len(instance.dispatch.first_connect) == fconn) self.assert_(len(instance.dispatch.checkout) == cout) self.assert_(len(instance.dispatch.checkin) == cin) p = self._queuepool_fixture() assert_listeners(p, 0, 0, 0, 0, 0) with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["connect", "first_connect", "checkout", "checkin"] ] ): p.add_listener(ListenAll()) assert_listeners(p, 1, 1, 1, 1, 1) with testing.expect_deprecated( *["PoolListener.%s is deprecated." % name for name in ["connect"]] ): p.add_listener(ListenConnect()) assert_listeners(p, 2, 2, 1, 1, 1) with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["first_connect"] ] ): p.add_listener(ListenFirstConnect()) assert_listeners(p, 3, 2, 2, 1, 1) with testing.expect_deprecated( *["PoolListener.%s is deprecated." % name for name in ["checkout"]] ): p.add_listener(ListenCheckOut()) assert_listeners(p, 4, 2, 2, 2, 1) with testing.expect_deprecated( *["PoolListener.%s is deprecated." % name for name in ["checkin"]] ): p.add_listener(ListenCheckIn()) assert_listeners(p, 5, 2, 2, 2, 2) del p snoop = ListenAll() with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["connect", "first_connect", "checkout", "checkin"] ] + [ "PoolListener is deprecated in favor of the PoolEvents " "listener interface. The Pool.listeners parameter " "will be removed" ] ): p = self._queuepool_fixture(listeners=[snoop]) assert_listeners(p, 1, 1, 1, 1, 1) c = p.connect() snoop.assert_total(1, 1, 1, 0) cc = c.connection snoop.assert_in(cc, True, True, True, False) c.close() snoop.assert_in(cc, True, True, True, True) del c, cc snoop.clear() # this one depends on immediate gc c = p.connect() cc = c.connection snoop.assert_in(cc, False, False, True, False) snoop.assert_total(0, 0, 1, 0) del c, cc lazy_gc() snoop.assert_total(0, 0, 1, 1) p.dispose() snoop.clear() c = p.connect() c.close() c = p.connect() snoop.assert_total(1, 0, 2, 1) c.close() snoop.assert_total(1, 0, 2, 2) # invalidation p.dispose() snoop.clear() c = p.connect() snoop.assert_total(1, 0, 1, 0) c.invalidate() snoop.assert_total(1, 0, 1, 1) c.close() snoop.assert_total(1, 0, 1, 1) del c lazy_gc() snoop.assert_total(1, 0, 1, 1) c = p.connect() snoop.assert_total(2, 0, 2, 1) c.close() del c lazy_gc() snoop.assert_total(2, 0, 2, 2) # detached p.dispose() snoop.clear() c = p.connect() snoop.assert_total(1, 0, 1, 0) c.detach() snoop.assert_total(1, 0, 1, 0) c.close() del c snoop.assert_total(1, 0, 1, 0) c = p.connect() snoop.assert_total(2, 0, 2, 0) c.close() del c snoop.assert_total(2, 0, 2, 1) # recreated p = p.recreate() snoop.clear() c = p.connect() snoop.assert_total(1, 1, 1, 0) c.close() snoop.assert_total(1, 1, 1, 1) c = p.connect() snoop.assert_total(1, 1, 2, 1) c.close() snoop.assert_total(1, 1, 2, 2) @testing.uses_deprecated(r".*Use the PoolEvents") def test_listeners_callables(self): def connect(dbapi_con, con_record): counts[0] += 1 def checkout(dbapi_con, con_record, con_proxy): counts[1] += 1 def checkin(dbapi_con, con_record): counts[2] += 1 i_all = dict(connect=connect, checkout=checkout, checkin=checkin) i_connect = dict(connect=connect) i_checkout = dict(checkout=checkout) i_checkin = dict(checkin=checkin) for cls in (pool.QueuePool, pool.StaticPool): counts = [0, 0, 0] def assert_listeners(p, total, conn, cout, cin): for instance in (p, p.recreate()): eq_(len(instance.dispatch.connect), conn) eq_(len(instance.dispatch.checkout), cout) eq_(len(instance.dispatch.checkin), cin) p = self._queuepool_fixture() assert_listeners(p, 0, 0, 0, 0) with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["connect", "checkout", "checkin"] ] ): p.add_listener(i_all) assert_listeners(p, 1, 1, 1, 1) with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["connect"] ] ): p.add_listener(i_connect) assert_listeners(p, 2, 1, 1, 1) with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["checkout"] ] ): p.add_listener(i_checkout) assert_listeners(p, 3, 1, 1, 1) with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["checkin"] ] ): p.add_listener(i_checkin) assert_listeners(p, 4, 1, 1, 1) del p with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["connect", "checkout", "checkin"] ] + [".*The Pool.listeners parameter will be removed"] ): p = self._queuepool_fixture(listeners=[i_all]) assert_listeners(p, 1, 1, 1, 1) c = p.connect() assert counts == [1, 1, 0] c.close() assert counts == [1, 1, 1] c = p.connect() assert counts == [1, 2, 1] with testing.expect_deprecated( *[ "PoolListener.%s is deprecated." % name for name in ["checkin"] ] ): p.add_listener(i_checkin) c.close() assert counts == [1, 2, 2] class PoolTest(PoolTestBase): def test_manager(self): with testing.expect_deprecated( r"The pool.manage function is deprecated," ): manager = pool.manage(MockDBAPI(), use_threadlocal=True) with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): c1 = manager.connect("foo.db") c2 = manager.connect("foo.db") c3 = manager.connect("bar.db") c4 = manager.connect("foo.db", bar="bat") c5 = manager.connect("foo.db", bar="hoho") c6 = manager.connect("foo.db", bar="bat") assert c1.cursor() is not None assert c1 is c2 assert c1 is not c3 assert c4 is c6 assert c4 is not c5 def test_manager_with_key(self): dbapi = MockDBAPI() with testing.expect_deprecated( r"The pool.manage function is deprecated," ): manager = pool.manage(dbapi, use_threadlocal=True) with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): c1 = manager.connect("foo.db", sa_pool_key="a") c2 = manager.connect("foo.db", sa_pool_key="b") c3 = manager.connect("bar.db", sa_pool_key="a") assert c1.cursor() is not None assert c1 is not c2 assert c1 is c3 eq_(dbapi.connect.mock_calls, [call("foo.db"), call("foo.db")]) def test_bad_args(self): with testing.expect_deprecated( r"The pool.manage function is deprecated," ): manager = pool.manage(MockDBAPI()) manager.connect(None) def test_non_thread_local_manager(self): with testing.expect_deprecated( r"The pool.manage function is deprecated," ): manager = pool.manage(MockDBAPI(), use_threadlocal=False) connection = manager.connect("foo.db") connection2 = manager.connect("foo.db") self.assert_(connection.cursor() is not None) self.assert_(connection is not connection2) def test_threadlocal_del(self): self._do_testthreadlocal(useclose=False) def test_threadlocal_close(self): self._do_testthreadlocal(useclose=True) def _do_testthreadlocal(self, useclose=False): dbapi = MockDBAPI() with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): for p in ( pool.QueuePool( creator=dbapi.connect, pool_size=3, max_overflow=-1, use_threadlocal=True, ), pool.SingletonThreadPool( creator=dbapi.connect, use_threadlocal=True ), ): c1 = p.connect() c2 = p.connect() self.assert_(c1 is c2) c3 = p.unique_connection() self.assert_(c3 is not c1) if useclose: c2.close() else: c2 = None c2 = p.connect() self.assert_(c1 is c2) self.assert_(c3 is not c1) if useclose: c2.close() else: c2 = None lazy_gc() if useclose: c1 = p.connect() c2 = p.connect() c3 = p.connect() c3.close() c2.close() self.assert_(c1.connection is not None) c1.close() c1 = c2 = c3 = None # extra tests with QueuePool to ensure connections get # __del__()ed when dereferenced if isinstance(p, pool.QueuePool): lazy_gc() self.assert_(p.checkedout() == 0) c1 = p.connect() c2 = p.connect() if useclose: c2.close() c1.close() else: c2 = None c1 = None lazy_gc() self.assert_(p.checkedout() == 0) def test_mixed_close(self): pool._refs.clear() with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): p = self._queuepool_fixture( pool_size=3, max_overflow=-1, use_threadlocal=True ) c1 = p.connect() c2 = p.connect() assert c1 is c2 c1.close() c2 = None assert p.checkedout() == 1 c1 = None lazy_gc() assert p.checkedout() == 0 lazy_gc() assert not pool._refs class QueuePoolTest(PoolTestBase): def test_threadfairy(self): with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): p = self._queuepool_fixture( pool_size=3, max_overflow=-1, use_threadlocal=True ) c1 = p.connect() c1.close() c2 = p.connect() assert c2.connection is not None def test_trick_the_counter(self): """this is a "flaw" in the connection pool; since threadlocal uses a single ConnectionFairy per thread with an open/close counter, you can fool the counter into giving you a ConnectionFairy with an ambiguous counter. i.e. its not true reference counting.""" with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): p = self._queuepool_fixture( pool_size=3, max_overflow=-1, use_threadlocal=True ) c1 = p.connect() c2 = p.connect() assert c1 is c2 c1.close() c2 = p.connect() c2.close() self.assert_(p.checkedout() != 0) c2.close() self.assert_(p.checkedout() == 0) @testing.requires.predictable_gc def test_weakref_kaboom(self): with testing.expect_deprecated( r".*Pool.use_threadlocal parameter is deprecated" ): p = self._queuepool_fixture( pool_size=3, max_overflow=-1, use_threadlocal=True ) c1 = p.connect() c2 = p.connect() c1.close() c2 = None del c1 del c2 gc_collect() assert p.checkedout() == 0 c3 = p.connect() assert c3 is not None class ExplicitAutoCommitDeprecatedTest(fixtures.TestBase): """test the 'autocommit' flag on select() and text() objects. Requires PostgreSQL so that we may define a custom function which modifies the database. """ __only_on__ = "postgresql" @classmethod def setup_class(cls): global metadata, foo metadata = MetaData(testing.db) foo = Table( "foo", metadata, Column("id", Integer, primary_key=True), Column("data", String(100)), ) metadata.create_all() testing.db.execute( "create function insert_foo(varchar) " "returns integer as 'insert into foo(data) " "values ($1);select 1;' language sql" ) def teardown(self): foo.delete().execute().close() @classmethod def teardown_class(cls): testing.db.execute("drop function insert_foo(varchar)") metadata.drop_all() def test_explicit_compiled(self): conn1 = testing.db.connect() conn2 = testing.db.connect() with testing.expect_deprecated( "The select.autocommit parameter is deprecated" ): conn1.execute(select([func.insert_foo("data1")], autocommit=True)) assert conn2.execute(select([foo.c.data])).fetchall() == [("data1",)] with testing.expect_deprecated( r"The SelectBase.autocommit method is deprecated," ): conn1.execute(select([func.insert_foo("data2")]).autocommit()) assert conn2.execute(select([foo.c.data])).fetchall() == [ ("data1",), ("data2",), ] conn1.close() conn2.close() def test_explicit_text(self): conn1 = testing.db.connect() conn2 = testing.db.connect() with testing.expect_deprecated( "The text.autocommit parameter is deprecated" ): conn1.execute( text("select insert_foo('moredata')", autocommit=True) ) assert conn2.execute(select([foo.c.data])).fetchall() == [ ("moredata",) ] conn1.close() conn2.close()

32.550167

79

0.548985

31f83afea1aa3491f5603b5bb0a45e21bcdc7769

9,487

py

Python

src/oci/data_integration/models/update_connection_from_adwc.py

ezequielramos/oci-python-sdk

cc4235cf217beaf9feed75760e9ce82610222762

[ "Apache-2.0", "BSD-3-Clause" ]

249

2017-09-11T22:06:05.000Z

2022-03-04T17:09:29.000Z

src/oci/data_integration/models/update_connection_from_adwc.py

ezequielramos/oci-python-sdk

cc4235cf217beaf9feed75760e9ce82610222762

[ "Apache-2.0", "BSD-3-Clause" ]

228

2017-09-11T23:07:26.000Z

2022-03-23T10:58:50.000Z

src/oci/data_integration/models/update_connection_from_adwc.py

ezequielramos/oci-python-sdk

cc4235cf217beaf9feed75760e9ce82610222762

[ "Apache-2.0", "BSD-3-Clause" ]

224

2017-09-27T07:32:43.000Z

2022-03-25T16:55:42.000Z

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from .update_connection_details import UpdateConnectionDetails from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class UpdateConnectionFromAdwc(UpdateConnectionDetails): """ The details to update an Autonomous Data Warehouse data asset connection. """ def __init__(self, **kwargs): """ Initializes a new UpdateConnectionFromAdwc object with values from keyword arguments. The default value of the :py:attr:`~oci.data_integration.models.UpdateConnectionFromAdwc.model_type` attribute of this class is ``ORACLE_ADWC_CONNECTION`` and it should not be changed. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param model_type: The value to assign to the model_type property of this UpdateConnectionFromAdwc. Allowed values for this property are: "ORACLE_ADWC_CONNECTION", "ORACLE_ATP_CONNECTION", "ORACLE_OBJECT_STORAGE_CONNECTION", "ORACLEDB_CONNECTION", "MYSQL_CONNECTION", "GENERIC_JDBC_CONNECTION", "BICC_CONNECTION", "AMAZON_S3_CONNECTION" :type model_type: str :param key: The value to assign to the key property of this UpdateConnectionFromAdwc. :type key: str :param model_version: The value to assign to the model_version property of this UpdateConnectionFromAdwc. :type model_version: str :param parent_ref: The value to assign to the parent_ref property of this UpdateConnectionFromAdwc. :type parent_ref: oci.data_integration.models.ParentReference :param name: The value to assign to the name property of this UpdateConnectionFromAdwc. :type name: str :param description: The value to assign to the description property of this UpdateConnectionFromAdwc. :type description: str :param object_status: The value to assign to the object_status property of this UpdateConnectionFromAdwc. :type object_status: int :param object_version: The value to assign to the object_version property of this UpdateConnectionFromAdwc. :type object_version: int :param identifier: The value to assign to the identifier property of this UpdateConnectionFromAdwc. :type identifier: str :param connection_properties: The value to assign to the connection_properties property of this UpdateConnectionFromAdwc. :type connection_properties: list[oci.data_integration.models.ConnectionProperty] :param registry_metadata: The value to assign to the registry_metadata property of this UpdateConnectionFromAdwc. :type registry_metadata: oci.data_integration.models.RegistryMetadata :param tns_alias: The value to assign to the tns_alias property of this UpdateConnectionFromAdwc. :type tns_alias: str :param tns_names: The value to assign to the tns_names property of this UpdateConnectionFromAdwc. :type tns_names: list[str] :param username: The value to assign to the username property of this UpdateConnectionFromAdwc. :type username: str :param password: The value to assign to the password property of this UpdateConnectionFromAdwc. :type password: str :param password_secret: The value to assign to the password_secret property of this UpdateConnectionFromAdwc. :type password_secret: oci.data_integration.models.SensitiveAttribute """ self.swagger_types = { 'model_type': 'str', 'key': 'str', 'model_version': 'str', 'parent_ref': 'ParentReference', 'name': 'str', 'description': 'str', 'object_status': 'int', 'object_version': 'int', 'identifier': 'str', 'connection_properties': 'list[ConnectionProperty]', 'registry_metadata': 'RegistryMetadata', 'tns_alias': 'str', 'tns_names': 'list[str]', 'username': 'str', 'password': 'str', 'password_secret': 'SensitiveAttribute' } self.attribute_map = { 'model_type': 'modelType', 'key': 'key', 'model_version': 'modelVersion', 'parent_ref': 'parentRef', 'name': 'name', 'description': 'description', 'object_status': 'objectStatus', 'object_version': 'objectVersion', 'identifier': 'identifier', 'connection_properties': 'connectionProperties', 'registry_metadata': 'registryMetadata', 'tns_alias': 'tnsAlias', 'tns_names': 'tnsNames', 'username': 'username', 'password': 'password', 'password_secret': 'passwordSecret' } self._model_type = None self._key = None self._model_version = None self._parent_ref = None self._name = None self._description = None self._object_status = None self._object_version = None self._identifier = None self._connection_properties = None self._registry_metadata = None self._tns_alias = None self._tns_names = None self._username = None self._password = None self._password_secret = None self._model_type = 'ORACLE_ADWC_CONNECTION' @property def tns_alias(self): """ Gets the tns_alias of this UpdateConnectionFromAdwc. The Autonomous Data Warehouse instance service name. :return: The tns_alias of this UpdateConnectionFromAdwc. :rtype: str """ return self._tns_alias @tns_alias.setter def tns_alias(self, tns_alias): """ Sets the tns_alias of this UpdateConnectionFromAdwc. The Autonomous Data Warehouse instance service name. :param tns_alias: The tns_alias of this UpdateConnectionFromAdwc. :type: str """ self._tns_alias = tns_alias @property def tns_names(self): """ Gets the tns_names of this UpdateConnectionFromAdwc. Array of service names that are available for selection in the tnsAlias property. :return: The tns_names of this UpdateConnectionFromAdwc. :rtype: list[str] """ return self._tns_names @tns_names.setter def tns_names(self, tns_names): """ Sets the tns_names of this UpdateConnectionFromAdwc. Array of service names that are available for selection in the tnsAlias property. :param tns_names: The tns_names of this UpdateConnectionFromAdwc. :type: list[str] """ self._tns_names = tns_names @property def username(self): """ Gets the username of this UpdateConnectionFromAdwc. The user name for the connection. :return: The username of this UpdateConnectionFromAdwc. :rtype: str """ return self._username @username.setter def username(self, username): """ Sets the username of this UpdateConnectionFromAdwc. The user name for the connection. :param username: The username of this UpdateConnectionFromAdwc. :type: str """ self._username = username @property def password(self): """ Gets the password of this UpdateConnectionFromAdwc. The password for the connection. :return: The password of this UpdateConnectionFromAdwc. :rtype: str """ return self._password @password.setter def password(self, password): """ Sets the password of this UpdateConnectionFromAdwc. The password for the connection. :param password: The password of this UpdateConnectionFromAdwc. :type: str """ self._password = password @property def password_secret(self): """ Gets the password_secret of this UpdateConnectionFromAdwc. :return: The password_secret of this UpdateConnectionFromAdwc. :rtype: oci.data_integration.models.SensitiveAttribute """ return self._password_secret @password_secret.setter def password_secret(self, password_secret): """ Sets the password_secret of this UpdateConnectionFromAdwc. :param password_secret: The password_secret of this UpdateConnectionFromAdwc. :type: oci.data_integration.models.SensitiveAttribute """ self._password_secret = password_secret def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

35.00738

248

0.65205

180b906b9fcd22cb470ea6ec6eb3f1b130759377

4,176

py

Python

spotify_client.py

leobeeson/music-time-machine

385faab5a55c6d78779cbb18bd7316ef0af8bf1c

[ "MIT" ]

null

spotify_client.py

leobeeson/music-time-machine

385faab5a55c6d78779cbb18bd7316ef0af8bf1c

[ "MIT" ]

null

spotify_client.py

leobeeson/music-time-machine

385faab5a55c6d78779cbb18bd7316ef0af8bf1c

[ "MIT" ]

null

import os from spotipy import Spotify from spotipy.oauth2 import SpotifyOAuth from typing import List from song_metadata_curator import SongMetadataCurator from dotenv import load_dotenv load_dotenv() CLIENT_ID = os.environ.get("SPOTIPY_CLIENT_ID") CLIENT_SECRET = os.environ.get("SPOTIPY_CLIENT_SECRET") REDERICT_URI = os.environ.get("SPOTIPY_REDIRECT_URI") SCOPE = "playlist-modify-private" class SpotifyClient(): def __init__(self) -> None: self.client_id = CLIENT_ID self.client_secret = CLIENT_SECRET self.redirect_uri = REDERICT_URI self.authenticate(SCOPE) self.get_access_token() self.create_spotipy_object() self.get_user_id() def authenticate(self, scope) -> SpotifyOAuth: oauth_manager = SpotifyOAuth( client_id = self.client_id, client_secret = self.client_secret, redirect_uri = self.redirect_uri, scope = scope, show_dialog = True, cache_path= "token.txt" ) self.oauth_manager = oauth_manager def get_access_token(self) -> None: self.oauth_manager.get_access_token(as_dict = False) def create_spotipy_object(self) -> None: self.spotify = Spotify(auth_manager = self.oauth_manager) def get_user_id(self) -> None: user_profile = self.spotify.current_user() user_id = user_profile["id"] self.user_id = user_id def get_all_spotify_song_ids(self, songs: List[dict]) -> List[dict]: metadata_curator = SongMetadataCurator() songs_with_uris = [] for song in songs: song_title = song["song_title"] song_title = metadata_curator.remove_apostrophes(song_title) artist_name = song["artist_name"] artist_name = metadata_curator.normalise_singer_collaboration(artist_name) track_uris = self.get_spotify_song_id(song_title, artist_name) song_with_uris = { "song_title": song_title, "artist_name": artist_name, "track_uris": track_uris } songs_with_uris.append(song_with_uris) return songs_with_uris def get_spotify_song_id(self, song_title: str, artist_name: str) -> List[str]: track_uris = [] response = self.spotify.search(q = f"track:{song_title} artist:{artist_name}", type = "track") try: items = response["tracks"]["items"] if len(items) > 0: for item in items: try: track_uri = item["uri"] track_uris.append(track_uri) except KeyError: print(f"NO SPOTIFY URI: {song_title} by {artist_name}.") except KeyError: print(f"TRACK NOT FOUND: {song_title} by {artist_name}") return track_uris def get_spotify_song_uris(self, song_ids: List[dict]) -> List[str]: song_uris = [] if len(song_ids) > 0: for song_id in song_ids: try: track_uris = song_id["track_uris"] try: track_uri = track_uris[0] except IndexError: continue except KeyError: continue song_uris.append(track_uri) return song_uris def create_empty_spotify_playlist(self, playlist_name: str) -> str: new_playlist = self.spotify.user_playlist_create( self.user_id, playlist_name, public = False ) playlist_id = new_playlist["id"] return playlist_id def create_spotify_playlist(self, song_uris: List[str], playlist_name: str) -> None: if len(song_uris) > 0: playlist_id = self.create_empty_spotify_playlist(playlist_name) snapshot_id = self.spotify.user_playlist_add_tracks( self.user_id, playlist_id, song_uris ) return snapshot_id

33.142857

102

0.589559

769c5246c6e2d8f03d88b36e38534c367e58dc1b

826

py

Python

0x03-caching/base_caching.py

JoseAVallejo12/holbertonschool-web_back_end

eb514784772352b8e4873d1f648726815ab69592

[ "MIT" ]

null

0x03-caching/base_caching.py

JoseAVallejo12/holbertonschool-web_back_end

eb514784772352b8e4873d1f648726815ab69592

[ "MIT" ]

null

0x03-caching/base_caching.py

JoseAVallejo12/holbertonschool-web_back_end

eb514784772352b8e4873d1f648726815ab69592

[ "MIT" ]

null

#!/usr/bin/python3 """ BaseCaching module.""" class BaseCaching(): """ BaseCaching defines: - constants of your caching system - where your data are stored (in a dictionary) """ MAX_ITEMS = 4 def __init__(self): """Initiliaze.""" self.cache_data = {} def print_cache(self): """ Print the cache """ print("Current cache:") for key in sorted(self.cache_data.keys()): print("{}: {}".format(key, self.cache_data.get(key))) def put(self, key, item): """ Add an item in the cache """ raise NotImplementedError("put must be implemented in your cache class") def get(self, key): """ Get an item by key """ raise NotImplementedError("get must be implemented in your cache class")

25.030303

80

0.572639

1728c92f7bab88c7237b5d9e66dcd3eae5c30263

11,025

py

Python

plotly/graph_objs/scatterpolargl/hoverlabel/_font.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/graph_objs/scatterpolargl/hoverlabel/_font.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/graph_objs/scatterpolargl/hoverlabel/_font.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

from plotly.basedatatypes import BaseTraceHierarchyType import copy class Font(BaseTraceHierarchyType): # color # ----- @property def color(self): """ The 'color' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self['color'] @color.setter def color(self, val): self['color'] = val # colorsrc # -------- @property def colorsrc(self): """ Sets the source reference on plot.ly for color . The 'colorsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['colorsrc'] @colorsrc.setter def colorsrc(self, val): self['colorsrc'] = val # family # ------ @property def family(self): """ HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on- premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". The 'family' property is a string and must be specified as: - A non-empty string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self['family'] @family.setter def family(self, val): self['family'] = val # familysrc # --------- @property def familysrc(self): """ Sets the source reference on plot.ly for family . The 'familysrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['familysrc'] @familysrc.setter def familysrc(self, val): self['familysrc'] = val # size # ---- @property def size(self): """ The 'size' property is a number and may be specified as: - An int or float in the interval [1, inf] - A tuple, list, or one-dimensional numpy array of the above Returns ------- int|float|numpy.ndarray """ return self['size'] @size.setter def size(self, val): self['size'] = val # sizesrc # ------- @property def sizesrc(self): """ Sets the source reference on plot.ly for size . The 'sizesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self['sizesrc'] @sizesrc.setter def sizesrc(self, val): self['sizesrc'] = val # property parent name # -------------------- @property def _parent_path_str(self): return 'scatterpolargl.hoverlabel' # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): """ Construct a new Font object Sets the font used in hover labels. Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.scatterpolargl.hoverlabel.Font color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- Font """ super(Font, self).__init__('font') # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.scatterpolargl.hoverlabel.Font constructor must be a dict or an instance of plotly.graph_objs.scatterpolargl.hoverlabel.Font""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop('skip_invalid', False) # Import validators # ----------------- from plotly.validators.scatterpolargl.hoverlabel import ( font as v_font ) # Initialize validators # --------------------- self._validators['color'] = v_font.ColorValidator() self._validators['colorsrc'] = v_font.ColorsrcValidator() self._validators['family'] = v_font.FamilyValidator() self._validators['familysrc'] = v_font.FamilysrcValidator() self._validators['size'] = v_font.SizeValidator() self._validators['sizesrc'] = v_font.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop('color', None) self['color'] = color if color is not None else _v _v = arg.pop('colorsrc', None) self['colorsrc'] = colorsrc if colorsrc is not None else _v _v = arg.pop('family', None) self['family'] = family if family is not None else _v _v = arg.pop('familysrc', None) self['familysrc'] = familysrc if familysrc is not None else _v _v = arg.pop('size', None) self['size'] = size if size is not None else _v _v = arg.pop('sizesrc', None) self['sizesrc'] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False

34.133127

75

0.562177

f889cff852fb0f077b9dca00a6ac7142bcdb8251

1,706

py

Python

aliyun-python-sdk-imm/aliyunsdkimm/request/v20170906/IndexTagRequest.py

sdk-team/aliyun-openapi-python-sdk

384730d707e6720d1676ccb8f552e6a7b330ec86

[ "Apache-2.0" ]

1

2019-12-23T12:36:43.000Z

aliyun-python-sdk-imm/aliyunsdkimm/request/v20170906/IndexTagRequest.py

sdk-team/aliyun-openapi-python-sdk

384730d707e6720d1676ccb8f552e6a7b330ec86

[ "Apache-2.0" ]

null

aliyun-python-sdk-imm/aliyunsdkimm/request/v20170906/IndexTagRequest.py

sdk-team/aliyun-openapi-python-sdk

384730d707e6720d1676ccb8f552e6a7b330ec86

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest class IndexTagRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'imm', '2017-09-06', 'IndexTag','imm') def get_SrcUris(self): return self.get_query_params().get('SrcUris') def set_SrcUris(self,SrcUris): self.add_query_param('SrcUris',SrcUris) def get_ModelId(self): return self.get_query_params().get('ModelId') def set_ModelId(self,ModelId): self.add_query_param('ModelId',ModelId) def get_Project(self): return self.get_query_params().get('Project') def set_Project(self,Project): self.add_query_param('Project',Project) def get_SetId(self): return self.get_query_params().get('SetId') def set_SetId(self,SetId): self.add_query_param('SetId',SetId) def get_Force(self): return self.get_query_params().get('Force') def set_Force(self,Force): self.add_query_param('Force',Force)

31.592593

67

0.745604

3d53bb7e17f847925ecefaa73daed9571e94271b

412

py

Python

products/urls.py

Code-Institute-Submissions/danielboots-fytletic

67c3000a4b681d7f76255ab11db841a7f2ba613e

[ "OLDAP-2.3" ]

1

2021-03-31T18:54:25.000Z

products/urls.py

Code-Institute-Submissions/danielboots-fytletic

67c3000a4b681d7f76255ab11db841a7f2ba613e

[ "OLDAP-2.3" ]

null

products/urls.py

Code-Institute-Submissions/danielboots-fytletic

67c3000a4b681d7f76255ab11db841a7f2ba613e

[ "OLDAP-2.3" ]

1

2021-03-31T11:00:11.000Z

from django.urls import path from . import views urlpatterns = [ path("", views.all_products, name="products"), path("<int:product_id>/", views.product_detail, name="product_detail"), path("add/", views.add_product, name="add_product"), path("edit/<int:product_id>/", views.edit_product, name="edit_product"), path("delete/<int:product_id>/", views.delete_product, name="delete_product"), ]

37.454545

82

0.701456

1298f143205819bcdf2d90e6a3f5cf7cefcc26eb

4,253

py

Python

exblox/trainer.py

lokijuhy/exblox

86dd358094381ac7c91d935a9c6d4c8d2380b291

[ "MIT" ]

null

exblox/trainer.py

lokijuhy/exblox

86dd358094381ac7c91d935a9c6d4c8d2380b291

[ "MIT" ]

null

exblox/trainer.py

lokijuhy/exblox

86dd358094381ac7c91d935a9c6d4c8d2380b291

[ "MIT" ]

null

from copy import deepcopy from sklearn.preprocessing import LabelEncoder from typing import Dict, Tuple from .architecture import Architecture, ArchitectureInterface from .ConfigurableComponent import ConfigurableComponent, ComponentInterface from .tuner import Tuner, TunerInterface from .predictor import Predictor class Trainer(ConfigurableComponent): def __init__(self, config: Dict, architecture: Architecture, tuner: Tuner = None): if not isinstance(config, dict): if isinstance(architecture, dict) or isinstance(tuner, dict): raise ValueError('It looks like the order of the arguments to `Trainer` is swapped. Please use' ' `Trainer(config, architecture, tuner)`.') raise ValueError('The first argument to Trainer must be a dictionary.') super().__init__(config) self.architecture = architecture self.tuner = tuner def fit(self, x, y) -> Tuple[Predictor, dict]: """ Train the `Architecture`, yielding a trained `Predictor` object and a `training_metadata` dictionary. If `Trainer` contains a `Tuner`, use it to fit the `Architecture`. Otherwise, `fit` the `Architecture` directly. Args: x: The training set. y: The labels for the training set. Returns: a trained `Predictor` object and a `training_metadata` dictionary. """ training_metadata = {} architecture = self.get_architecture() if self.tuner: trained_model, training_metadata = self.tuner.fit(architecture, x, y) else: trained_model = architecture.fit(x, y) predictor = Predictor(trained_model) return predictor, training_metadata def get_architecture(self) -> Architecture: """ Instantiate a new copy of an untrained architecture. Using this method rather than referencing `self.architecture` ensures that a single `Trainer` object can be used to train and yield multiple architectures, without overwriting the origianl (by reference). Returns: A new architecture object """ fresh_architecture = deepcopy(self.architecture) return fresh_architecture class SkorchTrainer(Trainer): def fit(self, x, y) -> Tuple[Predictor, Dict]: training_metadata = {} architecture = self.get_architecture() y = self.transform_y(y) if self.tuner: trained_model, training_metadata = self.tuner.fit(architecture, x, y) else: trained_model = architecture.fit(x, y) predictor = Predictor(trained_model) return predictor, training_metadata @staticmethod def transform_y(y): y = LabelEncoder().fit_transform(y) y = y.astype('float32') y = y.reshape((len(y), 1)) return y class TrainerInterface(ComponentInterface): registered_flavors = { 'Trainer': Trainer, 'SkorchTrainer': SkorchTrainer, } @classmethod def deserialize(cls, d: Dict[str, Dict]) -> Trainer: """ Instantiate a Trainer from dictionary containing keys ['Trainer', 'Architecture', 'Tuner']. Args: d: A dictionary with keys ['Trainer', 'Architecture', 'Tuner'], each containing configuration dictionaries. The configuration dictionaries contain the key 'flavor' describing the class name of the component to be instantiated, and key 'config' containing the object's config dictionary. The configuration dictionaries may also contain other keys, which must be added to the object by the subclass-ed deserialize method. Returns: A deserialized Trainer object. """ trainer_config = d['Trainer'] trainer_config = cls.validate_serialization_config(trainer_config) flavor_cls = cls.select_flavor(trainer_config['flavor']) architecture = ArchitectureInterface.deserialize(d['Architecture']) tuner = TunerInterface.deserialize(d['Tuner']) if 'Tuner' in d else None flavor_instance = flavor_cls(config=trainer_config['config'], architecture=architecture, tuner=tuner) return flavor_instance

38.663636

120

0.667294

67e5120a54b79a83a4ea125ee487839b5740af55

38,284

py

Python

python/paddle/tensor/random.py

zhenlin-work/Paddle

ed7a21dea0ddcffb6f7f33ce21c5c368f5c7866b

[ "Apache-2.0" ]

4

2021-02-08T13:07:15.000Z

2021-10-22T00:58:33.000Z

python/paddle/tensor/random.py

zhenlin-work/Paddle

ed7a21dea0ddcffb6f7f33ce21c5c368f5c7866b

[ "Apache-2.0" ]

2

2019-07-26T04:06:05.000Z

2019-07-29T04:25:24.000Z

python/paddle/tensor/random.py

zhenlin-work/Paddle

ed7a21dea0ddcffb6f7f33ce21c5c368f5c7866b

[ "Apache-2.0" ]

5

2021-12-10T11:20:06.000Z

2022-02-18T05:18:12.000Z

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: define random functions from ..fluid import core from ..fluid.framework import in_dygraph_mode, Variable, convert_np_dtype_to_dtype_, dygraph_only from ..fluid.layer_helper import LayerHelper from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, check_shape from ..fluid.layers import utils import paddle from paddle import _C_ops __all__ = [] def bernoulli(x, name=None): """ This OP returns a Tensor filled with random binary(0 or 1) number from a Bernoulli distribution. The input ``x`` is a tensor with probabilities for generating the random binary number. Each element in ``x`` should be in [0, 1], and the out is generated by: .. math:: out_i ~ Bernoulli (x_i) Args: x(Tensor): A tensor with probabilities for generating the random binary number. The data type should be float32, float64. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random binary number with the same shape and dtype as ``x``. Examples: .. code-block:: python import paddle paddle.set_device('cpu') # on CPU device paddle.seed(100) x = paddle.rand([2,3]) print(x) # [[0.55355281, 0.20714243, 0.01162981], # [0.51577556, 0.36369765, 0.26091650]] out = paddle.bernoulli(x) print(out) # [[1., 0., 1.], # [0., 1., 0.]] """ if in_dygraph_mode(): return _C_ops.bernoulli(x) check_variable_and_dtype(x, "x", ["float32", "float64"], "bernoulli") helper = LayerHelper("randint", **locals()) out = helper.create_variable_for_type_inference( dtype=x.dtype) # maybe set out to int32 ? helper.append_op( type='bernoulli', inputs={"X": x}, outputs={'Out': out}, attrs={}) out.stop_gradient = True return out def multinomial(x, num_samples=1, replacement=False, name=None): """ This OP returns a Tensor filled with random values sampled from a Multinomical distribution. The input ``x`` is a tensor with probabilities for generating the random number. Each element in ``x`` should be larger or equal to 0, but not all 0. ``replacement`` indicates whether it is a replaceable sample. If ``replacement`` is True, a category can be sampled more than once. Args: x(Tensor): A tensor with probabilities for generating the random number. The data type should be float32, float64. num_samples(int, optional): Number of samples, default is 1. replacement(bool, optional): Whether it is a replaceable sample, default is False. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with sampled category index after ``num_samples`` times samples. Examples: .. code-block:: python import paddle paddle.seed(100) # on CPU device x = paddle.rand([2,4]) print(x) # [[0.5535528 0.20714243 0.01162981 0.51577556] # [0.36369765 0.2609165 0.18905126 0.5621971 ]] paddle.seed(200) # on CPU device out1 = paddle.multinomial(x, num_samples=5, replacement=True) print(out1) # [[3 3 0 0 0] # [3 3 3 1 0]] # out2 = paddle.multinomial(x, num_samples=5) # InvalidArgumentError: When replacement is False, number of samples # should be less than non-zero categories paddle.seed(300) # on CPU device out3 = paddle.multinomial(x, num_samples=3) print(out3) # [[3 0 1] # [3 1 0]] """ assert core.is_compiled_with_rocm() == False, ( "multinomial op is not supported on ROCM yet.") if in_dygraph_mode(): return _C_ops.multinomial(x, 'num_samples', num_samples, 'replacement', replacement) check_variable_and_dtype(x, "x", ["float32", "float64"], "multinomial") helper = LayerHelper("multinomial", **locals()) out = helper.create_variable_for_type_inference( dtype=convert_np_dtype_to_dtype_('int64')) helper.append_op( type='multinomial', inputs={"X": x}, outputs={'Out': out}, attrs={'num_samples': num_samples, 'replacement': replacement}) out.stop_gradient = True return out def gaussian(shape, mean=0.0, std=1.0, dtype=None, name=None): """ This OP returns a Tensor filled with random values sampled from a Gaussian distribution, with ``shape`` and ``dtype``. Args: shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). mean (float|int, optional): Mean of the output tensor, default is 0.0. std (float|int, optional): Standard deviation of the output tensor, default is 1.0. seed (int, optional): Random seed of generator. dtype (str|np.dtype, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is None, use global default dtype (see ``get_default_dtype`` for details). name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random values sampled from a Gaussian distribution, with ``shape`` and ``dtype``. """ op_type_for_check = 'gaussian/standard_normal/randn/normal' seed = 0 if dtype is None: dtype = paddle.framework.get_default_dtype() if dtype not in ['float32', 'float64']: raise TypeError( "{} only supports [float32, float64], but the default dtype is {}" .format(op_type_for_check, dtype)) if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if in_dygraph_mode(): shape = utils.convert_shape_to_list(shape) return _C_ops.gaussian_random('shape', shape, 'mean', float(mean), 'std', float(std), 'seed', seed, 'dtype', dtype) check_shape(shape, op_type_for_check) check_dtype(dtype, 'dtype', ['float32', 'float64'], op_type_for_check) inputs = {} attrs = { 'mean': mean, 'std': std, 'seed': seed, 'dtype': dtype, 'use_mkldnn': False } utils.get_shape_tensor_inputs( inputs=inputs, attrs=attrs, shape=shape, op_type=op_type_for_check) helper = LayerHelper('gaussian', **locals()) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='gaussian_random', inputs=inputs, outputs={'Out': out}, attrs=attrs) out.stop_gradient = True return out def standard_normal(shape, dtype=None, name=None): """ This OP returns a Tensor filled with random values sampled from a standard normal distribution with mean 0 and standard deviation 1, with ``shape`` and ``dtype``. Args: shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). dtype (str|np.dtype, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is None, use global default dtype (see ``get_default_dtype`` for details). name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random values sampled from a standard normal distribution with mean 0 and standard deviation 1, with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle # example 1: attr shape is a list which doesn't contain Tensor. out1 = paddle.standard_normal(shape=[2, 3]) # [[-2.923464 , 0.11934398, -0.51249987], # random # [ 0.39632758, 0.08177969, 0.2692008 ]] # random # example 2: attr shape is a list which contains Tensor. dim1 = paddle.to_tensor([2], 'int64') dim2 = paddle.to_tensor([3], 'int32') out2 = paddle.standard_normal(shape=[dim1, dim2, 2]) # [[[-2.8852394 , -0.25898588], # random # [-0.47420555, 0.17683524], # random # [-0.7989969 , 0.00754541]], # random # [[ 0.85201347, 0.32320443], # random # [ 1.1399018 , 0.48336947], # random # [ 0.8086993 , 0.6868893 ]]] # random # example 3: attr shape is a Tensor, the data type must be int64 or int32. shape_tensor = paddle.to_tensor([2, 3]) out3 = paddle.standard_normal(shape_tensor) # [[-2.878077 , 0.17099959, 0.05111201] # random # [-0.3761474, -1.044801 , 1.1870178 ]] # random """ return gaussian(shape=shape, mean=0.0, std=1.0, dtype=dtype, name=name) def randn(shape, dtype=None, name=None): """ This OP returns a Tensor filled with random values sampled from a standard normal distribution with mean 0 and standard deviation 1, with ``shape`` and ``dtype``. Args: shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). dtype (str|np.dtype, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is None, use global default dtype (see ``get_default_dtype`` for details). name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random values sampled from a standard normal distribution with mean 0 and standard deviation 1, with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle # example 1: attr shape is a list which doesn't contain Tensor. out1 = paddle.randn(shape=[2, 3]) # [[-2.923464 , 0.11934398, -0.51249987], # random # [ 0.39632758, 0.08177969, 0.2692008 ]] # random # example 2: attr shape is a list which contains Tensor. dim1 = paddle.to_tensor([2], 'int64') dim2 = paddle.to_tensor([3], 'int32') out2 = paddle.randn(shape=[dim1, dim2, 2]) # [[[-2.8852394 , -0.25898588], # random # [-0.47420555, 0.17683524], # random # [-0.7989969 , 0.00754541]], # random # [[ 0.85201347, 0.32320443], # random # [ 1.1399018 , 0.48336947], # random # [ 0.8086993 , 0.6868893 ]]] # random # example 3: attr shape is a Tensor, the data type must be int64 or int32. shape_tensor = paddle.to_tensor([2, 3]) out3 = paddle.randn(shape_tensor) # [[-2.878077 , 0.17099959, 0.05111201] # random # [-0.3761474, -1.044801 , 1.1870178 ]] # random """ return standard_normal(shape, dtype, name) def normal(mean=0.0, std=1.0, shape=None, name=None): """ This OP returns a Tensor filled with random values sampled from a normal distribution with ``mean`` and ``std`` (standard deviation) . If ``mean`` is a Tensor, the output Tensor has the same shape and data type as ``mean``. If ``mean`` is not a Tensor and ``std`` is a Tensor, the output Tensor has the same shape and data type as ``std``. If ``mean`` and ``std`` are not a Tensor, the output Tensor has the same shape as ``shape``, with data type float32. If ``mean`` and ``std`` are Tensor, the num of elements of ``mean`` and ``std`` should be the same. Args: mean (float|Tensor, optional): The mean of the output Tensor's normal distribution. If ``mean`` is float, all elements of the output Tensor shared the same mean. If ``mean`` is a Tensor(data type supports float32, float64), it has per-element means. Default is 0.0 std (float|Tensor, optional): The standard deviation of the output Tensor's normal distribution. If ``std`` is float, all elements of the output Tensor shared the same standard deviation. If ``std`` is a Tensor(data type supports float32, float64), it has per-element standard deviations. Defaule is 1.0 shape (list|tuple|Tensor, optional): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). If ``mean`` or ``std`` is a Tensor, the shape of the output Tensor is the same as ``mean`` or ``std`` , attr ``shape`` is ignored. Default is None name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Tensor filled with random values sampled from a normal distribution with ``mean`` and ``std`` . Examples: .. code-block:: python import paddle out1 = paddle.normal(shape=[2, 3]) # [[ 0.17501129 0.32364586 1.561118 ] # random # [-1.7232178 1.1545963 -0.76156676]] # random mean_tensor = paddle.to_tensor([1.0, 2.0, 3.0]) out2 = paddle.normal(mean=mean_tensor) # [ 0.18644847 -1.19434458 3.93694787] # random std_tensor = paddle.to_tensor([1.0, 2.0, 3.0]) out3 = paddle.normal(mean=mean_tensor, std=std_tensor) # [1.00780561 3.78457445 5.81058198] # random """ if not in_dygraph_mode(): check_type(mean, 'mean', (int, float, Variable), 'normal') check_type(std, 'std', (int, float, Variable), 'normal') if isinstance(mean, Variable): check_dtype( mean.dtype, 'mean', ['float32', 'float64'], 'normal', "If mean is Tensor, it's data type only support float32, float64." ) if isinstance(std, Variable): check_dtype( std.dtype, 'std', ['float32', 'float64'], 'normal', "If std is Tensor, it's data type only support float32, float64." ) if shape is not None: check_shape(shape, 'normal') if isinstance(mean, Variable): if isinstance(std, Variable): if std.dtype != mean.dtype: std = paddle.cast(std, mean.dtype) mean_shape = paddle.shape(mean) std = paddle.reshape(std, mean_shape) else: std = float(std) out = standard_normal(paddle.shape(mean), mean.dtype, name) elif isinstance(std, Variable): mean = float(mean) out = standard_normal(paddle.shape(std), std.dtype, name) else: return gaussian(shape=shape, mean=mean, std=std, name=name) out = out * std + mean if not in_dygraph_mode(): out.stop_grediant = True return out def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None): """ This OP returns a Tensor filled with random values sampled from a uniform distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``. Examples: .. code-block:: text Input: shape = [1, 2] Output: result=[[0.8505902, 0.8397286]] Args: shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). dtype(str|np.dtype, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is None, use global default dtype (see ``get_default_dtype`` for details). min(float|int, optional): The lower bound on the range of random values to generate, ``min`` is included in the range. Default is -1.0. max(float|int, optional): The upper bound on the range of random values to generate, ``max`` is excluded in the range. Default is 1.0. seed(int, optional): Random seed used for generating samples. If seed is 0, it will use the seed of the global default generator (which can be set by paddle.seed). Note that if seed is not 0, this operator will always generate the same random numbers every time. Default is 0. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random values sampled from a uniform distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``. Raises: TypeError: If ``shape`` is not list, tuple, Tensor. TypeError: If ``dtype`` is not float32, float64. Examples: .. code-block:: python import paddle # example 1: # attr shape is a list which doesn't contain Tensor. out1 = paddle.uniform(shape=[3, 4]) # [[ 0.84524226, 0.6921872, 0.56528175, 0.71690357], # random # [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random # [ 0.433519, 0.39483607, -0.8660099, 0.83664286]] # random # example 2: # attr shape is a list which contains Tensor. dim1 = paddle.to_tensor([2], 'int64') dim2 = paddle.to_tensor([3], 'int32') out2 = paddle.uniform(shape=[dim1, dim2]) # [[-0.9951253, 0.30757582, 0.9899647 ], # random # [ 0.5864527, 0.6607096, -0.8886161]] # random # example 3: # attr shape is a Tensor, the data type must be int64 or int32. shape_tensor = paddle.to_tensor([2, 3]) out3 = paddle.uniform(shape_tensor) # [[-0.8517412, -0.4006908, 0.2551912 ], # random # [ 0.3364414, 0.36278176, -0.16085452]] # random """ if dtype is None: dtype = paddle.framework.get_default_dtype() if dtype not in ['float32', 'float64']: raise TypeError( "uniform/rand only supports [float32, float64], but the default dtype is {}". format(dtype)) if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if in_dygraph_mode(): shape = utils.convert_shape_to_list(shape) return _C_ops.uniform_random('shape', shape, 'min', float(min), 'max', float(max), 'seed', seed, 'dtype', dtype) check_type(shape, 'shape', (list, tuple, Variable), 'uniform/rand') check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform/rand') inputs = dict() attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype} utils.get_shape_tensor_inputs( inputs=inputs, attrs=attrs, shape=shape, op_type='uniform/rand') helper = LayerHelper("uniform", **locals()) out = helper.create_variable_for_type_inference(dtype) helper.append_op( type="uniform_random", inputs=inputs, attrs=attrs, outputs={"Out": out}) out.stop_gradient = True return out @dygraph_only def uniform_(x, min=-1.0, max=1.0, seed=0, name=None): """ This is the inplace version of OP ``uniform``, which returns a Tensor filled with random values sampled from a uniform distribution. The output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_tensor_uniform`. Args: x(Tensor): The input tensor to be filled with random values. min(float|int, optional): The lower bound on the range of random values to generate, ``min`` is included in the range. Default is -1.0. max(float|int, optional): The upper bound on the range of random values to generate, ``max`` is excluded in the range. Default is 1.0. seed(int, optional): Random seed used for generating samples. If seed is 0, it will use the seed of the global default generator (which can be set by paddle.seed). Note that if seed is not 0, this operator will always generate the same random numbers every time. Default is 0. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The input tensor x filled with random values sampled from a uniform distribution in the range [``min``, ``max``). Examples: .. code-block:: python import paddle # example: x = paddle.ones(shape=[3, 4]) x.uniform_() print(x) # [[ 0.84524226, 0.6921872, 0.56528175, 0.71690357], # random # [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random # [ 0.433519, 0.39483607, -0.8660099, 0.83664286]] # random """ return core.ops.uniform_random_inplace_(x, 'min', min, 'max', max, 'seed', seed) def randint(low=0, high=None, shape=[1], dtype=None, name=None): """ This OP returns a Tensor filled with random integers from a discrete uniform distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``. If ``high`` is None (the default), the range is [0, ``low``). Args: low (int): The lower bound on the range of random values to generate. The ``low`` is included in the range. If ``high`` is None, the range is [0, ``low``). Default is 0. high (int, optional): The upper bound on the range of random values to generate, the ``high`` is excluded in the range. Default is None (see above for behavior if high = None). Default is None. shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). Default is [1]. dtype (str|np.dtype, optional): The data type of the output tensor. Supported data types: int32, int64. If ``dytpe`` is None, the data type is int64. Default is None. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random integers from a discrete uniform distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle # example 1: # attr shape is a list which doesn't contain Tensor. out1 = paddle.randint(low=-5, high=5, shape=[3]) # [0, -3, 2] # random # example 2: # attr shape is a list which contains Tensor. dim1 = paddle.to_tensor([2], 'int64') dim2 = paddle.to_tensor([3], 'int32') out2 = paddle.randint(low=-5, high=5, shape=[dim1, dim2]) # [[0, -1, -3], # random # [4, -2, 0]] # random # example 3: # attr shape is a Tensor shape_tensor = paddle.to_tensor(3) out3 = paddle.randint(low=-5, high=5, shape=shape_tensor) # [-2, 2, 3] # random # example 4: # data type is int32 out4 = paddle.randint(low=-5, high=5, shape=[3], dtype='int32') # [-5, 4, -4] # random # example 5: # Input only one parameter # low=0, high=10, shape=[1], dtype='int64' out5 = paddle.randint(10) # [7] # random """ if high is None: if low <= 0: raise ValueError( "If high is None, low must be greater than 0, but received low = {0}.". format(low)) high = low low = 0 if dtype is None: dtype = 'int64' if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if in_dygraph_mode(): shape = utils.convert_shape_to_list(shape) return _C_ops.randint('shape', shape, 'low', low, 'high', high, 'seed', 0, 'dtype', dtype) check_shape(shape, 'randint') check_dtype(dtype, 'dtype', ['int32', 'int64'], 'randint') if low >= high: raise ValueError( "randint's low must less then high, but received low = {0}, " "high = {1}".format(low, high)) inputs = dict() attrs = {'low': low, 'high': high, 'seed': 0, 'dtype': dtype} utils.get_shape_tensor_inputs( inputs=inputs, attrs=attrs, shape=shape, op_type='randint') helper = LayerHelper("randint", **locals()) out = helper.create_variable_for_type_inference(dtype=dtype) helper.append_op( type='randint', inputs=inputs, outputs={'Out': out}, attrs=attrs) out.stop_gradient = True return out def randint_like(x, low=0, high=None, dtype=None, name=None): """ This OP returns a Tensor filled with random integers from a discrete uniform distribution in the range [``low``, ``high``), with the same shape as ``x``. (use ``dtype`` if ``dtype`` is not None) If ``high`` is None (the default), the range is [0, ``low``). Args: x (Tensor): The input tensor which specifies shape. The dtype of ``x`` can be bool, int32, int64, float16, float32, float64. low (int): The lower bound on the range of random values to generate. The ``low`` is included in the range. If ``high`` is None, the range is [0, ``low``). Default is 0. high (int, optional): The upper bound on the range of random values to generate, the ``high`` is excluded in the range. Default is None (see above for behavior if high = None). Default is None. dtype (str|np.dtype, optional): The data type of the output tensor. Supported data types: bool, int32, int64, float16, float32, float64. If ``dytpe`` is None, the data type is the same as x's data type. Default is None. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random integers from a discrete uniform distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle # example 1: # dtype is None and the dtype of x is float16 x = paddle.zeros((1,2)).astype("float16") out1 = paddle.randint_like(x, low=-5, high=5) print(out1) print(out1.dtype) # [[0, -3]] # random # paddle.float16 # example 2: # dtype is None and the dtype of x is float32 x = paddle.zeros((1,2)).astype("float32") out2 = paddle.randint_like(x, low=-5, high=5) print(out2) print(out2.dtype) # [[0, -3]] # random # paddle.float32 # example 3: # dtype is None and the dtype of x is float64 x = paddle.zeros((1,2)).astype("float64") out3 = paddle.randint_like(x, low=-5, high=5) print(out3) print(out3.dtype) # [[0, -3]] # random # paddle.float64 # example 4: # dtype is None and the dtype of x is int32 x = paddle.zeros((1,2)).astype("int32") out4 = paddle.randint_like(x, low=-5, high=5) print(out4) print(out4.dtype) # [[0, -3]] # random # paddle.int32 # example 5: # dtype is None and the dtype of x is int64 x = paddle.zeros((1,2)).astype("int64") out5 = paddle.randint_like(x, low=-5, high=5) print(out5) print(out5.dtype) # [[0, -3]] # random # paddle.int64 # example 6: # dtype is float64 and the dtype of x is float32 x = paddle.zeros((1,2)).astype("float32") out6 = paddle.randint_like(x, low=-5, high=5, dtype="float64") print(out6) print(out6.dtype) # [[0, -1]] # random # paddle.float64 # example 7: # dtype is bool and the dtype of x is float32 x = paddle.zeros((1,2)).astype("float32") out7 = paddle.randint_like(x, low=-5, high=5, dtype="bool") print(out7) print(out7.dtype) # [[0, -1]] # random # paddle.bool # example 8: # dtype is int32 and the dtype of x is float32 x = paddle.zeros((1,2)).astype("float32") out8 = paddle.randint_like(x, low=-5, high=5, dtype="int32") print(out8) print(out8.dtype) # [[0, -1]] # random # paddle.int32 # example 9: # dtype is int64 and the dtype of x is float32 x = paddle.zeros((1,2)).astype("float32") out9 = paddle.randint_like(x, low=-5, high=5, dtype="int64") print(out9) print(out9.dtype) # [[0, -1]] # random # paddle.int64 # example 10: # dtype is int64 and the dtype of x is bool x = paddle.zeros((1,2)).astype("bool") out10 = paddle.randint_like(x, low=-5, high=5, dtype="int64") print(out10) print(out10.dtype) # [[0, -1]] # random # paddle.int64 """ if high is None: if low <= 0: raise ValueError( "If high is None, low must be greater than 0, but received low = {0}.". format(low)) high = low low = 0 if dtype is None: dtype = x.dtype if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) shape = x.shape if low >= high: raise ValueError( "randint_like's low must less then high, but received low = {0}, " "high = {1}".format(low, high)) if in_dygraph_mode(): shape = utils.convert_shape_to_list(shape) out = _C_ops.randint('shape', shape, 'low', low, 'high', high, 'seed', 0, 'dtype', core.VarDesc.VarType.INT64) out = paddle.cast(out, dtype) return out check_shape(shape, 'randint_like') check_dtype(dtype, 'dtype', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'], 'randint_like') inputs = dict() attrs = { 'low': low, 'high': high, 'seed': 0, 'dtype': core.VarDesc.VarType.INT64 } utils.get_shape_tensor_inputs( inputs=inputs, attrs=attrs, shape=shape, op_type='randint_like') helper = LayerHelper("randint", **locals()) out = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.INT64) helper.append_op( type='randint', inputs=inputs, outputs={'Out': out}, attrs=attrs) out.stop_gradient = True out = paddle.cast(out, dtype) return out def randperm(n, dtype="int64", name=None): """ This OP returns a 1-D Tensor filled with random permutation values from 0 to n-1, with ``dtype``. Args: n (int): The upper bound (exclusive), and it should be greater than 0. dtype (str|np.dtype, optional): The data type of the output Tensor. Supported data types: int32, int64, float32, float64. Default is int64. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A 1-D Tensor filled with random permutation values from 0 to n-1, with ``dtype``. Examples: .. code-block:: python import paddle out1 = paddle.randperm(5) # [4, 1, 2, 3, 0] # random out2 = paddle.randperm(7, 'int32') # [1, 6, 2, 0, 4, 3, 5] # random """ if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if in_dygraph_mode(): return _C_ops.randperm('n', n, 'seed', 0, 'dtype', dtype) if n < 1: raise ValueError("The input n should be greater than 0 in randperm op.") check_dtype(dtype, 'dtype', ['int64', 'int32', 'float32', 'float64'], 'randperm') helper = LayerHelper("randperm", **locals()) out = helper.create_variable_for_type_inference(dtype) attrs = {'n': n, 'dtype': dtype, 'seed': 0} helper.append_op( type='randperm', inputs={}, outputs={'Out': out}, attrs=attrs) out.stop_gradient = True return out def rand(shape, dtype=None, name=None): """ This OP returns a Tensor filled with random values sampled from a uniform distribution in the range [0, 1), with ``shape`` and ``dtype``. Args: shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape`` is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If ``shape`` is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64). dtype (str|np.dtype, optional): The data type of the output Tensor. Supported data types: float32, float64. Default is None, use global default dtype (see ``get_default_dtype`` for details). name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor filled with random values sampled from a uniform distribution in the range [0, 1), with ``shape`` and ``dtype``. Examples: .. code-block:: python import paddle # example 1: attr shape is a list which doesn't contain Tensor. out1 = paddle.rand(shape=[2, 3]) # [[0.451152 , 0.55825245, 0.403311 ], # random # [0.22550228, 0.22106001, 0.7877319 ]] # random # example 2: attr shape is a list which contains Tensor. dim1 = paddle.to_tensor([2], 'int64') dim2 = paddle.to_tensor([3], 'int32') out2 = paddle.rand(shape=[dim1, dim2, 2]) # [[[0.8879919 , 0.25788337], # random # [0.28826773, 0.9712097 ], # random # [0.26438272, 0.01796806]], # random # [[0.33633623, 0.28654453], # random # [0.79109055, 0.7305809 ], # random # [0.870881 , 0.2984597 ]]] # random # example 3: attr shape is a Tensor, the data type must be int64 or int32. shape_tensor = paddle.to_tensor([2, 3]) out3 = paddle.rand(shape_tensor) # [[0.22920267, 0.841956 , 0.05981819], # random # [0.4836288 , 0.24573246, 0.7516129 ]] # random """ return uniform(shape, dtype, min=0.0, max=1.0, name=name)

40.72766

120

0.581522

4e282545478177cd7a079d2b658091c2e3a5328c

5,121

py

Python

Resources/app3.py

BankeUCI/SQLAlchemy-Challenge

ed961511bb605c400244c3af5d9253d478e0680a

[ "ADSL" ]

null

Resources/app3.py

BankeUCI/SQLAlchemy-Challenge

ed961511bb605c400244c3af5d9253d478e0680a

[ "ADSL" ]

null

Resources/app3.py

BankeUCI/SQLAlchemy-Challenge

ed961511bb605c400244c3af5d9253d478e0680a

[ "ADSL" ]

null

from time import strftime from flask import Flask, jsonify import sqlite3 import sqlalchemy from sqlalchemy import * from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, inspect, func import pandas as pd import numpy as np import datetime as dt ####################################################### # Database Setup ####################################################### engine = create_engine("sqlite:///hawaii.sqlite") # Reflect an existing database into a new model Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) # Classes automap found #Base.classes.keys() metadata = MetaData() # Save reference to table Measurement = Base.classes.measurement Station = Base.classes.station # Create our session (link) from python to the DB session = Session(engine) ############################################################# # Flask Setup ############################################################# app = Flask(__name__) ################################ # Flask Routes ################################ @app.route("/") def Home(): return( f"Available Routes:<br/>" f"/api/v1.0/precipitation<br/>" f"/api/v1.0/stations<br/>" f"/api/v1.0/tobs<br/>" f"/api/v1.0/<start><br/>" f"/api/v1.0/<start>/<end>" ) # Calculating date range for previous year date and last date in database def date_calc(): latest_date = session.query(func.max(Measurement.date)).all() # One year date range today = dt.date.today() # format date latest_date_fmt = today.replace(year=int(latest_date[0][0][:4]),\ month=int(latest_date[0][0][5:7]),\ day=int(latest_date[0][0][8:])) # One year ago from latest_date year_ago = latest_date_fmt-dt.timedelta(days=365) year_end = latest_date_fmt-strftime("%Y-%m-%d") startdate_year = year_ago.strftime("%Y-%m-%d") year_list = [startdate_year, year_end] return(tuple(year_list)) @app.route("/api/v1.0/precipitation") def precipitation(): # Start date and end date of previous year using date_calc function range = date_calc() end_date = range[1] start_date = range[0] # Converting query results to a dictionary using `date` as the key and `prcp` as the value results = session.query(Measurement.date, Measurement.station, Measurement.prcp).\ filter(Measurement.date <= end_date).\ filter(Measurement.date >= start_date).all() # Return the JSON representation of your dictionary. list = [] for result in results: dict = {"Date":result[0],"Station":result[1],"Precipitation":result[2]} list.append(dict) return jsonify(list) # Returning a JSON list of stations from the dataset. @app.route("/api/v1.0/stations") def stations(): stations = session.query(Station.station, Station.name).all() list=[] for station in stations: dict = {"Station ID:":stations[0], "Station Name": stations[1]} list.append(dict) return jsonify(list) @app.route("/api/v1.0/tobs") def tobs(): # Last year's most active station date and temperature query range = date_calc() end_date = range[1] start_date = range[0] tobs = session.query(Measurement.date, Measurement.tobs).\ filter(Measurement.date <= end_date).\ filter(Measurement.date >= start_date).all() list = [] for temp in tobs: dict = {"date": temp[0], "tobs": temp[1]} list.append(dict) #JSON list of temperature observations (TOBS) for the previous year. return jsonify(list) # JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range @app.route("/api/v1.0/<start>") def tempstart(start): ##### When given the start only, calculate `TMIN`, `TAVG`, and `TMAX` for all dates greater than and equal to the start date results = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\ filter(Measurement.date >= start).order_by(Measurement.date.desc()).all() print(f"MIN, MAX, AVG Temperatures") for temps in results: dict = {"Min Temp": results[0][0], "Avg Temp": results[0][1], "Max Temp": results[0][2]} return jsonify(dict) @app.route("/api/v1.0/<start>/<end>") def tempstartend(start,end): # Given the start and the end date, calculate the `TMIN`, `TAVG`, and `TMAX` for dates between the start and end date inclusive results = session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\ filter(Measurement.date >= start, Measurement.date <= end).order_by(Measurement.date.desc()).all() for temps in results: dict = {"Mini Temp": results[0][0], "Avg Temp": results[0][1], "Max Temp": results[0][2]} return jsonify(dict) if __name__ == '__main__': app.run(debug=True)

33.470588

131

0.614528

61f6a68c6974fef1d035a5278d1bd5dafeee12bb

98

py

Python

Functions advanced - Lab/Character combinations.py

DiyanKalaydzhiev23/Advanced---Python

ed2c60bb887c49e5a87624719633e2b8432f6f6b

[ "MIT" ]

null

Functions advanced - Lab/Character combinations.py

DiyanKalaydzhiev23/Advanced---Python

ed2c60bb887c49e5a87624719633e2b8432f6f6b

[ "MIT" ]

null

Functions advanced - Lab/Character combinations.py

DiyanKalaydzhiev23/Advanced---Python

ed2c60bb887c49e5a87624719633e2b8432f6f6b

[ "MIT" ]

null

from itertools import permutations [print(''.join(el)) for el in list(permutations(input()))]

24.5

59

0.714286

34cd924d7c3e222ae3e964c6c497d33e7aff9b5c

660

py

Python

implementation/server/cms/migrations/0013_contentpage_image.py

Aincient/cleo

933ef372fa7847d943206d72bfb03c201dbafbd6

[ "Apache-2.0" ]

null

implementation/server/cms/migrations/0013_contentpage_image.py

Aincient/cleo

933ef372fa7847d943206d72bfb03c201dbafbd6

[ "Apache-2.0" ]

null

implementation/server/cms/migrations/0013_contentpage_image.py

Aincient/cleo

933ef372fa7847d943206d72bfb03c201dbafbd6

[ "Apache-2.0" ]

3

2018-10-01T12:04:36.000Z

2021-01-07T09:30:50.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.12 on 2018-06-29 14:21 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('wagtailimages', '0020_add-verbose-name'), ('cms', '0012_auto_20180625_0816'), ] operations = [ migrations.AddField( model_name='contentpage', name='image', field=models.ForeignKey(blank=True, help_text='Header image', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image'), ), ]

28.695652

175

0.657576

2d38101b743df3d7ccfcfb00b64b6839e8bb30db

780

py

Python

posts/migrations/0003_comment_users_who_liked_post_users_who_liked.py

Eugust/mini_social

fc9ba094c75a82e952707e0e898f90fb8cbb654a

[ "MIT" ]

null

posts/migrations/0003_comment_users_who_liked_post_users_who_liked.py

Eugust/mini_social

fc9ba094c75a82e952707e0e898f90fb8cbb654a

[ "MIT" ]

null

posts/migrations/0003_comment_users_who_liked_post_users_who_liked.py

Eugust/mini_social

fc9ba094c75a82e952707e0e898f90fb8cbb654a

[ "MIT" ]

null

# Generated by Django 4.0.1 on 2022-01-21 02:13 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('posts', '0002_initial'), ] operations = [ migrations.AddField( model_name='comment', name='users_who_liked', field=models.ManyToManyField(blank=True, related_name='users_who_liked_comment', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='post', name='users_who_liked', field=models.ManyToManyField(blank=True, related_name='users_who_liked_post', to=settings.AUTH_USER_MODEL), ), ]

30

122

0.657692

297b704a60faf39c37015cda8c07d3c91181701e

15,575

py

Python

qa/rpc-tests/wallet.py

Xrunner1/luckycore-0.12.1.3

f7fe1e74f533a384b14a667202779608389eeea5

[ "MIT" ]

3

2019-11-25T18:31:45.000Z

2020-11-14T16:58:14.000Z

qa/rpc-tests/wallet.py

Xrunner1/luckycore-0.12.1.3

f7fe1e74f533a384b14a667202779608389eeea5

[ "MIT" ]

1

2020-08-03T05:22:22.000Z

qa/rpc-tests/wallet.py

Xrunner1/luckycore-0.12.1.3

f7fe1e74f533a384b14a667202779608389eeea5

[ "MIT" ]

4

2019-06-08T06:52:18.000Z

2021-08-21T14:19:33.000Z

#!/usr/bin/env python2 # coding=utf-8 # ^^^^^^^^^^^^ TODO remove when supporting only Python3 # Copyright (c) 2014-2015 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * class WalletTest (BitcoinTestFramework): def check_fee_amount(self, curr_balance, balance_with_fee, fee_per_byte, tx_size): """Return curr_balance after asserting the fee was in range""" fee = balance_with_fee - curr_balance target_fee = fee_per_byte * tx_size if fee < target_fee: raise AssertionError("Fee of %s LUCKY too low! (Should be %s LUCKY)"%(str(fee), str(target_fee))) # allow the node's estimation to be at most 2 bytes off if fee > fee_per_byte * (tx_size + 2): raise AssertionError("Fee of %s LUCKY too high! (Should be %s LUCKY)"%(str(fee), str(target_fee))) return curr_balance def setup_chain(self): print("Initializing test directory "+self.options.tmpdir) initialize_chain_clean(self.options.tmpdir, 4) def setup_network(self, split=False): self.nodes = start_nodes(3, self.options.tmpdir) connect_nodes_bi(self.nodes,0,1) connect_nodes_bi(self.nodes,1,2) connect_nodes_bi(self.nodes,0,2) self.is_network_split=False self.sync_all() def run_test (self): # Check that there's no UTXO on none of the nodes assert_equal(len(self.nodes[0].listunspent()), 0) assert_equal(len(self.nodes[1].listunspent()), 0) assert_equal(len(self.nodes[2].listunspent()), 0) print "Mining blocks..." self.nodes[0].generate(1) walletinfo = self.nodes[0].getwalletinfo() assert_equal(walletinfo['immature_balance'], 500) assert_equal(walletinfo['balance'], 0) self.sync_all() self.nodes[1].generate(101) self.sync_all() assert_equal(self.nodes[0].getbalance(), 500) assert_equal(self.nodes[1].getbalance(), 500) assert_equal(self.nodes[2].getbalance(), 0) # Check that only first and second nodes have UTXOs assert_equal(len(self.nodes[0].listunspent()), 1) assert_equal(len(self.nodes[1].listunspent()), 1) assert_equal(len(self.nodes[2].listunspent()), 0) # Send 210 LUCKY from 0 to 2 using sendtoaddress call. # Second transaction will be child of first, and will require a fee self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 110) self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 100) walletinfo = self.nodes[0].getwalletinfo() assert_equal(walletinfo['immature_balance'], 0) # Have node0 mine a block, thus it will collect its own fee. self.nodes[0].generate(1) self.sync_all() # Exercise locking of unspent outputs unspent_0 = self.nodes[2].listunspent()[0] unspent_0 = {"txid": unspent_0["txid"], "vout": unspent_0["vout"]} self.nodes[2].lockunspent(False, [unspent_0]) assert_raises(JSONRPCException, self.nodes[2].sendtoaddress, self.nodes[2].getnewaddress(), 200) assert_equal([unspent_0], self.nodes[2].listlockunspent()) self.nodes[2].lockunspent(True, [unspent_0]) assert_equal(len(self.nodes[2].listlockunspent()), 0) # Have node1 generate 100 blocks (so node0 can recover the fee) self.nodes[1].generate(100) self.sync_all() # node0 should end up with 1000 LUCKY in block rewards plus fees, but # minus the 210 plus fees sent to node2 assert_equal(self.nodes[0].getbalance(), 1000-210) assert_equal(self.nodes[2].getbalance(), 210) # Node0 should have two unspent outputs. # Create a couple of transactions to send them to node2, submit them through # node1, and make sure both node0 and node2 pick them up properly: node0utxos = self.nodes[0].listunspent(1) assert_equal(len(node0utxos), 2) # create both transactions txns_to_send = [] for utxo in node0utxos: inputs = [] outputs = {} inputs.append({ "txid" : utxo["txid"], "vout" : utxo["vout"]}) outputs[self.nodes[2].getnewaddress("from1")] = utxo["amount"] raw_tx = self.nodes[0].createrawtransaction(inputs, outputs) txns_to_send.append(self.nodes[0].signrawtransaction(raw_tx)) # Have node 1 (miner) send the transactions self.nodes[1].sendrawtransaction(txns_to_send[0]["hex"], True) self.nodes[1].sendrawtransaction(txns_to_send[1]["hex"], True) # Have node1 mine a block to confirm transactions: self.nodes[1].generate(1) self.sync_all() assert_equal(self.nodes[0].getbalance(), 0) assert_equal(self.nodes[2].getbalance(), 1000) assert_equal(self.nodes[2].getbalance("from1"), 1000-210) # Send 100 LUCKY normal address = self.nodes[0].getnewaddress("test") fee_per_byte = Decimal('0.001') / 1000 self.nodes[2].settxfee(fee_per_byte * 1000) txid = self.nodes[2].sendtoaddress(address, 100, "", "", False) self.nodes[2].generate(1) self.sync_all() node_2_bal = self.check_fee_amount(self.nodes[2].getbalance(), Decimal('900'), fee_per_byte, count_bytes(self.nodes[2].getrawtransaction(txid))) assert_equal(self.nodes[0].getbalance(), Decimal('100')) # Send 100 LUCKY with subtract fee from amount txid = self.nodes[2].sendtoaddress(address, 100, "", "", True) self.nodes[2].generate(1) self.sync_all() node_2_bal -= Decimal('100') assert_equal(self.nodes[2].getbalance(), node_2_bal) node_0_bal = self.check_fee_amount(self.nodes[0].getbalance(), Decimal('200'), fee_per_byte, count_bytes(self.nodes[2].getrawtransaction(txid))) # Sendmany 100 LUCKY txid = self.nodes[2].sendmany('from1', {address: 100}, 0, "", []) self.nodes[2].generate(1) self.sync_all() node_0_bal += Decimal('100') node_2_bal = self.check_fee_amount(self.nodes[2].getbalance(), node_2_bal - Decimal('100'), fee_per_byte, count_bytes(self.nodes[2].getrawtransaction(txid))) assert_equal(self.nodes[0].getbalance(), node_0_bal) # Sendmany 100 LUCKY with subtract fee from amount txid = self.nodes[2].sendmany('from1', {address: 100}, 0, "", [address]) self.nodes[2].generate(1) self.sync_all() node_2_bal -= Decimal('100') assert_equal(self.nodes[2].getbalance(), node_2_bal) node_0_bal = self.check_fee_amount(self.nodes[0].getbalance(), node_0_bal + Decimal('100'), fee_per_byte, count_bytes(self.nodes[2].getrawtransaction(txid))) # Test ResendWalletTransactions: # Create a couple of transactions, then start up a fourth # node (nodes[3]) and ask nodes[0] to rebroadcast. # EXPECT: nodes[3] should have those transactions in its mempool. txid1 = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 1) txid2 = self.nodes[1].sendtoaddress(self.nodes[0].getnewaddress(), 1) sync_mempools(self.nodes) self.nodes.append(start_node(3, self.options.tmpdir)) connect_nodes_bi(self.nodes, 0, 3) sync_blocks(self.nodes) relayed = self.nodes[0].resendwallettransactions() assert_equal(set(relayed), {txid1, txid2}) sync_mempools(self.nodes) assert(txid1 in self.nodes[3].getrawmempool()) # Exercise balance rpcs assert_equal(self.nodes[0].getwalletinfo()["unconfirmed_balance"], 1) assert_equal(self.nodes[0].getunconfirmedbalance(), 1) #check if we can list zero value tx as available coins #1. create rawtx #2. hex-changed one output to 0.0 #3. sign and send #4. check if recipient (node0) can list the zero value tx usp = self.nodes[1].listunspent() inputs = [{"txid":usp[0]['txid'], "vout":usp[0]['vout']}] outputs = {self.nodes[1].getnewaddress(): 499.998, self.nodes[0].getnewaddress(): 11.11} rawTx = self.nodes[1].createrawtransaction(inputs, outputs).replace("c0833842", "00000000") #replace 11.11 with 0.0 (int32) decRawTx = self.nodes[1].decoderawtransaction(rawTx) signedRawTx = self.nodes[1].signrawtransaction(rawTx) decRawTx = self.nodes[1].decoderawtransaction(signedRawTx['hex']) zeroValueTxid= decRawTx['txid'] sendResp = self.nodes[1].sendrawtransaction(signedRawTx['hex']) self.sync_all() self.nodes[1].generate(1) #mine a block self.sync_all() unspentTxs = self.nodes[0].listunspent() #zero value tx must be in listunspents output found = False for uTx in unspentTxs: if uTx['txid'] == zeroValueTxid: found = True assert_equal(uTx['amount'], Decimal('0')) assert(found) #do some -walletbroadcast tests stop_nodes(self.nodes) wait_bitcoinds() self.nodes = start_nodes(3, self.options.tmpdir, [["-walletbroadcast=0"],["-walletbroadcast=0"],["-walletbroadcast=0"]]) connect_nodes_bi(self.nodes,0,1) connect_nodes_bi(self.nodes,1,2) connect_nodes_bi(self.nodes,0,2) self.sync_all() txIdNotBroadcasted = self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 2) txObjNotBroadcasted = self.nodes[0].gettransaction(txIdNotBroadcasted) self.nodes[1].generate(1) #mine a block, tx should not be in there self.sync_all() assert_equal(self.nodes[2].getbalance(), node_2_bal) #should not be changed because tx was not broadcasted #now broadcast from another node, mine a block, sync, and check the balance self.nodes[1].sendrawtransaction(txObjNotBroadcasted['hex']) self.nodes[1].generate(1) self.sync_all() node_2_bal += 2 txObjNotBroadcasted = self.nodes[0].gettransaction(txIdNotBroadcasted) assert_equal(self.nodes[2].getbalance(), node_2_bal) #create another tx txIdNotBroadcasted = self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), 2) #restart the nodes with -walletbroadcast=1 stop_nodes(self.nodes) wait_bitcoinds() self.nodes = start_nodes(3, self.options.tmpdir) connect_nodes_bi(self.nodes,0,1) connect_nodes_bi(self.nodes,1,2) connect_nodes_bi(self.nodes,0,2) sync_blocks(self.nodes) self.nodes[0].generate(1) sync_blocks(self.nodes) node_2_bal += 2 #tx should be added to balance because after restarting the nodes tx should be broadcastet assert_equal(self.nodes[2].getbalance(), node_2_bal) #send a tx with value in a string (PR#6380 +) txId = self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), "2") txObj = self.nodes[0].gettransaction(txId) assert_equal(txObj['amount'], Decimal('-2')) txId = self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), "0.0001") txObj = self.nodes[0].gettransaction(txId) assert_equal(txObj['amount'], Decimal('-0.0001')) #check if JSON parser can handle scientific notation in strings txId = self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), "1e-4") txObj = self.nodes[0].gettransaction(txId) assert_equal(txObj['amount'], Decimal('-0.0001')) try: txId = self.nodes[0].sendtoaddress(self.nodes[2].getnewaddress(), "1f-4") except JSONRPCException as e: assert("Invalid amount" in e.error['message']) else: raise AssertionError("Must not parse invalid amounts") try: self.nodes[0].generate("2") raise AssertionError("Must not accept strings as numeric") except JSONRPCException as e: assert("not an integer" in e.error['message']) # Import address and private key to check correct behavior of spendable unspents # 1. Send some coins to generate new UTXO address_to_import = self.nodes[2].getnewaddress() txid = self.nodes[0].sendtoaddress(address_to_import, 1) self.nodes[0].generate(1) self.sync_all() # 2. Import address from node2 to node1 self.nodes[1].importaddress(address_to_import) # 3. Validate that the imported address is watch-only on node1 assert(self.nodes[1].validateaddress(address_to_import)["iswatchonly"]) # 4. Check that the unspents after import are not spendable assert_array_result(self.nodes[1].listunspent(), {"address": address_to_import}, {"spendable": False}) # 5. Import private key of the previously imported address on node1 priv_key = self.nodes[2].dumpprivkey(address_to_import) self.nodes[1].importprivkey(priv_key) # 6. Check that the unspents are now spendable on node1 assert_array_result(self.nodes[1].listunspent(), {"address": address_to_import}, {"spendable": True}) #check if wallet or blochchain maintenance changes the balance self.sync_all() blocks = self.nodes[0].generate(2) self.sync_all() balance_nodes = [self.nodes[i].getbalance() for i in range(3)] block_count = self.nodes[0].getblockcount() # Check modes: # - True: unicode escaped as \u.... # - False: unicode directly as UTF-8 for mode in [True, False]: self.nodes[0].ensure_ascii = mode # unicode check: Basic Multilingual Plane, Supplementary Plane respectively for s in [u'рыба', u'𝅘𝅥𝅯']: addr = self.nodes[0].getaccountaddress(s) label = self.nodes[0].getaccount(addr) assert_equal(label.encode('utf-8'), s.encode('utf-8')) # TODO remove encode(...) when supporting only Python3 assert(s in self.nodes[0].listaccounts().keys()) self.nodes[0].ensure_ascii = True # restore to default # maintenance tests maintenance = [ '-rescan', '-reindex', '-zapwallettxes=1', '-zapwallettxes=2', '-salvagewallet', ] for m in maintenance: print "check " + m stop_nodes(self.nodes) wait_bitcoinds() self.nodes = start_nodes(3, self.options.tmpdir, [[m]] * 3) while m == '-reindex' and [block_count] * 3 != [self.nodes[i].getblockcount() for i in range(3)]: # reindex will leave rpc warm up "early"; Wait for it to finish time.sleep(0.1) assert_equal(balance_nodes, [self.nodes[i].getbalance() for i in range(3)]) # Exercise listsinceblock with the last two blocks coinbase_tx_1 = self.nodes[0].listsinceblock(blocks[0]) assert_equal(coinbase_tx_1["lastblock"], blocks[1]) assert_equal(len(coinbase_tx_1["transactions"]), 1) assert_equal(coinbase_tx_1["transactions"][0]["blockhash"], blocks[1]) assert_equal(len(self.nodes[0].listsinceblock(blocks[1])["transactions"]), 0) if __name__ == '__main__': WalletTest ().main ()

44.5

165

0.635634

069374ad8e83fa6933463c6fb8ec9ed23c193d5b

428

py

Python

examples/yield.py

scottluskcis/learning-python

034f332817430454d55729f8f98fded4c310094e

[ "MIT" ]

null

examples/yield.py

scottluskcis/learning-python

034f332817430454d55729f8f98fded4c310094e

[ "MIT" ]

null

examples/yield.py

scottluskcis/learning-python

034f332817430454d55729f8f98fded4c310094e

[ "MIT" ]

null

students = [] def read_file(): try: f = open("students.txt", "r") # read text from a file using access mode r for student in read_students(f): students.append(student) f.close() except Exception: print("Could not read file") # use generator with yield to read each line in the file def read_students(f): for line in f: yield line read_file() print(students)

20.380952

82

0.616822

32cb23cbfa9bdef4728e85d0014123652e4aefea

4,646

py

Python

python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py

worldlove521/Paddle

c7f1f3ed0c897073cc7ae8ec60a13a8217dffe7d

[ "Apache-2.0" ]

1

2019-03-14T02:29:12.000Z

python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py

VonRosenchild/Paddle

4ef6f738c3ce37aa7eee845b90ee78941f6eb43e

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py

VonRosenchild/Paddle

4ef6f738c3ce37aa7eee845b90ee78941f6eb43e

[ "Apache-2.0" ]

1

2019-03-14T02:29:15.000Z

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import math from op_test import OpTest def quantize_max_abs(x, max_range): scale = np.max(np.abs(x).flatten()) y = np.round(x / scale * max_range) return y, scale def dequantize_max_abs(x, scale, max_range): y = (scale / max_range) * x return y def channel_wise_quantize_max_abs(x, quant_bit=8): scales = [] for i in range(x.shape[0]): scales.append(np.max(np.abs(x[i])).astype("float32")) y = x.copy() max_range = math.pow(2, quant_bit - 1) - 1 for i, scale in enumerate(scales): y[i] = np.round(y[i] / scale * max_range) return y, scales def channel_wise_dequantize_max_abs(x, scales, quant_bits, activation_scale=None): y = x.copy() for i in range(x.shape[0]): y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * y[i] if activation_scale is not None: y *= activation_scale / (math.pow(2, quant_bits[1] - 1) - 1) return y class TestFakeChannelWiseDequantizeMaxAbsOpTwoScales(OpTest): def set_args(self): self.quant_bits = [8, 8] self.data_type = "float32" self.activation_scale = 0.7861 def setUp(self): self.set_args() self.op_type = "fake_channel_wise_dequantize_max_abs" x = np.random.randn(4, 3, 64, 64).astype(self.data_type) yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0]) ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits, self.activation_scale) self.inputs = { 'X': yq, 'Scales': [("scales0", np.array(scales).astype(self.data_type)), ("scales1", np.array( [self.activation_scale]).astype(self.data_type))] } self.attrs = {'quant_bits': self.quant_bits} self.outputs = {'Out': ydq} def test_check_output(self): self.check_output() class TestFakeChannelWiseDequantizeMaxAbsOpOneScale(OpTest): def set_args(self): self.quant_bits = [8] self.data_type = "float32" def setUp(self): self.set_args() self.op_type = "fake_channel_wise_dequantize_max_abs" x = np.random.randn(4, 3, 64, 64).astype(self.data_type) yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0]) ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits) self.inputs = { 'X': yq, 'Scales': [("scales0", np.array(scales).astype(self.data_type))] } self.attrs = {'quant_bits': self.quant_bits} self.outputs = {'Out': ydq} def test_check_output(self): self.check_output() class TestFakeDequantizeMaxAbsOp(OpTest): def set_args(self): self.num_bits = 8 self.max_range = math.pow(2, self.num_bits - 1) - 1 self.data_type = "float32" def setUp(self): self.set_args() self.op_type = "fake_dequantize_max_abs" x = np.random.randn(31, 65).astype(self.data_type) yq, scale = quantize_max_abs(x, self.max_range) ydq = dequantize_max_abs(yq, scale, self.max_range) self.inputs = {'X': yq, 'Scale': np.array(scale).astype(self.data_type)} self.attrs = {'max_range': self.max_range} self.outputs = {'Out': ydq} def test_check_output(self): self.check_output() class TestFakeDequantizeMaxAbsOpDouble(TestFakeDequantizeMaxAbsOp): def set_args(self): self.num_bits = 8 self.max_range = math.pow(2, self.num_bits - 1) - 1 self.data_type = "float64" class TestFakeDequantizeMaxAbsOp5Bits(TestFakeDequantizeMaxAbsOp): def set_args(self): self.num_bits = 5 self.max_range = math.pow(2, self.num_bits - 1) - 1 self.data_type = "float32" if __name__ == "__main__": unittest.main()

32.041379

80

0.625484

988774dbc19dd9cd88b384e2d409e67a1a23b5bb

1,384

py

Python

preprocess/tacotron/hyperparams.py

RothIdan/adaptive_voice_conversion

06fe2af31fa6a2b3363dcad942c62d9dc388ee65

[ "Apache-2.0" ]

366

2019-02-22T04:27:03.000Z

2022-03-31T08:48:02.000Z

preprocess/tacotron/hyperparams.py

RothIdan/adaptive_voice_conversion

06fe2af31fa6a2b3363dcad942c62d9dc388ee65

[ "Apache-2.0" ]

37

2019-04-22T07:21:30.000Z

2022-03-29T09:57:41.000Z

preprocess/tacotron/hyperparams.py

RothIdan/adaptive_voice_conversion

06fe2af31fa6a2b3363dcad942c62d9dc388ee65

[ "Apache-2.0" ]

78

2019-03-15T01:21:38.000Z

2022-03-17T03:55:38.000Z

# -*- coding: utf-8 -*- #/usr/bin/python2 ''' By kyubyong park. kbpark.linguist@gmail.com. https://www.github.com/kyubyong/tacotron ''' class Hyperparams: '''Hyper parameters''' # pipeline prepro = False # if True, run `python prepro.py` first before running `python train.py`. vocab = "PE abcdefghijklmnopqrstuvwxyz'.?" # P: Padding E: End of Sentence # data data = "/data/private/voice/LJSpeech-1.0" # data = "/data/private/voice/nick" test_data = 'harvard_sentences.txt' max_duration = 10.0 top_db = 15 # signal processing sr = 24000 # Sample rate. n_fft = 2048 # fft points (samples) frame_shift = 0.0125 # seconds frame_length = 0.05 # seconds hop_length = int(sr*frame_shift) # samples. win_length = int(sr*frame_length) # samples. n_mels = 512 # Number of Mel banks to generate power = 1.2 # Exponent for amplifying the predicted magnitude n_iter = 100 # Number of inversion iterations preemphasis = .97 # or None max_db = 100 ref_db = 20 # model embed_size = 256 # alias = E encoder_num_banks = 16 decoder_num_banks = 8 num_highwaynet_blocks = 4 r = 5 # Reduction factor. Paper => 2, 3, 5 dropout_rate = .5 # training scheme lr = 0.001 # Initial learning rate. logdir = "logdir/01" sampledir = 'samples' batch_size = 32

26.113208

93

0.642341

269e90797463e0ff68b93b6aa24e73c3c1de11ac

1,577

py

Python

.history/ClassFiles/OOP/Encapsulation_20210105153638.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

.history/ClassFiles/OOP/Encapsulation_20210105153638.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

.history/ClassFiles/OOP/Encapsulation_20210105153638.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

''' Encapsulation : Part 1 Encapsulation is the process of restricting access to methods and variables in a class in order to prevent direct data modification so it prevents accidental data modification. Encapsulation basically allows the internal representation of an object to be hidden from the view outside of the objects definition. Public methods and variables can be accessed from anywhere within the program. Private methods and variables are accessible from their own class. Double underscore prefix before object name makes it private' Encapsulation Part 2: 40 ''' # class Cars: # def __init__(self,speed, color): # self.speed = speed # self.color = color # def set_speed(self,value): # self.speed = value # def get_speed(self): # return self.speed # Encapsulation Part 2: 40 class Cars: def __init__(self,speed, color): self.speed = speed self.color = color def set_speed(self,value): self.speed = value def get_speed(self): return self.speed ford = Cars(250,"green") nissan = Cars(300,"red") toyota = Cars(350, "blue") ford.set_speed(450) # If I wanted to chang he value of the speed after the instantiantion, I can do that by using the name of the instance and the method. # ford.speed = 500 # I can also access the speed variable directly without the method and change the value. I'm able to do this because there is no encapsulation in place. print(ford.get_speed()) # print(ford) # 500

25.031746

176

0.686113

743dc4a6682b3893e1967f2eabcaf3a2f7784640

1,059

py

Python

contrastive.py

804463592/pytorch-siamese-master

34181ded641820cd6c3f0ea6255ac67a832c561b

[ "MIT" ]

1

2018-12-24T06:34:59.000Z

contrastive.py

804463592/pytorch-siamese-master

34181ded641820cd6c3f0ea6255ac67a832c561b

[ "MIT" ]

null

contrastive.py

804463592/pytorch-siamese-master

34181ded641820cd6c3f0ea6255ac67a832c561b

[ "MIT" ]

2

2019-05-21T14:44:05.000Z

2022-02-12T06:25:54.000Z

import torch import torch.nn class ContrastiveLoss(torch.nn.Module): """ Contrastive loss function. Based on: """ def __init__(self, margin=1.0): super(ContrastiveLoss, self).__init__() self.margin = margin def check_type_forward(self, in_types): assert len(in_types) == 3 x0_type, x1_type, y_type = in_types assert x0_type.size() == x1_type.shape assert x1_type.size()[0] == y_type.shape[0] assert x1_type.size()[0] > 0 assert x0_type.dim() == 2 assert x1_type.dim() == 2 assert y_type.dim() == 1 def forward(self, x0, x1, y): #self.check_type_forward((x0, x1, y)) # TODO: forward # euclidian distance diff = x0 - x1 dist_sq = torch.sum(torch.pow(diff, 2), 1) dist = torch.sqrt(dist_sq) mdist = self.margin - dist dist = torch.clamp(mdist, min=0.0) loss = y * dist_sq + (1 - y) * torch.pow(dist, 2) loss = torch.sum(loss) / 2.0 / x0.size()[0] return loss

27.153846

57

0.567517

80cca9b6fbad2e4fe22b3f266fd1341e9ff06822

5,726

py

Python

Source/boost_1_33_1/libs/python/test/map_indexing_suite.py

spxuw/RFIM

32b78fbb90c7008b1106b0cff4f8023ae83c9b6d

[ "MIT" ]

null

Source/boost_1_33_1/libs/python/test/map_indexing_suite.py

spxuw/RFIM

32b78fbb90c7008b1106b0cff4f8023ae83c9b6d

[ "MIT" ]

null

Source/boost_1_33_1/libs/python/test/map_indexing_suite.py

spxuw/RFIM

32b78fbb90c7008b1106b0cff4f8023ae83c9b6d

[ "MIT" ]

null

# Copyright Joel de Guzman 2004. Distributed under the Boost # Software License, Version 1.0. (See accompanying # file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt) ''' ##################################################################### # Check an object that we will use as container element ##################################################################### >>> from map_indexing_suite_ext import * >>> assert "map_indexing_suite_IntMap_entry" in dir() >>> assert "map_indexing_suite_TestMap_entry" in dir() >>> assert "map_indexing_suite_XMap_entry" in dir() >>> x = X('hi') >>> x hi >>> x.reset() # a member function that modifies X >>> x reset >>> x.foo() # another member function that modifies X >>> x foo # test that a string is implicitly convertible # to an X >>> x_value('bochi bochi') 'gotya bochi bochi' ##################################################################### # Iteration ##################################################################### >>> def print_xmap(xmap): ... s = '[ ' ... for x in xmap: ... s += repr(x) ... s += ' ' ... s += ']' ... print s ##################################################################### # Setting (adding entries) ##################################################################### >>> xm = XMap() >>> xm['joel'] = 'apple' >>> xm['tenji'] = 'orange' >>> xm['mariel'] = 'grape' >>> xm['tutit'] = 'banana' >>> xm['kim'] = 'kiwi' >>> print_xmap(xm) [ (joel, apple) (kim, kiwi) (mariel, grape) (tenji, orange) (tutit, banana) ] ##################################################################### # Changing an entry ##################################################################### >>> xm['joel'] = 'pineapple' >>> print_xmap(xm) [ (joel, pineapple) (kim, kiwi) (mariel, grape) (tenji, orange) (tutit, banana) ] ##################################################################### # Deleting an entry ##################################################################### >>> del xm['joel'] >>> print_xmap(xm) [ (kim, kiwi) (mariel, grape) (tenji, orange) (tutit, banana) ] ##################################################################### # adding an entry ##################################################################### >>> xm['joel'] = 'apple' >>> print_xmap(xm) [ (joel, apple) (kim, kiwi) (mariel, grape) (tenji, orange) (tutit, banana) ] ##################################################################### # Indexing ##################################################################### >>> len(xm) 5 >>> xm['joel'] apple >>> xm['tenji'] orange >>> xm['mariel'] grape >>> xm['tutit'] banana >>> xm['kim'] kiwi ##################################################################### # Calling a mutating function of a container element ##################################################################### >>> xm['joel'].reset() >>> xm['joel'] reset ##################################################################### # Copying a container element ##################################################################### >>> x = X(xm['mariel']) >>> x grape >>> x.foo() >>> x foo >>> xm['mariel'] # should not be changed to 'foo' grape ##################################################################### # Referencing a container element ##################################################################### >>> x = xm['mariel'] >>> x grape >>> x.foo() >>> x foo >>> xm['mariel'] # should be changed to 'foo' foo >>> xm['mariel'] = 'grape' # take it back >>> xm['joel'] = 'apple' # take it back ##################################################################### # Contains ##################################################################### >>> assert 'joel' in xm >>> assert 'mariel' in xm >>> assert 'tenji' in xm >>> assert 'tutit' in xm >>> assert 'kim' in xm >>> assert not 'X' in xm >>> assert not 12345 in xm ##################################################################### # Some references to the container elements ##################################################################### >>> z0 = xm['joel'] >>> z1 = xm['mariel'] >>> z2 = xm['tenji'] >>> z3 = xm['tutit'] >>> z4 = xm['kim'] >>> z0 # proxy apple >>> z1 # proxy grape >>> z2 # proxy orange >>> z3 # proxy banana >>> z4 # proxy kiwi ##################################################################### # Delete some container element ##################################################################### >>> del xm['tenji'] >>> print_xmap(xm) [ (joel, apple) (kim, kiwi) (mariel, grape) (tutit, banana) ] >>> del xm['tutit'] >>> print_xmap(xm) [ (joel, apple) (kim, kiwi) (mariel, grape) ] ##################################################################### # Show that the references are still valid ##################################################################### >>> z0 # proxy apple >>> z1 # proxy grape >>> z2 # proxy detached orange >>> z3 # proxy detached banana >>> z4 # proxy kiwi ##################################################################### # Show that iteration allows mutable access to the elements ##################################################################### >>> for x in xm: ... x.data().reset() >>> print_xmap(xm) [ (joel, reset) (kim, reset) (mariel, reset) ] ##################################################################### # END.... ##################################################################### ''' def run(args = None): import sys import doctest if args is not None: sys.argxm = args return doctest.testmod(sys.modules.get(__name__)) if __name__ == '__main__': print 'running...' import sys status = run()[0] if (status == 0): print "Done." sys.exit(status)

26.509259

81

0.367097

c2621d6fbec00acc175d0f71f681cf90e3685dd8

819

py

Python

instagram_api/response/model/broadcast.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

13

2019-08-07T21:24:34.000Z

2020-12-12T12:23:50.000Z

instagram_api/response/model/broadcast.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

instagram_api/response/model/broadcast.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

from ..mapper import PropertyMapper, ApiInterfaceBase from ..mapper.types import Timestamp, AnyType from .user import User __all__ = ['Broadcast', 'BroadcastInterface'] class BroadcastInterface(ApiInterfaceBase): broadcast_owner: User broadcast_status: str cover_frame_url: str published_time: str broadcast_message: str muted: AnyType media_id: int id: str rtmp_playback_url: str dash_abr_playback_url: str dash_playback_url: str ranked_position: AnyType organic_tracking_token: str seen_ranked_position: AnyType viewer_count: int dash_manifest: str expire_at: Timestamp encoding_tag: str total_unique_viewer_count: int internal_only: bool number_of_qualities: int class Broadcast(PropertyMapper, BroadcastInterface): pass

22.75

53

0.749695

022fb8314549d1e47e23953cdc0148181111b001

6,025

py

Python

tests/functional/test_plugins_host.py

arenadata/adcm

a499caa30adc2a53e7b3f46c96a865f9e4079e4e

[ "Apache-2.0" ]

16

2019-11-28T18:05:21.000Z

2021-12-08T18:09:18.000Z

tests/functional/test_plugins_host.py

arenadata/adcm

a499caa30adc2a53e7b3f46c96a865f9e4079e4e

[ "Apache-2.0" ]

1,127

2019-11-29T08:57:25.000Z

2022-03-31T20:21:32.000Z

tests/functional/test_plugins_host.py

arenadata/adcm

a499caa30adc2a53e7b3f46c96a865f9e4079e4e

[ "Apache-2.0" ]

10

2019-11-28T18:05:06.000Z

2022-01-13T06:16:40.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for adcm_host plugin""" # pylint:disable=redefined-outer-name import adcm_client.base import allure import pytest from adcm_client.objects import ADCMClient, Bundle, Provider from adcm_pytest_plugin.utils import get_data_dir, wait_until_step_succeeds @pytest.fixture() def bundle(sdk_client_fs: ADCMClient) -> Bundle: """Upload bundle and create 4 provider objects""" bundle = sdk_client_fs.upload_from_fs(get_data_dir(__file__)) bundle.provider_create(name="first_p") bundle.provider_create(name="second_p") bundle.provider_create(name="third_p") bundle.provider_create(name="forth_p") return bundle @pytest.fixture() def first_p(bundle: Bundle): """First provider""" return bundle.provider(name="first_p") @pytest.fixture() def second_p(bundle: Bundle): """Second provider""" return bundle.provider(name="second_p") @pytest.fixture() def third_p(bundle: Bundle): """Third provider""" return bundle.provider(name="third_p") @pytest.fixture() def forth_p(bundle: Bundle): """Forth provider""" return bundle.provider(name="forth_p") def test_create_one_host(second_p: Provider): """Test scenario: 1. Create three providers 2. Create host on one of the providers 3. Ensure host exists """ hostname = "second_h" with allure.step('Run action create host'): second_p.action(name="create_host").run(config_diff={'fqdn': hostname}).try_wait() second_h = second_p.host(fqdn=hostname) with allure.step('Check if host is created'): assert second_h.provider().id == second_p.id assert second_h.fqdn == hostname def test_create_multi_host_and_delete_one(first_p: Provider, third_p: Provider): """Test scenario: 1. Create three providers 2. Create two host from first providers 3. Create one host from third provider 4. Remove one of host binded to first provider 5. Check that host has been removed 6. Check that other hosts still there. """ with allure.step('Create two host from first providers'): first_p.action(name="create_host").run(config_diff={'fqdn': "one_one"}).try_wait() first_p.action(name="create_host").run(config_diff={'fqdn': "one_two"}).try_wait() with allure.step('Create one host from third provider'): third_p.action(name="create_host").run(config_diff={'fqdn': "three_one"}).try_wait() with allure.step('Remove one of host binded to first provider'): one_two = first_p.host(fqdn="one_two") one_two.action(name="remove_host").run().try_wait() with allure.step('Check that host has been removed'): assert first_p.host(fqdn="one_one").fqdn == "one_one" with allure.step('Check that other hosts still there'): assert third_p.host(fqdn="three_one").fqdn == "three_one" with pytest.raises(adcm_client.base.ObjectNotFound): first_p.host(fqdn="one_two") def _assert_that_object_exists(get_object_func, *args, **kwargs): try: obj = get_object_func(*args, **kwargs) except adcm_client.base.ObjectNotFound as error: raise AssertionError("Object still not found") from error if obj is None: raise AssertionError("Object is None") def test_check_host_lock_during_operations(forth_p: Provider): """Test scenario: 1. Create provider 2. Create host first host on provider 3. Run job that creates the second host on provider 4. Wait until second host will be created. 5. Check that both host is locked 6. Wait for job to be finished without errors 7. Check that both hosts is free 8. Run remove action on one of hosts 9. Check that host under action is locked, while other host is free 10. Wait for job to be finished without errors 11. Check that remaining host is free. """ with allure.step('Create host first host on provider'): forth_p.action(name="create_host").run(config_diff={'fqdn': "forth_one"}).try_wait() with allure.step('Run job that creates the second host on provider'): job = forth_p.action(name="create_host").run(config={'fqdn': "forth_two", 'sleep': 2}) with allure.step('Wait until second host will be created'): wait_until_step_succeeds(_assert_that_object_exists, period=0.5, get_object_func=forth_p.host, fqdn="forth_two") forth_two_h = forth_p.host(fqdn="forth_two") forth_one_h = forth_p.host(fqdn='forth_one') with allure.step('Check that both host has is locked'): assert forth_one_h.locked is True assert forth_two_h.locked is True with allure.step('Wait for job to be finished without errors'): job.try_wait() with allure.step('Check that both hosts is free'): forth_one_h.reread() forth_two_h.reread() assert forth_one_h.locked is False assert forth_two_h.locked is False with allure.step('Run remove action on one of hosts'): job = forth_one_h.action(name="remove_host").run(config={"sleep": 2}) with allure.step('Check that host under action is locked, while other host is free'): forth_one_h.reread() forth_two_h.reread() assert forth_one_h.locked is True assert forth_two_h.locked is False with allure.step('Wait for job to be finished without errors'): job.try_wait() with allure.step('Check that remaining host is free'): forth_two_h.reread() assert forth_two_h.locked is False

39.123377

120

0.7039

3aa6d4570748042d8122fc00520e86bb6de40db2

13,566

py

Python

trunk/swen610/toolshare/views/tool_controller.py

leon486/toolshare

13f6ab1afb96a5f8294c121ff6916a1fcab5c2bc

[ "MIT" ]

null

trunk/swen610/toolshare/views/tool_controller.py

leon486/toolshare

13f6ab1afb96a5f8294c121ff6916a1fcab5c2bc

[ "MIT" ]

null

trunk/swen610/toolshare/views/tool_controller.py

leon486/toolshare

13f6ab1afb96a5f8294c121ff6916a1fcab5c2bc

[ "MIT" ]

null

from django.shortcuts import render, get_object_or_404, redirect from django.contrib.auth.decorators import login_required from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger from toolshare.models.tool import Tool from toolshare.models.user import User from toolshare.models.reservation import Reservation from toolshare.views.base_controller import BaseController from toolshare.forms.forms_tool import ToolRegistrationForm from toolshare.forms.forms_tool import BorrowToolForm, ChangeToolForm,ChangeToolAvailabilityForm import datetime from datetime import date, timedelta as td from django.contrib import messages from django.utils import timezone import pdb import pytz from toolshare.forms.forms_tool import ChangeAvailabilityForm from toolshare.utils import EmailSender # Create your views here. class ToolController(BaseController): PAGE_SIZE = 4 @staticmethod @login_required def register_tool(request): user = User.objects.get(pk=request.user.id) if request.method == 'POST': pass registration_form = ToolRegistrationForm(request.POST, request.FILES) if registration_form.is_valid(): # Generate the new-user from the Form new_tool = registration_form.save(commit=False) new_tool.owner = user new_tool.status = 'A' if new_tool.shed is not None: new_tool.pickup_location = 'At Shed' new_tool.save() return redirect('/toolshare/list-owned-tools') else: # Show the registration-form dummy_tool = Tool() dummy_tool.pickup_location = user.pickup_location registration_form = ToolRegistrationForm(instance=dummy_tool) registration_form.fields['shed'].queryset = user.shed_set return render(request, 'toolshare/tool-registration.html', { 'registration_form': registration_form }) @staticmethod @login_required def find_tool(request): user = User.objects.get(pk=request.user.id) if request.method == 'GET': search_for = request.GET.get('search_for') to_date = request.GET.get('to_date') from_date = request.GET.get('from_date') #Match empty fields to make query work if to_date == '': to_date = from_date if from_date == '': from_date = to_date #tools = Tool.objects.all() tools = Tool.objects.all().exclude(status='D') if search_for is not None: tools = tools.filter(name__contains=search_for) #Find all reservations during the specified dates if (to_date is not None and to_date != '') and (from_date is not None and from_date != ''): reservations = Reservation.objects.filter(start_date__lte=to_date, end_date__gte=from_date, status='A') #filter them from the results for r in reservations: tools = tools.exclude(reservation=r) # Only tools in the share-zone tools_shared_zone = list() for tool in tools: owner = User.objects.get(pk=tool.owner.id) if owner.share_zone == user.share_zone: tools_shared_zone.append(tool) tools = tools_shared_zone tool_paginator = Paginator(tools, ToolController.PAGE_SIZE) page = request.GET.get('page') try: tool_page = tool_paginator.page(page) except PageNotAnInteger: tool_page = tool_paginator.page(1) except EmptyPage: # If page is out of range (e.g. 9999), # deliver last page of results. tool_page = tool_paginator.page(tool_paginator.num_pages) search_for = request.GET.get('search_for') if search_for is None: search_for = '' if from_date is None: from_date = '' if to_date is None: to_date = '' return render(request, 'toolshare/find_tool.html', { 'tool_count': len(tools), 'tool_page': tool_page, 'search_for': search_for, 'to_date': to_date, 'from_date': from_date }) return render(request, 'toolshare/find_tool.html') @staticmethod @login_required def list_owned_tools(request): if request.method == 'GET': user = User.objects.get(pk=request.user.id) user_tools = user.tool_set.all() tool_paginator = Paginator(user_tools, ToolController.PAGE_SIZE) page = request.GET.get('page') try: tool_page = tool_paginator.page(page) except PageNotAnInteger: tool_page = tool_paginator.page(1) except EmptyPage: # If page is out of range (e.g. 9999), # deliver last page of results. tool_page = tool_paginator.page(tool_paginator.num_pages) return render(request, 'toolshare/list-owned-tools.html', { 'tool_page': tool_page, 'user_tools': user_tools }) def tool_detail(request, tool_id): if request.method == 'GET': tool = Tool.objects.get(pk=tool_id) return render(request, 'toolshare/tool-detail.html', { 'tool':tool }) @staticmethod @login_required def change_tool_info(request, tool_id): tool = Tool.objects.get(pk= tool_id) if request.method == 'POST': changeToolInfo = ChangeToolForm(request.POST, request.FILES) if changeToolInfo.is_valid(): newtool = changeToolInfo.save(commit=False) #tool.name = newtool.name tool.description = newtool.description tool.category = newtool.category tool.status = newtool.status tool.special_instructions = newtool.special_instructions tool.pickup_location = newtool.pickup_location if "my_picture" in request.FILES: tool.picture = request.FILES['my_picture'] tool.save() return redirect('/toolshare/tool-detail/%s'%tool_id) else: changeToolInfo = ChangeToolForm(instance=tool) return render(request, 'toolshare/tool-info.html',{ 'changeToolInfo' : changeToolInfo, 'tool':tool }) @staticmethod @login_required def change_tool_availability(request, tool_id): tool = Tool.objects.get(pk= tool_id) changeToolAvail_form = ChangeToolAvailabilityForm(requested_tool_id=tool_id) if request.method == 'POST': Flag = False sharedTool = Tool.objects.get(pk = tool_id) startdate = request.POST['start_date'] startDate = datetime.datetime.strptime(startdate, "%m/%d/%Y").replace(tzinfo=pytz.UTC) enddate = request.POST['end_date'] endDate = datetime.datetime.strptime(enddate, "%m/%d/%Y").replace(tzinfo=pytz.UTC) # Get all the reservation list of the tool tool_reservations = Reservation.objects.filter(tool_id = tool_id) for reserve in tool_reservations: if reserve.status == 'RP' and reserve.borrower != request.user: if (startDate >= reserve.start_date and startDate <= reserve.end_date) or (startDate <= reserve.start_date and startDate <= reserve.end_date): messages.add_message(request, messages.ERROR, 'You cannot change the tool availability.') Flag = False else: Flag = True else: Flag = True if Flag: for reserve in tool_reservations: if reserve.borrower != request.user: if reserve.status == 'P' or reserve.status == 'A': if (startDate >= reserve.start_date and startDate <= reserve.end_date) or (startDate <= reserve.start_date and startDate <= reserve.end_date): reserve.cancel_msg = 'Your tool reservation has been canceled since the owner changed the availability.' reserve.status = 'C' reserve.save() newReservation = Reservation() newReservation.status = 'A' newReservation.tool = sharedTool newReservation.start_date = startDate newReservation.end_date = endDate newReservation.borrower = request.user newReservation.lender = request.user newReservation.save() return redirect('/toolshare/change-tool-availability/%s'%tool_id) else: return render(request, 'toolshare/change-tool-availability.html', {'tool': tool,'changeToolAvail_form': changeToolAvail_form,'tool_id': tool_id,'current_date': datetime.datetime.now().strftime('%m/%d/%Y')}) @staticmethod @login_required def deactivate_tool(request, tool_id): tool = Tool.objects.get(pk=tool_id) user = User.objects.get(pk=request.user.id) if tool.owner == user: tool.status = 'D' tool.save() return redirect('/toolshare/tool-detail/%s'%tool_id) #return render(request, 'toolshare/tool-detail.html',{ # 'tool':tool #}) @staticmethod @login_required def change_availability_tool(request, tool_id): tool = Tool.objects.get(pk= tool_id) user = User.objects.get(pk = request.user.id) if request.method == 'POST': change_avail_form = ChangeAvailabilityForm(request.POST, requested_tool_id=tool_id) if change_avail_form.is_valid(): new_reservation = change_avail_form.save(commit=False) new_reservation.tool = tool new_reservation.borrower = user if tool.shed is not None: new_reservation.lender = tool.shed.coordinator else: new_reservation.lender = user new_reservation.status = 'A' # Cancel all approved and pending reservations reservations = Reservation.objects.filter( tool_id=tool.id, end_date__gt=new_reservation.start_date, start_date__lt=new_reservation.end_date ) for reservation in reservations: if (new_reservation.start_date <= reservation.end_date and new_reservation.end_date >= reservation.start_date): if reservation.status == 'A': reservation.status = 'C' reservation.cancel_msg = 'The owner cancelled this reservation.' EmailSender.send_cancel_request_email(reservation) elif reservation.status == 'P': reservation.status = 'R' reservation.reject_msg = 'The owner rejected this reservation.' EmailSender.send_approve_reject_request_email(reservation) reservation.save() # Save the owner-reservation new_reservation.save() # Display the success message a_start = new_reservation.start_date.strftime('%m/%d/%Y') a_end = new_reservation.end_date.strftime('%m/%d/%Y') messages.add_message(request, messages.SUCCESS, 'You changed the availability of your tool between {0} - {1}. \n"{2}" reservations were cancelled.'.format(a_start, a_end, len(reservations))) return redirect('/toolshare/tool-detail/%s' % tool_id) else: change_avail_form = ChangeAvailabilityForm(requested_tool_id = tool_id) return render(request, 'toolshare/change-tool-availability.html', { 'tool': tool, 'changeToolAvail_form': change_avail_form, 'tool_id': tool_id, 'current_date': datetime.datetime.now().strftime('%m/%d/%Y') }) @staticmethod @login_required def activate_deactivate_tool(request, tool_id): tool = Tool.objects.get(pk=tool_id) if tool.status != 'D': now = timezone.now() reservations = Reservation.objects.filter(tool_id = tool.id, status ="A", start_date__gt = now) if len(reservations) > 0: messages.add_message(request, messages.WARNING, 'There are pending/approved reservations for the tool. Cancel the reservations first.') return redirect('/toolshare/tool-detail/%s'%tool_id) tool.status = 'D' tool.save() return redirect('/toolshare/tool-detail/%s'%tool_id) else: tool.status = 'A' tool.save() return redirect('/toolshare/tool-detail/%s'%tool_id)

44.045455

218

0.581675

ae0c6cb688c671cb936e70764582a5805af662fe

2,793

py

Python

evaluation.py

suikei-wang/Towards-Interpretable-Attention-Networks-for-Cervical-Cancer-Analysis

30b69394cc3fe339d2bc9b4c3b17cd345d088dff

[ "MIT" ]

1

2022-01-19T10:01:15.000Z

evaluation.py

suikei-wang/Towards-Interpretable-Attention-Networks-for-Cervical-Cancer-Analysis

30b69394cc3fe339d2bc9b4c3b17cd345d088dff

[ "MIT" ]

null

evaluation.py

suikei-wang/Towards-Interpretable-Attention-Networks-for-Cervical-Cancer-Analysis

30b69394cc3fe339d2bc9b4c3b17cd345d088dff

[ "MIT" ]

null

import torch import torch.nn as nn from tqdm import tqdm from sklearn.metrics import precision_recall_fscore_support as score from models.ResNet50 import initialize_model as ResNet50 from models.Pretrained import initialize_model as Pretrained from models.DenseNet import initialize_model as DenseNet from dataLoader import get_dataloaders from train_evaluate import evaluate device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # evaluation print("Evaluation on validation and test set") print("=" * 20) num_classes = 5 # num_classes = 7 # for smear dataset batch_size = 16 criterion = nn.CrossEntropyLoss() model, input_size = ResNet50(num_classes) model.load_state_dict(torch.load('weights/baseline1')) # model.load_state_dict(torch.load('weights/baseline2')) # model.load_state_dict(torch.load('weights/baseline3')) # model.load_state_dict(torch.load('weights/baseline4')) # model.load_state_dict(torch.load('weights/model1')) # model.load_state_dict(torch.load('weights/model2')) # model.load_state_dict(torch.load('weights/model3')) # model.load_state_dict(torch.load('weights/model4')) model = model.to(device) generate_validation_labels = True dataloaders, class_name = get_dataloaders(input_size, batch_size, True) val_loss, val_top1, val_top5, val_labels = evaluate(model, dataloaders['val'], criterion, is_labelled = True, generate_labels = generate_validation_labels, k = 5) epoch_loss, top1_acc, top5_acc, test_labels = evaluate(model, dataloaders['test'], criterion, is_labelled = True, generate_labels = True, k = 5) print("Top 1 accuracy on test set is", top1_acc) # Get the confusion matrix from test confusion_matrix = {x: [0,0,0,0,0] for x in class_name} # confusion_matrix = {x: [0,0,0,0,0,0,0] for x in class_name} for smear dataset running_top1_correct = 0 loader = dataloaders['test'] labels_array = [] pred_array = [] for inputs, labels in tqdm(loader): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _, preds = torch.topk(outputs, k=1, dim=1) for i in range(len(labels)): original_label = int(labels[i]) labels_array.append(original_label) pred_array.append(int(preds[i])) confusion_matrix[class_name[original_label]][int(preds[i])] += 1 running_top1_correct += torch.sum(preds[:, 0] == labels.data) precision, recall, fscore, support = score(labels_array, pred_array) epoch_top1_acc = float(running_top1_correct.double() / len(loader.dataset)) percentage = {x: [y /sum(confusion_matrix[x]) for y in confusion_matrix[x]] for x in confusion_matrix.keys()} print() print("Confusion matrix") print("=" * 20) print(percentage) print() print("Precision:", precision) print("Recall", recall) print("F1-Score", fscore) print("Support:", support)

38.260274

162

0.750806

484c48c58fa612238c60a077f17149b568a61036

1,838

py

Python

AlgorithmsPractice/python/307_(Binary Indexed Tree)_hard_Range Sum Query - Mutable.py

YangXiaoo/NoteBook

37056acad7a05b876832f72ac34d3d1a41e0dd22

[ "CNRI-Python", "RSA-MD", "CECILL-B" ]

58

2019-03-03T04:42:23.000Z

2022-01-13T04:36:31.000Z

AlgorithmsPractice/python/307_(Binary Indexed Tree)_hard_Range Sum Query - Mutable.py

YangXiaoo/NoteBook

37056acad7a05b876832f72ac34d3d1a41e0dd22

[ "CNRI-Python", "RSA-MD", "CECILL-B" ]

null

AlgorithmsPractice/python/307_(Binary Indexed Tree)_hard_Range Sum Query - Mutable.py

YangXiaoo/NoteBook

37056acad7a05b876832f72ac34d3d1a41e0dd22

[ "CNRI-Python", "RSA-MD", "CECILL-B" ]

28

2019-08-11T01:25:00.000Z

2021-08-22T06:46:06.000Z

''' Given an integer array nums, find the sum of the elements between indices i and j (i ≤ j), inclusive. The update(i, val) function modifies nums by updating the element at index i to val. Example: Given nums = [1, 3, 5] sumRange(0, 2) -> 9 update(1, 2) sumRange(0, 2) -> 8 Note: The array is only modifiable by the update function. You may assume the number of calls to update and sumRange function is distributed evenly. ''' # 2018-9-2 # 307. Range Sum Query - Mutable # Binary Indexed Tree # 关键在于 k -= (k & -k) 和 k += (k & -k), 前者用于update后者用于sum class NumArray: def __init__(self, nums): """ :type nums: List[int] """ self.nums = nums self.lens = len(nums) self.BIT = [0] * (self.lens + 1) # map(update, range(self.lens), nums) for i in range(self.lens): k = i + 1 while k <= self.lens: self.BIT[k] += nums[i] k += (k & -k) def update(self, i, val): """ :type i: int :type val: int :rtype: void """ diff = val - self.nums[i] self.nums[i] = val i += 1 while i <= self.lens: self.BIT[i] += diff i += (i & -i) # print(self.BIT) def sumRange(self, i, j): """ :type i: int :type j: int :rtype: int """ res = 0 j += 1 while j > 0: res += self.BIT[j] j -= (j & -j) while i > 0: res -= self.BIT[i] i -= (i & -i) return res # Your NumArray object will be instantiated and called as such: nums = [1, 3, 5] obj = NumArray(nums) obj.update(1, 2) obj.update(1, 44) obj.update(2, 44) r1 = obj.sumRange(0, 2) print(r1,obj.nums)

21.372093

101

0.492383

9c02a3199e40bc5c0475fbfec11cca7f64846ff4

1,649

py

Python

homeassistant/components/kira/remote.py

tbarbette/core

8e58c3aa7bc8d2c2b09b6bd329daa1c092d52d3c

[ "Apache-2.0" ]

22,481

2020-03-02T13:09:59.000Z

2022-03-31T23:34:28.000Z

homeassistant/components/kira/remote.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

homeassistant/components/kira/remote.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,411

2020-03-02T14:19:20.000Z

2022-03-31T22:46:07.000Z

"""Support for Keene Electronics IR-IP devices.""" import functools as ft import logging from homeassistant.components import remote from homeassistant.const import CONF_DEVICE, CONF_NAME from homeassistant.helpers.entity import Entity from . import CONF_REMOTE, DOMAIN _LOGGER = logging.getLogger(__name__) def setup_platform(hass, config, add_entities, discovery_info=None): """Set up the Kira platform.""" if discovery_info: name = discovery_info.get(CONF_NAME) device = discovery_info.get(CONF_DEVICE) kira = hass.data[DOMAIN][CONF_REMOTE][name] add_entities([KiraRemote(device, kira)]) return True class KiraRemote(Entity): """Remote representation used to send commands to a Kira device.""" def __init__(self, name, kira): """Initialize KiraRemote class.""" _LOGGER.debug("KiraRemote device init started for: %s", name) self._name = name self._kira = kira @property def name(self): """Return the Kira device's name.""" return self._name def update(self): """No-op.""" def send_command(self, command, **kwargs): """Send a command to one device.""" for single_command in command: code_tuple = (single_command, kwargs.get(remote.ATTR_DEVICE)) _LOGGER.info("Sending Command: %s to %s", *code_tuple) self._kira.sendCode(code_tuple) async def async_send_command(self, command, **kwargs): """Send a command to a device.""" return await self.hass.async_add_executor_job( ft.partial(self.send_command, command, **kwargs) )

30.537037

73

0.664645

2cdcf09b0a31b7161fd71c5fafba940a98504cbf

846

py

Python

evennia/comms/migrations/0011_auto_20170217_2039.py

fermuch/evennia

8961baa0a5b9b5419f864a144f080acc68a7ad0f

[ "BSD-3-Clause" ]

null

evennia/comms/migrations/0011_auto_20170217_2039.py

fermuch/evennia

8961baa0a5b9b5419f864a144f080acc68a7ad0f

[ "BSD-3-Clause" ]

null

evennia/comms/migrations/0011_auto_20170217_2039.py

fermuch/evennia

8961baa0a5b9b5419f864a144f080acc68a7ad0f

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.11 on 2017-02-17 20:39 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('scripts', '0007_auto_20150403_2339'), ('comms', '0010_auto_20161206_1912'), ] operations = [ migrations.AddField( model_name='msg', name='db_receivers_scripts', field=models.ManyToManyField(blank=True, help_text='script_receivers', null=True, related_name='receiver_script_set', to='scripts.ScriptDB'), ), migrations.AddField( model_name='msg', name='db_sender_scripts', field=models.ManyToManyField(blank=True, db_index=True, null=True, related_name='sender_script_set', to='scripts.ScriptDB', verbose_name='sender(script)'), ), ]

31.333333

167

0.638298

716bbdee9ebe571b5d4cbb828941b16c87ead194

2,148

py

Python

2018/Day 12/solution.py

JohnBehnke/Advent-of-Code-2017

f16f5e2b58dc8d267995d242d64f63e19aca9ba9

[ "MIT" ]

1

2020-12-13T02:19:05.000Z

2018/Day 12/solution.py

JohnBehnke/Advent-of-Code-2017

f16f5e2b58dc8d267995d242d64f63e19aca9ba9

[ "MIT" ]

null

2018/Day 12/solution.py

JohnBehnke/Advent-of-Code-2017

f16f5e2b58dc8d267995d242d64f63e19aca9ba9

[ "MIT" ]

null

''' initial state: #..#.#..##......###...### ...## => # ..#.. => # .#... => # .#.#. => # .#.## => # .##.. => # .#### => # #.#.# => # #.### => # ##.#. => # ##.## => # ###.. => # ###.# => # ####. => # ''' def bootstrap(inputFile): rules = {} initalState = ['.','.','.'] for line in open(inputFile): if 'initial state' in line: state = list(line.strip().split(': ')[1]) initalState.extend(state) initalState.extend(['.','.','.','.','.']) if '=>' in line: rules[line.strip().split(' => ')[0]] = line.strip().split(' => ')[1] return rules, initalState if __name__ == '__main__': rules, initalState = bootstrap('input.txt') generationLimit, currentGeneration = 20, 0 while currentGeneration <= generationLimit: nextGeneration = [] for i in range(len(initalState)): nextGeneration.append(initalState[i]) if len(nextGeneration) >= 5: leftPots = ''.join(nextGeneration[-5:-3]) centerPot = ''.join(nextGeneration[-3]) rightPots = ''.join(nextGeneration[-2:]) combinedPots = leftPots + centerPot + rightPots nextGeneration[-3] = rules[combinedPots] print ''.join(nextGeneration) initalState = nextGeneration[:] currentGeneration += 1 ''' 0: ...#..#.#..##......###...###........... 1: ...#...#....#.....#..#..#..#........... 2: ...##..##...##....#..#..#..##.......... 3: ..#.#...#..#.#....#..#..#...#.......... 4: ...#.#..#...#.#...#..#..##..##......... 5: ....#...##...#.#..#..#...#...#......... 6: ....##.#.#....#...#..##..##..##........ 7: ...#..###.#...##..#...#...#...#........ 8: ...#....##.#.#.#..##..##..##..##....... 9: ...##..#..#####....#...#...#...#....... 10: ..#.#..#...#.##....##..##..##..##...... 11: ...#...##...#.#...#.#...#...#...#...... 12: ...##.#.#....#.#...#.#..##..##..##..... 13: ..#..###.#....#.#...#....#...#...#..... 14: ..#....##.#....#.#..##...##..##..##.... 15: ..##..#..#.#....#....#..#.#...#...#.... 16: .#.#..#...#.#...##...#...#.#..##..##... 17: ..#...##...#.#.#.#...##...#....#...#... 18: ..##.#.#....#####.#.#.#...##...##..##.. 19: .#..###.#..#.#.#######.#.#.#..#.#...#.. 20: .#....##....#####...#######....#.#..##. '''

26.85

71

0.318901

17a1e0e64ddc02e8d37884357e0d79638cc2082e

28,871

py

Python

pnacl/driver/pnacl-driver.py

MicrohexHQ/nacl_contracts

3efab5eecb3cf7ba43f2d61000e65918aa4ba77a

[ "BSD-3-Clause" ]

6

2015-02-06T23:41:01.000Z

2015-10-21T03:08:51.000Z

pnacl/driver/pnacl-driver.py

MicrohexHQ/nacl_contracts

3efab5eecb3cf7ba43f2d61000e65918aa4ba77a

[ "BSD-3-Clause" ]

null

pnacl/driver/pnacl-driver.py

MicrohexHQ/nacl_contracts

3efab5eecb3cf7ba43f2d61000e65918aa4ba77a

[ "BSD-3-Clause" ]

1

2019-10-02T08:41:50.000Z

#!/usr/bin/python # Copyright (c) 2012 The Native Client Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # # IMPORTANT NOTE: If you make local mods to this file, you must run: # % pnacl/build.sh driver # in order for them to take effect in the scons build. This command # updates the copy in the toolchain/ tree. # import re import subprocess from driver_tools import AddHostBinarySearchPath, DefaultOutputName, \ DriverChain, GetArch, ParseArgs, ParseTriple, Run, RunDriver, RunWithEnv, \ TempNameGen, UnrecognizedOption from driver_env import env from driver_log import DriverOpen, Log import filetype import pathtools EXTRA_ENV = { 'ALLOW_TRANSLATE': '0', # Allow bitcode translation before linking. # It doesn't normally make sense to do this. 'ALLOW_NATIVE' : '0', # Allow native objects (.S,.s,.o) to be in the # linker line for .pexe generation. # It doesn't normally make sense to do this. # CXX_EH_MODE specifies how to deal with C++ exception handling: # * 'none': Strips out use of C++ exception handling. # * 'sjlj': Enables the setjmp()+longjmp()-based implementation of # C++ exception handling. This is supported in PNaCl's stable # ABI. # * 'zerocost': Enables the zero-cost implementation of C++ # exception handling. This is not supported in PNaCl's stable # ABI. 'CXX_EH_MODE': 'none', 'FORCE_INTERMEDIATE_LL': '0', # Produce an intermediate .ll file # Useful for debugging. # NOTE: potentially different code paths and bugs # might be triggered by this 'LANGUAGE' : '', # C or CXX (set by SetTool) 'INCLUDE_CXX_HEADERS': '0', # This is set by RunCC. # Command-line options 'GCC_MODE' : '', # '' (default), '-E', '-c', or '-S' 'STDINC' : '1', # Include standard headers (-nostdinc sets to 0) 'STDINCCXX' : '1', # Include standard cxx headers (-nostdinc++ sets to 0) 'USE_STDLIB' : '1', # Include standard libraries (-nostdlib sets to 0) 'STDLIB' : '', # C++ Standard Library. 'STDLIB_TRUNC': '', # C++ Standard Library, truncated to pass as -lXXX. 'STDLIB_IDIR' : '', # C++ Standard Library include directory. # Note: the above C++ Standard Library # settings use a default if their value # remains uset. 'DEFAULTLIBS' : '1', # Link with default libraries 'DIAGNOSTIC' : '0', # Diagnostic flag detected 'PIC' : '0', # Generate PIC # TODO(robertm): Switch the default to 1 'NO_ASM' : '0', # Disallow use of inline assembler 'NEED_DASH_E' : '0', # Used for stdin inputs, which must have an explicit # type set (using -x) unless -E is specified. 'VERBOSE' : '0', # Verbose (-v) 'SHOW_VERSION': '0', # Version (--version) 'PTHREAD' : '0', # use pthreads? 'INPUTS' : '', # Input files 'OUTPUT' : '', # Output file 'UNMATCHED' : '', # Unrecognized parameters 'BIAS_NONE' : '', 'BIAS_ARM' : '-D__arm__ -D__ARM_ARCH_7A__ -D__ARMEL__', 'BIAS_MIPS32' : '-D__MIPS__ -D__mips__ -D__MIPSEL__', 'BIAS_X8632' : '-D__i386__ -D__i386 -D__i686 -D__i686__ -D__pentium4__', 'BIAS_X8664' : '-D__amd64__ -D__amd64 -D__x86_64__ -D__x86_64 -D__core2__', 'BIAS_ARM_NONSFI': '${BIAS_ARM} -D__native_client_nonsfi__', 'BIAS_X8632_NONSFI': '${BIAS_X8632} -D__native_client_nonsfi__', 'FRONTEND_TRIPLE' : 'le32-unknown-nacl', 'OPT_LEVEL' : '', # Default for most tools is 0, but we need to know # if it's explicitly set or not when the driver # is only used for linking + translating. 'CC_FLAGS' : '-O${#OPT_LEVEL ? ${OPT_LEVEL} : 0} ' + '-fno-vectorize -fno-slp-vectorize ' + '-fno-common ${PTHREAD ? -pthread} ' + '-nostdinc ${BIAS_%BIAS%} ' + # BUG: http://code.google.com/p/nativeclient/issues/detail?id=2345 # it would be better to detect asm use inside clang # as some uses of asm are borderline legit, e.g. # <prototype> asm("<function-name>"); '${NO_ASM ? -Dasm=ASM_FORBIDDEN -D__asm__=ASM_FORBIDDEN} ' + '-target ${FRONTEND_TRIPLE}', 'ISYSTEM' : '${ISYSTEM_USER} ${STDINC ? ${ISYSTEM_BUILTIN}}', 'ISYSTEM_USER' : '', # System include directories specified by # using the -isystem flag. 'ISYSTEM_BUILTIN': '${BASE_USR}/local/include ' + '${ISYSTEM_CLANG} ' + '${ISYSTEM_CXX} ' + '${BASE_USR}/include ' + '${BASE_SDK}/include ', 'ISYSTEM_CLANG' : '${BASE_LLVM}/lib/clang/3.4/include', 'ISYSTEM_CXX' : '${INCLUDE_CXX_HEADERS && STDINCCXX ? ${ISYSTEM_CXX_include_paths}}', 'ISYSTEM_CXX_include_paths' : '${BASE_USR}/include/c++/${STDLIB_IDIR} ' + '${BASE_USR}/include/c++/${STDLIB_IDIR}/arm-none-linux-gnueabi ' + '${BASE_USR}/include/c++/${STDLIB_IDIR}/backward', # Only propagate opt level to linker if explicitly set, so that the # linker will know if an opt level was explicitly set or not. 'LD_FLAGS' : '${#OPT_LEVEL ? -O${OPT_LEVEL}} -static ' + '${PIC ? -fPIC} ${@AddPrefix:-L:SEARCH_DIRS} ' + '--pnacl-exceptions=${CXX_EH_MODE}', 'SEARCH_DIRS' : '', # Directories specified using -L # Library Strings 'EMITMODE' : '${!USE_STDLIB ? nostdlib : static}', # This is setup so that LD_ARGS_xxx is evaluated lazily. 'LD_ARGS' : '${LD_ARGS_%EMITMODE%}', # ${ld_inputs} signifies where to place the objects and libraries # provided on the command-line. 'LD_ARGS_nostdlib': '-nostdlib ${ld_inputs}', 'LD_ARGS_static': '${CXX_EH_MODE==zerocost ? -l:crt1_for_eh.x : -l:crt1.x} ' + '-l:crti.bc -l:crtbegin.bc ' '${CXX_EH_MODE==sjlj ? -l:sjlj_eh_redirect.bc : ' '${CXX_EH_MODE==none ? -l:unwind_stubs.bc}} ' + '${ld_inputs} ' + '--start-group ${STDLIBS} --end-group', 'LLVM_PASSES_TO_DISABLE': '', # Flags for translating to native .o files. 'TRANSLATE_FLAGS' : '-O${#OPT_LEVEL ? ${OPT_LEVEL} : 0}', 'STDLIBS' : '${DEFAULTLIBS ? ' '${LIBSTDCPP} ${LIBPTHREAD} ${LIBNACL} ${LIBC} ${LIBPNACLMM}}', 'LIBSTDCPP' : '${IS_CXX ? -l${STDLIB_TRUNC} -lm }', 'LIBC' : '-lc', 'LIBNACL' : '-lnacl', 'LIBPNACLMM': '-lpnaclmm', # Enabled/disabled by -pthreads 'LIBPTHREAD': '${PTHREAD ? -lpthread}', # IS_CXX is set by pnacl-clang and pnacl-clang++ programmatically 'CC' : '${IS_CXX ? ${CLANGXX} : ${CLANG}}', 'RUN_CC': '${CC} -emit-llvm ${mode} ${CC_FLAGS} ' + '${@AddPrefix:-isystem :ISYSTEM} ' + '-x${typespec} "${infile}" -o ${output}', } def AddLLVMPassDisableFlag(*args): env.append('LLVM_PASSES_TO_DISABLE', *args) env.append('LD_FLAGS', *args) def AddLDFlag(*args): env.append('LD_FLAGS', *args) def AddTranslatorFlag(*args): # pass translator args to ld in case we go all the way to .nexe env.append('LD_FLAGS', *['-Wt,' + a for a in args]) # pass translator args to translator in case we go to .o env.append('TRANSLATE_FLAGS', *args) def AddCCFlag(*args): env.append('CC_FLAGS', *args) def AddDiagnosticFlag(*args): env.append('CC_FLAGS', *args) env.set('DIAGNOSTIC', '1') def SetTarget(*args): arch = ParseTriple(args[0]) env.set('FRONTEND_TRIPLE', args[0]) AddLDFlag('--target=' + args[0]) def SetStdLib(*args): """Set the C++ Standard Library.""" lib = args[0] assert lib == 'libc++' or lib == 'libstdc++', ( 'Invalid C++ standard library: -stdlib=%s' % lib) env.set('STDLIB', lib) env.set('STDLIB_TRUNC', lib[3:]) if lib == 'libc++': env.set('STDLIB_IDIR', 'v1') if env.getbool('IS_CXX'): # libc++ depends on pthread for C++11 features as well as some # exception handling (which may get removed later by the PNaCl ABI # simplification) and initialize-once. env.set('PTHREAD', '1') elif lib == 'libstdc++': env.set('STDLIB_IDIR', '4.6.2') def IsPortable(): return env.getone('FRONTEND_TRIPLE').startswith('le32-') stdin_count = 0 def AddInputFileStdin(): global stdin_count # When stdin is an input, -x or -E must be given. forced_type = filetype.GetForcedFileType() if not forced_type: # Only allowed if -E is specified. forced_type = 'c' env.set('NEED_DASH_E', '1') stdin_name = '__stdin%d__' % stdin_count env.append('INPUTS', stdin_name) filetype.ForceFileType(stdin_name, forced_type) stdin_count += 1 def IsStdinInput(f): return f.startswith('__stdin') and f.endswith('__') def HandleDashX(arg): if arg == 'none': filetype.SetForcedFileType(None) return filetype.SetForcedFileType(filetype.GCCTypeToFileType(arg)) def AddVersionFlag(*args): env.set('SHOW_VERSION', '1') AddDiagnosticFlag(*args) def AddBPrefix(prefix): """ Add a path to the list searched for host binaries and include dirs. """ AddHostBinarySearchPath(prefix) prefix = pathtools.normalize(prefix) if pathtools.isdir(prefix) and not prefix.endswith('/'): prefix += '/' # Add prefix/ to the library search dir if it exists if pathtools.isdir(prefix): env.append('SEARCH_DIRS', prefix) # Add prefix/include to isystem if it exists include_dir = prefix + 'include' if pathtools.isdir(include_dir): env.append('ISYSTEM_USER', include_dir) CustomPatterns = [ ( '--driver=(.+)', "env.set('CC', pathtools.normalize($0))\n"), ( '--pnacl-allow-native', "env.set('ALLOW_NATIVE', '1')"), ( '--pnacl-allow-translate', "env.set('ALLOW_TRANSLATE', '1')"), ( '--pnacl-frontend-triple=(.+)', SetTarget), ( ('-target','(.+)'), SetTarget), ( ('--target=(.+)'), SetTarget), ( '--pnacl-exceptions=(none|sjlj|zerocost)', "env.set('CXX_EH_MODE', $0)"), # TODO(mseaborn): Remove "--pnacl-allow-exceptions", which is # superseded by "--pnacl-exceptions". ( '--pnacl-allow-exceptions', "env.set('CXX_EH_MODE', 'zerocost')"), ( '(--pnacl-allow-nexe-build-id)', AddLDFlag), ( '(--pnacl-disable-abi-check)', AddLDFlag), ( '(--pnacl-disable-pass=.+)', AddLLVMPassDisableFlag), ( '(--pnacl-allow-dev-intrinsics)', AddLDFlag), ] GCCPatterns = [ ( '-o(.+)', "env.set('OUTPUT', pathtools.normalize($0))"), ( ('-o', '(.+)'), "env.set('OUTPUT', pathtools.normalize($0))"), ( '-E', "env.set('GCC_MODE', '-E')"), ( '-S', "env.set('GCC_MODE', '-S')"), ( '-c', "env.set('GCC_MODE', '-c')"), ( '-allow-asm', "env.set('NO_ASM', '0')"), ( '-nostdinc', "env.set('STDINC', '0')"), ( '-nostdinc\+\+', "env.set('STDINCCXX', '0')"), ( '-nostdlib', "env.set('USE_STDLIB', '0')"), ( '-nodefaultlibs', "env.set('DEFAULTLIBS', '0')"), ( '-?-stdlib=(.*)', SetStdLib), ( ('-?-stdlib', '(.*)'), SetStdLib), # Flags to pass to native linker ( '(-Wn,.*)', AddLDFlag), ( '-rdynamic', "env.append('LD_FLAGS', '-export-dynamic')"), # Flags to pass to pnacl-translate ( '-Wt,(.*)', AddTranslatorFlag), ( ('-Xtranslator','(.*)'), AddTranslatorFlag), # We don't care about -fPIC, but pnacl-ld and pnacl-translate do. ( '-fPIC', "env.set('PIC', '1')"), # We must include -l, -Xlinker, and -Wl options into the INPUTS # in the order they appeared. This is the exactly behavior of gcc. # For example: gcc foo.c -Wl,--start-group -lx -ly -Wl,--end-group # ( '(-l.+)', "env.append('INPUTS', $0)"), ( ('(-l)','(.+)'), "env.append('INPUTS', $0+$1)"), ( ('-Xlinker','(.*)'), "env.append('INPUTS', '-Xlinker=' + $0)"), ( '(-Wl,.*)', "env.append('INPUTS', $0)"), ( '(-Bstatic)', "env.append('INPUTS', $0)"), ( '(-Bdynamic)', "env.append('INPUTS', $0)"), ( '-O([sz])', "env.set('OPT_LEVEL', $0)\n"), ( '-O([0-3])', "env.set('OPT_LEVEL', $0)\n"), ( '-O([0-9]+)', "env.set('OPT_LEVEL', '3')\n"), ( '-O', "env.set('OPT_LEVEL', '1')\n"), ( ('-isystem', '(.*)'), "env.append('ISYSTEM_USER', pathtools.normalize($0))"), ( '-isystem(.+)', "env.append('ISYSTEM_USER', pathtools.normalize($0))"), ( ('-I', '(.+)'), "env.append('CC_FLAGS', '-I'+pathtools.normalize($0))"), ( '-I(.+)', "env.append('CC_FLAGS', '-I'+pathtools.normalize($0))"), # NOTE: the -iquote =DIR syntax (substitute = with sysroot) doesn't work. # Clang just says: ignoring nonexistent directory "=DIR" ( ('-iquote', '(.+)'), "env.append('CC_FLAGS', '-iquote', pathtools.normalize($0))"), ( ('-iquote(.+)'), "env.append('CC_FLAGS', '-iquote', pathtools.normalize($0))"), ( ('-idirafter', '(.+)'), "env.append('CC_FLAGS', '-idirafter'+pathtools.normalize($0))"), ( '-idirafter(.+)', "env.append('CC_FLAGS', '-idirafter'+pathtools.normalize($0))"), ( ('(-include)','(.+)'), AddCCFlag), ( ('(-include.+)'), AddCCFlag), ( '(-g)', AddCCFlag), ( '(-W.*)', AddCCFlag), ( '(-w)', AddCCFlag), ( '(-std=.*)', AddCCFlag), ( '(-ansi)', AddCCFlag), ( ('(-D)','(.*)'), AddCCFlag), ( '(-D.+)', AddCCFlag), ( ('(-U)','(.*)'), AddCCFlag), ( '(-U.+)', AddCCFlag), ( '(-f.*)', AddCCFlag), ( '(-pedantic)', AddCCFlag), ( '(-pedantic-errors)', AddCCFlag), ( '(-g.*)', AddCCFlag), ( '(-v|--v)', "env.append('CC_FLAGS', $0)\n" "env.set('VERBOSE', '1')"), ( '(-pthreads?)', "env.set('PTHREAD', '1')"), # No-op: accepted for compatibility in case build scripts pass it. ( '-static', ""), ( ('-B','(.*)'), AddBPrefix), ( ('-B(.+)'), AddBPrefix), ( ('-L','(.+)'), "env.append('SEARCH_DIRS', pathtools.normalize($0))"), ( '-L(.+)', "env.append('SEARCH_DIRS', pathtools.normalize($0))"), ( '(-Wp,.*)', AddCCFlag), ( '(-Xpreprocessor .*)', AddCCFlag), ( ('(-Xclang)', '(.*)'), AddCCFlag), # Accept and ignore default flags ( '-m32', ""), ( '-emit-llvm', ""), ( '(-MG)', AddCCFlag), ( '(-MMD)', AddCCFlag), ( '(-MM?)', "env.append('CC_FLAGS', $0)\n" "env.set('GCC_MODE', '-E')"), ( '(-MP)', AddCCFlag), ( ('(-MQ)','(.*)'), AddCCFlag), ( '(-MD)', AddCCFlag), ( ('(-MT)','(.*)'), AddCCFlag), ( ('(-MF)','(.*)'), "env.append('CC_FLAGS', $0, pathtools.normalize($1))"), ( ('-x', '(.+)'), HandleDashX), ( '-x(.+)', HandleDashX), ( ('(-mllvm)', '(.+)'), AddCCFlag), # Ignore these gcc flags ( '(-msse)', ""), ( '(-march=armv7-a)', ""), ( '(-pipe)', ""), ( '(-s)', AddLDFlag), ( '(--strip-all)', AddLDFlag), ( '(--strip-debug)', AddLDFlag), # Ignore these assembler flags ( '(-Qy)', ""), ( ('(--traditional-format)', '.*'), ""), ( '(-gstabs)', ""), ( '(--gstabs)', ""), ( '(-gdwarf2)', ""), ( '(--gdwarf2)', ""), ( '(--fatal-warnings)', ""), ( '(-meabi=.*)', ""), ( '(-mfpu=.*)', ""), ( '(-mfloat-abi=.+)', AddCCFlag), # GCC diagnostic mode triggers ( '(-print-.*)', AddDiagnosticFlag), ( '(--print.*)', AddDiagnosticFlag), ( '(-dumpspecs)', AddDiagnosticFlag), ( '(--version)', AddVersionFlag), # These are preprocessor flags which should be passed to the frontend, but # should not prevent the usual -i flags (which DIAGNOSTIC mode does) ( '(-d[DIMNU])', AddCCFlag), ( '(-d.*)', AddDiagnosticFlag), # Catch all other command-line arguments ( '(-.+)', "env.append('UNMATCHED', $0)"), # Standard input ( '-', AddInputFileStdin), # Input Files # Call ForceFileType for all input files at the time they are # parsed on the command-line. This ensures that the gcc "-x" # setting is correctly applied. ( '(.*)', "env.append('INPUTS', pathtools.normalize($0))\n" "filetype.ForceFileType(pathtools.normalize($0))"), ] def CheckSetup(): if not env.has('IS_CXX'): Log.Fatal('"pnacl-driver" cannot be used directly. ' 'Use pnacl-clang or pnacl-clang++.') def DriverOutputTypes(driver_flag, compiling_to_native): output_type_map = { ('-E', False) : 'pp', ('-E', True) : 'pp', ('-c', False) : 'po', ('-c', True) : 'o', ('-S', False) : 'll', ('-S', True) : 's', ('', False) : 'pexe', ('', True) : 'nexe', } return output_type_map[(driver_flag, compiling_to_native)] def ReadDriverRevision(): rev_file = env.getone('DRIVER_REV_FILE') # Might be an SVN version or a GIT hash (depending on the NaCl src client) nacl_ver = DriverOpen(rev_file, 'rb').readlines()[0] m = re.search(r'\[SVN\].*/native_client:\s*(\d+)', nacl_ver) if m: return m.group(1) m = re.search(r'\[GIT\].*/native_client.git:\s*(\w+)', nacl_ver) if m: return m.group(1) # fail-fast: if the REV file exists but regex search failed, # we need to fix the regex to get nacl-version. if not m: Log.Fatal('Failed to parse REV file to get nacl-version.') def main(argv): env.update(EXTRA_ENV) CheckSetup() ParseArgs(argv, CustomPatterns + GCCPatterns) # "configure", especially when run as part of a toolchain bootstrap # process, will invoke gcc with various diagnostic options and # parse the output. In these cases we do not alter the incoming # commandline. It is also important to not emit spurious messages. if env.getbool('DIAGNOSTIC'): if env.getbool('SHOW_VERSION'): code, stdout, stderr = Run(env.get('CC') + env.get('CC_FLAGS'), redirect_stdout=subprocess.PIPE) out = stdout.split('\n') nacl_version = ReadDriverRevision() out[0] += ' nacl-version=%s' % nacl_version stdout = '\n'.join(out) print stdout, else: Run(env.get('CC') + env.get('CC_FLAGS')) return 0 unmatched = env.get('UNMATCHED') if len(unmatched) > 0: UnrecognizedOption(*unmatched) # If -arch was given, we are compiling directly to native code compiling_to_native = GetArch() is not None if env.getbool('ALLOW_NATIVE') and not compiling_to_native: Log.Fatal("--pnacl-allow-native without -arch is not meaningful.") if not env.get('STDLIB'): # Default C++ Standard Library. SetStdLib('libc++') inputs = env.get('INPUTS') output = env.getone('OUTPUT') if len(inputs) == 0: if env.getbool('VERBOSE'): # -v can be invoked without any inputs. Runs the original # command without modifying the commandline for this case. Run(env.get('CC') + env.get('CC_FLAGS')) return 0 else: Log.Fatal('No input files') gcc_mode = env.getone('GCC_MODE') output_type = DriverOutputTypes(gcc_mode, compiling_to_native) needs_linking = (gcc_mode == '') if env.getbool('NEED_DASH_E') and gcc_mode != '-E': Log.Fatal("-E or -x required when input is from stdin") # There are multiple input files and no linking is being done. # There will be multiple outputs. Handle this case separately. if not needs_linking: # Filter out flags inputs = [f for f in inputs if not IsFlag(f)] if output != '' and len(inputs) > 1: Log.Fatal('Cannot have -o with -c, -S, or -E and multiple inputs: %s', repr(inputs)) for f in inputs: if IsFlag(f): continue intype = filetype.FileType(f) if not filetype.IsSourceType(intype): if ((output_type == 'pp' and intype != 'S') or (output_type == 'll') or (output_type == 'po' and intype != 'll') or (output_type == 's' and intype not in ('ll','po','S')) or (output_type == 'o' and intype not in ('ll','po','S','s'))): Log.Fatal("%s: Unexpected type of file for '%s'", pathtools.touser(f), gcc_mode) if output == '': f_output = DefaultOutputName(f, output_type) else: f_output = output namegen = TempNameGen([f], f_output) CompileOne(f, output_type, namegen, f_output) return 0 # Linking case assert(needs_linking) assert(output_type in ('pso','so','pexe','nexe')) if output == '': output = pathtools.normalize('a.out') namegen = TempNameGen(inputs, output) # Compile all source files (c/c++/ll) to .po for i in xrange(0, len(inputs)): if IsFlag(inputs[i]): continue intype = filetype.FileType(inputs[i]) if filetype.IsSourceType(intype) or intype == 'll': inputs[i] = CompileOne(inputs[i], 'po', namegen) # Compile all .s/.S to .o if env.getbool('ALLOW_NATIVE'): for i in xrange(0, len(inputs)): if IsFlag(inputs[i]): continue intype = filetype.FileType(inputs[i]) if intype in ('s','S'): inputs[i] = CompileOne(inputs[i], 'o', namegen) # We should only be left with .po and .o and libraries for f in inputs: if IsFlag(f): continue intype = filetype.FileType(f) if intype in ('o','s','S') or filetype.IsNativeArchive(f): if not env.getbool('ALLOW_NATIVE'): Log.Fatal('%s: Native object files not allowed in link. ' 'Use --pnacl-allow-native to override.', pathtools.touser(f)) assert(intype in ('po','o','so','ldscript') or filetype.IsArchive(f)) # Fix the user-specified linker arguments ld_inputs = [] for f in inputs: if f.startswith('-Xlinker='): ld_inputs.append(f[len('-Xlinker='):]) elif f.startswith('-Wl,'): ld_inputs += f[len('-Wl,'):].split(',') else: ld_inputs.append(f) if env.getbool('ALLOW_NATIVE'): ld_inputs.append('--pnacl-allow-native') # Invoke the linker env.set('ld_inputs', *ld_inputs) ld_args = env.get('LD_ARGS') ld_flags = env.get('LD_FLAGS') RunDriver('ld', ld_flags + ld_args + ['-o', output]) return 0 def IsFlag(f): return f.startswith('-') def CompileOne(infile, output_type, namegen, output = None): if output is None: output = namegen.TempNameForInput(infile, output_type) chain = DriverChain(infile, output, namegen) SetupChain(chain, filetype.FileType(infile), output_type) chain.run() return output def RunCC(infile, output, mode): intype = filetype.FileType(infile) typespec = filetype.FileTypeToGCCType(intype) include_cxx_headers = (env.get('LANGUAGE') == 'CXX') or (intype == 'c++') env.setbool('INCLUDE_CXX_HEADERS', include_cxx_headers) if IsStdinInput(infile): infile = '-' RunWithEnv("${RUN_CC}", infile=infile, output=output, mode=mode, typespec=typespec) def RunLLVMAS(infile, output): if IsStdinInput(infile): infile = '-' # This is a bitcode only step - so get rid of "-arch xxx" which # might be inherited from the current invocation RunDriver('as', [infile, '-o', output], suppress_inherited_arch_args=True) def RunNativeAS(infile, output): if IsStdinInput(infile): infile = '-' RunDriver('as', [infile, '-o', output]) def RunTranslate(infile, output, mode): if not env.getbool('ALLOW_TRANSLATE'): Log.Fatal('%s: Trying to convert bitcode to an object file before ' 'bitcode linking. This is supposed to wait until ' 'translation. Use --pnacl-allow-translate to override.', pathtools.touser(infile)) args = env.get('TRANSLATE_FLAGS') + [mode, '--allow-llvm-bitcode-input', infile, '-o', output] if env.getbool('PIC'): args += ['-fPIC'] RunDriver('translate', args) def RunOpt(infile, outfile, pass_list): filtered_list = [pass_option for pass_option in pass_list if pass_option not in env.get('LLVM_PASSES_TO_DISABLE')] RunDriver('opt', filtered_list + [infile, '-o', outfile]) def SetupChain(chain, input_type, output_type): assert(output_type in ('pp','ll','po','s','o')) cur_type = input_type # source file -> pp if filetype.IsSourceType(cur_type) and output_type == 'pp': chain.add(RunCC, 'cpp', mode='-E') cur_type = 'pp' if cur_type == output_type: return # source file -> ll if (filetype.IsSourceType(cur_type) and (env.getbool('FORCE_INTERMEDIATE_LL') or output_type == 'll')): chain.add(RunCC, 'll', mode='-S') cur_type = 'll' if cur_type == output_type: return # ll -> po if cur_type == 'll': chain.add(RunLLVMAS, 'po') cur_type = 'po' if cur_type == output_type: return # source file -> po (we also force native output to go through this phase if filetype.IsSourceType(cur_type) and output_type in ('po', 'o', 's'): chain.add(RunCC, 'po', mode='-c') cur_type = 'po' if cur_type == output_type: return # po -> o if (cur_type == 'po' and output_type == 'o'): # If we aren't using biased bitcode, then at least -expand-byval # must be run to work with the PPAPI shim calling convention. if IsPortable(): chain.add(RunOpt, 'expand.po', pass_list=['-expand-byval']) chain.add(RunTranslate, 'o', mode='-c') cur_type = 'o' if cur_type == output_type: return # po -> s if cur_type == 'po': # If we aren't using biased bitcode, then at least -expand-byval # must be run to work with the PPAPI shim calling convention. if IsPortable(): chain.add(RunOpt, 'expand.po', pass_list=['-expand-byval']) chain.add(RunTranslate, 's', mode='-S') cur_type = 's' if cur_type == output_type: return # S -> s if cur_type == 'S': chain.add(RunCC, 's', mode='-E') cur_type = 's' if output_type == 'pp': return if cur_type == output_type: return # s -> o if cur_type == 's' and output_type == 'o': chain.add(RunNativeAS, 'o') cur_type = 'o' if cur_type == output_type: return Log.Fatal("Unable to compile .%s to .%s", input_type, output_type) def get_help(argv): tool = env.getone('SCRIPT_NAME') if '--help-full' in argv: # To get ${CC}, etc. env.update(EXTRA_ENV) code, stdout, stderr = Run('"${CC}" -help', redirect_stdout=subprocess.PIPE, redirect_stderr=subprocess.STDOUT, errexit=False) return stdout else: return """ This is a "GCC-compatible" driver using clang under the hood. Usage: %s [options] <inputs> ... BASIC OPTIONS: -o <file> Output to <file>. -E Only run the preprocessor. -S Generate bitcode assembly. -c Generate bitcode object. -I <dir> Add header search path. -L <dir> Add library search path. -D<key>[=<val>] Add definition for the preprocessor. -W<id> Toggle warning <id>. -f<feature> Enable <feature>. -Wl,<arg> Pass <arg> to the linker. -Xlinker <arg> Pass <arg> to the linker. -Wt,<arg> Pass <arg> to the translator. -Xtranslator <arg> Pass <arg> to the translator. -Wp,<arg> Pass <arg> to the preprocessor. -Xpreprocessor,<arg> Pass <arg> to the preprocessor. -x <language> Treat subsequent input files as having type <language>. -static Produce a static executable (the default). -Bstatic Link subsequent libraries statically. -Bdynamic Link subsequent libraries dynamically. -fPIC Ignored (only used by translator backend) (accepted for compatibility). -pipe Ignored (for compatibility). -O<n> Optimation level <n>: 0, 1, 2, 3, 4 or s. -g Generate complete debug information. -gline-tables-only Generate debug line-information only (allowing for stack traces). -flimit-debug-info Generate limited debug information. -save-temps Keep intermediate compilation results. -v Verbose output / show commands. -h | --help Show this help. --help-full Show underlying clang driver's help message (warning: not all options supported). """ % (tool)

36.361461

84

0.575214

fa00c6fe83ece6c4f2c78553594c5611ca0520f9

406

py

Python

tests/test_table.py

refgenie/refgenconf

727fe1f219780b46fe3743a906dcd6238f1734f6

[ "BSD-2-Clause" ]

3

2019-07-12T16:46:37.000Z

2019-11-20T20:39:43.000Z

tests/test_table.py

databio/refgenconf

727fe1f219780b46fe3743a906dcd6238f1734f6

[ "BSD-2-Clause" ]

80

2019-05-14T20:59:44.000Z

2020-06-16T17:09:52.000Z

tests/test_table.py

databio/refgenconf

727fe1f219780b46fe3743a906dcd6238f1734f6

[ "BSD-2-Clause" ]

4

2019-05-15T15:22:15.000Z

2020-05-26T14:28:48.000Z

class TestAliasTable: def test_alias_table_dimensions(self, my_rgc): assert len(my_rgc.genomes_list()) == my_rgc.genome_aliases_table.row_count assert len(my_rgc.genome_aliases_table.columns) == 2 class TestAssetTable: def test_asset_table_dimensions(self, my_rgc): assert my_rgc.genome_aliases_table.row_count == len( my_rgc.list_assets_by_genome() )

33.833333

82

0.724138

fef3a2cc60bf0b7c0d25c6aef3f775622d353afd

885

py

Python

tests/test_metrics.py

Marcnuth/AutoQuant

b2fbade31a86c16b1287675cb7483afeb0e43663

[ "MIT" ]

1

2021-11-07T04:01:44.000Z

tests/test_metrics.py

Marcnuth/AutoQuant

b2fbade31a86c16b1287675cb7483afeb0e43663

[ "MIT" ]

null

tests/test_metrics.py

Marcnuth/AutoQuant

b2fbade31a86c16b1287675cb7483afeb0e43663

[ "MIT" ]

3

2021-11-25T15:03:19.000Z

2021-12-24T02:17:51.000Z

from autoquant.metric import gross_rate_of_return, CAGR, max_drawdown, beta, annualized_rate_of_return, avg_annualized_rate_of_return from datetime import date from autoquant import Market def test_metrics(): assert gross_rate_of_return(1, 11) == 10 assert CAGR(1, 11, date(2021, 11, 1), date(2023, 5, 1)) - 4.96 < 0.01 assert max_drawdown([1, 2, 3, 4, 5, 6, 7]) == 0 assert max_drawdown([7, 6, 5, 4, 3, 2, 1]) - 0.85 < 0.01 assert max_drawdown([10, 8]) - 0.2 < 0.01 def test_beta(): beta(Market.SH, '000002', start=date(2020, 1, 1), end=date(2021, 1, 1)) def test_aror(): data = annualized_rate_of_return(market=Market.SZ, code='000002', start_year=2010, end_year=2021) assert abs(data - 0.087) < 1e-3 data = avg_annualized_rate_of_return(market=Market.SZ, code='000002', start_year=2010, end_year=2021) assert abs(data - 0.135) < 1e-3

35.4

133

0.685876

777bfe6d87f543429edf42a166f84a626c881d76

293,868

py

Python

venv/Lib/site-packages/tensorflow/python/training/gen_training_ops.py

golam-shovon/Targated_Advertising

903b42ec04c98e10fcacd126eb8ba52720ac3cc5

[ "BSD-4-Clause-UC" ]

1

2019-04-11T13:23:09.000Z

Lib/site-packages/tensorflow/python/training/gen_training_ops.py

caiyongji/Anaconda-py36.5-tensorflow-built-env

f4eb40b5ca3f49dfc929ff3ad2b4bb877e9663e2

[ "PSF-2.0" ]

null

Lib/site-packages/tensorflow/python/training/gen_training_ops.py

caiyongji/Anaconda-py36.5-tensorflow-built-env

f4eb40b5ca3f49dfc929ff3ad2b4bb877e9663e2

[ "PSF-2.0" ]

null

"""Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. """ import collections as _collections import six as _six from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import errors as _errors from tensorflow.python.framework import tensor_shape as _tensor_shape from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 # Needed to trigger the call to _set_call_cpp_shape_fn. from tensorflow.python.framework import common_shapes as _common_shapes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.deprecation import deprecated_endpoints from tensorflow.python.util import dispatch as _dispatch from tensorflow.python.util.tf_export import tf_export def apply_ada_max(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the AdaMax algorithm. m_t <- beta1 * m_{t-1} + (1 - beta1) * g v_t <- max(beta2 * v_{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). m: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). v: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). beta1_power: A `Tensor`. Must have the same type as `var`. Must be a scalar. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. beta1: A `Tensor`. Must have the same type as `var`. Momentum factor. Must be a scalar. beta2: A `Tensor`. Must have the same type as `var`. Momentum factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `var`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_ada_max op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyAdaMax", var=var, m=m, v=v, beta1_power=beta1_power, lr=lr, beta1=beta1, beta2=beta2, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyAdaMax", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_ada_max_eager_fallback(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_ada_max op does not support eager execution. Arg 'out' is a ref.") def apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the adadelta scheme. accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update; Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). accum_update: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `var`. Decay factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `var`. Constant factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_adadelta op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyAdadelta", var=var, accum=accum, accum_update=accum_update, lr=lr, rho=rho, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyAdadelta", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_adadelta_eager_fallback(var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_adadelta op does not support eager execution. Arg 'out' is a ref.") def apply_adagrad(var, accum, lr, grad, use_locking=False, update_slots=True, name=None): r"""Update '*var' according to the adagrad scheme. accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. update_slots: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_adagrad op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if update_slots is None: update_slots = True update_slots = _execute.make_bool(update_slots, "update_slots") _, _, _op = _op_def_lib._apply_op_helper( "ApplyAdagrad", var=var, accum=accum, lr=lr, grad=grad, use_locking=use_locking, update_slots=update_slots, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking"), "update_slots", _op.get_attr("update_slots")) _execute.record_gradient( "ApplyAdagrad", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_adagrad_eager_fallback(var, accum, lr, grad, use_locking=False, update_slots=True, name=None, ctx=None): raise RuntimeError("apply_adagrad op does not support eager execution. Arg 'out' is a ref.") def apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, use_locking=False, name=None): r"""Update '*var' according to the proximal adagrad scheme. Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). gradient_accumulator: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). gradient_squared_accumulator: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). grad: A `Tensor`. Must have the same type as `var`. The gradient. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. global_step: A `Tensor` of type `int64`. Training step number. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_adagrad_da op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyAdagradDA", var=var, gradient_accumulator=gradient_accumulator, gradient_squared_accumulator=gradient_squared_accumulator, grad=grad, lr=lr, l1=l1, l2=l2, global_step=global_step, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyAdagradDA", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_adagrad_da_eager_fallback(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_adagrad_da op does not support eager execution. Arg 'out' is a ref.") def apply_adam(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=False, use_nesterov=False, name=None): r"""Update '*var' according to the Adam algorithm. $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). m: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). v: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). beta1_power: A `Tensor`. Must have the same type as `var`. Must be a scalar. beta2_power: A `Tensor`. Must have the same type as `var`. Must be a scalar. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. beta1: A `Tensor`. Must have the same type as `var`. Momentum factor. Must be a scalar. beta2: A `Tensor`. Must have the same type as `var`. Momentum factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `var`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, uses the nesterov update. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_adam op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ApplyAdam", var=var, m=m, v=v, beta1_power=beta1_power, beta2_power=beta2_power, lr=lr, beta1=beta1, beta2=beta2, epsilon=epsilon, grad=grad, use_locking=use_locking, use_nesterov=use_nesterov, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking"), "use_nesterov", _op.get_attr("use_nesterov")) _execute.record_gradient( "ApplyAdam", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_adam_eager_fallback(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=False, use_nesterov=False, name=None, ctx=None): raise RuntimeError("apply_adam op does not support eager execution. Arg 'out' is a ref.") def apply_add_sign(var, m, lr, alpha, sign_decay, beta, grad, use_locking=False, name=None): r"""Update '*var' according to the AddSign update. m_t <- beta1 * m_{t-1} + (1 - beta1) * g update <- (alpha + sign_decay * sign(g) *sign(m)) * g variable <- variable - lr_t * update Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). m: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. alpha: A `Tensor`. Must have the same type as `var`. Must be a scalar. sign_decay: A `Tensor`. Must have the same type as `var`. Must be a scalar. beta: A `Tensor`. Must have the same type as `var`. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and m tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_add_sign op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyAddSign", var=var, m=m, lr=lr, alpha=alpha, sign_decay=sign_decay, beta=beta, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyAddSign", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_add_sign_eager_fallback(var, m, lr, alpha, sign_decay, beta, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_add_sign op does not support eager execution. Arg 'out' is a ref.") def apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) mg <- rho * mg_{t-1} + (1-rho) * grad ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). mg: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). ms: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). mom: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `var`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `var`. epsilon: A `Tensor`. Must have the same type as `var`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_centered_rms_prop op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyCenteredRMSProp", var=var, mg=mg, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyCenteredRMSProp", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_centered_rms_prop_eager_fallback(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_centered_rms_prop op does not support eager execution. Arg 'out' is a ref.") def apply_ftrl(var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False, name=None): r"""Update '*var' according to the Ftrl-proximal scheme. accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). linear: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). grad: A `Tensor`. Must have the same type as `var`. The gradient. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regulariation. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regulariation. Must be a scalar. lr_power: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_ftrl op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyFtrl", var=var, accum=accum, linear=linear, grad=grad, lr=lr, l1=l1, l2=l2, lr_power=lr_power, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyFtrl", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_ftrl_eager_fallback(var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_ftrl op does not support eager execution. Arg 'out' is a ref.") def apply_ftrl_v2(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None): r"""Update '*var' according to the Ftrl-proximal scheme. grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage linear += grad_with_shrinkage + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). linear: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). grad: A `Tensor`. Must have the same type as `var`. The gradient. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regulariation. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 shrinkage regulariation. Must be a scalar. l2_shrinkage: A `Tensor`. Must have the same type as `var`. lr_power: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_ftrl_v2 op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyFtrlV2", var=var, accum=accum, linear=linear, grad=grad, lr=lr, l1=l1, l2=l2, l2_shrinkage=l2_shrinkage, lr_power=lr_power, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyFtrlV2", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_ftrl_v2_eager_fallback(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_ftrl_v2 op does not support eager execution. Arg 'out' is a ref.") def apply_gradient_descent(var, alpha, delta, use_locking=False, name=None): r"""Update '*var' by subtracting 'alpha' * 'delta' from it. Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). alpha: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. delta: A `Tensor`. Must have the same type as `var`. The change. use_locking: An optional `bool`. Defaults to `False`. If `True`, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_gradient_descent op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyGradientDescent", var=var, alpha=alpha, delta=delta, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyGradientDescent", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_gradient_descent_eager_fallback(var, alpha, delta, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_gradient_descent op does not support eager execution. Arg 'out' is a ref.") def apply_momentum(var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None): r"""Update '*var' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum + grad var -= lr * accum Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. momentum: A `Tensor`. Must have the same type as `var`. Momentum. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_momentum op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ApplyMomentum", var=var, accum=accum, lr=lr, grad=grad, momentum=momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking"), "use_nesterov", _op.get_attr("use_nesterov")) _execute.record_gradient( "ApplyMomentum", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_momentum_eager_fallback(var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None, ctx=None): raise RuntimeError("apply_momentum op does not support eager execution. Arg 'out' is a ref.") def apply_power_sign(var, m, lr, logbase, sign_decay, beta, grad, use_locking=False, name=None): r"""Update '*var' according to the AddSign update. m_t <- beta1 * m_{t-1} + (1 - beta1) * g update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g variable <- variable - lr_t * update Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). m: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. logbase: A `Tensor`. Must have the same type as `var`. Must be a scalar. sign_decay: A `Tensor`. Must have the same type as `var`. Must be a scalar. beta: A `Tensor`. Must have the same type as `var`. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and m tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_power_sign op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyPowerSign", var=var, m=m, lr=lr, logbase=logbase, sign_decay=sign_decay, beta=beta, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyPowerSign", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_power_sign_eager_fallback(var, m, lr, logbase, sign_decay, beta, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_power_sign op does not support eager execution. Arg 'out' is a ref.") def apply_proximal_adagrad(var, accum, lr, l1, l2, grad, use_locking=False, name=None): r"""Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_proximal_adagrad op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyProximalAdagrad", var=var, accum=accum, lr=lr, l1=l1, l2=l2, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyProximalAdagrad", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_proximal_adagrad_eager_fallback(var, accum, lr, l1, l2, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_proximal_adagrad op does not support eager execution. Arg 'out' is a ref.") def apply_proximal_gradient_descent(var, alpha, l1, l2, delta, use_locking=False, name=None): r"""Update '*var' as FOBOS algorithm with fixed learning rate. prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). alpha: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. delta: A `Tensor`. Must have the same type as `var`. The change. use_locking: An optional `bool`. Defaults to `False`. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_proximal_gradient_descent op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyProximalGradientDescent", var=var, alpha=alpha, l1=l1, l2=l2, delta=delta, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyProximalGradientDescent", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_proximal_gradient_descent_eager_fallback(var, alpha, l1, l2, delta, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_proximal_gradient_descent op does not support eager execution. Arg 'out' is a ref.") def apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). ms: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). mom: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `var`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `var`. epsilon: A `Tensor`. Must have the same type as `var`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("apply_rms_prop op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ApplyRMSProp", var=var, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "ApplyRMSProp", _inputs_flat, _attrs, _result, name) _result, = _result return _result def apply_rms_prop_eager_fallback(var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None, ctx=None): raise RuntimeError("apply_rms_prop op does not support eager execution. Arg 'out' is a ref.") def resource_apply_ada_max(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the AdaMax algorithm. m_t <- beta1 * m_{t-1} + (1 - beta1) * g v_t <- max(beta2 * v_{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) Args: var: A `Tensor` of type `resource`. Should be from a Variable(). m: A `Tensor` of type `resource`. Should be from a Variable(). v: A `Tensor` of type `resource`. Should be from a Variable(). beta1_power: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Must be a scalar. lr: A `Tensor`. Must have the same type as `beta1_power`. Scaling factor. Must be a scalar. beta1: A `Tensor`. Must have the same type as `beta1_power`. Momentum factor. Must be a scalar. beta2: A `Tensor`. Must have the same type as `beta1_power`. Momentum factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `beta1_power`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `beta1_power`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAdaMax", name, _ctx._post_execution_callbacks, var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_ada_max_eager_fallback( var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAdaMax", var=var, m=m, v=v, beta1_power=beta1_power, lr=lr, beta1=beta1, beta2=beta2, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_ada_max_eager_fallback(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_ada_max """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([beta1_power, lr, beta1, beta2, epsilon, grad], _ctx) (beta1_power, lr, beta1, beta2, epsilon, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) m = _ops.convert_to_tensor(m, _dtypes.resource) v = _ops.convert_to_tensor(v, _dtypes.resource) _inputs_flat = [var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyAdaMax", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the adadelta scheme. accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update; Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). accum_update: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `lr`. Decay factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `lr`. Constant factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAdadelta", name, _ctx._post_execution_callbacks, var, accum, accum_update, lr, rho, epsilon, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_adadelta_eager_fallback( var, accum, accum_update, lr, rho, epsilon, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAdadelta", var=var, accum=accum, accum_update=accum_update, lr=lr, rho=rho, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_adadelta_eager_fallback(var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_adadelta """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, rho, epsilon, grad], _ctx) (lr, rho, epsilon, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) accum_update = _ops.convert_to_tensor(accum_update, _dtypes.resource) _inputs_flat = [var, accum, accum_update, lr, rho, epsilon, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyAdadelta", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_adagrad(var, accum, lr, grad, use_locking=False, update_slots=True, name=None): r"""Update '*var' according to the adagrad scheme. accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. update_slots: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAdagrad", name, _ctx._post_execution_callbacks, var, accum, lr, grad, "use_locking", use_locking, "update_slots", update_slots) return _result except _core._FallbackException: try: return resource_apply_adagrad_eager_fallback( var, accum, lr, grad, use_locking=use_locking, update_slots=update_slots, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if update_slots is None: update_slots = True update_slots = _execute.make_bool(update_slots, "update_slots") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAdagrad", var=var, accum=accum, lr=lr, grad=grad, use_locking=use_locking, update_slots=update_slots, name=name) return _op _result = None return _result def resource_apply_adagrad_eager_fallback(var, accum, lr, grad, use_locking=False, update_slots=True, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_adagrad """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if update_slots is None: update_slots = True update_slots = _execute.make_bool(update_slots, "update_slots") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, grad], _ctx) (lr, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, grad] _attrs = ("T", _attr_T, "use_locking", use_locking, "update_slots", update_slots) _result = _execute.execute(b"ResourceApplyAdagrad", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, use_locking=False, name=None): r"""Update '*var' according to the proximal adagrad scheme. Args: var: A `Tensor` of type `resource`. Should be from a Variable(). gradient_accumulator: A `Tensor` of type `resource`. Should be from a Variable(). gradient_squared_accumulator: A `Tensor` of type `resource`. Should be from a Variable(). grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The gradient. lr: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `grad`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `grad`. L2 regularization. Must be a scalar. global_step: A `Tensor` of type `int64`. Training step number. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAdagradDA", name, _ctx._post_execution_callbacks, var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_adagrad_da_eager_fallback( var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAdagradDA", var=var, gradient_accumulator=gradient_accumulator, gradient_squared_accumulator=gradient_squared_accumulator, grad=grad, lr=lr, l1=l1, l2=l2, global_step=global_step, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_adagrad_da_eager_fallback(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_adagrad_da """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([grad, lr, l1, l2], _ctx) (grad, lr, l1, l2) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) gradient_accumulator = _ops.convert_to_tensor(gradient_accumulator, _dtypes.resource) gradient_squared_accumulator = _ops.convert_to_tensor(gradient_squared_accumulator, _dtypes.resource) global_step = _ops.convert_to_tensor(global_step, _dtypes.int64) _inputs_flat = [var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyAdagradDA", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_adam(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=False, use_nesterov=False, name=None): r"""Update '*var' according to the Adam algorithm. $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ Args: var: A `Tensor` of type `resource`. Should be from a Variable(). m: A `Tensor` of type `resource`. Should be from a Variable(). v: A `Tensor` of type `resource`. Should be from a Variable(). beta1_power: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Must be a scalar. beta2_power: A `Tensor`. Must have the same type as `beta1_power`. Must be a scalar. lr: A `Tensor`. Must have the same type as `beta1_power`. Scaling factor. Must be a scalar. beta1: A `Tensor`. Must have the same type as `beta1_power`. Momentum factor. Must be a scalar. beta2: A `Tensor`. Must have the same type as `beta1_power`. Momentum factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `beta1_power`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `beta1_power`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, uses the nesterov update. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAdam", name, _ctx._post_execution_callbacks, var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, "use_locking", use_locking, "use_nesterov", use_nesterov) return _result except _core._FallbackException: try: return resource_apply_adam_eager_fallback( var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=use_locking, use_nesterov=use_nesterov, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAdam", var=var, m=m, v=v, beta1_power=beta1_power, beta2_power=beta2_power, lr=lr, beta1=beta1, beta2=beta2, epsilon=epsilon, grad=grad, use_locking=use_locking, use_nesterov=use_nesterov, name=name) return _op _result = None return _result def resource_apply_adam_eager_fallback(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=False, use_nesterov=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_adam """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _attr_T, _inputs_T = _execute.args_to_matching_eager([beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad], _ctx) (beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) m = _ops.convert_to_tensor(m, _dtypes.resource) v = _ops.convert_to_tensor(v, _dtypes.resource) _inputs_flat = [var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad] _attrs = ("T", _attr_T, "use_locking", use_locking, "use_nesterov", use_nesterov) _result = _execute.execute(b"ResourceApplyAdam", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_adam_with_amsgrad(var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the Adam algorithm. $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ $$vhat_t := max{vhat_{t-1}, v_t}$$ $$variable := variable - lr_t * m_t / (\sqrt{vhat_t} + \epsilon)$$ Args: var: A `Tensor` of type `resource`. Should be from a Variable(). m: A `Tensor` of type `resource`. Should be from a Variable(). v: A `Tensor` of type `resource`. Should be from a Variable(). vhat: A `Tensor` of type `resource`. Should be from a Variable(). beta1_power: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Must be a scalar. beta2_power: A `Tensor`. Must have the same type as `beta1_power`. Must be a scalar. lr: A `Tensor`. Must have the same type as `beta1_power`. Scaling factor. Must be a scalar. beta1: A `Tensor`. Must have the same type as `beta1_power`. Momentum factor. Must be a scalar. beta2: A `Tensor`. Must have the same type as `beta1_power`. Momentum factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `beta1_power`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `beta1_power`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAdamWithAmsgrad", name, _ctx._post_execution_callbacks, var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_adam_with_amsgrad_eager_fallback( var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAdamWithAmsgrad", var=var, m=m, v=v, vhat=vhat, beta1_power=beta1_power, beta2_power=beta2_power, lr=lr, beta1=beta1, beta2=beta2, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_adam_with_amsgrad_eager_fallback(var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_adam_with_amsgrad """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad], _ctx) (beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) m = _ops.convert_to_tensor(m, _dtypes.resource) v = _ops.convert_to_tensor(v, _dtypes.resource) vhat = _ops.convert_to_tensor(vhat, _dtypes.resource) _inputs_flat = [var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyAdamWithAmsgrad", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_add_sign(var, m, lr, alpha, sign_decay, beta, grad, use_locking=False, name=None): r"""Update '*var' according to the AddSign update. m_t <- beta1 * m_{t-1} + (1 - beta1) * g update <- (alpha + sign_decay * sign(g) *sign(m)) * g variable <- variable - lr_t * update Args: var: A `Tensor` of type `resource`. Should be from a Variable(). m: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. alpha: A `Tensor`. Must have the same type as `lr`. Must be a scalar. sign_decay: A `Tensor`. Must have the same type as `lr`. Must be a scalar. beta: A `Tensor`. Must have the same type as `lr`. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and m tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyAddSign", name, _ctx._post_execution_callbacks, var, m, lr, alpha, sign_decay, beta, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_add_sign_eager_fallback( var, m, lr, alpha, sign_decay, beta, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyAddSign", var=var, m=m, lr=lr, alpha=alpha, sign_decay=sign_decay, beta=beta, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_add_sign_eager_fallback(var, m, lr, alpha, sign_decay, beta, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_add_sign """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, alpha, sign_decay, beta, grad], _ctx) (lr, alpha, sign_decay, beta, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) m = _ops.convert_to_tensor(m, _dtypes.resource) _inputs_flat = [var, m, lr, alpha, sign_decay, beta, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyAddSign", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) mg <- rho * mg_{t-1} + (1-rho) * grad ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom Args: var: A `Tensor` of type `resource`. Should be from a Variable(). mg: A `Tensor` of type `resource`. Should be from a Variable(). ms: A `Tensor` of type `resource`. Should be from a Variable(). mom: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `lr`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `lr`. epsilon: A `Tensor`. Must have the same type as `lr`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyCenteredRMSProp", name, _ctx._post_execution_callbacks, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_centered_rms_prop_eager_fallback( var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyCenteredRMSProp", var=var, mg=mg, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_centered_rms_prop_eager_fallback(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_centered_rms_prop """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, rho, momentum, epsilon, grad], _ctx) (lr, rho, momentum, epsilon, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) mg = _ops.convert_to_tensor(mg, _dtypes.resource) ms = _ops.convert_to_tensor(ms, _dtypes.resource) mom = _ops.convert_to_tensor(mom, _dtypes.resource) _inputs_flat = [var, mg, ms, mom, lr, rho, momentum, epsilon, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyCenteredRMSProp", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_ftrl(var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False, name=None): r"""Update '*var' according to the Ftrl-proximal scheme. accum_new = accum + grad * grad linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). linear: A `Tensor` of type `resource`. Should be from a Variable(). grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The gradient. lr: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `grad`. L1 regulariation. Must be a scalar. l2: A `Tensor`. Must have the same type as `grad`. L2 regulariation. Must be a scalar. lr_power: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyFtrl", name, _ctx._post_execution_callbacks, var, accum, linear, grad, lr, l1, l2, lr_power, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_ftrl_eager_fallback( var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyFtrl", var=var, accum=accum, linear=linear, grad=grad, lr=lr, l1=l1, l2=l2, lr_power=lr_power, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_ftrl_eager_fallback(var, accum, linear, grad, lr, l1, l2, lr_power, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_ftrl """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([grad, lr, l1, l2, lr_power], _ctx) (grad, lr, l1, l2, lr_power) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) linear = _ops.convert_to_tensor(linear, _dtypes.resource) _inputs_flat = [var, accum, linear, grad, lr, l1, l2, lr_power] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyFtrl", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_ftrl_v2(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None): r"""Update '*var' according to the Ftrl-proximal scheme. grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage linear += grad_with_shrinkage + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). linear: A `Tensor` of type `resource`. Should be from a Variable(). grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The gradient. lr: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `grad`. L1 regulariation. Must be a scalar. l2: A `Tensor`. Must have the same type as `grad`. L2 shrinkage regulariation. Must be a scalar. l2_shrinkage: A `Tensor`. Must have the same type as `grad`. lr_power: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyFtrlV2", name, _ctx._post_execution_callbacks, var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_ftrl_v2_eager_fallback( var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyFtrlV2", var=var, accum=accum, linear=linear, grad=grad, lr=lr, l1=l1, l2=l2, l2_shrinkage=l2_shrinkage, lr_power=lr_power, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_ftrl_v2_eager_fallback(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_ftrl_v2 """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([grad, lr, l1, l2, l2_shrinkage, lr_power], _ctx) (grad, lr, l1, l2, l2_shrinkage, lr_power) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) linear = _ops.convert_to_tensor(linear, _dtypes.resource) _inputs_flat = [var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyFtrlV2", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_gradient_descent(var, alpha, delta, use_locking=False, name=None): r"""Update '*var' by subtracting 'alpha' * 'delta' from it. Args: var: A `Tensor` of type `resource`. Should be from a Variable(). alpha: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. delta: A `Tensor`. Must have the same type as `alpha`. The change. use_locking: An optional `bool`. Defaults to `False`. If `True`, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyGradientDescent", name, _ctx._post_execution_callbacks, var, alpha, delta, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_gradient_descent_eager_fallback( var, alpha, delta, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyGradientDescent", var=var, alpha=alpha, delta=delta, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_gradient_descent_eager_fallback(var, alpha, delta, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_gradient_descent """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([alpha, delta], _ctx) (alpha, delta) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) _inputs_flat = [var, alpha, delta] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyGradientDescent", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_keras_momentum(var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None): r"""Update '*var' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum - lr * grad var += accum Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. momentum: A `Tensor`. Must have the same type as `lr`. Momentum. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var + momentum * accum, so in the end, the var you get is actually var + momentum * accum. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyKerasMomentum", name, _ctx._post_execution_callbacks, var, accum, lr, grad, momentum, "use_locking", use_locking, "use_nesterov", use_nesterov) return _result except _core._FallbackException: try: return resource_apply_keras_momentum_eager_fallback( var, accum, lr, grad, momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyKerasMomentum", var=var, accum=accum, lr=lr, grad=grad, momentum=momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name) return _op _result = None return _result def resource_apply_keras_momentum_eager_fallback(var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_keras_momentum """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, grad, momentum], _ctx) (lr, grad, momentum) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, grad, momentum] _attrs = ("T", _attr_T, "use_locking", use_locking, "use_nesterov", use_nesterov) _result = _execute.execute(b"ResourceApplyKerasMomentum", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_momentum(var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None): r"""Update '*var' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum + grad var -= lr * accum Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. momentum: A `Tensor`. Must have the same type as `lr`. Momentum. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyMomentum", name, _ctx._post_execution_callbacks, var, accum, lr, grad, momentum, "use_locking", use_locking, "use_nesterov", use_nesterov) return _result except _core._FallbackException: try: return resource_apply_momentum_eager_fallback( var, accum, lr, grad, momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyMomentum", var=var, accum=accum, lr=lr, grad=grad, momentum=momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name) return _op _result = None return _result def resource_apply_momentum_eager_fallback(var, accum, lr, grad, momentum, use_locking=False, use_nesterov=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_momentum """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, grad, momentum], _ctx) (lr, grad, momentum) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, grad, momentum] _attrs = ("T", _attr_T, "use_locking", use_locking, "use_nesterov", use_nesterov) _result = _execute.execute(b"ResourceApplyMomentum", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_power_sign(var, m, lr, logbase, sign_decay, beta, grad, use_locking=False, name=None): r"""Update '*var' according to the AddSign update. m_t <- beta1 * m_{t-1} + (1 - beta1) * g update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g variable <- variable - lr_t * update Args: var: A `Tensor` of type `resource`. Should be from a Variable(). m: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. logbase: A `Tensor`. Must have the same type as `lr`. Must be a scalar. sign_decay: A `Tensor`. Must have the same type as `lr`. Must be a scalar. beta: A `Tensor`. Must have the same type as `lr`. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and m tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyPowerSign", name, _ctx._post_execution_callbacks, var, m, lr, logbase, sign_decay, beta, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_power_sign_eager_fallback( var, m, lr, logbase, sign_decay, beta, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyPowerSign", var=var, m=m, lr=lr, logbase=logbase, sign_decay=sign_decay, beta=beta, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_power_sign_eager_fallback(var, m, lr, logbase, sign_decay, beta, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_power_sign """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, logbase, sign_decay, beta, grad], _ctx) (lr, logbase, sign_decay, beta, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) m = _ops.convert_to_tensor(m, _dtypes.resource) _inputs_flat = [var, m, lr, logbase, sign_decay, beta, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyPowerSign", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, use_locking=False, name=None): r"""Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `lr`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `lr`. L2 regularization. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyProximalAdagrad", name, _ctx._post_execution_callbacks, var, accum, lr, l1, l2, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_proximal_adagrad_eager_fallback( var, accum, lr, l1, l2, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyProximalAdagrad", var=var, accum=accum, lr=lr, l1=l1, l2=l2, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_proximal_adagrad_eager_fallback(var, accum, lr, l1, l2, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_proximal_adagrad """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, l1, l2, grad], _ctx) (lr, l1, l2, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, l1, l2, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyProximalAdagrad", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_proximal_gradient_descent(var, alpha, l1, l2, delta, use_locking=False, name=None): r"""Update '*var' as FOBOS algorithm with fixed learning rate. prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} Args: var: A `Tensor` of type `resource`. Should be from a Variable(). alpha: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `alpha`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `alpha`. L2 regularization. Must be a scalar. delta: A `Tensor`. Must have the same type as `alpha`. The change. use_locking: An optional `bool`. Defaults to `False`. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyProximalGradientDescent", name, _ctx._post_execution_callbacks, var, alpha, l1, l2, delta, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_proximal_gradient_descent_eager_fallback( var, alpha, l1, l2, delta, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyProximalGradientDescent", var=var, alpha=alpha, l1=l1, l2=l2, delta=delta, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_proximal_gradient_descent_eager_fallback(var, alpha, l1, l2, delta, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_proximal_gradient_descent """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([alpha, l1, l2, delta], _ctx) (alpha, l1, l2, delta) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) _inputs_flat = [var, alpha, l1, l2, delta] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyProximalGradientDescent", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None): r"""Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom Args: var: A `Tensor` of type `resource`. Should be from a Variable(). ms: A `Tensor` of type `resource`. Should be from a Variable(). mom: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `lr`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `lr`. epsilon: A `Tensor`. Must have the same type as `lr`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceApplyRMSProp", name, _ctx._post_execution_callbacks, var, ms, mom, lr, rho, momentum, epsilon, grad, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_apply_rms_prop_eager_fallback( var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceApplyRMSProp", var=var, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, use_locking=use_locking, name=name) return _op _result = None return _result def resource_apply_rms_prop_eager_fallback(var, ms, mom, lr, rho, momentum, epsilon, grad, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_apply_rms_prop """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, rho, momentum, epsilon, grad], _ctx) (lr, rho, momentum, epsilon, grad) = _inputs_T var = _ops.convert_to_tensor(var, _dtypes.resource) ms = _ops.convert_to_tensor(ms, _dtypes.resource) mom = _ops.convert_to_tensor(mom, _dtypes.resource) _inputs_flat = [var, ms, mom, lr, rho, momentum, epsilon, grad] _attrs = ("T", _attr_T, "use_locking", use_locking) _result = _execute.execute(b"ResourceApplyRMSProp", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False, name=None): r"""var: Should be from a Variable(). Args: var: A `Tensor` of type `resource`. accum: A `Tensor` of type `resource`. Should be from a Variable(). accum_update: A `Tensor` of type `resource`. : Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Learning rate. Must be a scalar. rho: A `Tensor`. Must have the same type as `lr`. Decay factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `lr`. Constant factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyAdadelta", name, _ctx._post_execution_callbacks, var, accum, accum_update, lr, rho, epsilon, grad, indices, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_adadelta_eager_fallback( var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyAdadelta", var=var, accum=accum, accum_update=accum_update, lr=lr, rho=rho, epsilon=epsilon, grad=grad, indices=indices, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_adadelta_eager_fallback(var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_adadelta """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, rho, epsilon, grad], _ctx) (lr, rho, epsilon, grad) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) accum_update = _ops.convert_to_tensor(accum_update, _dtypes.resource) _inputs_flat = [var, accum, accum_update, lr, rho, epsilon, grad, indices] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyAdadelta", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_adagrad(var, accum, lr, grad, indices, use_locking=False, update_slots=True, name=None): r"""Update relevant entries in '*var' and '*accum' according to the adagrad scheme. That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Learning rate. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. update_slots: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyAdagrad", name, _ctx._post_execution_callbacks, var, accum, lr, grad, indices, "use_locking", use_locking, "update_slots", update_slots) return _result except _core._FallbackException: try: return resource_sparse_apply_adagrad_eager_fallback( var, accum, lr, grad, indices, use_locking=use_locking, update_slots=update_slots, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if update_slots is None: update_slots = True update_slots = _execute.make_bool(update_slots, "update_slots") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyAdagrad", var=var, accum=accum, lr=lr, grad=grad, indices=indices, use_locking=use_locking, update_slots=update_slots, name=name) return _op _result = None return _result def resource_sparse_apply_adagrad_eager_fallback(var, accum, lr, grad, indices, use_locking=False, update_slots=True, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_adagrad """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if update_slots is None: update_slots = True update_slots = _execute.make_bool(update_slots, "update_slots") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, grad], _ctx) (lr, grad) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, grad, indices] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking, "update_slots", update_slots) _result = _execute.execute(b"ResourceSparseApplyAdagrad", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, use_locking=False, name=None): r"""Update entries in '*var' and '*accum' according to the proximal adagrad scheme. Args: var: A `Tensor` of type `resource`. Should be from a Variable(). gradient_accumulator: A `Tensor` of type `resource`. Should be from a Variable(). gradient_squared_accumulator: A `Tensor` of type `resource`. Should be from a Variable(). grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. lr: A `Tensor`. Must have the same type as `grad`. Learning rate. Must be a scalar. l1: A `Tensor`. Must have the same type as `grad`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `grad`. L2 regularization. Must be a scalar. global_step: A `Tensor` of type `int64`. Training step number. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyAdagradDA", name, _ctx._post_execution_callbacks, var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_adagrad_da_eager_fallback( var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyAdagradDA", var=var, gradient_accumulator=gradient_accumulator, gradient_squared_accumulator=gradient_squared_accumulator, grad=grad, indices=indices, lr=lr, l1=l1, l2=l2, global_step=global_step, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_adagrad_da_eager_fallback(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_adagrad_da """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([grad, lr, l1, l2], _ctx) (grad, lr, l1, l2) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) gradient_accumulator = _ops.convert_to_tensor(gradient_accumulator, _dtypes.resource) gradient_squared_accumulator = _ops.convert_to_tensor(gradient_squared_accumulator, _dtypes.resource) global_step = _ops.convert_to_tensor(global_step, _dtypes.int64) _inputs_flat = [var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyAdagradDA", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None): r"""Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom Args: var: A `Tensor` of type `resource`. Should be from a Variable(). mg: A `Tensor` of type `resource`. Should be from a Variable(). ms: A `Tensor` of type `resource`. Should be from a Variable(). mom: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `lr`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `lr`. epsilon: A `Tensor`. Must have the same type as `lr`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var, ms and mom. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyCenteredRMSProp", name, _ctx._post_execution_callbacks, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_centered_rms_prop_eager_fallback( var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyCenteredRMSProp", var=var, mg=mg, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, indices=indices, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_centered_rms_prop_eager_fallback(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_centered_rms_prop """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, rho, momentum, epsilon, grad], _ctx) (lr, rho, momentum, epsilon, grad) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) mg = _ops.convert_to_tensor(mg, _dtypes.resource) ms = _ops.convert_to_tensor(ms, _dtypes.resource) mom = _ops.convert_to_tensor(mom, _dtypes.resource) _inputs_flat = [var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyCenteredRMSProp", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_ftrl(var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False, name=None): r"""Update relevant entries in '*var' according to the Ftrl-proximal scheme. That is for rows we have grad for, we update var, accum and linear as follows: accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). linear: A `Tensor` of type `resource`. Should be from a Variable(). grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. lr: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `grad`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `grad`. L2 regularization. Must be a scalar. lr_power: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyFtrl", name, _ctx._post_execution_callbacks, var, accum, linear, grad, indices, lr, l1, l2, lr_power, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_ftrl_eager_fallback( var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyFtrl", var=var, accum=accum, linear=linear, grad=grad, indices=indices, lr=lr, l1=l1, l2=l2, lr_power=lr_power, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_ftrl_eager_fallback(var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_ftrl """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([grad, lr, l1, l2, lr_power], _ctx) (grad, lr, l1, l2, lr_power) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) linear = _ops.convert_to_tensor(linear, _dtypes.resource) _inputs_flat = [var, accum, linear, grad, indices, lr, l1, l2, lr_power] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyFtrl", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_ftrl_v2(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None): r"""Update relevant entries in '*var' according to the Ftrl-proximal scheme. That is for rows we have grad for, we update var, accum and linear as follows: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage linear += grad_with_shrinkage + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). linear: A `Tensor` of type `resource`. Should be from a Variable(). grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. lr: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `grad`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `grad`. L2 shrinkage regulariation. Must be a scalar. l2_shrinkage: A `Tensor`. Must have the same type as `grad`. lr_power: A `Tensor`. Must have the same type as `grad`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyFtrlV2", name, _ctx._post_execution_callbacks, var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_ftrl_v2_eager_fallback( var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyFtrlV2", var=var, accum=accum, linear=linear, grad=grad, indices=indices, lr=lr, l1=l1, l2=l2, l2_shrinkage=l2_shrinkage, lr_power=lr_power, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_ftrl_v2_eager_fallback(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_ftrl_v2 """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([grad, lr, l1, l2, l2_shrinkage, lr_power], _ctx) (grad, lr, l1, l2, l2_shrinkage, lr_power) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) linear = _ops.convert_to_tensor(linear, _dtypes.resource) _inputs_flat = [var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyFtrlV2", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_keras_momentum(var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False, name=None): r"""Update relevant entries in '*var' and '*accum' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. That is for rows we have grad for, we update var and accum as follows: accum = accum * momentum - lr * grad var += accum Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Learning rate. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. momentum: A `Tensor`. Must have the same type as `lr`. Momentum. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var + momentum * accum, so in the end, the var you get is actually var + momentum * accum. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyKerasMomentum", name, _ctx._post_execution_callbacks, var, accum, lr, grad, indices, momentum, "use_locking", use_locking, "use_nesterov", use_nesterov) return _result except _core._FallbackException: try: return resource_sparse_apply_keras_momentum_eager_fallback( var, accum, lr, grad, indices, momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyKerasMomentum", var=var, accum=accum, lr=lr, grad=grad, indices=indices, momentum=momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name) return _op _result = None return _result def resource_sparse_apply_keras_momentum_eager_fallback(var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_keras_momentum """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, grad, momentum], _ctx) (lr, grad, momentum) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, grad, indices, momentum] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking, "use_nesterov", use_nesterov) _result = _execute.execute(b"ResourceSparseApplyKerasMomentum", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_momentum(var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False, name=None): r"""Update relevant entries in '*var' and '*accum' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. That is for rows we have grad for, we update var and accum as follows: accum = accum * momentum + grad var -= lr * accum Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Learning rate. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. momentum: A `Tensor`. Must have the same type as `lr`. Momentum. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyMomentum", name, _ctx._post_execution_callbacks, var, accum, lr, grad, indices, momentum, "use_locking", use_locking, "use_nesterov", use_nesterov) return _result except _core._FallbackException: try: return resource_sparse_apply_momentum_eager_fallback( var, accum, lr, grad, indices, momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyMomentum", var=var, accum=accum, lr=lr, grad=grad, indices=indices, momentum=momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name) return _op _result = None return _result def resource_sparse_apply_momentum_eager_fallback(var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_momentum """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, grad, momentum], _ctx) (lr, grad, momentum) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, grad, indices, momentum] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking, "use_nesterov", use_nesterov) _result = _execute.execute(b"ResourceSparseApplyMomentum", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, indices, use_locking=False, name=None): r"""Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. That is for rows we have grad for, we update var and accum as follows: accum += grad * grad prox_v = var prox_v -= lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} Args: var: A `Tensor` of type `resource`. Should be from a Variable(). accum: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Learning rate. Must be a scalar. l1: A `Tensor`. Must have the same type as `lr`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `lr`. L2 regularization. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyProximalAdagrad", name, _ctx._post_execution_callbacks, var, accum, lr, l1, l2, grad, indices, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_proximal_adagrad_eager_fallback( var, accum, lr, l1, l2, grad, indices, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyProximalAdagrad", var=var, accum=accum, lr=lr, l1=l1, l2=l2, grad=grad, indices=indices, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_proximal_adagrad_eager_fallback(var, accum, lr, l1, l2, grad, indices, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_proximal_adagrad """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, l1, l2, grad], _ctx) (lr, l1, l2, grad) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) accum = _ops.convert_to_tensor(accum, _dtypes.resource) _inputs_flat = [var, accum, lr, l1, l2, grad, indices] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyProximalAdagrad", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_proximal_gradient_descent(var, alpha, l1, l2, grad, indices, use_locking=False, name=None): r"""Sparse update '*var' as FOBOS algorithm with fixed learning rate. That is for rows we have grad for, we update var as follows: prox_v = var - alpha * grad var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} Args: var: A `Tensor` of type `resource`. Should be from a Variable(). alpha: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `alpha`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `alpha`. L2 regularization. Must be a scalar. grad: A `Tensor`. Must have the same type as `alpha`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyProximalGradientDescent", name, _ctx._post_execution_callbacks, var, alpha, l1, l2, grad, indices, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_proximal_gradient_descent_eager_fallback( var, alpha, l1, l2, grad, indices, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyProximalGradientDescent", var=var, alpha=alpha, l1=l1, l2=l2, grad=grad, indices=indices, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_proximal_gradient_descent_eager_fallback(var, alpha, l1, l2, grad, indices, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_proximal_gradient_descent """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([alpha, l1, l2, grad], _ctx) (alpha, l1, l2, grad) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) _inputs_flat = [var, alpha, l1, l2, grad, indices] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyProximalGradientDescent", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def resource_sparse_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None): r"""Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom Args: var: A `Tensor` of type `resource`. Should be from a Variable(). ms: A `Tensor` of type `resource`. Should be from a Variable(). mom: A `Tensor` of type `resource`. Should be from a Variable(). lr: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `lr`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `lr`. epsilon: A `Tensor`. Must have the same type as `lr`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `lr`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var, ms and mom. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: The created Operation. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "ResourceSparseApplyRMSProp", name, _ctx._post_execution_callbacks, var, ms, mom, lr, rho, momentum, epsilon, grad, indices, "use_locking", use_locking) return _result except _core._FallbackException: try: return resource_sparse_apply_rms_prop_eager_fallback( var, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=use_locking, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "ResourceSparseApplyRMSProp", var=var, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, indices=indices, use_locking=use_locking, name=name) return _op _result = None return _result def resource_sparse_apply_rms_prop_eager_fallback(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function resource_sparse_apply_rms_prop """ _ctx = ctx if ctx else _context.context() if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _attr_T, _inputs_T = _execute.args_to_matching_eager([lr, rho, momentum, epsilon, grad], _ctx) (lr, rho, momentum, epsilon, grad) = _inputs_T _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx) var = _ops.convert_to_tensor(var, _dtypes.resource) ms = _ops.convert_to_tensor(ms, _dtypes.resource) mom = _ops.convert_to_tensor(mom, _dtypes.resource) _inputs_flat = [var, ms, mom, lr, rho, momentum, epsilon, grad, indices] _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking", use_locking) _result = _execute.execute(b"ResourceSparseApplyRMSProp", 0, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _result = None return _result def sparse_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False, name=None): r"""var: Should be from a Variable(). Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). accum_update: A mutable `Tensor`. Must have the same type as `var`. : Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Learning rate. Must be a scalar. rho: A `Tensor`. Must have the same type as `var`. Decay factor. Must be a scalar. epsilon: A `Tensor`. Must have the same type as `var`. Constant factor. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_adadelta op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyAdadelta", var=var, accum=accum, accum_update=accum_update, lr=lr, rho=rho, epsilon=epsilon, grad=grad, indices=indices, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyAdadelta", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_adadelta_eager_fallback(var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_adadelta op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_adagrad(var, accum, lr, grad, indices, use_locking=False, update_slots=True, name=None): r"""Update relevant entries in '*var' and '*accum' according to the adagrad scheme. That is for rows we have grad for, we update var and accum as follows: $$accum += grad * grad$$ $$var -= lr * grad * (1 / sqrt(accum))$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Learning rate. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. update_slots: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_adagrad op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if update_slots is None: update_slots = True update_slots = _execute.make_bool(update_slots, "update_slots") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyAdagrad", var=var, accum=accum, lr=lr, grad=grad, indices=indices, use_locking=use_locking, update_slots=update_slots, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking"), "update_slots", _op.get_attr("update_slots")) _execute.record_gradient( "SparseApplyAdagrad", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_adagrad_eager_fallback(var, accum, lr, grad, indices, use_locking=False, update_slots=True, name=None, ctx=None): raise RuntimeError("sparse_apply_adagrad op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, use_locking=False, name=None): r"""Update entries in '*var' and '*accum' according to the proximal adagrad scheme. Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). gradient_accumulator: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). gradient_squared_accumulator: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. lr: A `Tensor`. Must have the same type as `var`. Learning rate. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. global_step: A `Tensor` of type `int64`. Training step number. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_adagrad_da op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyAdagradDA", var=var, gradient_accumulator=gradient_accumulator, gradient_squared_accumulator=gradient_squared_accumulator, grad=grad, indices=indices, lr=lr, l1=l1, l2=l2, global_step=global_step, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyAdagradDA", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_adagrad_da_eager_fallback(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_adagrad_da op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None): r"""Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) $$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$ $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ $$var <- var - mom$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). mg: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). ms: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). mom: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `var`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `var`. epsilon: A `Tensor`. Must have the same type as `var`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var, ms and mom. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_centered_rms_prop op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyCenteredRMSProp", var=var, mg=mg, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, indices=indices, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyCenteredRMSProp", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_centered_rms_prop_eager_fallback(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_centered_rms_prop op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_ftrl(var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False, name=None): r"""Update relevant entries in '*var' according to the Ftrl-proximal scheme. That is for rows we have grad for, we update var, accum and linear as follows: $$accum_new = accum + grad * grad$$ $$linear += grad + (accum_{new}^{-lr_{power}} - accum^{-lr_{power}} / lr * var$$ $$quadratic = 1.0 / (accum_{new}^{lr_{power}} * lr) + 2 * l2$$ $$var = (sign(linear) * l1 - linear) / quadratic\ if\ |linear| > l1\ else\ 0.0$$ $$accum = accum_{new}$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). linear: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. lr_power: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_ftrl op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyFtrl", var=var, accum=accum, linear=linear, grad=grad, indices=indices, lr=lr, l1=l1, l2=l2, lr_power=lr_power, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyFtrl", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_ftrl_eager_fallback(var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_ftrl op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_ftrl_v2(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None): r"""Update relevant entries in '*var' according to the Ftrl-proximal scheme. That is for rows we have grad for, we update var, accum and linear as follows: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage linear += grad_with_shrinkage + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). linear: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 shrinkage regulariation. Must be a scalar. l2_shrinkage: A `Tensor`. Must have the same type as `var`. lr_power: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_ftrl_v2 op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyFtrlV2", var=var, accum=accum, linear=linear, grad=grad, indices=indices, lr=lr, l1=l1, l2=l2, l2_shrinkage=l2_shrinkage, lr_power=lr_power, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyFtrlV2", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_ftrl_v2_eager_fallback(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_ftrl_v2 op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_momentum(var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False, name=None): r"""Update relevant entries in '*var' and '*accum' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. That is for rows we have grad for, we update var and accum as follows: $$accum = accum * momentum + grad$$ $$var -= lr * accum$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Learning rate. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. momentum: A `Tensor`. Must have the same type as `var`. Momentum. Must be a scalar. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_momentum op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") if use_nesterov is None: use_nesterov = False use_nesterov = _execute.make_bool(use_nesterov, "use_nesterov") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyMomentum", var=var, accum=accum, lr=lr, grad=grad, indices=indices, momentum=momentum, use_locking=use_locking, use_nesterov=use_nesterov, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking"), "use_nesterov", _op.get_attr("use_nesterov")) _execute.record_gradient( "SparseApplyMomentum", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_momentum_eager_fallback(var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False, name=None, ctx=None): raise RuntimeError("sparse_apply_momentum op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, indices, use_locking=False, name=None): r"""Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. That is for rows we have grad for, we update var and accum as follows: $$accum += grad * grad$$ $$prox_v = var$$ $$prox_v -= lr * grad * (1 / sqrt(accum))$$ $$var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). accum: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Learning rate. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_proximal_adagrad op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyProximalAdagrad", var=var, accum=accum, lr=lr, l1=l1, l2=l2, grad=grad, indices=indices, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyProximalAdagrad", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_proximal_adagrad_eager_fallback(var, accum, lr, l1, l2, grad, indices, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_proximal_adagrad op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_proximal_gradient_descent(var, alpha, l1, l2, grad, indices, use_locking=False, name=None): r"""Sparse update '*var' as FOBOS algorithm with fixed learning rate. That is for rows we have grad for, we update var as follows: $$prox_v = var - alpha * grad$$ $$var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). alpha: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. l1: A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. l2: A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. use_locking: An optional `bool`. Defaults to `False`. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_proximal_gradient_descent op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyProximalGradientDescent", var=var, alpha=alpha, l1=l1, l2=l2, grad=grad, indices=indices, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyProximalGradientDescent", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_proximal_gradient_descent_eager_fallback(var, alpha, l1, l2, grad, indices, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_proximal_gradient_descent op does not support eager execution. Arg 'out' is a ref.") def sparse_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None): r"""Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) $$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$ $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$ $$var <- var - mom$$ Args: var: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Should be from a Variable(). ms: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). mom: A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). lr: A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. rho: A `Tensor`. Must have the same type as `var`. Decay rate. Must be a scalar. momentum: A `Tensor`. Must have the same type as `var`. epsilon: A `Tensor`. Must have the same type as `var`. Ridge term. Must be a scalar. grad: A `Tensor`. Must have the same type as `var`. The gradient. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var, ms and mom. use_locking: An optional `bool`. Defaults to `False`. If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `var`. """ _ctx = _context._context if _ctx is not None and _ctx._eager_context.is_eager: raise RuntimeError("sparse_apply_rms_prop op does not support eager execution. Arg 'out' is a ref.") # Add nodes to the TensorFlow graph. if use_locking is None: use_locking = False use_locking = _execute.make_bool(use_locking, "use_locking") _, _, _op = _op_def_lib._apply_op_helper( "SparseApplyRMSProp", var=var, ms=ms, mom=mom, lr=lr, rho=rho, momentum=momentum, epsilon=epsilon, grad=grad, indices=indices, use_locking=use_locking, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"), "use_locking", _op.get_attr("use_locking")) _execute.record_gradient( "SparseApplyRMSProp", _inputs_flat, _attrs, _result, name) _result, = _result return _result def sparse_apply_rms_prop_eager_fallback(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, use_locking=False, name=None, ctx=None): raise RuntimeError("sparse_apply_rms_prop op does not support eager execution. Arg 'out' is a ref.") def _InitOpDefLibrary(op_list_proto_bytes): op_list = _op_def_pb2.OpList() op_list.ParseFromString(op_list_proto_bytes) _op_def_registry.register_op_list(op_list) op_def_lib = _op_def_library.OpDefLibrary() op_def_lib.add_op_list(op_list) return op_def_lib # op { # name: "ApplyAdaMax" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "m" # type_attr: "T" # is_ref: true # } # input_arg { # name: "v" # type_attr: "T" # is_ref: true # } # input_arg { # name: "beta1_power" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "beta1" # type_attr: "T" # } # input_arg { # name: "beta2" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyAdadelta" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum_update" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "rho" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyAdagrad" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # attr { # name: "update_slots" # type: "bool" # default_value { # b: true # } # } # } # op { # name: "ApplyAdagradDA" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "gradient_accumulator" # type_attr: "T" # is_ref: true # } # input_arg { # name: "gradient_squared_accumulator" # type_attr: "T" # is_ref: true # } # input_arg { # name: "grad" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "l1" # type_attr: "T" # } # input_arg { # name: "l2" # type_attr: "T" # } # input_arg { # name: "global_step" # type: DT_INT64 # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyAdam" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "m" # type_attr: "T" # is_ref: true # } # input_arg { # name: "v" # type_attr: "T" # is_ref: true # } # input_arg { # name: "beta1_power" # type_attr: "T" # } # input_arg { # name: "beta2_power" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "beta1" # type_attr: "T" # } # input_arg { # name: "beta2" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # attr { # name: "use_nesterov" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyAddSign" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "m" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "alpha" # type_attr: "T" # } # input_arg { # name: "sign_decay" # type_attr: "T" # } # input_arg { # name: "beta" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyCenteredRMSProp" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "mg" # type_attr: "T" # is_ref: true # } # input_arg { # name: "ms" # type_attr: "T" # is_ref: true # } # input_arg { # name: "mom" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "rho" # type_attr: "T" # } # input_arg { # name: "momentum" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyFtrl" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum" # type_attr: "T" # is_ref: true # } # input_arg { # name: "linear" # type_attr: "T" # is_ref: true # } # input_arg { # name: "grad" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "l1" # type_attr: "T" # } # input_arg { # name: "l2" # type_attr: "T" # } # input_arg { # name: "lr_power" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyFtrlV2" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum" # type_attr: "T" # is_ref: true # } # input_arg { # name: "linear" # type_attr: "T" # is_ref: true # } # input_arg { # name: "grad" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "l1" # type_attr: "T" # } # input_arg { # name: "l2" # type_attr: "T" # } # input_arg { # name: "l2_shrinkage" # type_attr: "T" # } # input_arg { # name: "lr_power" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyGradientDescent" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "alpha" # type_attr: "T" # } # input_arg { # name: "delta" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyMomentum" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # input_arg { # name: "momentum" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # attr { # name: "use_nesterov" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyPowerSign" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "m" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "logbase" # type_attr: "T" # } # input_arg { # name: "sign_decay" # type_attr: "T" # } # input_arg { # name: "beta" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyProximalAdagrad" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "accum" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "l1" # type_attr: "T" # } # input_arg { # name: "l2" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyProximalGradientDescent" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "alpha" # type_attr: "T" # } # input_arg { # name: "l1" # type_attr: "T" # } # input_arg { # name: "l2" # type_attr: "T" # } # input_arg { # name: "delta" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ApplyRMSProp" # input_arg { # name: "var" # type_attr: "T" # is_ref: true # } # input_arg { # name: "ms" # type_attr: "T" # is_ref: true # } # input_arg { # name: "mom" # type_attr: "T" # is_ref: true # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "rho" # type_attr: "T" # } # input_arg { # name: "momentum" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # output_arg { # name: "out" # type_attr: "T" # is_ref: true # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # } # op { # name: "ResourceApplyAdaMax" # input_arg { # name: "var" # type: DT_RESOURCE # } # input_arg { # name: "m" # type: DT_RESOURCE # } # input_arg { # name: "v" # type: DT_RESOURCE # } # input_arg { # name: "beta1_power" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "beta1" # type_attr: "T" # } # input_arg { # name: "beta2" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # is_stateful: true # } # op { # name: "ResourceApplyAdadelta" # input_arg { # name: "var" # type: DT_RESOURCE # } # input_arg { # name: "accum" # type: DT_RESOURCE # } # input_arg { # name: "accum_update" # type: DT_RESOURCE # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "rho" # type_attr: "T" # } # input_arg { # name: "epsilon" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # is_stateful: true # } # op { # name: "ResourceApplyAdagrad" # input_arg { # name: "var" # type: DT_RESOURCE # } # input_arg { # name: "accum" # type: DT_RESOURCE # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: "grad" # type_attr: "T" # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_FLOAT # type: DT_DOUBLE # type: DT_INT32 # type: DT_UINT8 # type: DT_INT16 # type: DT_INT8 # type: DT_COMPLEX64 # type: DT_INT64 # type: DT_QINT8 # type: DT_QUINT8 # type: DT_QINT32 # type: DT_BFLOAT16 # type: DT_UINT16 # type: DT_COMPLEX128 # type: DT_HALF # type: DT_UINT32 # type: DT_UINT64 # } # } # } # attr { # name: "use_locking" # type: "bool" # default_value { # b: false # } # } # attr { # name: "update_slots" # type: "bool" # default_value { # b: true # } # } # is_stateful: true # } # op { # name: "ResourceApplyAdagradDA" # input_arg { # name: "var" # type: DT_RESOURCE # } # input_arg { # name: "gradient_accumulator" # type: DT_RESOURCE # } # input_arg { # name: "gradient_squared_accumulator" # type: DT_RESOURCE # } # input_arg { # name: "grad" # type_attr: "T" # } # input_arg { # name: "lr" # type_attr: "T" # } # input_arg { # name: 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"use_locking" # type: "bool" # default_value { # b: false # } # } # } _op_def_lib = _InitOpDefLibrary(b"\n\304\001\n\013ApplyAdaMax\022\013\n\003var\"\001T\200\001\001\022\t\n\001m\"\001T\200\001\001\022\t\n\001v\"\001T\200\001\001\022\020\n\013beta1_power\"\001T\022\007\n\002lr\"\001T\022\n\n\005beta1\"\001T\022\n\n\005beta2\"\001T\022\014\n\007epsilon\"\001T\022\t\n\004grad\"\001T\032\013\n\003out\"\001T\200\001\001\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\027\n\013use_locking\022\004bool\032\002(\000\n\265\001\n\rApplyAdadelta\022\013\n\003var\"\001T\200\001\001\022\r\n\005accum\"\001T\200\001\001\022\024\n\014accum_update\"\001T\200\001\001\022\007\n\002lr\"\001T\022\010\n\003rho\"\001T\022\014\n\007epsilon\"\001T\022\t\n\004grad\"\001T\032\013\n\003out\"\001T\200\001\001\" 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\n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\210\001\001\n\360\001\n\031ResourceSparseApplyFtrlV2\022\007\n\003var\030\024\022\t\n\005accum\030\024\022\n\n\006linear\030\024\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\022\007\n\002lr\"\001T\022\007\n\002l1\"\001T\022\007\n\002l2\"\001T\022\021\n\014l2_shrinkage\"\001T\022\r\n\010lr_power\"\001T\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\210\001\001\n\340\001\n ResourceSparseApplyKerasMomentum\022\007\n\003var\030\024\022\t\n\005accum\030\024\022\007\n\002lr\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\022\r\n\010momentum\"\001T\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\"\030\n\014use_nesterov\022\004bool\032\002(\000\210\001\001\n\333\001\n\033ResourceSparseApplyMomentum\022\007\n\003var\030\024\022\t\n\005accum\030\024\022\007\n\002lr\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\022\r\n\010momentum\"\001T\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\"\030\n\014use_nesterov\022\004bool\032\002(\000\210\001\001\n\313\001\n\"ResourceSparseApplyProximalAdagrad\022\007\n\003var\030\024\022\t\n\005accum\030\024\022\007\n\002lr\"\001T\022\007\n\002l1\"\001T\022\007\n\002l2\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\" 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\n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\n\200\002\n\032SparseApplyCenteredRMSProp\022\013\n\003var\"\001T\200\001\001\022\n\n\002mg\"\001T\200\001\001\022\n\n\002ms\"\001T\200\001\001\022\013\n\003mom\"\001T\200\001\001\022\007\n\002lr\"\001T\022\010\n\003rho\"\001T\022\r\n\010momentum\"\001T\022\014\n\007epsilon\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\032\013\n\003out\"\001T\200\001\001\" 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\n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\n\376\001\n\021SparseApplyFtrlV2\022\013\n\003var\"\001T\200\001\001\022\r\n\005accum\"\001T\200\001\001\022\016\n\006linear\"\001T\200\001\001\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\022\007\n\002lr\"\001T\022\007\n\002l1\"\001T\022\007\n\002l2\"\001T\022\021\n\014l2_shrinkage\"\001T\022\r\n\010lr_power\"\001T\032\013\n\003out\"\001T\200\001\001\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\n\345\001\n\023SparseApplyMomentum\022\013\n\003var\"\001T\200\001\001\022\r\n\005accum\"\001T\200\001\001\022\007\n\002lr\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\022\r\n\010momentum\"\001T\032\013\n\003out\"\001T\200\001\001\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\"\030\n\014use_nesterov\022\004bool\032\002(\000\n\325\001\n\032SparseApplyProximalAdagrad\022\013\n\003var\"\001T\200\001\001\022\r\n\005accum\"\001T\200\001\001\022\007\n\002lr\"\001T\022\007\n\002l1\"\001T\022\007\n\002l2\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\032\013\n\003out\"\001T\200\001\001\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\n\321\001\n\"SparseApplyProximalGradientDescent\022\013\n\003var\"\001T\200\001\001\022\n\n\005alpha\"\001T\022\007\n\002l1\"\001T\022\007\n\002l2\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\032\013\n\003out\"\001T\200\001\001\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000\n\354\001\n\022SparseApplyRMSProp\022\013\n\003var\"\001T\200\001\001\022\n\n\002ms\"\001T\200\001\001\022\013\n\003mom\"\001T\200\001\001\022\007\n\002lr\"\001T\022\010\n\003rho\"\001T\022\r\n\010momentum\"\001T\022\014\n\007epsilon\"\001T\022\t\n\004grad\"\001T\022\023\n\007indices\"\010Tindices\032\013\n\003out\"\001T\200\001\001\" \n\001T\022\004type:\025\n\0232\021\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\"\030\n\010Tindices\022\004type:\006\n\0042\002\003\t\"\027\n\013use_locking\022\004bool\032\002(\000")

36.830179

23,622

0.609195

16355f52eea0add171343a06f37239cbd62c3878

7,892

py

Python

external/eospy/eospy/keys.py

unification-com/haiku-node-prototype

ea77aa90f6b3f08d004be1c24e6b8d62e83bc66b

[ "MIT" ]

3

2018-06-15T18:02:05.000Z

2018-07-06T02:32:18.000Z

external/eospy/eospy/keys.py

unification-com/haiku-node-prototype

ea77aa90f6b3f08d004be1c24e6b8d62e83bc66b

[ "MIT" ]

4

2018-08-17T06:51:34.000Z

2018-08-17T08:39:24.000Z

external/eospy/eospy/keys.py

unification-com/haiku-node-prototype

ea77aa90f6b3f08d004be1c24e6b8d62e83bc66b

[ "MIT" ]

null

import base58 import os import ecdsa import re from binascii import hexlify, unhexlify from .utils import sha256, ripemd160, str_to_hex, hex_to_int import hashlib import time import struct def check_wif(key) : if isinstance(key, str) : try : EOSKey(key) return True except Exception as ex: pass return False class EOSKey : def __init__(self, private_str='') : ''' ''' if private_str : private_key, format, key_type = self._parse_key(private_str) self._sk = ecdsa.SigningKey.from_string(unhexlify(private_key), curve=ecdsa.SECP256k1) else : prng = self._create_entropy() self._sk = ecdsa.SigningKey.generate(curve=ecdsa.SECP256k1, entropy=prng) self._vk = self._sk.get_verifying_key() def __str__(self) : return self.to_public() def _parse_key(self, private_str) : ''' ''' match = re.search('^PVT_([A-Za-z0-9]+)_([A-Za-z0-9]+)$', private_str) if not match : # legacy WIF - format version_key = self._check_decode(private_str, 'sha256x2') # ensure first 2 chars == 0x80 version = int(version_key[0:2],16) if not version == 0x80 : raise ValueError('Expected version 0x80, instead got {0}', version) private_key = version_key[2:] key_type = 'K1' format = 'WIF' else : key_type, key_string = match.groups() private_key = self._check_decode(key_string, key_type) format = 'PVT' return (private_key, format, key_type) def _create_entropy(self) : ''' ''' ba = bytearray(os.urandom(32)) seed = sha256(ba) return ecdsa.util.PRNG(seed) def _check_encode(self, key_buffer, key_type=None) : ''' ''' if isinstance(key_buffer, bytes) : key_buffer = key_buffer.decode() check = key_buffer if key_type == 'sha256x2' : first_sha = sha256(unhexlify(check)) chksum = sha256(unhexlify(first_sha))[:8] else : if key_type : check += hexlify(bytearray(key_type,'utf-8')).decode() chksum = ripemd160(unhexlify(check))[:8] return base58.b58encode(unhexlify(key_buffer+chksum)) def _check_decode(self, key_string, key_type=None) : ''' ''' buffer = hexlify(base58.b58decode(key_string)).decode() chksum = buffer[-8:] key = buffer[:-8] if key_type == 'sha256x2' : # legacy first_sha = sha256(unhexlify(key)) newChk = sha256(unhexlify(first_sha))[:8] else : check = key if key_type : check += hexlify(bytearray(key_type, 'utf-8')).decode() newChk = ripemd160(unhexlify(check))[:8] #print('newChk: '+newChk) if chksum != newChk : raise ValueError('checksums do not match: {0} != {1}'.format(chksum, newChk)) return key def _recover_key(self, digest, signature, i) : ''' Recover the public key from the sig http://www.secg.org/sec1-v2.pdf ''' curve = ecdsa.SECP256k1.curve G = ecdsa.SECP256k1.generator order = ecdsa.SECP256k1.order yp = (i %2) r, s = ecdsa.util.sigdecode_string(signature, order) x = r + (i // 2 ) * order alpha = ((x * x * x) + (curve.a() * x) + curve.b()) % curve.p() beta = ecdsa.numbertheory.square_root_mod_prime(alpha, curve.p()) y = beta if (beta - yp) % 2 == 0 else curve.p() - beta # generate R R = ecdsa.ellipticcurve.Point(curve, x, y, order) e = ecdsa.util.string_to_number(digest) # compute Q Q = ecdsa.numbertheory.inverse_mod(r, order) * (s * R + (-e % order) * G) # verify message if not ecdsa.VerifyingKey.from_public_point(Q, curve=ecdsa.SECP256k1).verify_digest(signature, digest, sigdecode=ecdsa.util.sigdecode_string) : return None return ecdsa.VerifyingKey.from_public_point(Q, curve=ecdsa.SECP256k1) def _recovery_pubkey_param(self, digest, signature) : ''' Use to derive a number that will allow for the easy recovery of the public key from the signature ''' for i in range(0,4) : p = self._recover_key(digest, signature, i) if (p.to_string() == self._vk.to_string() ) : return i def _compress_pubkey(self) : ''' ''' order = self._sk.curve.generator.order() p = self._vk.pubkey.point x_str = ecdsa.util.number_to_string(p.x(), order) hex_data = bytearray(chr(2 + (p.y() & 1)), 'utf-8') compressed = hexlify(hex_data + x_str).decode() return compressed def to_public(self) : ''' ''' cmp = self._compress_pubkey() return 'EOS' + self._check_encode(cmp).decode() def to_wif(self) : ''' ''' pri_key = '80' + hexlify(self._sk.to_string()).decode() return self._check_encode(pri_key, 'sha256x2').decode() def sign(self, digest) : ''' ''' cnt = 0 # convert digest to hex string digest = unhexlify(digest) while 1 : cnt +=1 if not cnt % 10 : print('Still searching for a signature. Tried {} times.'.format(cnt)) # get deterministic k k = ecdsa.rfc6979.generate_k( self._sk.curve.generator.order(), self._sk.privkey.secret_multiplier, hashlib.sha256, bytearray(sha256(digest + struct.pack('d', time.time())), 'utf-8') # use time to randomize ) # sign the message sigder = self._sk.sign_digest(digest, sigencode=ecdsa.util.sigencode_der, k=k) # reformat sig r, s = ecdsa.util.sigdecode_der(sigder, self._sk.curve.generator.order()) sig = ecdsa.util.sigencode_string(r, s, self._sk.curve.generator.order()) # ensure signature is canonical if isinstance(sigder[5],int) : lenR = sigder[3] else : lenR = str_to_hex(sigder[3]) if isinstance(sigder[5 + lenR], int) : lenS = sigder[5 + lenR] else : lenS = str_to_hex(sigder[5 + lenR]) if lenR is 32 and lenS is 32 : # derive recover parameter i = self._recovery_pubkey_param(digest, sig) # compressed i += 4 # compact i += 27 break # pack sigstr = struct.pack('<B', i) + sig # encode return 'SIG_K1_' + self._check_encode(hexlify(sigstr), 'K1').decode() def verify(self, encoded_sig, digest) : ''' ''' # remove SIG_ prefix encoded_sig = encoded_sig[4:] # remove curve prefix curvePre = encoded_sig[:3].strip('_') if curvePre != 'K1' : raise TypeError('Unsupported curve prefix {}'.format(curvePre)) decoded_sig = self._check_decode(encoded_sig[3:], curvePre) # first 2 bytes are recover param recover_param = hex_to_int(decoded_sig[:2]) - 4 - 27 # use sig sig = decoded_sig[2:] # verify sig p = self._recover_key(unhexlify(digest), unhexlify(sig), recover_param) return p.verify_digest(unhexlify(sig), unhexlify(digest), sigdecode=ecdsa.util.sigdecode_string)

38.125604

132

0.542448

2dc69b5f4275014944d2c32708689e9b9af13f42

103

py

Python

Chapter03/forking_process.py

ibiscum/Learning-Concurrency-in-Python

d3f0320ad2a80c46b37de331bf335b80df0d3ed9

[ "MIT" ]

null

Chapter03/forking_process.py

ibiscum/Learning-Concurrency-in-Python

d3f0320ad2a80c46b37de331bf335b80df0d3ed9

[ "MIT" ]

null

Chapter03/forking_process.py

ibiscum/Learning-Concurrency-in-Python

d3f0320ad2a80c46b37de331bf335b80df0d3ed9

[ "MIT" ]

null

import time import multiprocess def my_process(): print("My Process Starting") time.sleep(5)

12.875

32

0.708738

2b23102b80e801618580ecb628d9431c2efc066b

15,018

py

Python

examples/stencil_grid/stencil_kernel.py

zanellia/ctree

d53d29d972ac06b60d8fc4fb81f5553d2ffbfda0

[ "BSD-2-Clause" ]

16

2015-01-11T21:10:26.000Z

2021-09-28T11:39:56.000Z

examples/stencil_grid/stencil_kernel.py

zanellia/ctree

d53d29d972ac06b60d8fc4fb81f5553d2ffbfda0

[ "BSD-2-Clause" ]

12

2015-01-01T01:55:49.000Z

2019-06-23T16:41:12.000Z

examples/stencil_grid/stencil_kernel.py

mbdriscoll/ctree

e15538ecdf3aaa3bb2f210701d29334fd5e4ec40

[ "BSD-2-Clause" ]

4

2015-07-23T20:48:36.000Z

2021-08-02T12:17:37.000Z

""" This version was taken from the stencil_specializer project and has all asp stuff removed in order to work on a direct c-tree llvm implementation The main driver, intercepts the kernel() call and invokes the other components. Stencil kernel classes are subclassed from the StencilKernel class defined here. At initialization time, the text of the kernel() method is parsed into a Python AST, then converted into a StencilModel by stencil_python_front_end. The kernel() function is replaced by shadow_kernel(), which intercepts future calls to kernel(). During each call to kernel(), stencil_unroll_neighbor_iter is called to unroll neighbor loops, stencil_convert is invoked to convert the model to C++, and an external compiler tool is invoked to generate a binary which then efficiently completes executing the call. The binary is cached for future calls. """ import numpy import math import inspect import ast # from examples.stencil_grid.stencil_python_front_end import * # from examples.stencil_grid.stencil_unroll_neighbor_iter import * # from examples.stencil_grid.stencil_optimize_cpp import * # from examples.stencil_grid.stencil_convert import * from copy import deepcopy import logging logging.basicConfig(level=20) from ctree.transformations import PyBasicConversions from ctree.jit import LazySpecializedFunction from ctree.c.types import * from ctree.frontend import get_ast from ctree.visitors import NodeTransformer, NodeVisitor from ctree.c.nodes import * from ctree.omp.nodes import * # logging.basicConfig(filename='tmp.txt', # filemode='w', # format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s', # datefmt='%H:%M:%S', # level=20) class StencilConvert(LazySpecializedFunction): def __init__(self, func, entry_point, input_grids, output_grid, constants): self.input_grids = input_grids self.output_grid = output_grid self.constants = constants super(StencilConvert, self).__init__(get_ast(func)) def args_to_subconfig(self, args): conf = () for arg in args: conf += ((len(arg), arg.dtype, arg.ndim, arg.shape),) return conf def get_tuning_driver(self): from ctree.tune import ( BruteForceTuningDriver, IntegerParameter, MinimizeTime ) params = [IntegerParameter("block_factor", 4, 8), IntegerParameter("unroll_factor", 1, 4)] return BruteForceTuningDriver(params, MinimizeTime()) def transform(self, tree, program_config): """Convert the Python AST to a C AST.""" param_types = [] for arg in program_config[0]: param_types.append(NdPointer(arg[1], arg[2], arg[3])) kernel_sig = FuncType(Void(), param_types) tune_cfg = program_config[1] # block_factor = 2**tune_cfg['block_factor'] unroll_factor = 2**tune_cfg['unroll_factor'] for transformer in [StencilTransformer(self.input_grids, self.output_grid, self.constants ), PyBasicConversions()]: tree = transformer.visit(tree) first_For = tree.find(For) inner_For = FindInnerMostLoop().find(first_For) # self.block(inner_For, first_For, block_factor) self.unroll(inner_For, unroll_factor) # remove self param # TODO: Better way to do this? params = tree.find(FunctionDecl, name="kernel").params params.pop(0) self.gen_array_macro_definition(tree, params) entry_point = tree.find(FunctionDecl, name="kernel") entry_point.set_typesig(kernel_sig) return tree, entry_point.get_type().as_ctype() def gen_array_macro_definition(self, tree, arg_names): first_for = tree.find(For) for index, arg in enumerate(self.input_grids + (self.output_grid,)): defname = "_%s_array_macro" % arg_names[index] params = ','.join(["_d"+str(x) for x in range(arg.dim)]) params = "(%s)" % params calc = "((_d%d)" % (arg.dim - 1) for x in range(arg.dim - 1): dim = str(int(arg.data.strides[x]/arg.data.itemsize)) calc += "+((_d%s) * %s)" % (str(x), dim) calc += ")" first_for.insert_before(Define(defname+params, calc)) def unroll(self, for_node, factor): # Determine the leftover iterations after unrolling initial = for_node.init.right.value end = for_node.test.right.value leftover_begin = int((end - initial + 1) / factor) * factor + initial new_end = leftover_begin - 1 new_incr = AddAssign(SymbolRef(for_node.incr.arg.name), factor) new_body = for_node.body[:] for x in range(1, factor): new_extension = deepcopy(for_node.body) new_extension = map(UnrollReplacer(for_node.init.left.name, x).visit, new_extension) new_body.extend(new_extension) leftover_For = For(Assign(for_node.init.left, Constant(leftover_begin)), for_node.test, for_node.incr, for_node.body) for_node.test = LtE(for_node.init.left.name, new_end) for_node.incr = new_incr for_node.body = new_body if not leftover_begin >= end: for_node.body.append(leftover_For) #def block(self, tree, factor): class FindInnerMostLoop(NodeVisitor): def __init__(self): self.inner_most = None def find(self, node): self.visit(node) return self.inner_most def visit_For(self, node): self.inner_most = node map(self.visit, node.body) class UnrollReplacer(NodeTransformer): def __init__(self, loopvar, incr): self.loopvar = loopvar self.incr = incr self.in_new_scope = False self.inside_for = False super(UnrollReplacer, self).__init__() def visit_SymbolRef(self, node): if node.name == self.loopvar: return Add(node, Constant(self.incr)) return SymbolRef(node.name) class StencilTransformer(NodeTransformer): def __init__(self, input_grids, output_grid, constants): # TODO: Give these wrapper classes? self.input_grids = input_grids self.output_grid = output_grid self.ghost_depth = output_grid.ghost_depth self.next_fresh_var = 0 self.output_index = None self.neighbor_grid_name = None self.kernel_target = None self.offset_list = None self.var_list = [] self.input_dict = {} self.constants = constants super(StencilTransformer, self).__init__() def visit_FunctionDef(self, node): for index, arg in enumerate(node.args.args[1:]): # PYTHON3 vs PYTHON2 if hasattr(arg, 'arg'): arg = arg.arg else: arg = arg.id if index < len(self.input_grids): self.input_dict[arg] = self.input_grids[index] else: self.output_grid_name = arg node.body = list(map(self.visit, node.body)) return node def gen_fresh_var(self): self.next_fresh_var += 1 return "x%d" % self.next_fresh_var def visit_For(self, node): if type(node.iter) is ast.Call and \ type(node.iter.func) is ast.Attribute: if node.iter.func.attr is 'interior_points': dim = len(self.output_grid.shape) self.kernel_target = node.target.id curr_node = None ret_node = None for d in range(dim): target = SymbolRef(self.gen_fresh_var()) self.var_list.append(target.name) for_loop = For( Assign(SymbolRef(target.name, Int()), Constant(self.ghost_depth)), LtE(target, Constant( self.output_grid.shape[d] - self.ghost_depth - 1) ), PostInc(target), []) if d == 0: ret_node = for_loop else: curr_node.body = [for_loop] if d == dim - 2: curr_node.body.insert(0, OmpParallelFor()) elif d == dim - 1: curr_node.body.insert(0, OmpIvDep()) curr_node = for_loop self.output_index = self.gen_fresh_var() pt = [SymbolRef(x) for x in self.var_list] macro = self.gen_array_macro(self.output_grid_name, pt) curr_node.body = [Assign(SymbolRef(self.output_index, Int()), macro)] for elem in map(self.visit, node.body): if type(elem) == list: curr_node.body.extend(elem) else: curr_node.body.append(elem) self.kernel_target = None return ret_node elif node.iter.func.attr is 'neighbors': neighbors_id = node.iter.args[1].n grid_name = node.iter.func.value.id grid = self.input_dict[grid_name] zero_point = tuple([0 for x in range(grid.dim)]) self.neighbor_target = node.target.id self.neighbor_grid_name = grid_name body = [] statement = node.body[0] for x in grid.neighbors(zero_point, neighbors_id): self.offset_list = list(x) for statement in node.body: body.append(self.visit(deepcopy(statement))) self.neighbor_target = None return body return node # Handle array references def visit_Subscript(self, node): grid_name = node.value.id target = node.slice.value if isinstance(target, ast.Name): target = target.id if target == self.kernel_target: if grid_name is self.output_grid_name: return ArrayRef(SymbolRef(self.output_grid_name), SymbolRef(self.output_index)) elif grid_name in self.input_dict: grid = self.input_dict[grid_name] pt = list(map(lambda x: SymbolRef(x), self.var_list)) index = self.gen_array_macro(grid_name, pt) return ArrayRef(SymbolRef(grid_name), index) elif grid_name == self.neighbor_grid_name: pt = list(map(lambda x, y: Add(SymbolRef(x), SymbolRef(y)), self.var_list, self.offset_list)) index = self.gen_array_macro(grid_name, pt) return ArrayRef(SymbolRef(grid_name), index) elif isinstance(target, ast.Call): return ArrayRef(SymbolRef(grid_name), self.visit(target)) return node def visit_Call(self, node): if node.func.id == 'distance': zero_point = tuple([0 for _ in range(len(self.offset_list))]) return Constant(int(self.distance(zero_point, self.offset_list))) elif node.func.id == 'int': return Cast(Int(), self.visit(node.args[0])) node.args = list(map(self.visit, node.args)) return node def distance(self, x, y): return math.sqrt(sum([(x[i]-y[i])**2 for i in range(0, len(x))])) def gen_array_macro(self, arg, point): name = "_%s_array_macro" % arg return FunctionCall(SymbolRef(name), point) def visit_AugAssign(self, node): # TODO: Handle all types? value = self.visit(node.value) # HACK to get this to work, PyBasicConversions will skip this AugAssign node # TODO Figure out why value = PyBasicConversions().visit(value) if type(node.op) is ast.Add: return AddAssign(self.visit(node.target), value) if type(node.op) is ast.Sub: return SubAssign(self.visit(node.target), value) def visit_Assign(self, node): target = PyBasicConversions().visit(self.visit(node.targets[0])) value = PyBasicConversions().visit(self.visit(node.value)) return Assign(target, value) def visit_Name(self, node): if node.id in self.constants.keys(): return Constant(self.constants[node.id]) return node # may want to make this inherit from something else... class StencilKernel(object): def __init__(self, with_cilk=False): # we want to raise an exception if there is no kernel() # method defined. try: dir(self).index("kernel") except ValueError: raise Exception("No kernel method defined.") # get text of kernel() method and parse into a StencilModel # self.kernel_src = inspect.getsource(self.kernel) # print(self.kernel_src) # self.kernel_ast = ast.parse(self.remove_indentation(self.kernel_src)) # print(ast.dump(self.kernel_ast, include_attributes=True)) # self.model = StencilPythonFrontEnd().parse(self.kernel_ast) # print(ast.dump(self.model, include_attributes=True)) self.model = self.kernel # print(self.new_kernel) self.pure_python = False self.pure_python_kernel = self.kernel self.should_unroll = True self.should_cacheblock = False self.block_size = 1 # replace kernel with shadow version self.kernel = self.shadow_kernel self.specialized_sizes = None self.with_cilk = with_cilk self.constants = {} def shadow_kernel(self, *args): if self.pure_python: return self.pure_python_kernel(*args) if not self.specialized_sizes or\ self.specialized_sizes != [y.shape for y in args]: self.specialized = StencilConvert( self.model, "kernel", args[0:-1], args[-1], self.constants) self.specialized_sizes = [arg.shape for arg in args] with Timer() as t: self.specialized(*[arg.data for arg in args]) self.specialized.report(time=t) import time class Timer: def __enter__(self): self.start = time.clock() return self def __exit__(self, *args): self.end = time.clock() self.interval = self.end - self.start

38.409207

96

0.583233

cc16e36d108c0b762fb5424a885b8ba7348ef602

6,647

py

Python

monet/graph.py

stjordanis/MONeT-1

98a5c7d149ca19c8c64069dbd8f27ce7f97bf3af

[ "MIT" ]

161

2020-10-28T02:21:50.000Z

2022-03-11T05:06:16.000Z

monet/graph.py

kiminh/MONeT

83302c12e8fd3d1c8b2496928c843f0e84226cc8

[ "MIT" ]

4

2020-10-28T02:27:43.000Z

2021-03-31T00:04:43.000Z

monet/graph.py

kiminh/MONeT

83302c12e8fd3d1c8b2496928c843f0e84226cc8

[ "MIT" ]

15

2020-10-28T02:32:12.000Z

2021-12-23T13:20:23.000Z

import torch from functools import lru_cache class Node: def __init__(self, shape): self.shape = shape class Input(Node): def __repr__(self): return '<Input %s>' % str(list(self.shape)) class Param(Node): def __repr__(self): return '<Param %s>' % str(list(self.shape)) class ComputeNode(Node): class Arg: pass class V(Arg): def __init__(self, v): self.value = v def __repr__(self): return '<V %s>' % str(self.value) class D(Arg): def __init__(self, index, requires_grad=False): self.index = index self.requires_grad = requires_grad def __repr__(self): return '<D %d %d>' % (self.index, self.requires_grad) def __init__(self, shape, nodeid, op, args, has_backward, is_depthwise=False): super().__init__(shape) self._op = op self._args = args self.id = nodeid self._has_backward = has_backward self._is_depthwise = is_depthwise @property @lru_cache(maxsize=512) def op(self): return self._op @property def args(self): return self._args @property def has_backward(self): return self._has_backward @property @lru_cache(maxsize=512) def is_depthwise(self): return self._is_depthwise @property @lru_cache(maxsize=128) def dependencies(self): return [(a.index, a.requires_grad) for a in self._args if isinstance(a, self.D)] def __repr__(self): return '<Op %s %s>' % (str(self._op), str(list(self.shape))) class Graph: def __init__(self): self._nodes = [] self._outputs = [] def _add_node(self, node): self._nodes.append(node) return len(self._nodes)-1 def _add_input(self, shape): return self._add_node(Input(shape)) def _add_param(self, shape): return self._add_node(Param(shape)) def _add_op(self, shape, op, args, has_backward=False, is_depthwise=False): nodeid = len(self._nodes) return self._add_node(ComputeNode(shape, nodeid, op, args, has_backward, is_depthwise)) def _add_output(self, output_id): self._outputs.append(output_id) @property def nodes(self): return self._nodes @classmethod def create(cls, model, input_shape=(3, 224, 224)): # create a graph of the forward pass # JIT trace the model args = (torch.ones((23,) + input_shape),) graph, torch_out = torch.jit._get_trace_graph(model, args, _force_outplace=False, _return_inputs_states=False) torch._C._jit_pass_constant_propagation(graph) torch._C._jit_pass_inline(graph) torch._C._jit_pass_dce(graph) torch._C._jit_pass_lint(graph) params = torch.jit._unique_state_dict(model) assert len(list(graph.inputs())) == len(args) + len(params) node_id = {} r = cls() arg_and_param_shape = [list(a.shape) for a in args] + [list(p.shape) for p in params.values()] for k, i in enumerate(graph.inputs()): if k < len(args): node_id[i.unique()] = r._add_input([-1]+arg_and_param_shape[k][1:]) else: node_id[i.unique()] = r._add_param(arg_and_param_shape[k]) const = {} # Track connected nodes in the graph track = set() track.add("input.1") for node in graph.nodes(): if node.kind()!="aten::size": for ip in node.inputs(): if ip.debugName() in track or "input" in ip.debugName(): track.add(node.output().debugName()) if "input" in node.output().debugName(): track.add(node.output().debugName()) list_contents = {} for n in graph.nodes(): assert n.kind() != 'prim::GetAttr' if n.kind() == 'prim::Constant': const[n.output().unique()] = n['value'] if n.hasAttribute('value') else None elif len(n.kind()) > 6 and n.kind()[:6] == 'aten::': args = [] for i in n.inputs(): iu = i.unique() if iu in list_contents: iu_list = list_contents[iu] else: iu_list = [iu] for iu in iu_list: if iu in const: args.append(ComputeNode.V(const[iu])) elif iu in node_id: if i.debugName() not in track and (not isinstance(r._nodes[node_id[iu]], Input)) and (not isinstance(r._nodes[node_id[iu]], Param)): # Doing this for addmm and transpose for ii in i.node().inputs(): iiu = ii.unique() assert (isinstance(r._nodes[node_id[iiu]], Input) or isinstance(r._nodes[node_id[iiu]], Param)) == True args.append(ComputeNode.D(node_id[iiu], ii.requires_grad())) else: args.append(ComputeNode.D(node_id[iu], i.requires_grad())) else: raise ValueError('Nodes %s disconnected' % repr(i)) has_backward = False if n.output().debugName() in track: has_backward = True # Identify depthwise conv is_depthwise = False if n.kind() == "aten::_convolution": assert isinstance(args[8], ComputeNode.V) if args[8].value > 1 and args[8].value == r.nodes[args[0].index].shape[1]: is_depthwise = True # Add node to graph node_id[n.output().unique()] = r._add_op([s if s != 23 else -1 for s in n.output().type().sizes()], n.kind(), args, has_backward, is_depthwise) elif n.kind() in ['prim::ListConstruct', 'prim::TupleConstruct']: list_contents[n.output().unique()] = [i.unique() for i in n.inputs()] else: print('Unknown OP', n.kind(), n) # Identify outputs for op in graph.outputs(): if op.node().kind()[:6] == 'aten::': r._add_output(node_id[op.unique()]) elif op.node().kind() == 'prim::TupleConstruct': for i in op.node().inputs(): r._add_output(node_id[i.unique()]) return r

35.92973

197

0.531368

eedf083efa2a3dfc8c3bd3e271315707a901d0bb

8,641

py

Python

text2array/iterators.py

kmkurn/text2array

910c349fcbd85299b137c132f218a26860211a6b

[ "Apache-2.0" ]

null

text2array/iterators.py

kmkurn/text2array

910c349fcbd85299b137c132f218a26860211a6b

[ "Apache-2.0" ]

null

text2array/iterators.py

kmkurn/text2array

910c349fcbd85299b137c132f218a26860211a6b

[ "Apache-2.0" ]

1

2021-02-27T08:53:59.000Z

# Copyright 2019 Kemal Kurniawan # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from collections import defaultdict from random import Random from typing import Any, Callable, Iterable, Iterator, Optional, Sequence, Sized import statistics as stat import warnings from . import Batch, Sample class BatchIterator(Iterable[Batch], Sized): """Iterator that produces batches of samples. Example: >>> from text2array import BatchIterator >>> samples = [ ... {'ws': ['a']}, ... {'ws': ['a', 'b']}, ... {'ws': ['b', 'b']}, ... ] >>> iter_ = BatchIterator(samples, batch_size=2) >>> for b in iter_: ... print(list(b)) ... [{'ws': ['a']}, {'ws': ['a', 'b']}] [{'ws': ['b', 'b']}] Args: samples (~typing.Iterable[Sample]): Iterable of samples to batch. batch_size: Maximum number of samples in each batch. Note: When ``samples`` is an instance of `~typing.Sized`, this iterator can be passed to `len` to get the number of batches. Otherwise, a `TypeError` is raised. """ def __init__(self, samples: Iterable[Sample], batch_size: int = 1) -> None: if batch_size <= 0: raise ValueError("batch size must be greater than 0") self._samples = samples self._bsz = batch_size @property def batch_size(self) -> int: return self._bsz def __len__(self) -> int: n = len(self._samples) # type: ignore b = self._bsz return n // b + (1 if n % b != 0 else 0) def __iter__(self) -> Iterator[Batch]: it, exhausted = iter(self._samples), False while not exhausted: batch = Batch() while not exhausted and len(batch) < self._bsz: try: batch.append(next(it)) except StopIteration: exhausted = True if batch: yield batch class ShuffleIterator(Iterable[Any], Sized): r"""Iterator that shuffles a sequence of items before iterating. When ``key`` is not given, this iterator performs ordinary shuffling using `random.shuffle`. Otherwise, a noisy sorting is performed. The items are sorted ascending by the value of the given key, plus some random noise :math:`\epsilon \sim` Uniform :math:`(-z, z)`, where :math:`z` equals ``scale`` times the standard deviation of key values. This formulation means that ``scale`` regulates how noisy the sorting is. The larger it is, the more noisy the sorting becomes, i.e. it resembles random shuffling more closely. In an extreme case where ``scale=0``, this method just sorts the items by ``key``. This method is useful when working with text data, where we want to shuffle the dataset and also minimize padding by ensuring that sentences of similar lengths are not too far apart. Example: >>> from random import Random >>> from text2array import ShuffleIterator >>> samples = [ ... {'ws': ['a', 'b', 'b']}, ... {'ws': ['a']}, ... {'ws': ['a', 'a', 'b', 'b', 'b', 'b']}, ... ] >>> iter_ = ShuffleIterator(samples, key=lambda s: len(s['ws']), rng=Random(1234)) >>> for s in iter_: ... print(s) ... {'ws': ['a']} {'ws': ['a', 'a', 'b', 'b', 'b', 'b']} {'ws': ['a', 'b', 'b']} Args: items (~typing.Sequence[Any]): Sequence of items to shuffle and iterate over. key (typing.Callable[[Any], int]): Callable to get the key value of an item. scale: Value to regulate the noise of the sorting. Must not be negative. rng: Random number generator to use for shuffling. Set this to ensure reproducibility. If not given, an instance of `~random.Random` with the default seed is used. """ def __init__( self, items: Sequence[Any], key: Optional[Callable[[Any], int]] = None, scale: float = 1.0, rng: Optional[Random] = None, ) -> None: if scale < 0: raise ValueError("scale cannot be less than 0") if rng is None: # pragma: no cover rng = Random() self._items = items self._key = key self._scale = scale self._rng = rng def __len__(self) -> int: return len(self._items) def __iter__(self) -> Iterator[Sample]: if self._key is None: self._shuffle() else: self._shuffle_by_key() return iter(self._items) def _shuffle(self) -> None: self._items = list(self._items) self._rng.shuffle(self._items) def _shuffle_by_key(self) -> None: assert self._key is not None std = stat.stdev(self._key(s) for s in self._items) z = self._scale * std noises = [self._rng.uniform(-z, z) for _ in range(len(self._items))] indices = list(range(len(self._items))) indices.sort(key=lambda i: self._key(self._items[i]) + noises[i]) # type: ignore shuf_items = [self._items[i] for i in indices] self._items = shuf_items class BucketIterator(Iterable[Batch], Sized): """Iterator that batches together samples from the same bucket. Example: >>> from text2array import BucketIterator >>> samples = [ ... {'ws': ['a']}, ... {'ws': ['a', 'b']}, ... {'ws': ['b']}, ... {'ws': ['c']}, ... {'ws': ['b', 'b']}, ... ] >>> iter_ = BucketIterator(samples, key=lambda s: len(s['ws']), batch_size=2) >>> for b in iter_: ... print(list(b)) ... [{'ws': ['a']}, {'ws': ['b']}] [{'ws': ['c']}] [{'ws': ['a', 'b']}, {'ws': ['b', 'b']}] Args: samples (~typing.Iterable[Sample]): Iterable of samples to batch. key (typing.Callable[[Sample], Any]): Callable to get the bucket key of a sample. batch_size: Maximum number of samples in each batch. shuffle_bucket: Whether to shuffle every bucket before batching. rng: Random number generator to use for shuffling. Set this to ensure reproducibility. If not given, an instance of `~random.Random` with the default seed is used. sort_bucket: Whether to sort every bucket before batching. When both ``shuffle_bucket`` and ``sort_bucket`` is ``True``, sorting will be ignored (but don't rely on this behavior). sort_bucket_by (typing.Callable[[Sample], Any]): Callable acting as the sort key if ``sort_bucket=True``. Note: When ``samples`` is an instance of `~typing.Sized`, this iterator can be passed to `len` to get the number of batches. Otherwise, a `TypeError` is raised. """ def __init__( self, samples: Iterable[Sample], key: Callable[[Sample], Any], batch_size: int = 1, shuffle_bucket: bool = False, rng: Optional[Random] = None, sort_bucket: bool = False, sort_bucket_by: Optional[Callable[[Sample], Any]] = None, ) -> None: if rng is None: # pragma: no cover rng = Random() if shuffle_bucket and sort_bucket: warnings.warn( "Both shuffle_bucket and sort_bucket is True; sort_bucket will have no effect" ) self._bsz = batch_size self._shuf = shuffle_bucket self._rng = rng bucket_dict = defaultdict(list) for s in samples: bucket_dict[key(s)].append(s) self._buckets = bucket_dict.values() if sort_bucket: for bkt in self._buckets: bkt.sort(key=sort_bucket_by) @property def batch_size(self): return self._bsz def __len__(self): return sum(len(BatchIterator(ss, self._bsz)) for ss in self._buckets) def __iter__(self): for ss in self._buckets: if self._shuf: self._rng.shuffle(ss) yield from BatchIterator(ss, self._bsz)

35.126016

95

0.580257

2b43eb1dbe6d09ada6337047d2da34382852a6ee

1,392

py

Python

Server_Client/client.py

mattgarbecki/RoundnetPositionTracker

8e54c5fdb91e497184b66c9ff09e0e356d4d0e37

[ "MIT" ]

null

Server_Client/client.py

mattgarbecki/RoundnetPositionTracker

8e54c5fdb91e497184b66c9ff09e0e356d4d0e37

[ "MIT" ]

null

Server_Client/client.py

mattgarbecki/RoundnetPositionTracker

8e54c5fdb91e497184b66c9ff09e0e356d4d0e37

[ "MIT" ]

1

2020-05-13T23:51:06.000Z

import requests import socket import astropy.io.misc.asdf.tags.time.tests.test_time import sensordroid def getDataPoint(): sensordroid return def runClient(address, port): # Create a TCP/IP socket sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Connect the socket to the port where the server is listening server_address = (address, port) print('connecting to {} port {}'.format(*server_address)) sock.connect(server_address) while True: # CHANGE MESSAGE TO BE THE DATA THATS SENDING message = input() if message == "quit": print("Closing client") sock.sendall("quit".encode("UTF-8")) break message = message + "%" + str(time.time()) sock.sendall(message.encode("UTF-8")) sock.close() def manageClient(): LINK = "http://localhost:3500" URL = LINK + "/address" # PIPE UI INPUT HERE FOR GAME NAME NAME = input("game name: ") PARAMS = {'name':NAME} r = requests.get(url = URL, params = PARAMS) if r.status_code != 200: print("ERROR: No Connection") quit() if r.text == "NONAME" or r.text == "INVALID": print("ERROR: gamename not found") quit() DATA = r.text.strip("[]").split(",") runClient(DATA[0].strip("\""), int(DATA[1])) if __name__ == "__main__": manageClient()

24.857143

66

0.604167

b7bef69db510c9e9ac644ec846c76e072b675620

2,263

py

Python

slack_differ.py

robobario/slack_differ

60ca4389a9faf36b5050847f88adcfc32775122e

[ "MIT" ]

null

slack_differ.py

robobario/slack_differ

60ca4389a9faf36b5050847f88adcfc32775122e

[ "MIT" ]

null

slack_differ.py

robobario/slack_differ

60ca4389a9faf36b5050847f88adcfc32775122e

[ "MIT" ]

null

#!/usr/bin/python import sh from os import listdir, mkdir from os.path import join, realpath, dirname, isdir, isfile import json import sys print "running slack_differ" dname = dirname(realpath(__file__)) print "dirname : " + dname script_dir = join(dname,'scripts') print "script_dir : " + script_dir if not isdir(script_dir): print "no script dir found" scripts = [] else: scripts = [f for f in listdir(script_dir) if f.endswith(".json")] for script in scripts: try: script_metadata = json.load(open(join(script_dir, script))) old_file = join(dname, script + ".old") new_file = join(dname, script + ".new") new_out = open(new_file,'w') command = sh.Command(join(script_dir,script_metadata["script"])) print("running " + join(script_dir,script_metadata["script"])) command(_out=new_out) if not isfile(old_file): sh.cp(new_file,old_file) diff = sh.diff(old_file,new_file,_ok_code=[0,1]) if len(diff) > 0: message = str(open(new_file).read()) payload = { "channel":script_metadata["channel"], "username":script_metadata["user"], "text": script_metadata["title"], "icon_emoji":script_metadata["emoji"], "attachments" : [ { "color":"good", "fields":[ { "title":"new value", "value":message, "short":False } ] } ] } payload = "payload=" + json.dumps(payload) sh.mv(new_file,old_file) sh.curl(["curl", "-X", "POST" ,"--data-urlencode" ,payload,script_metadata["slack_url"]]) except Exception as e: print "failed on " + script +" :" + str(e) if len(sys.argv) > 1 and sys.argv[1] == "example": print("initialising example") if not isdir(script_dir): mkdir(script_dir) script = open(join(script_dir,"example.sh"),'w') script.write("#!/bin/bash\necho 'hi'") metafile = open(join(script_dir,"example.json"),'w') meta = { "channel":"#example", "user":"examplebot", "title":"script title", "emoji":":metal:", "slack_url":"https://hook.slack.com/XXX/YYY", "script":"example.sh" } metafile.write(json.dumps(meta))

31.873239

95

0.592134

6002076e36db07619287501f4e07304697ad3b4d

282

py

Python

fms/fms/doctype/announcement/announcement.py

sagar30051991/KF-FMS

e6e41343e2f90b09c73ef4f7a7ec35fd7be81a18

[ "MIT" ]

null

fms/fms/doctype/announcement/announcement.py

sagar30051991/KF-FMS

e6e41343e2f90b09c73ef4f7a7ec35fd7be81a18

[ "MIT" ]

null

fms/fms/doctype/announcement/announcement.py

sagar30051991/KF-FMS

e6e41343e2f90b09c73ef4f7a7ec35fd7be81a18

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2015, New Indictrans Technology Pvt Ltd and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document class Announcement(Document): pass

25.636364

72

0.787234

faf3274822022da014b74e39ab2416be71ca1782

1,584

py

Python

src/modtorsiflex/__init__.py

cathedralpkg/TorsiFlex

35f59b8813befdaac7a1c985216a7ead3012fb53

[ "Unlicense" ]

1

2022-02-02T01:04:36.000Z

src/modtorsiflex/__init__.py

cathedralpkg/TorsiFlex

35f59b8813befdaac7a1c985216a7ead3012fb53

[ "Unlicense" ]

null

src/modtorsiflex/__init__.py

cathedralpkg/TorsiFlex

35f59b8813befdaac7a1c985216a7ead3012fb53

[ "Unlicense" ]

3

2021-12-28T14:21:32.000Z

2022-03-14T13:18:07.000Z

''' --------------------------- Licensing and Distribution --------------------------- Program name: TorsiFlex Version : 2021.3 License : MIT/x11 Copyright (c) 2021, David Ferro Costas (david.ferro@usc.es) and Antonio Fernandez Ramos (qf.ramos@usc.es) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --------------------------- *----------------------------------* | Module : modtorsiflex | | Sub-module : __init__ | | Last Update: 2021/11/22 (Y/M/D) | | Main Author: David Ferro-Costas | *----------------------------------* Just a __init__ to generate the module '''

36.837209

78

0.688131

8bc98ef72a8c42cb4393d39ec6ac3e02d7565424

17,497

py

Python

plugins/modules/oci_dns_resolver_actions.py

slmjy/oci-ansible-collection

349c91e2868bf4706a6e3d6fb3b47fc622bfe11b

[ "Apache-2.0" ]

108

2020-05-19T20:46:10.000Z

2022-03-25T14:10:01.000Z

plugins/modules/oci_dns_resolver_actions.py

slmjy/oci-ansible-collection

349c91e2868bf4706a6e3d6fb3b47fc622bfe11b

[ "Apache-2.0" ]

90

2020-06-14T22:07:11.000Z

2022-03-07T05:40:29.000Z

plugins/modules/oci_dns_resolver_actions.py

slmjy/oci-ansible-collection

349c91e2868bf4706a6e3d6fb3b47fc622bfe11b

[ "Apache-2.0" ]

42

2020-08-30T23:09:12.000Z

2022-03-25T16:58:01.000Z

#!/usr/bin/python # Copyright (c) 2020, 2021 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_dns_resolver_actions short_description: Perform actions on a Resolver resource in Oracle Cloud Infrastructure description: - Perform actions on a Resolver resource in Oracle Cloud Infrastructure - For I(action=change_compartment), moves a resolver into a different compartment along with its protected default view and any endpoints. Zones in the default view are not moved. Requires a `PRIVATE` scope query parameter. version_added: "2.9.0" author: Oracle (@oracle) options: resolver_id: description: - The OCID of the target resolver. type: str aliases: ["id"] required: true compartment_id: description: - The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the compartment into which the resolver, along with its protected default view and resolver endpoints, should be moved. type: str required: true scope: description: - Specifies to operate only on resources that have a matching DNS scope. type: str choices: - "GLOBAL" - "PRIVATE" action: description: - The action to perform on the Resolver. type: str required: true choices: - "change_compartment" extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: Perform action change_compartment on resolver oci_dns_resolver_actions: # required resolver_id: "ocid1.resolver.oc1..xxxxxxEXAMPLExxxxxx" compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx" action: change_compartment # optional scope: GLOBAL """ RETURN = """ resolver: description: - Details of the Resolver resource acted upon by the current operation returned: on success type: complex contains: compartment_id: description: - The OCID of the owning compartment. returned: on success type: str sample: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx" attached_vcn_id: description: - The OCID of the attached VCN. returned: on success type: str sample: "ocid1.attachedvcn.oc1..xxxxxxEXAMPLExxxxxx" display_name: description: - The display name of the resolver. returned: on success type: str sample: display_name_example freeform_tags: description: - Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see L(Resource Tags,https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm). - "**Example:** `{\\"Department\\": \\"Finance\\"}`" returned: on success type: dict sample: {'Department': 'Finance'} defined_tags: description: - Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see L(Resource Tags,https://docs.cloud.oracle.com/Content/General/Concepts/resourcetags.htm). - "**Example:** `{\\"Operations\\": {\\"CostCenter\\": \\"42\\"}}`" returned: on success type: dict sample: {'Operations': {'CostCenter': 'US'}} id: description: - The OCID of the resolver. returned: on success type: str sample: "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx" time_created: description: - "The date and time the resource was created in \\"YYYY-MM-ddThh:mm:ssZ\\" format with a Z offset, as defined by RFC 3339." - "**Example:** `2016-07-22T17:23:59:60Z`" returned: on success type: str sample: "2013-10-20T19:20:30+01:00" time_updated: description: - "The date and time the resource was last updated in \\"YYYY-MM-ddThh:mm:ssZ\\" format with a Z offset, as defined by RFC 3339." - "**Example:** `2016-07-22T17:23:59:60Z`" returned: on success type: str sample: "2013-10-20T19:20:30+01:00" lifecycle_state: description: - The current state of the resource. returned: on success type: str sample: ACTIVE _self: description: - The canonical absolute URL of the resource. returned: on success type: str sample: _self_example default_view_id: description: - The OCID of the default view. returned: on success type: str sample: "ocid1.defaultview.oc1..xxxxxxEXAMPLExxxxxx" is_protected: description: - A Boolean flag indicating whether or not parts of the resource are unable to be explicitly managed. returned: on success type: bool sample: true endpoints: description: - Read-only array of endpoints for the resolver. returned: on success type: complex contains: name: description: - The name of the resolver endpoint. Must be unique, case-insensitive, within the resolver. returned: on success type: str sample: name_example endpoint_type: description: - The type of resolver endpoint. VNIC is currently the only supported type. returned: on success type: str sample: VNIC forwarding_address: description: - An IP address from which forwarded queries may be sent. For VNIC endpoints, this IP address must be part of the subnet and will be assigned by the system if unspecified when isForwarding is true. returned: on success type: str sample: forwarding_address_example is_forwarding: description: - A Boolean flag indicating whether or not the resolver endpoint is for forwarding. returned: on success type: bool sample: true is_listening: description: - A Boolean flag indicating whether or not the resolver endpoint is for listening. returned: on success type: bool sample: true listening_address: description: - An IP address to listen to queries on. For VNIC endpoints this IP address must be part of the subnet and will be assigned by the system if unspecified when isListening is true. returned: on success type: str sample: listening_address_example compartment_id: description: - The OCID of the owning compartment. This will match the resolver that the resolver endpoint is under and will be updated if the resolver's compartment is changed. returned: on success type: str sample: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx" time_created: description: - "The date and time the resource was created in \\"YYYY-MM-ddThh:mm:ssZ\\" format with a Z offset, as defined by RFC 3339." - "**Example:** `2016-07-22T17:23:59:60Z`" returned: on success type: str sample: "2013-10-20T19:20:30+01:00" time_updated: description: - "The date and time the resource was last updated in \\"YYYY-MM-ddThh:mm:ssZ\\" format with a Z offset, as defined by RFC 3339." - "**Example:** `2016-07-22T17:23:59:60Z`" returned: on success type: str sample: "2013-10-20T19:20:30+01:00" lifecycle_state: description: - The current state of the resource. returned: on success type: str sample: ACTIVE _self: description: - The canonical absolute URL of the resource. returned: on success type: str sample: _self_example subnet_id: description: - The OCID of a subnet. Must be part of the VCN that the resolver is attached to. returned: on success type: str sample: "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" attached_views: description: - The attached views. Views are evaluated in order. returned: on success type: complex contains: view_id: description: - The OCID of the view. returned: on success type: str sample: "ocid1.view.oc1..xxxxxxEXAMPLExxxxxx" rules: description: - Rules for the resolver. Rules are evaluated in order. returned: on success type: complex contains: client_address_conditions: description: - A list of CIDR blocks. The query must come from a client within one of the blocks in order for the rule action to apply. returned: on success type: list sample: [] qname_cover_conditions: description: - A list of domain names. The query must be covered by one of the domains in order for the rule action to apply. returned: on success type: list sample: [] action: description: - "The action determines the behavior of the rule. If a query matches a supplied condition, the action will apply. If there are no conditions on the rule, all queries are subject to the specified action. * `FORWARD` - Matching requests will be forwarded from the source interface to the destination address." returned: on success type: str sample: FORWARD destination_addresses: description: - IP addresses to which queries should be forwarded. Currently limited to a single address. returned: on success type: list sample: [] source_endpoint_name: description: - Case-insensitive name of an endpoint, that is a sub-resource of the resolver, to use as the forwarding interface. The endpoint must have isForwarding set to true. returned: on success type: str sample: source_endpoint_name_example sample: { "compartment_id": "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx", "attached_vcn_id": "ocid1.attachedvcn.oc1..xxxxxxEXAMPLExxxxxx", "display_name": "display_name_example", "freeform_tags": {'Department': 'Finance'}, "defined_tags": {'Operations': {'CostCenter': 'US'}}, "id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx", "time_created": "2013-10-20T19:20:30+01:00", "time_updated": "2013-10-20T19:20:30+01:00", "lifecycle_state": "ACTIVE", "_self": "_self_example", "default_view_id": "ocid1.defaultview.oc1..xxxxxxEXAMPLExxxxxx", "is_protected": true, "endpoints": [{ "name": "name_example", "endpoint_type": "VNIC", "forwarding_address": "forwarding_address_example", "is_forwarding": true, "is_listening": true, "listening_address": "listening_address_example", "compartment_id": "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx", "time_created": "2013-10-20T19:20:30+01:00", "time_updated": "2013-10-20T19:20:30+01:00", "lifecycle_state": "ACTIVE", "_self": "_self_example", "subnet_id": "ocid1.subnet.oc1..xxxxxxEXAMPLExxxxxx" }], "attached_views": [{ "view_id": "ocid1.view.oc1..xxxxxxEXAMPLExxxxxx" }], "rules": [{ "client_address_conditions": [], "qname_cover_conditions": [], "action": "FORWARD", "destination_addresses": [], "source_endpoint_name": "source_endpoint_name_example" }] } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import ( oci_common_utils, oci_wait_utils, ) from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIActionsHelperBase, get_custom_class, ) try: from oci.dns import DnsClient from oci.dns.models import ChangeResolverCompartmentDetails HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class ResolverActionsHelperGen(OCIActionsHelperBase): """ Supported actions: change_compartment """ @staticmethod def get_module_resource_id_param(): return "resolver_id" def get_module_resource_id(self): return self.module.params.get("resolver_id") def get_get_fn(self): return self.client.get_resolver def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_resolver, resolver_id=self.module.params.get("resolver_id"), ) def change_compartment(self): action_details = oci_common_utils.convert_input_data_to_model_class( self.module.params, ChangeResolverCompartmentDetails ) return oci_wait_utils.call_and_wait( call_fn=self.client.change_resolver_compartment, call_fn_args=(), call_fn_kwargs=dict( resolver_id=self.module.params.get("resolver_id"), change_resolver_compartment_details=action_details, scope=self.module.params.get("scope"), ), waiter_type=oci_wait_utils.NONE_WAITER_KEY, operation="{0}_{1}".format( self.module.params.get("action").upper(), oci_common_utils.ACTION_OPERATION_KEY, ), waiter_client=self.get_waiter_client(), resource_helper=self, wait_for_states=self.get_action_desired_states( self.module.params.get("action") ), ) ResolverActionsHelperCustom = get_custom_class("ResolverActionsHelperCustom") class ResourceHelper(ResolverActionsHelperCustom, ResolverActionsHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec( supports_create=False, supports_wait=False ) module_args.update( dict( resolver_id=dict(aliases=["id"], type="str", required=True), compartment_id=dict(type="str", required=True), scope=dict(type="str", choices=["GLOBAL", "PRIVATE"]), action=dict(type="str", required=True, choices=["change_compartment"]), ) ) module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_helper = ResourceHelper( module=module, resource_type="resolver", service_client_class=DnsClient, namespace="dns", ) result = resource_helper.perform_action(module.params.get("action")) module.exit_json(**result) if __name__ == "__main__": main()

39.675737

151

0.563125

7024726e6c75726b1d6913cb263146591311183c

418

py

Python

__Training__/Python - HackerRank/2. Basic Data Types/Finding the percentage.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

__Training__/Python - HackerRank/2. Basic Data Types/Finding the percentage.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

__Training__/Python - HackerRank/2. Basic Data Types/Finding the percentage.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

# https://www.hackerrank.com/challenges/finding-the-percentage/problem n = int(input()) # 3 student_marks = {} for _ in range(n): name, *scores = input().split() # Krishna 67 68 69 # Arjun 70 98 63 # Malika 52 56 60 scores = list(map(float, scores)) student_marks[name] = sum(scores) / len(scores) query_name = input() # Malika print("{:.2f}".format(student_marks[query_name])) # 56.00

17.416667

70

0.643541

e87f922c17b9089a645b1b05a99fd83e86d74ecf

2,065

py

Python

holmes/validators/blacklist.py

scorphus/holmes-api

6b3c76d4299fecf2d8799d7b5c3c6a6442cacd59

[ "MIT" ]

null

holmes/validators/blacklist.py

scorphus/holmes-api

6b3c76d4299fecf2d8799d7b5c3c6a6442cacd59

[ "MIT" ]

null

holmes/validators/blacklist.py

scorphus/holmes-api

6b3c76d4299fecf2d8799d7b5c3c6a6442cacd59

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from holmes.utils import get_domain_from_url, is_valid from holmes.validators.base import Validator from holmes.utils import _ class BlackListValidator(Validator): @classmethod def get_blacklist_parsed_value(cls, value): return ', '.join([ '<a href="%s" target="_blank">Link #%s</a>' % (url, index) for index, url in enumerate(value) ]) @classmethod def get_violation_definitions(cls): return { 'blacklist.domains': { 'title': _('Domain Blacklist'), 'description': _('Some links are blacklisted: %s'), 'value_parser': cls.get_blacklist_parsed_value, 'category': _('SEO'), 'generic_description': _( 'Detected domain blacklisted hyperlinks. ' 'Links with this violation are those that have anchors ' 'to websites added in Holmes\'s Black List configuration.' ), 'unit': 'list' } } @classmethod def get_default_violations_values(cls, config): return { 'blacklist.domains': { 'value': config.BLACKLIST_DOMAIN, 'description': config.get_description('BLACKLIST_DOMAIN') } } def validate(self): blacklist_domains = self.get_violation_pref('blacklist.domains') domains = [] links = self.get_links() for link in links: href = link.get('href') if not is_valid(href): continue link_domain, link_domain_url = get_domain_from_url(href) if link_domain in blacklist_domains: domains.append(href) if domains: self.add_violation( key='blacklist.domains', value=domains, points=100 * len(domains) ) def get_links(self): return self.review.data.get('page.all_links', None)

29.927536

78

0.550121

5943feb9a555751ad97da8b8829466a0b0e894d1

2,262

py

Python

transitland/test_operator.py

transit-land/onestop-id-python-client

d03d8759d0758803519c51c6970213946a4078d4

[ "MIT" ]

null

transitland/test_operator.py

transit-land/onestop-id-python-client

d03d8759d0758803519c51c6970213946a4078d4

[ "MIT" ]

null

transitland/test_operator.py

transit-land/onestop-id-python-client

d03d8759d0758803519c51c6970213946a4078d4

[ "MIT" ]

null

"""Test Operator.""" import unittest import util from operator import Operator class TestOperator(unittest.TestCase): def setUp(self): self.expect = util.example_export() def _sanity(self, entity): """Perform sanity checks! After bootstrap_gtfs or from_json...""" # More extensive checks, since json export includes nearly everything. assert entity.geohash() == '9qs' assert entity.onestop() == self.expect['onestopId'] assert len(entity.identifiers()) == 1 # Routes assert len(entity.routes()) == 5 expect = ['r-9qsczp-40', 'r-9qt1-50', 'r-9qsb-20', 'r-9qscy-30', 'r-9qscy-10'] for i in entity.routes(): assert i.onestop() in expect assert len(i.identifiers()) == 1 # Stops assert len(entity.stops()) == 9 for i in entity.stops(): assert i.onestop() in self.expect['serves'] assert len(i.identifiers()) == 1 def test_init(self): entity = Operator() def test_geohash(self): entity = util.example_feed().operator(self.expect['onestopId']) assert entity.geohash() == '9qs' def test_from_json(self): feed = util.example_feed() entity = util.example_feed().operator(self.expect['onestopId']) roundtrip = Operator.from_json(entity.json()) self._sanity(roundtrip) def test_json(self): entity = util.example_feed().operator(self.expect['onestopId']) data = entity.json() for k in ['name','onestopId','type']: assert data[k] == self.expect[k] assert len(data['features']) == 14 def test_routes(self): entity = util.example_feed().operator(self.expect['onestopId']) assert len(entity.routes()) == 5 def test_route(self): entity = util.example_feed().operator(self.expect['onestopId']) for i in entity.routes(): assert entity.route(i.onestop()) with self.assertRaises(ValueError): entity.route('none') def test_stops(self): entity = util.example_feed().operator(self.expect['onestopId']) assert len(entity.stops()) == 9 def test_stop(self): entity = util.example_feed().operator(self.expect['onestopId']) for i in entity.stops(): assert entity.stop(i.onestop()) with self.assertRaises(ValueError): entity.stop('none')

31.859155

74

0.651636

cca0cfd65ae7e00b14583c214174fdf2b0828e7f

633

py

Python

vcx/wrappers/python3/setup.py

evernym/indy-sdk

714d449353518f929d9787d3156af785e2a42ccb

[ "Apache-2.0" ]

5

2018-04-09T12:26:28.000Z

2019-06-12T01:45:30.000Z

vcx/wrappers/python3/setup.py

evernym/indy-sdk

714d449353518f929d9787d3156af785e2a42ccb

[ "Apache-2.0" ]

9

2019-01-22T22:31:54.000Z

2019-04-11T21:45:09.000Z

vcx/wrappers/python3/setup.py

evernym/indy-sdk

714d449353518f929d9787d3156af785e2a42ccb

[ "Apache-2.0" ]

19

2018-04-25T16:08:43.000Z

2022-01-11T10:18:38.000Z

from setuptools import setup, find_packages import os PKG_VERSION = os.environ.get('PACKAGE_VERSION') or '0.2.4' PKG_NAME = os.environ.get('PACKAGE_NAME') or 'python3-wrapper-vcx' def get_version(): try: return os.environ['VCX_VERSION'] except KeyError: return '0.2.0' setup( name=PKG_NAME, version=get_version(), description='Python 3 wrapper for libcxs', long_description='None...for now', author='Devin Fisher, Ryan Marsh, Mark Hadley, Doug Wightman', author_email='ryan.marsh@evernym.com', include_package_data=True, packages=find_packages(exclude=['demo', 'tests']) )

26.375

66

0.696682

67d3173dc87d11acdf55f9134ba7a7426c31319b

12,769

py

Python

src/platform_vision/scripts/platform_vision/SlaveCameraController.py

ahmohamed1/activeStereoVisionPlatform

6c928ca242e4de68c7b15a8748bff1d9f7fa1382

[ "MIT" ]

null

src/platform_vision/scripts/platform_vision/SlaveCameraController.py

ahmohamed1/activeStereoVisionPlatform

6c928ca242e4de68c7b15a8748bff1d9f7fa1382

[ "MIT" ]

null

src/platform_vision/scripts/platform_vision/SlaveCameraController.py

ahmohamed1/activeStereoVisionPlatform

6c928ca242e4de68c7b15a8748bff1d9f7fa1382

[ "MIT" ]

null

#!/usr/bin/env python import rospy import cv2 import sys import numpy as np import math import argparse from geometry_msgs.msg import Twist from geometry_msgs.msg import Vector3, Pose2D from sensor_msgs.msg import Image from cv_bridge import CvBridge, CvBridgeError from std_msgs.msg import Int64 from std_msgs.msg import Float64 from std_msgs.msg import Bool import os.path import platform_vision.PNCC as PNCC import platform_vision.BaseFeatureMatching as BaseFeatureMatching from platform_controller.vergincyDepthClass import DrawTrackingSystem VERBOSE = True DEBUG = True class SlaveCameraController: def __init__(self, activeTilitController=False,algorithmToUse= 'PNCC', scaleDown = 0): self.drawTrackingSystem = DrawTrackingSystem() cv2.namedWindow('Slave Camera', cv2.WINDOW_NORMAL) cv2.resizeWindow('Slave Camera', (640,420)) cv2.moveWindow('Slave Camera', 1000, 600) self.motorPublisher = rospy.Publisher('/right/pan/move', Float64, queue_size=2) self.left_image_sub = rospy.Subscriber('/stereo/left/image_raw', Image, self.left_image_callback) self.right_image_sub = rospy.Subscriber('/stereo/right/image_raw', Image, self.right_image_callback) self.templateSizeSub = rospy.Subscriber('/templateSize', Pose2D, self.templateSizeCallBack) self.bridge = CvBridge() self.OnTargetPublisher = rospy.Publisher('/right/onTarget', Bool, queue_size=1) self.fileExcite = False self.activeTilitController = activeTilitController if self.activeTilitController: self.slaveTiltMotorPub = rospy.Publisher('/right/tilt/move', Float64, queue_size=2) self.motorMinLimitTilt = -37 self.motorMaxLimitTilt = 37 self.left_image = None self.right_image = None self.savenumber = 0 self.motorCountForTerminate = 0 # Define the pyramid algorithm self.ScaleDown = scaleDown if self.ScaleDown > 0: # self.imageSize = np.array([920, 640]) self.imageSize = np.array([2048/self.ScaleDown , 1080/self.ScaleDown]) self.templateSize = 80 self.thresholdMotorController = np.array([20,6]) pyramidLevel = 4 self.scaleTemplate = 0.5 else: self.imageSize = np.array([2048 , 1080]) self.templateSize = 200 self.thresholdMotorController = np.array([50,10]) pyramidLevel = 7 self.scaleTemplate = 1.0 self.algorithmToUse = algorithmToUse self.featueMatchingAlgorithmState = False if self.algorithmToUse == 'PNCC': self.fastMatchingPyramid = PNCC.FastMatchingPyramid(self.imageSize, pyramidLevel=pyramidLevel, windowSize=self.templateSize, grayImage = False, showImage = True,drawDifferencesInImage= True, operatingName = 'Slave ') elif self.algorithmToUse == 'kaze' or self.algorithmToUse == 'FLANN' or self.algorithmToUse == 'Brute': self.featueMatchingAlgorithmState = True self.trackingFeature = BaseFeatureMatching.BaseFeatureMatching() self.exponatialGain = [0.0025, 0.0035] self.mapExponatialValue = [0.3, 0.35] self.motorMinLimit = -75 self.motorMaxLimit = 75 self.currentPos = [0.0, 0.0] self.stepDistance = 0.0001 self.motorPos = [Float64(), Float64()] i = 0 r = rospy.Rate(10) # 10hz while (i < 5): self.motorPos[0].data = self.currentPos[0] self.motorPos[1].data = self.currentPos[1] # set the motor to the zero position self.motorPublisher.publish(self.motorPos[0]) self.slaveTiltMotorPub.publish(self.motorPos[1]) r.sleep() i +=1 self.currentPos[1] = 8 while (i < 5): self.motorPos[1].data = self.currentPos[1] # set the motor to the zero position self.slaveTiltMotorPub.publish(self.motorPos[1]) r.sleep() i +=1 # sleep for 0.5 seconds rospy.sleep(.5) self.terminateButton = 1 def __del__(self): for i in range(10): self.currentPos = [0.0, 0.0] self.motorPublisher.publish(self.motorPos[0]) self.slaveTiltMotorPub.publish(self.motorPos[1]) def saveImage(self, templateImage): self.savenumber += 1 tempImgStr = '/home/abdulla/dev/Data/' + str(self.savenumber) + 'template.jpg' leftImgStr = '/home/abdulla/dev/Data/' + str(self.savenumber) + 'left.jpg' rightImgStr = '/home/abdulla/dev/Data/' + str(self.savenumber) + 'right.jpg' self.fileExcite = os.path.isfile(tempImgStr) while self.fileExcite: self.savenumber += 1 tempImgStr = '/home/abdulla/dev/Data/' + str(self.savenumber) + 'template.jpg' self.fileExcite = os.path.isfile(tempImgStr) print (self.savenumber) cv2.imwrite(tempImgStr, templateImage) cv2.imwrite(rightImgStr, self.right_image) cv2.imwrite(leftImgStr, self.left_image) print ('Image saved') def my_mouse_callback(self, event,x,y,flags,param): if event==cv2.EVENT_LBUTTONDOWN: if self.algorithmToUse == 'PNCC': self.saveImage(self.fastMatchingPyramid.getTemplate()) pass if event==cv2.EVENT_RBUTTONDOWN: self.terminateButton += 1 def moveToZero(self, pan=0.0, tilt=0.0): print('Motor move to ZERO position!!!') for i in range(10): self.motorPos[0].data = pan self.motorPos[1].data = tilt self.currentPos = [0.0, 0.0] # set the motor to the zero position self.motorPublisher.publish(self.motorPos[0]) self.slaveTiltMotorPub.publish(self.motorPos[1]) def moveMotor(self,value): state = False speed = 0 speed = np.sign(-value) * math.exp(abs(value)*self.exponatialGain[0])*self.mapExponatialValue[0] if abs(value) > self.thresholdMotorController[0] : self.currentPos[0] += speed self.motorPos[0].data = self.currentPos[0] # print("Motor speed: ", self.currentPos) if self.currentPos[0] < self.motorMaxLimit and self.currentPos[0] > self.motorMinLimit : self.motorPublisher.publish(self.motorPos[0]) elif abs(value) < self.thresholdMotorController[0] and abs(value) > self.thresholdMotorController[1]: self.currentPos[0] -= value * 0.001 self.motorPos[0].data = self.currentPos[0] # print("Motor speed: ", self.currentPos) self.motorPublisher.publish(self.motorPos[0]) else: # print ("Pan Center Position") state = True return state def TiltMoveMotor(self,value): state = False TiltSpeed = 0 TiltSpeed = np.sign(-value) * math.exp(abs(value)*self.exponatialGain[1])*self.mapExponatialValue[1] if abs(value) > self.thresholdMotorController[0] : self.currentPos[1] += TiltSpeed self.motorPos[1].data = self.currentPos[1] # print("Motor speed: ", self.TiltCurrentPos) if self.currentPos[1] < self.motorMaxLimitTilt and self.currentPos[1] > self.motorMinLimitTilt: self.slaveTiltMotorPub.publish(self.motorPos[1]) elif abs(value) < self.thresholdMotorController[0] and abs(value) > self.thresholdMotorController[1]: self.currentPos[1] -= value * 0.001 self.motorPos[1].data = self.currentPos[1] # print("Motor speed: ", self.TiltCurrentPos) self.slaveTiltMotorPub.publish(self.motorPos[1]) else: # print ("Tilt Center Position") state = True return state def convertROSToCV(self, data): try: cv_image = self.bridge.imgmsg_to_cv2(data, 'bgr8') if self.ScaleDown: return cv2.resize(cv_image, (self.imageSize[0], self.imageSize[1])) else: return cv_image except CvBridgeError, e: print e def left_image_callback(self, data): self.left_image = self.convertROSToCV(data) def right_image_callback(self, data): self.right_image = self.convertROSToCV(data) def templateSizeCallBack(self, data): # templateSize = data.data # self.moveToZero(pan=5, tilt=10) if self.ScaleDown > 0: templateSize = [data.x/self.ScaleDown, data.y/self.ScaleDown] else: templateSize = [data.x, data.y] if self.algorithmToUse == 'PNCC': # self.fastMatchingPyramid.setTemplateSize(int((templateSize*self.scaleTemplate)/1.8)) self.fastMatchingPyramid.setTemplateSize2D(templateSize) def computeTheCenterUsingDifferentAlgorithm(self, template,image): centerPoint = None if self.algorithmToUse == 'PNCC': self.fastMatchingPyramid.createTemplate(template, self.imageSize/2) # cv2.imshow("template image", self.fastMatchingPyramid.getTemplate()) # print("1") _img, centerPoint = self.fastMatchingPyramid.trackObject(image) # print("2") cv2.imshow('Slave Camera', _img) elif self.algorithmToUse == 'feature' or self.featueMatchingAlgorithmState: Size = self.templateSize template = template[self.imageSize[1]/2-Size:self.imageSize[1]/2+Size, self.imageSize[0]/2-Size:self.imageSize[0]/2+Size ] # _img, centerPoint = self.trackingFeature.BruteForceMatchingwithSIFTDescriptorsandRatioTest(template, image) _img, centerPoint = self.trackingFeature.algorithmDictionary[self.algorithmToUse](template, image) # kaze, FLANN, Brute # _img, centerPoint = self.trackingFeature.kaze_match(template, image) cv2.imshow('Slave Camera', _img) return centerPoint def trackObject(self, visualAttention = False): if visualAttention: self.motorCountForTerminate =0; rate = rospy.Rate(60) # 10hz cv2.setMouseCallback('Slave Camera', self.my_mouse_callback) panMotorstate = None tiltMotorState = None while not rospy.is_shutdown(): rate.sleep() # Publish the coordinate self.drawTrackingSystem.calculateThePosition() # print [x,y,z] if self.left_image is not None and self.right_image is not None: centerPoint = self.computeTheCenterUsingDifferentAlgorithm(self.left_image,self.right_image ) differences = self.calculateDifferences(centerPoint) panMotorstate = self.moveMotor(differences[0]) if self.activeTilitController: tiltMotorState = self.TiltMoveMotor(differences[1]) ikey = cv2.waitKey(3) if ikey == ord('q'): self.moveToZero() exit() elif ikey == ord('s'): if self.algorithmToUse == 'PNCC': # self.saveImage(self.fastMatchingPyramid.getTemplate()) pass if self.terminateButton == 2 : break # if visualAttention == True: boolState = Bool() boolState.data = False self.OnTargetPublisher.publish(boolState) if panMotorstate and tiltMotorState: self.motorCountForTerminate += 1 # print("count: " , self.motorCountForTerminate) if self.motorCountForTerminate > 35: # print('Target centered') self.motorCountForTerminate = 0 boolState.data = True self.OnTargetPublisher.publish(boolState) # break [x,y,z] = self.drawTrackingSystem.calculateThePosition() return [x,y,z] def calculateDifferences(self, centerPoint): if centerPoint is not None: return centerPoint - self.imageSize/2 else: return np.array([0 , 0]) def buildPyramid(self, image, maxleval): """Build image pyramid for level [0,...,maxlevel] """ imgpyr = [image] aux = image for i in range(0,maxleval): aux = cv2.pyrDown(aux) imgpyr.append(aux) imgpyr.reverse() return imgpyr

41.457792

134

0.608662

19f9dec0a5382290dfe8ea07a917e0029022e9b7

2,249

py

Python

jupyterlab/PreviewSearchPage.py

Larz60p/MakerProjectApril2019

2fd4d68aa66c1f4ad3b01f6a9589a078319280d7

[ "MIT" ]

1

2019-04-25T22:53:52.000Z

jupyterlab/PreviewSearchPage.py

Larz60p/MakerProjectApril2019

2fd4d68aa66c1f4ad3b01f6a9589a078319280d7

[ "MIT" ]

null

jupyterlab/PreviewSearchPage.py

Larz60p/MakerProjectApril2019

2fd4d68aa66c1f4ad3b01f6a9589a078319280d7

[ "MIT" ]

null

# PreviesSearchPage.py from selenium import webdriver from selenium.webdriver.common.by import By from bs4 import BeautifulSoup import BusinessPaths import time import PrettifyPage import CreateDict import json import sys class PreviewSearchPage: def __init__(self): self.bpath = BusinessPaths.BusinessPaths() self.pp = PrettifyPage.PrettifyPage() self.cd = CreateDict.CreateDict() self.analyze_page() def start_browser(self): caps = webdriver.DesiredCapabilities().FIREFOX caps["marionette"] = True self.browser = webdriver.Firefox(capabilities=caps) def stop_browser(self): self.browser.close() def save_page(self, filename): soup = BeautifulSoup(self.browser.page_source, "lxml") with filename.open('w') as fp: fp.write(self.pp.prettify(soup, 2)) def analyze_page(self): self.start_browser() self.get_search_page('Andover') self.stop_browser() def get_search_page(self, searchitem): # pick city with multiple pages url = self.bpath.base_url self.browser.get(url) time.sleep(2) print(f'Main Page URL: {self.browser.current_url}') self.browser.find_element(By.XPATH, '/html/body/div[2]/div[4]/div/form/div/div/span[1]/select/option[3]').click() searchbox = self.browser.find_element(By.XPATH, '//*[@id="query"]') searchbox.clear() searchbox.send_keys(searchitem) self.browser.find_element(By.XPATH, '/html/body/div[2]/div[4]/div/form/div/div/span[3]/button').click() time.sleep(2) print(f'Results Page 1 URL: {self.browser.current_url}') # get page 2 # find next page button and click self.browser.find_element(By.XPATH, '/html/body/div[2]/div/div[2]/div[3]/div[2]/div/span[1]/a/icon').click() time.sleep(2) print(f'Results Page 2 URL: {self.browser.current_url}') # Get url of a detail page self.browser.find_element(By.XPATH, '/html/body/div[2]/div/div[2]/table/tbody/tr[1]/td[1]/a').click() time.sleep(2) print(f'Detail Page URL: {self.browser.current_url}') if __name__ == '__main__': PreviewSearchPage()

34.075758

121

0.649622

d78f98c344ff3704bf6b81bef097dee15b204be1

6,805

py

Python

zeitsprung/scraping.py

munterfinger/zeitsprung

8b1a539069cd0d5508b5ce419fc2ba8c26aaecf2

[ "MIT" ]

null

zeitsprung/scraping.py

munterfinger/zeitsprung

8b1a539069cd0d5508b5ce419fc2ba8c26aaecf2

[ "MIT" ]

109

2020-10-03T16:41:30.000Z

2021-09-16T21:03:34.000Z

zeitsprung/scraping.py

munterfinger/zeitsprung

8b1a539069cd0d5508b5ce419fc2ba8c26aaecf2

[ "MIT" ]

null

from bs4 import BeautifulSoup from datetime import datetime, timezone from io import BytesIO from json import loads from pathlib import Path from pydub import AudioSegment from requests import get from time import sleep from typing import Union from zeitsprung.base import Base from zeitsprung.database import SQLiteEngine class Scraper(Base): """Class for scraping and preprocessing the data from the 'www.zeitsprung.fm' website.""" def __init__(self, data_folder: str, update_interval: int = 24*60*60, reset: bool = False, verbose: bool = True) -> None: """ Class constructor for the Scraper class. Parameters ---------- data_folder : str Folder to store the database and audio files. Is created or if existing, the files will bind to this. update_interval : int, default 24*60*60 Interval to wait for updating after the last episode is fetched. reset : bool, default False Ignore and reset an existing database.? verbose : bool, default True Print messages about the activities conducted by a class instance. """ super().__init__(verbose) self.created_at = datetime.now(timezone.utc).replace(microsecond=0).isoformat() self.data_folder = Path(data_folder) self.db = SQLiteEngine(self.data_folder / 'zeitsprung.db') self.update_interval = update_interval self.verbose = verbose if (self.data_folder / 'zeitsprung.db').exists() and reset: self._print(f"Overwriting existing directory structure in '{data_folder}'.") Path(data_folder).mkdir(parents=True, exist_ok=True) (Path(data_folder) / 'audio').mkdir(parents=True, exist_ok=True) self.db.setup_schema() elif (self.data_folder / 'zeitsprung.db').exists() and not reset: self._print(f"Binding to existing directory structure in '{data_folder}'.") else: self._print(f"Creating directory structure in '{data_folder}'.") Path(data_folder).mkdir(parents=True, exist_ok=True) (Path(data_folder) / 'audio').mkdir(parents=True, exist_ok=True) self.db.setup_schema() self.current_episode = self.db.query_last_episode_id() def __str__(self) -> str: """ Print function of the class. Returns ------- str A string, which describes the class instance. """ return f"Scraper created at '{self.created_at}' with db connection to " \ f"'{self.db.db_file}', current episode is 'ZS{self.current_episode}'." def get_episode_meta(self, i: int) -> Union[list, None]: """ Gets the episodes meta data (title, description, publication and modified at date) and stores it to the database. Parameters ---------- i : int Number of the episode (i - 1). Returns ------- list: List containing the meta data of the episode. """ url = f"https://www.zeitsprung.fm/podcast/zs{'0'+str(i) if i < 10 else str(i)}/" self._print(f'Requesting meta data of episode {i}: {url}') html_doc = get(url) if html_doc.status_code == 200: soup = BeautifulSoup(html_doc.content, 'html.parser') script_content = loads(soup.find("script").contents[0]) title = soup.find('title').get_text(strip=True).split(":") return [ i, datetime.fromisoformat(self.search_key('datePublished', script_content['@graph'])), datetime.fromisoformat(self.search_key('dateModified', script_content['@graph'])), title[0], title[1][1:], soup.find("meta", {"property": "og:description"}).get('content'), soup.find("meta", {"property": "og:url"}).get('content'), None if soup.find("ul", {"class": "episode_download_list"}) is None else soup.find( "ul", {"class": "episode_download_list"}).find_all('a')[0].get('href') ] else: # html_doc.status_code == 404: return None @staticmethod def search_key(key, dict_obj): for entry in dict_obj: if key in entry: return entry[key] def get_episode_audio(self, url: str) -> Union[AudioSegment, None]: """ Downloads the audio of a specified episode. Parameters ---------- url : str URL to download the audio from. Returns ------- AudioSegment: The audio of the episode. """ if url is not None: self._print(f"Fetching audio file from {url}") audio_mp3 = BytesIO(get(url, allow_redirects=True).content) audio = AudioSegment.from_file(audio_mp3) return audio else: self._print('No audio file available for this episode.') return None def save_episode_audio(self, audio: AudioSegment, file_name: str) -> None: """ Save the audio file of an episode and as '.wav' file. Parameters ---------- audio : AudioSegment Audio file to save. file_name : str File name with path, where the file should be saved to. Returns ------- None """ self._print(f"Exporting audio sequence to file '{file_name}'") audio.export(file_name, format="wav") def run(self) -> None: """ Start the scraper, which will download the meta data and audio files of all not yet existing episodes in the database. Returns ------- None """ while True: meta_row = self.get_episode_meta(self.current_episode + 1) if meta_row is not None: self.db.insert_meta_row(meta_row) audio = self.get_episode_audio(meta_row[7]) if audio is not None: audio_row = [ self.current_episode + 1, self.data_folder / 'audio' / f'{str(self.current_episode + 1).zfill(3)}.wav', round(audio.duration_seconds), audio.frame_rate, audio.frame_width ] self.save_episode_audio(audio, audio_row[1]) self.db.insert_audio_row(audio_row) self.current_episode += 1 else: self._print(f"Episode not yet published, pausing for {int(self.update_interval/(60*60))} hours.") sleep(self.update_interval)

36.390374

116

0.574137

e5d9ec84f3a938f06ee5218780c541b0b6a8fb9a

1,789

py

Python

decrypt_oracle/setup.py

Alan01252/aws-encryption-sdk-python

67cf41715b9841d374be07e39fd9ded9fb824280

[ "Apache-2.0" ]

null

decrypt_oracle/setup.py

Alan01252/aws-encryption-sdk-python

67cf41715b9841d374be07e39fd9ded9fb824280

[ "Apache-2.0" ]

null

decrypt_oracle/setup.py

Alan01252/aws-encryption-sdk-python

67cf41715b9841d374be07e39fd9ded9fb824280

[ "Apache-2.0" ]

null

"""API Gateway + Lambda decryption oracle using the AWS Encryption SDK for Python.""" import os import re from setuptools import find_packages, setup VERSION_RE = re.compile(r"""__version__ = ['"]([0-9.]+)['"]""") HERE = os.path.abspath(os.path.dirname(__file__)) def read(*args): """Read complete file contents.""" return open(os.path.join(HERE, *args)).read() def get_version(): """Read the version from this module.""" init = read("src", "aws_encryption_sdk_decrypt_oracle", "__init__.py") return VERSION_RE.search(init).group(1) def get_requirements(): """Read the requirements file.""" requirements = read("requirements-actual.txt") return [r for r in requirements.strip().splitlines()] setup( name="aws-encryption-sdk-decrypt-oracle", packages=find_packages("src"), package_dir={"": "src"}, version=get_version(), author="Amazon Web Services", maintainer="Amazon Web Services", author_email="aws-cryptools@amazon.com", url="https://github.com/awslabs/aws-encryption-sdk-python", description="API Gateway + Lambda decryption oracle using the AWS Encryption SDK for Python", long_description=read("README.rst"), keywords="aws-encryption-sdk aws kms encryption", license="Apache License 2.0", install_requires=get_requirements(), classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Natural Language :: English", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: Implementation :: CPython", "Topic :: Security", "Topic :: Security :: Cryptography", ], )

33.12963

97

0.667412

59270d72e76d7b6eb00ef832efda884d1036031a

5,243

py

Python

models/train_classifier.py

dakcicek/disaster_response

c1abd82a22fa8be912eea362664106c60acc4f3d

[ "MIT" ]

null

models/train_classifier.py

dakcicek/disaster_response

c1abd82a22fa8be912eea362664106c60acc4f3d

[ "MIT" ]

null

models/train_classifier.py

dakcicek/disaster_response

c1abd82a22fa8be912eea362664106c60acc4f3d

[ "MIT" ]

null

#!/usr/bin/env python # # ML Pipeline Preparation import nltk nltk.download('punkt') nltk.download('wordnet') import re import sys import pickle import pandas as pd import numpy as np from sqlalchemy import create_engine import nltk nltk.download(['punkt', 'wordnet', 'stopwords']) from nltk.corpus import stopwords from nltk.stem.wordnet import WordNetLemmatizer from nltk.stem.porter import PorterStemmer from nltk import word_tokenize, sent_tokenize from sklearn.pipeline import Pipeline from sklearn.multioutput import MultiOutputClassifier from sklearn.metrics import precision_score, recall_score, f1_score from sklearn.model_selection import learning_curve,GridSearchCV,RandomizedSearchCV from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.metrics import make_scorer from time import time from nltk.stem import SnowballStemmer from string import punctuation from sklearn.metrics import accuracy_score from sklearn.metrics import classification_report from sklearn.ensemble import AdaBoostClassifier def load_data(database_filepath): """ This function loads data from given database path and returns a dataframe Input: database_filepath: database file path Output: X: traing message list Y: training target category names """ # load data from database engine = create_engine('sqlite:///'+ database_filepath) df = pd.read_sql_table('DisasterResponse',engine) # define features and target X = df.message y = df.iloc[:,4:] category_names = list(df.columns[4:]) return X, y def tokenize(text): """ Tokenization function to process the text data to normalize, lemmatize, and tokenize text. Input: Text data Output: List of clean tokens """ # remove punctations text = ''.join([c for c in text if c not in punctuation]) #tokenize text tokens = word_tokenize(text) # initiate lemmatizer lemmatizer = WordNetLemmatizer() clean_tokens = [] for token in tokens: # lemmatize, normalize case, and remove leading/trailing white space clean_tok = lemmatizer.lemmatize(token).lower().strip() clean_tokens.append(clean_tok) return clean_tokens def build_pipeline(): print('Building pipeline..') """ improved_pipeline = Pipeline([ ('vect', CountVectorizer(tokenizer=tokenize, stop_words='english')), ('tfidf', TfidfTransformer(norm='l2')), ('clf', MultiOutputClassifier(RandomForestClassifier(random_state=42, n_estimators=60,max_depth=4,n_jobs=4,verbose=3 ))) ]) return improved_pipeline """ # model pipeline grid_search_pipeline = Pipeline([ ('vect', CountVectorizer(tokenizer=tokenize, stop_words='english')), ('tfidf', TfidfTransformer(norm='l2')), ('clf', MultiOutputClassifier((AdaBoostClassifier()))) ]) parameters = { 'clf__estimator__n_estimators' : [5,10,50], 'clf__estimator__learning_rate' : [0.5,1], } grid_search_clf = GridSearchCV(grid_search_pipeline, parameters) return grid_search_clf def evaluate_model(model, X_test, Y_test): """ Prints the classification report for the given model and test data Input: model: trained model X_test: test data for the predication Y_test: true test labels for the X_test data Output: None """ #Accuracy over all y_pred = model.predict(X_test) print('Overall Accuracy: {}'.format( accuracy_score(Y_test.iloc[:,1:].values, np.array([x[1:] for x in y_pred])))) # Calculate the accuracy for each of them. for i in range(len(Y_test.columns)): print("Category Name:", Y_test.columns[i],"\n", classification_report(Y_test.iloc[:, i].values, y_pred[:, i])) print('Accuracy of %25s: %.2f' %(Y_test.columns[i], accuracy_score(Y_test.iloc[:, i].values, y_pred[:,i]))) def save_model(model, model_file_name): pickle.dump(model, open(model_file_name, 'wb')) def main(): if len(sys.argv) == 3: database_filepath, model_filepath = sys.argv[1:] print('Loading data...\n DATABASE: {}'.format(database_filepath)) X, Y = load_data(database_filepath) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2) print('Building model...') model = build_pipeline() print('Training model...') model.fit(X_train, Y_train) print('Evaluating model...') evaluate_model(model, X_test, Y_test) print('Saving model...\n MODEL: {}'.format(model_filepath)) save_model(model, model_filepath) print('Trained model saved!') else: print('Please provide the filepath of the disaster messages database ' 'as the first argument and the filepath of the pickle file to ' 'save the model to as the second argument. \n\nExample: python ' 'train_classifier.py ../data/DisasterResponse.db classifier.pkl') if __name__ == '__main__': main()

32.565217

128

0.692924

12514602e85629e507f4571067c346e3dc161f50

6,641

py

Python

utils/tag_util.py

LindgeW/BiaffineNER

0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf

[ "Apache-2.0" ]

13

2020-10-24T08:09:27.000Z

2022-03-04T02:56:50.000Z

utils/tag_util.py

LindgeW/BiaffineNER

0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf

[ "Apache-2.0" ]

4

2021-04-10T10:06:37.000Z

2022-03-08T10:25:53.000Z

utils/tag_util.py

LindgeW/BiaffineNER

0ae179e9ff731362f6c8ba6d0b24485ad45e8bbf

[ "Apache-2.0" ]

3

2020-10-26T06:38:17.000Z

2021-08-19T18:29:49.000Z

''' CWS / POS标签规范： 1、BI 2、BIS 3、BIES NER标签规范： 1、BIO 2、BISO 3、BIESO ''' def bi2bies(bi_tags): tag_len = len(bi_tags) for i, t in enumerate(bi_tags): if t == 'B': if i + 1 == tag_len or 'I' != bi_tags[i+1]: bi_tags[i] = 'S' elif t == 'I': if i + 1 == tag_len or 'I' != bi_tags[i+1]: bi_tags[i] = 'E' return bi_tags # BIO -> BIOES def bio2bioes(bio_tags): tag_len = len(bio_tags) for i, t in enumerate(bio_tags): if 'B-' in t and (i+1 == tag_len or 'I-' not in bio_tags[i+1]): _type = bio_tags[i].split('-')[1] bio_tags[i] = 'S-' + _type elif 'I-' in t and (i+1 == tag_len or 'I-' not in bio_tags[i+1]): _type = bio_tags[i].split('-')[1] bio_tags[i] = 'E-' + _type return bio_tags # =============================CWS============================== # # BI def extract_cws_bi_span(tag_seq): spans = [] s = 0 n = len(tag_seq) start = False for i, tag in enumerate(tag_seq): if tag == 'B': s = i if i + 1 == n or tag_seq[i + 1] != 'I': spans.append((s, i)) start = False else: start = True elif tag == 'I': if start: if i + 1 == n or tag_seq[i + 1] != 'I': spans.append((s, i)) start = False else: start = False return spans # BIS def extract_cws_bis_span(tag_seq): spans = [] s = 0 n = len(tag_seq) start = False for i, tag in enumerate(tag_seq): if tag == 'S': spans.append((i, i)) start = False elif tag == 'B': s = i start = True elif tag == 'I': if start: if i + 1 == n or tag_seq[i + 1] != 'I': spans.append((s, i)) start = False else: start = False return spans # BIES / BMES def extract_cws_bies_span(tag_seq): spans = [] s = 0 start = False for i, tag in enumerate(tag_seq): if tag == 'S': spans.append((i, i)) start = False elif tag == 'B': s = i start = True elif tag == 'E': if start: spans.append((s, i)) start = False else: if tag not in ['I', 'M']: start = False return spans # =============================NER============================== # def extract_ner_bio_span(tag_seq: list): span_res = [] n = len(tag_seq) s = 0 type_b = None start = False for i, tag in enumerate(tag_seq): if tag == 'O': start = False elif tag.startswith('B-'): s = i type_b = tag.split('-')[1] if i + 1 == n or not tag_seq[i+1].startswith('I-'): span_res.append((s, i, type_b)) start = False else: start = True elif tag.startswith('I-'): if start and tag.split('-')[1] == type_b: if i + 1 == n or not tag_seq[i+1].startswith('I-'): span_res.append((s, i, type_b)) start = False return span_res def extract_ner_biso_span(tag_seq): spans = [] s = 0 n = len(tag_seq) start = False type_b = None for i, tag in enumerate(tag_seq): if tag == 'O': start = False elif tag.startswith('S-'): spans.append((i, i, tag.split('-')[1])) start = False elif tag.startswith('B-'): s = i start = True type_b = tag.split('-')[1] elif tag.startswith('I-'): if start and type_b == tag.split('-')[1]: if i + 1 == n or not tag_seq[i+1].startswith('I-'): spans.append((s, i, type_b)) start = False return spans def extract_ner_bieso_span(tag_seq): spans = [] s = 0 start = False type_b = None for i, tag in enumerate(tag_seq): if tag == 'O': start = False elif tag.startswith('S-'): spans.append((i, i, tag.split('-')[1])) start = False elif tag.startswith('B-'): s = i type_b = tag.split('-')[1] start = True elif tag.startswith('E-'): if start and tag.split('-')[1] == type_b: spans.append((s, i, type_b)) start = False else: if tag.split('-')[1] != type_b: start = False return spans def seq_match(main_str, sub_strs: list): N = len(main_str) match_spans = set() for sub_str in sub_strs: L = len(sub_str) for i in range(N - L + 1): if main_str[i: i+L] == sub_str: match_spans.add((i, i+L-1)) return match_spans def span2tags(spans, seq_len, default_tag='O'): ''' :param spans: [(s, e, cls), ...] :param seq_len: sequence length :param default_tag: default tag in sequence :return: ''' tags = [default_tag] * seq_len for one_span in spans: if len(one_span) == 3: s, e, cls = one_span cls = '-'+cls elif len(one_span) == 2: s, e = one_span cls = '' else: raise ValueError tags[s] = 'B' + cls tags[e] = 'E' + cls if s == e: tags[s] = 'S' + cls elif s < e: tags[s+1: e] = ['I' + cls] * (e - s - 1) else: raise IndexError return tags def test_(): # x = 'I I S B S I B X I S B I I S S B I S S B I I'.split() # x = 'I I B B X I B B I I B I B I B B I B I B I I'.split() # x = 'B I E S B E S S B I I E B I S B E S E'.split() # y = extract_cws_bi_span(x) # y = extract_cws_bis_span(x) # y = extract_cws_bies_span(x) # x = 'O I-per S-org S-org I-per S-org B-loc I-loc E-loc O B-org E-org O B-per E-per B-loc E-per O S-LOC E-LOC'.split() x = 'O B-PER I-PER I-LOC B-PER I-PER B-LOC B-ORG I-ORG O B-LOC I-LOC I-PER'.split() y = extract_ner_bio_span(x) # y = extract_ner_biso_span(x) # y = extract_ner_bieso_span(x) print(y) spans = [(1, 2), (3, 3), (4, 6)] tags = span2tags(spans, 10) print(tags) main_str = '我是一个目前在阿里巴巴实习的研究生，方向是NLP' sub_str = ['阿里巴巴', 'NLP'] res = seq_match(main_str, sub_str) tags = span2tags(res, len(main_str)) print(res) print(tags)

26.043137

123

0.455504

1853d6c5b5a086a995ab5e8a253edec8d84dccfa

4,032

py

Python

mistral/rpc/kombu/kombu_listener.py

saadkhan44/mistral

4d95c032c7dafbc1a0079af17424db1d9a5851c5

[ "Apache-2.0" ]

3

2015-08-28T04:57:56.000Z

2017-03-27T10:59:56.000Z

mistral/rpc/kombu/kombu_listener.py

saadkhan44/mistral

4d95c032c7dafbc1a0079af17424db1d9a5851c5

[ "Apache-2.0" ]

21

2015-04-14T22:41:53.000Z

2019-02-20T09:30:10.000Z

mistral/rpc/kombu/kombu_listener.py

saadkhan44/mistral

4d95c032c7dafbc1a0079af17424db1d9a5851c5

[ "Apache-2.0" ]

12

2015-08-14T02:27:37.000Z

2020-12-31T10:09:21.000Z

# Copyright (c) 2016 Intel Corporation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import itertools from kombu.mixins import ConsumerMixin import six import threading from oslo_log import log as logging from mistral.rpc.kombu import base as kombu_base LOG = logging.getLogger(__name__) class KombuRPCListener(ConsumerMixin): def __init__(self, connections, callback_queue): self._results = {} self._connections = itertools.cycle(connections) self._callback_queue = callback_queue self._thread = None self.connection = six.next(self._connections) self.ready = threading.Event() def add_listener(self, correlation_id): self._results[correlation_id] = six.moves.queue.Queue() def remove_listener(self, correlation_id): if correlation_id in self._results: del self._results[correlation_id] def get_consumers(self, Consumer, channel): consumers = [Consumer( self._callback_queue, callbacks=[self.on_message], accept=['pickle', 'json'] )] self.ready.set() return consumers def start(self): if self._thread is None: self._thread = threading.Thread(target=self.run) self._thread.daemon = True self._thread.start() def on_message(self, response, message): """Callback on response. This method is automatically called when a response is incoming and decides if it is the message we are waiting for - the message with the result. :param response: the body of the amqp message already deserialized by kombu :param message: the plain amqp kombu.message with additional information """ LOG.debug("Got response: {0}".format(response)) try: message.ack() except Exception as e: LOG.exception("Failed to acknowledge AMQP message: %s", e) else: LOG.debug("AMQP message acknowledged.") correlation_id = message.properties['correlation_id'] queue = self._results.get(correlation_id) if queue: result = { kombu_base.TYPE: 'error' if message.properties.get('type') == 'error' else None, kombu_base.RESULT: response } queue.put(result) else: LOG.debug( "Got a response, but seems like no process is waiting for" "it [correlation_id={0}]".format(correlation_id) ) def get_result(self, correlation_id, timeout): return self._results[correlation_id].get(block=True, timeout=timeout) def on_connection_error(self, exc, interval): self.ready.clear() self.connection = six.next(self._connections) LOG.debug("Broker connection failed: %s", exc) LOG.debug( "Sleeping for %s seconds, then retrying connection", interval ) def wait_ready(self, timeout=10.0): """Waits for the listener to successfully declare the consumer :param timeout: timeout for waiting in seconds :return: same as :func:`~threading.Event.wait` :rtype: bool """ if self.ready.wait(timeout=timeout): return self.connection else: return False

31.5

78

0.62004

06712c8de3e40d29268f0df73a91cbf96d883c9a

2,207

py

Python

numpy_serial.py

berquist/gemm

4eca3f6c4f20199b8d64d8cf78f926d79131f69f

[ "MIT" ]

null

numpy_serial.py

berquist/gemm

4eca3f6c4f20199b8d64d8cf78f926d79131f69f

[ "MIT" ]

null

numpy_serial.py

berquist/gemm

4eca3f6c4f20199b8d64d8cf78f926d79131f69f

[ "MIT" ]

null

from __future__ import division from __future__ import print_function import time import numpy as np def numpy_vec(scale, X, Y, result): result = (scale * X) + Y return def numpy_loop(N, scale, X, Y, result): for i in range(N): result[i] = (scale * X[i]) + Y[i] return def main(): N = 2 * 1000 * 1000 num_run_times = 5 # penalty for using a double (np.float64)? scale = 2.0 arrayX = np.empty(shape=(N), dtype=np.float32) arrayY = np.empty(shape=(N), dtype=np.float32) result = np.empty(shape=(N), dtype=np.float32) print('Number of repeats: {}'.format(num_run_times)) # initialize array values # ...do it the naive way for now for i in range(N): arrayX[i] = i arrayY[i] = i result[i] = 0.0 list_X = [i for i in range(N)] list_Y = [i for i in range(N)] list_r = [0.0 for i in range(N)] min_numpy_vec = 1.0e30 for i in range(num_run_times): start_time = time.time() numpy_vec(scale, arrayX, arrayY, result) end_time = time.time() min_numpy_vec = min(min_numpy_vec, end_time - start_time) print('[numpy vec]:\t\t{}'.format(min_numpy_vec)) # clear out the buffer result[:] = 0.0 min_numpy_loop = 1.0e30 for i in range(num_run_times): start_time = time.time() numpy_loop(N, scale, arrayX, arrayY, result) end_time = time.time() min_numpy_loop = min(min_numpy_loop, end_time - start_time) print('[numpy loop]:\t\t{}'.format(min_numpy_loop)) # clear out the buffer result[:] = 0.0 min_list_loop = 1.0e30 for i in range(num_run_times): start_time = time.time() numpy_loop(N, scale, list_X, list_Y, list_r) end_time = time.time() min_list_loop = min(min_list_loop, end_time - start_time) print('[list loop]:\t\t{}'.format(min_list_loop)) print('[numpy loop]/[numpy vec]:\t\t{}'.format(min_numpy_loop / min_numpy_vec)) print('[numpy loop]/[list loop]:\t\t{}'.format(min_numpy_loop / min_list_loop)) print('[list loop]/[numpy vec]:\t\t{}'.format(min_list_loop / min_numpy_vec)) return if __name__ == '__main__': main()

25.079545

83

0.615768

725c0b570f414f846ae3ba4da2e911dfdd4cd0e3

9,706

py

Python

setup.py

roschler/mythril

586b321a9468716946bf67592ac2be6c8502ed4c

[ "MIT" ]

null

setup.py

roschler/mythril

586b321a9468716946bf67592ac2be6c8502ed4c

[ "MIT" ]

null

setup.py

roschler/mythril

586b321a9468716946bf67592ac2be6c8502ed4c

[ "MIT" ]

null

from setuptools import setup, find_packages from setuptools.command.install import install import os # Package version (vX.Y.Z). It must match git tag being used for CircleCI # deployment; otherwise the build will failed. VERSION = "v0.17.5" class VerifyVersionCommand(install): """Custom command to verify that the git tag matches our version""" description = 'verify that the git tag matches our version' def run(self): tag = os.getenv('CIRCLE_TAG') if (tag != VERSION): info = "Git tag: {0} does not match the version of this app: {1}".format(tag, VERSION) sys.exit(info) long_description = ''' Mythril is a security analysis tool for Ethereum smart contracts. It uses concolic analysis to detect various types of issues. Use it to analyze source code or as a nmap-style black-box blockchain scanner (an "ethermap" if you will). Installation and setup ---------------------- Install from Pypi: .. code:: bash $ pip install mythril Or, clone the GitHub repo to install the newest master branch: .. code:: bash $ git clone https://github.com/b-mueller/mythril/ $ cd mythril $ python setup.py install Note that Mythril requires Python 3.5 to work. Function signatures ~~~~~~~~~~~~~~~~~~~ Whenever you disassemble or analyze binary code, Mythril will try to resolve function names using its local signature database. The database must be provided at ``~/.mythril/signatures.json``. You can start out with the `default file <signatures.json>`__ as follows: :: $ cd ~/.mythril $ wget https://raw.githubusercontent.com/b-mueller/mythril/master/signatures.json When you analyze Solidity code, new function signatures are added to the database automatically. Security analysis ----------------- Run ``myth -x`` with one of the input options described below to run the analysis. This will run the Python modules in the `/analysis/modules <https://github.com/b-mueller/mythril/tree/master/mythril/analysis/modules>`__ directory. Mythril detects a range of `security issues <security_checks.md>`__, including integer underflows, owner-overwrite-to-Ether-withdrawal, and others. However, the analysis will not detect business logic issues and is not equivalent to formal verification. Analyzing Solidity code ~~~~~~~~~~~~~~~~~~~~~~~ In order to work with Solidity source code files, the `solc command line compiler <http://solidity.readthedocs.io/en/develop/using-the-compiler.html>`__ needs to be installed and in path. You can then provide the source file(s) as positional arguments, e.g.: .. code:: bash $ myth -x myContract.sol Alternatively, compile the code on `Remix <http://remix.ethereum.org>`__ and pass the runtime binary code to Mythril: .. code:: bash $ myth -x -c "0x5060(...)" If you have multiple interdependent contracts, pass them to Mythril as separate input files. Mythril will map the first contract to address "0x0000(..)", the second one to "0x1111(...)", and so forth (make sure that contract addresses are set accordingly in the source). The contract passed in the first argument will be executed as the "main" contract. .. code:: bash $ myth -x myContract.sol myLibrary.sol Working with on-chain contracts ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To analyze contracts on the blockchain you need an Ethereum node. By default, Mythril will query a local node via RPC. Alternatively, you can use `INFURA <https://infura.io>`__: :: $ myth --infura-mainnet -x -a 0x5c436ff914c458983414019195e0f4ecbef9e6dd If you are planning to do batch operations or use the contract search features, running a `go-ethereum <https://github.com/ethereum/go-ethereum>`__ node is recommended. Start your local node as follows: .. code:: bash $ geth --rpc --rpcapi eth,debug --syncmode fast Specify the target contract with the ``-a`` option: .. code:: bash $ myth -x -a 0x5c436ff914c458983414019195e0f4ecbef9e6dd -v1 Adding the ``-l`` flag will cause Mythril to automatically retrieve dependencies, such as library contracts: .. code:: bash $ myth -x -a 0xEbFD99838cb0c132016B9E117563CB41f2B02264 -l -v1 Control flow graph ------------------ The ``-g FILENAME`` option generates an `interactive jsViz graph <http://htmlpreview.github.io/?https://github.com/b-mueller/mythril/blob/master/static/mythril.html>`__: .. code:: bash $ myth -g ./graph.html -a 0xEbFD99838cb0c132016B9E117563CB41f2B02264 -l .. figure:: https://raw.githubusercontent.com/b-mueller/mythril/master/static/callgraph7.png :alt: Call graph callgraph [STRIKEOUT:The "bounce" effect, while awesome (and thus enabled by default), sometimes messes up the graph layout.] Try adding the ``--enable-physics`` flag for a very entertaining "bounce" effect that unfortunately completely destroys usability. Blockchain exploration ---------------------- Mythril builds its own contract database to enable fast search operations. This enables operations like those described in the `legendary "Mitch Brenner" blog post <https://medium.com/@rtaylor30/how-i-snatched-your-153-037-eth-after-a-bad-tinder-date-d1d84422a50b>`__ in [STRIKEOUT:seconds] minutes instead of days. Unfortunately, the initial sync process is slow. You don't need to sync the whole blockchain right away though: If you abort the syncing process with ``ctrl+c``, it will be auto-resumed the next time you run the ``--init-db`` command. .. code:: bash $ myth --init-db Starting synchronization from latest block: 4323706 Processing block 4323000, 3 individual contracts in database (...) The default behavior is to only sync contracts with a non-zero balance. You can disable this behavior with the ``--sync-all`` flag, but be aware that this will result in a huge (as in: dozens of GB) database. Searching from the command line ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The search feature allows you to find contract instances that contain specific function calls and opcode sequences. It supports simple boolean expressions, such as: .. code:: bash $ myth --search "func#changeMultisig(address)#" $ myth --search "code#PUSH1 0x50,POP#" $ myth --search "func#changeMultisig(address)# and code#PUSH1 0x50#" Reading contract storage ~~~~~~~~~~~~~~~~~~~~~~~~ You can read the contents of storage slots from a deployed contract as follows. .. code:: bash ./myth --storage 0 -a "0x76799f77587738bfeef09452df215b63d2cfb08a" 0x0000000000000000000000000000000000000000000000000000000000000003 Utilities --------- Disassembler ~~~~~~~~~~~~ Use the ``-d`` flag to disassemble code. The disassembler accepts a bytecode string or a contract address as its input. .. code:: bash $ myth -d -c "0x6060" 0 PUSH1 0x60 Specifying an address via ``-a ADDRESS`` will download the contract code from your node. .. code:: bash $ myth -d -a "0x2a0c0dbecc7e4d658f48e01e3fa353f44050c208" 0 PUSH1 0x60 2 PUSH1 0x40 4 MSTORE (...) 1135 - FUNCTION safeAdd(uint256,uint256) - 1136 CALLVALUE 1137 ISZERO Finding cross-references ~~~~~~~~~~~~~~~~~~~~~~~~ It is often useful to find other contracts referenced by a particular contract. E.g.: .. code:: bash $ myth --search "code#DELEGATECALL#" Matched contract with code hash 07459966443977122e639cbf7804c446 Address: 0x76799f77587738bfeef09452df215b63d2cfb08a, balance: 1000000000000000 $ myth --xrefs -a 0x76799f77587738bfeef09452df215b63d2cfb08a 5b9e8728e316bbeb692d22daaab74f6cbf2c4691 Calculating function hashes ~~~~~~~~~~~~~~~~~~~~~~~~~~~ To print the Keccak hash for a given function signature: .. code:: bash $ myth --hash "setOwner(address)" 0x13af4035 Credit ------ - JSON RPC library is adapted from `ethjsonrpc <https://github.com/ConsenSys/ethjsonrpc>`__ (it doesn't seem to be maintained anymore, and I needed to make some changes to it). - The signature data in ``signatures.json`` was initially obtained from the `Ethereum Function Signature Database <https://www.4byte.directory>`__. ''' setup( name='mythril', version=VERSION[1:], description='Security analysis tool for Ethereum smart contracts', long_description=long_description, url='https://github.com/b-mueller/mythril', author='Bernhard Mueller', author_email='bernhard.mueller11@gmail.com', license='MIT', classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Science/Research', 'Topic :: Software Development :: Disassemblers', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], keywords='hacking disassembler security ethereum', packages=find_packages(exclude=['contrib', 'docs', 'tests']), install_requires=[ 'ethereum>=2.3.0', 'ZODB>=5.3.0', 'z3-solver>=4.5', 'laser-ethereum>=0.17.5', 'requests', 'BTrees', 'py-solc', 'plyvel', 'pytest', 'eth_abi>=1.0.0', 'eth-utils>=1.0.1', 'eth-account>=0.1.0a2', 'eth-hash>=0.1.0', 'eth-keyfile>=0.5.1', 'eth-keys>=0.2.0b3', 'eth-rlp>=0.1.0', 'eth-tester>=0.1.0b21', 'coverage', 'jinja2', 'attrs', 'rlp<1.0.0' ], python_requires='>=3.5', extras_require={ }, include_package_data=True, scripts=['myth'], cmdclass = { 'verify': VerifyVersionCommand, } )

28.297376

110

0.69081

aa0d7e637ae90024f2569283fe896cec9a02645c

145

py

Python

src/common/rucio/lfn.py

ESCAPE-WP2/rucio-analysis

81e6a7c9df790e5ec0833b21f30843f6ae906855

[ "MIT" ]

4

2021-04-14T06:29:27.000Z

2022-03-23T17:38:16.000Z

src/common/rucio/lfn.py

ESCAPE-WP2/rucio-analysis

81e6a7c9df790e5ec0833b21f30843f6ae906855

[ "MIT" ]

4

2020-10-06T17:43:49.000Z

2021-04-07T16:45:18.000Z

src/common/rucio/lfn.py

ESCAPE-WP2/rucio-analysis

81e6a7c9df790e5ec0833b21f30843f6ae906855

[ "MIT" ]

1

2021-07-22T20:46:15.000Z

class LFN(): def __init__(self, abspath): self._abspath = abspath @property def abspath(self): return self._abspath

18.125

32

0.613793

30d970d302bb42c9f0b6fee3fefedf099b849f4b

1,064

py

Python

pylearn2/utils/bit_strings.py

jacobpeplinskiV2/pylearn2

478903d84bfa2b317e78be4e0d78588c2f58496e

[ "BSD-3-Clause" ]

null

pylearn2/utils/bit_strings.py

jacobpeplinskiV2/pylearn2

478903d84bfa2b317e78be4e0d78588c2f58496e

[ "BSD-3-Clause" ]

null

pylearn2/utils/bit_strings.py

jacobpeplinskiV2/pylearn2

478903d84bfa2b317e78be4e0d78588c2f58496e

[ "BSD-3-Clause" ]

null

"""Utilities for manipulating binary strings/masks.""" __author__ = "David Warde-Farley" __copyright__ = "Copyright 2012, Universite de Montreal" __credits__ = ["David Warde-Farley"] __license__ = "3-clause BSD" __email__ = "wardefar@iro" __maintainer__ = "David Warde-Farley" import numpy as np from six.moves import xrange def all_bit_strings(bits, dtype='uint8'): """ Create a matrix of all binary strings of a given width as the rows. Parameters ---------- bits : int The number of bits to count through. dtype : str or dtype object The dtype of the returned array. Returns ------- bit_strings : ndarray, shape (2 ** bits, bits) The numbers from 0 to 2 ** bits - 1 as binary numbers, most significant bit first. Notes ----- Obviously the memory requirements of this are exponential in the first argument, so use with caution. """ return np.array([[int(x) for x in np.binary_repr(i, width=bits)] for i in xrange(0, 2 ** bits)], dtype=dtype)

28

74

0.650376

1cf3113af9ae1fb20330e2b6dd79f573019fab4e

2,482

py

Python

products_and_services_client/models/currency.py

pitzer42/opbk-br-quickstart

b3f86b2e5f82a6090aaefb563614e174a452383c

[ "MIT" ]

2

2021-02-07T23:58:36.000Z

2021-02-08T01:03:25.000Z

products_and_services_client/models/currency.py

pitzer42/opbk-br-quickstart

b3f86b2e5f82a6090aaefb563614e174a452383c

[ "MIT" ]

null

products_and_services_client/models/currency.py

pitzer42/opbk-br-quickstart

b3f86b2e5f82a6090aaefb563614e174a452383c

[ "MIT" ]

null

# coding: utf-8 """ API's OpenData do Open Banking Brasil As API's descritas neste documento são referentes as API's da fase OpenData do Open Banking Brasil. # noqa: E501 OpenAPI spec version: 1.0.0-rc5.2 Contact: apiteam@swagger.io Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class Currency(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { } attribute_map = { } def __init__(self): # noqa: E501 """Currency - a model defined in Swagger""" # noqa: E501 self.discriminator = None def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(Currency, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, Currency): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

29.2

117

0.555197

bfc6268b5ed6b41ceff83e9e92afb39a3b80d509

285

wsgi

Python

application.wsgi

dangoldin/bots

f0f80edb97ad48ed25862d79385b019497df135d

[ "MIT" ]

2

2016-06-29T04:02:49.000Z

2018-11-26T02:48:49.000Z

application.wsgi

dangoldin/bots

f0f80edb97ad48ed25862d79385b019497df135d

[ "MIT" ]

null

application.wsgi

dangoldin/bots

f0f80edb97ad48ed25862d79385b019497df135d

[ "MIT" ]

null

import os, sys, logging logging.basicConfig(stream=sys.stderr) PROJECT_DIR = '/var/www/bots' activate_this = os.path.join(PROJECT_DIR, 'bin', 'activate_this.py') execfile(activate_this, dict(__file__=activate_this)) sys.path.append(PROJECT_DIR) from bots import app as application

23.75

68

0.785965

a9786de68f0fb8651492a8f2146adf40a7baabe6

2,441

py

Python

Intergovernmental/Internet/scripts/create-filtered-lists.py

public-law/datasets

664c3bc3b7b4d4bb1e7001127fba7d321940ce77

[ "CC-BY-4.0" ]

1

2022-03-27T06:42:31.000Z

Intergovernmental/Internet/scripts/create-filtered-lists.py

public-law/datasets

664c3bc3b7b4d4bb1e7001127fba7d321940ce77

[ "CC-BY-4.0" ]

1

2022-03-26T19:20:02.000Z

Intergovernmental/Internet/scripts/create-filtered-lists.py

public-law/datasets

664c3bc3b7b4d4bb1e7001127fba7d321940ce77

[ "CC-BY-4.0" ]

null

#!/usr/bin/env python import json import re from typing import Pattern INPUT_FILE = "../all-asns-org-names.json_lines" EDU = re.compile( r""" academy | college | \bedu | \-esd | institut | library | school | schule | suny | universidad | university """, re.IGNORECASE | re.VERBOSE, ) GOV = re.compile( r""" administration | agency | authority | board | bureau | city\Wof | commission | county | court | department | \bDNIC\b | gov | justica | justice | ministerio | ministry | municipal | police | policia | public\Wutilities\Wcommission | revenue | state\Wof | territory | tribunal | united nations """, re.IGNORECASE | re.VERBOSE, ) MEDIA = re.compile( r""" broadcasting | journal | \bmedia\b | news | \bpress | publish | reuters | televis | times | verlag | washpost | zdf """, re.IGNORECASE | re.VERBOSE, ) NGO = re.compile( r""" association | \bassoc\b | church | committee | credit\bunion | foundation | \bfund\b | nonprofit | non-profit | society | \bUN\b | united\bnation """, re.IGNORECASE | re.VERBOSE, ) DATA_FILTERS = [ {"regex": EDU, "file_slug": "edu"}, {"regex": GOV, "file_slug": "gov"}, {"regex": MEDIA, "file_slug": "media"}, {"regex": NGO, "file_slug": "ngo"}, ] def json_line_to_tsv(line: str) -> str: """Produce the standard TSV format: Country ASN Name + OrgName """ data = json.loads(line) return f"{data['Country']}\t{data['ASNumber']}\t{data['ASName']} {data['OrgName']}".strip() for data_filter in DATA_FILTERS: regex: Pattern[str] = data_filter["regex"] slug: str = data_filter["file_slug"] output_file = f"../asn-{slug}-list.tsv" with open(INPUT_FILE, "r", encoding="utf8") as f: records = f.readlines() matching_records = [r for r in records if regex.search(r)] tsv_records = [json_line_to_tsv(j) for j in matching_records] with open(output_file, "w", encoding="utf8") as f: f.writelines([record + "\n" for record in tsv_records])

21.60177

95

0.523146

e62afce86729b04baeb4333a9d1f4846e57bb592

2,319

py

Python

iis-3rdparty-madis/src/main/resources/eu/dnetlib/iis/3rdparty/scripts/madis/functions/aggregate/util.py

mpol/iis

fbf7129bbd131fbf824a0d3fc8a0afde367794e2

[ "Apache-2.0" ]

20

2015-09-19T21:17:23.000Z

2022-03-01T10:37:59.000Z

iis-3rdparty-madis/src/main/resources/eu/dnetlib/iis/3rdparty/scripts/madis/functions/aggregate/util.py

mpol/iis

fbf7129bbd131fbf824a0d3fc8a0afde367794e2

[ "Apache-2.0" ]

1,054

2015-09-11T06:51:27.000Z

2022-03-30T09:46:54.000Z

iis-3rdparty-madis/src/main/resources/eu/dnetlib/iis/3rdparty/scripts/madis/functions/aggregate/util.py

mpol/iis

fbf7129bbd131fbf824a0d3fc8a0afde367794e2

[ "Apache-2.0" ]

80

2015-12-09T12:41:52.000Z

2022-02-16T11:46:42.000Z

__docformat__ = 'reStructuredText en' class expandgroups: """ .. function:: expandgroups(args) -> args Expands the contents of groups. Usefull for debugging group related queries. Examples: Directed graph: >>> table1(''' ... 1 2 ... 2 3 ... 1 4 ... 2 5 ... 3 3 ... ''') >>> sql("select expandgroups(a,b) from table1") C1 | C2 ------- 1 | 2 2 | 3 1 | 4 2 | 5 3 | 3 >>> sql("select expandgroups(a,b) as gr from table1") gr1 | gr2 --------- 1 | 2 2 | 3 1 | 4 2 | 5 3 | 3 >>> sql("select a,expandgroups(b) as gr from table1 group by a") a | gr ------ 1 | 2 1 | 4 2 | 3 2 | 5 3 | 3 """ registered=True def __init__(self): self.rows=[] def step(self, *args): self.rows.append(args) def final(self): yield tuple(('C'+str(x) for x in xrange(1,len(self.rows[0])+1))) for r in self.rows: yield r class showgroups: """ .. function:: showgroups(args) -> string Shows the contents of groups. Usefull for debugging group related queries. Examples: Directed graph: >>> table1(''' ... 1 2 ... 2 3 ... 1 4 ... 2 5 ... 3 3 ... ''') >>> sql("select showgroups(a,b) from table1") # doctest: +NORMALIZE_WHITESPACE showgroups(a,b) -------------------- <BLANKLINE> 1 2 2 3 1 4 2 5 3 3 >>> sql("select showgroups(b) as gr from table1 group by a") gr ---- <BLANKLINE> 2 4 <BLANKLINE> 3 5 <BLANKLINE> 3 """ registered=True def __init__(self): self.rows=[] def step(self, *args): self.rows.append(args) def final(self): return '\n'+'\n'.join(['\t'.join([unicode(x) for x in r]) for r in self.rows]) if not ('.' in __name__): """ This is needed to be able to test the function, put it at the end of every new function you create """ import sys import setpath from functions import * testfunction() if __name__ == "__main__": reload(sys) sys.setdefaultencoding('utf-8') import doctest doctest.testmod()

17.70229

86

0.490298

6a0d7571c975ed90297c5548a8e3a9bc0f83406d

1,080

py

Python

tests/zad2_ParameterizedPackage.py

cati97/laboratorium-7-cati97

0eaa3ca43a8868a96f32bfe9462e441b56d694fc

[ "MIT" ]

null

tests/zad2_ParameterizedPackage.py

cati97/laboratorium-7-cati97

0eaa3ca43a8868a96f32bfe9462e441b56d694fc

[ "MIT" ]

null

tests/zad2_ParameterizedPackage.py

cati97/laboratorium-7-cati97

0eaa3ca43a8868a96f32bfe9462e441b56d694fc

[ "MIT" ]

1

2020-11-17T16:37:32.000Z

import unittest from src.sample.zad2 import * from nose.tools import assert_equal from parameterized import parameterized, parameterized_class @parameterized([ (1, 'I'), (2, 'II'), (3, 'III'), (4, 'IV'), (5, 'V'), (6, 'VI'), (9, 'IX'), (10, 'X'), ]) def test_first_method(input, expected): assert_equal(roman(input), expected) class RomanParameterizedPackage(unittest.TestCase): @parameterized.expand([ ("small", 27, 'XXVII'), ("medium", 163, 'CLXIII'), ("large", 911, 'CMXI'), ]) def test_expand(self, name, input, expected): self.assertEqual(roman(input), expected) @parameterized_class(('number', 'expected'), [ (48, "XLVIII"), (49, "XLIX"), (59, "LIX"), (93, "XCIII"), (141, "CXLI"), (402, "CDII"), (575, "DLXXV"), (1024, "MXXIV"), (3000, "MMM"), ]) class RomanParameterizedPackage2(unittest.TestCase): def test_other_parameterized(self): self.assertEqual(roman(self.number), self.expected) if __name__ == '__main__': unittest.main()

21.176471

60

0.593519

41d18c2267ff48dbc29b9e8be1174d8b90b29038

4,307

py

Python

trajopt/envs/quanser/qube/ctrl.py

JoeMWatson/trajopt

8b98718721e0c373cd7dc01a35f42447c1134713

[ "MIT" ]

1

2019-10-17T08:42:17.000Z

trajopt/envs/quanser/qube/ctrl.py

JoeMWatson/trajopt

8b98718721e0c373cd7dc01a35f42447c1134713

[ "MIT" ]

null

trajopt/envs/quanser/qube/ctrl.py

JoeMWatson/trajopt

8b98718721e0c373cd7dc01a35f42447c1134713

[ "MIT" ]

null

import autograd.numpy as np from trajopt.envs.quanser.qube.base import QubeDynamics class PDCtrl: """ Slightly tweaked PD controller (increases gains if `x_des` not reachable). Accepts `th_des` and drives Qube to `x_des = (th_des, 0.0, 0.0, 0.0)` Flag `done` is set when `|x_des - x| < tol`. Tweak: increase P-gain on `th` if velocity is zero but the goal is still not reached (useful for counteracting resistance from the power cord). """ def __init__(self, K=None, th_des=0.0, tol=5e-2): self.done = False self.K = K if K is not None else [5.0, 0.0, 0.5, 0.0] self.th_des = th_des self.tol = tol def __call__(self, x): th, al, thd, ald = x K, th_des, tol = self.K, self.th_des, self.tol all_but_th_squared = al ** 2 + thd ** 2 + ald ** 2 err = np.sqrt((th_des - th) ** 2 + all_but_th_squared) if not self.done and err < tol: self.done = True elif th_des and np.sqrt(all_but_th_squared) < tol / 5.0: # Increase P-gain on `th` when struggling to reach `th_des` K[0] += 0.01 * K[0] return np.array([K[0]*(th_des - th) - K[1]*al - K[2]*thd - K[3]*ald]) class GoToLimCtrl: """Go to joint limits by applying `u_max`; save limit value in `th_lim`.""" def __init__(self, positive=True): self.done = False self.th_lim = 10.0 self.sign = 1 if positive else -1 self.u_max = 1.0 self.cnt = 0 self.cnt_done = 200 def __call__(self, x): th, _, thd, _ = x if np.abs(th - self.th_lim) > 0: self.cnt = 0 self.th_lim = th else: self.cnt += 1 self.done = self.cnt == self.cnt_done return np.array([self.sign * self.u_max]) class CalibrCtrl: """Go to joint limits, find midpoint, go to the midpoint.""" def __init__(self): self.done = False self.go_right = GoToLimCtrl(positive=True) self.go_left = GoToLimCtrl(positive=False) self.go_center = PDCtrl() def __call__(self, x): u = np.array([0.0]) if not self.go_right.done: u = self.go_right(x) elif not self.go_left.done: u = self.go_left(x) elif not self.go_center.done: if self.go_center.th_des == 0.0: self.go_center.th_des = \ (self.go_left.th_lim + self.go_right.th_lim) / 2 u = self.go_center(x) elif not self.done: self.done = True return u class EnergyCtrl: """PD controller on energy.""" def __init__(self, Er, mu, a_max): self.Er = Er # reference energy (J) self.mu = mu # P-gain on the energy (m/s/J) self.a_max = a_max # max acceleration of the pendulum pivot (m/s^2) self._dyn = QubeDynamics() # dynamics parameters of the robot def __call__(self, x): _, al, _, ald = x Jp = self._dyn.Mp * self._dyn.Lp ** 2 / 12 Ek = 0.5 * Jp * ald ** 2 Ep = 0.5 * self._dyn.Mp * self._dyn.g * self._dyn.Lp * (1. - np.cos(al + np.pi)) E = Ek + Ep acc = np.clip(self.mu * (E - self.Er) * np.sign(ald * np.cos(al + np.pi)), -self.a_max, self.a_max) trq = self._dyn.Mr * self._dyn.Lr * acc voltage = self._dyn.Rm / self._dyn.km * trq return np.array([voltage]) class SwingUpCtrl: """Hybrid controller (EnergyCtrl, PDCtrl) switching based on alpha.""" def __init__(self, ref_energy, energy_gain, acc_max, alpha_max_pd_enable=20.0, pd_gain=None): # Set up the energy pumping controller self.en_ctrl = EnergyCtrl(ref_energy, energy_gain, acc_max) # Set up the PD controller cos_al_delta = np.cos(2. * np.pi - np.deg2rad(alpha_max_pd_enable)) self.pd_enabled = lambda cos_al: cos_al > cos_al_delta pd_gain = pd_gain if pd_gain is not None else [-1.5, 25.0, -1.5, 2.5] self.pd_ctrl = PDCtrl(K=pd_gain) def __call__(self, obs): th, al, th_d, al_d = obs if self.pd_enabled(np.cos(al)): obs[1] = (2. * np.pi * al) if al > np.pi else al return self.pd_ctrl(obs) else: return self.en_ctrl(obs)

33.648438

88

0.565823

7d3d8d37b2a9b590f7db08e40c9c28ababb1383e

2,734

py

Python

sub_rgbd_and_cloud.py

felixchenfy/ros_pub_and_sub_rgbd_and_cloud

28e481689e81a08c491f859a78c0058c86d4ebee

[ "MIT" ]

8

2019-12-23T09:42:38.000Z

2021-09-06T20:49:30.000Z

sub_rgbd_and_cloud.py

felixchenfy/ros_pub_and_sub_rgbd_and_cloud

28e481689e81a08c491f859a78c0058c86d4ebee

[ "MIT" ]

null

sub_rgbd_and_cloud.py

felixchenfy/ros_pub_and_sub_rgbd_and_cloud

28e481689e81a08c491f859a78c0058c86d4ebee

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' This script gives exampe code for subscribing: (1) Color image. (2) Depth image. (3) Camera info. (4) Point cloud (subscribed as open3d format). ''' from utils.lib_ros_rgbd_pub_and_sub import ColorImageSubscriber, DepthImageSubscriber, CameraInfoSubscriber from utils.lib_ros_point_cloud_pub_and_sub import PointCloudSubscriber from utils.lib_rgbd import MyCameraInfo import numpy as np import rospy # -- Set ROS topic names for subscribing. NS = "test_data/" # ROS topic namespace. COLOR_TOPIC_NAME = NS + "color" DEPTH_TOPIC_NAME = NS + "depth" CAMERA_INFO_TOPIC_NAME = NS + "camera_info" CLOUD_TOPIC_NAME = NS + "point_cloud" # -- Subscribe data and print. def main(): # -- Set subscribers. sub_color = ColorImageSubscriber(COLOR_TOPIC_NAME) sub_depth = DepthImageSubscriber(DEPTH_TOPIC_NAME) sub_camera_info = CameraInfoSubscriber(CAMERA_INFO_TOPIC_NAME) sub_cloud = PointCloudSubscriber(CLOUD_TOPIC_NAME) # -- Loop and subscribe. cnt_1, cnt_2, cnt_3, cnt_4 = 0, 0, 0, 0 # color, depth, camera_info, cloud while not rospy.is_shutdown(): # Color. if sub_color.has_image(): color = sub_color.get_image() cnt_1 += 1 rospy.loginfo("Subscribe {}: color image, " "shape={}".format( cnt_1, color.shape)) # Depth. if sub_depth.has_image(): depth = sub_depth.get_image() cnt_2 += 1 rospy.loginfo("Subscribe {}: depth image, " "shape={}".format( cnt_2, depth.shape)) # Camera_info. if sub_camera_info.has_camera_info(): ros_camera_info = sub_camera_info.get_camera_info() cnt_3 += 1 rospy.loginfo("Subscribe {}: camera_info, " "fx={}, fy={}.".format( cnt_3, ros_camera_info.K[0], ros_camera_info.K[4], )) my_camera_info = MyCameraInfo(ros_camera_info=ros_camera_info) # Point_cloud. if sub_cloud.has_cloud(): open3d_cloud = sub_cloud.get_cloud() cnt_4 += 1 num_points = np.asarray(open3d_cloud.points).shape[0] rospy.loginfo("Subscribe {}: point cloud, " "{} points.".format( cnt_4, num_points)) rospy.sleep(0.1) if __name__ == '__main__': node_name = "sub_rgbd_and_cloud" rospy.init_node(node_name) main() rospy.logwarn("Node `{}` stops.".format(node_name))

32.164706

107

0.581931

d33333631f3719c0dc35826c097597615bc54dcc

31,634

py

Python

tensor2tensor/utils/decoding.py

YunseokJANG/tensor2tensor

2451614b930c73b2b8dd891b4fc5838d99a151a6

[ "Apache-2.0" ]

1

2021-07-13T17:37:11.000Z

tensor2tensor/utils/decoding.py

YunseokJANG/tensor2tensor

2451614b930c73b2b8dd891b4fc5838d99a151a6

[ "Apache-2.0" ]

null

tensor2tensor/utils/decoding.py

YunseokJANG/tensor2tensor

2451614b930c73b2b8dd891b4fc5838d99a151a6

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2018 The Tensor2Tensor Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Decoding utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import operator import os import re import time import numpy as np import six from six.moves import input # pylint: disable=redefined-builtin from tensor2tensor.data_generators import problem as problem_lib from tensor2tensor.data_generators import text_encoder from tensor2tensor.data_generators import text_problems from tensor2tensor.utils import mlperf_log from tensor2tensor.utils import registry import tensorflow as tf FLAGS = tf.flags.FLAGS # Number of samples to draw for an image input (in such cases as captioning) IMAGE_DECODE_LENGTH = 100 def decode_hparams(overrides=""): """Hyperparameters for decoding.""" hp = tf.contrib.training.HParams( save_images=False, log_results=True, extra_length=100, batch_size=0, beam_size=4, alpha=0.6, eos_penalty=0.0, block_size=0, guess_and_check_top_k=0, guess_and_check_epsilon=-1, return_beams=False, write_beam_scores=False, max_input_size=-1, identity_output=False, num_samples=-1, delimiter="\n", decode_to_file=None, decode_in_memory=False, summaries_log_dir="decode", # Directory to write hook summaries. shards=1, # How many shards of data to decode (treating 1 as None). shard_id=0, # Which shard are we decoding if more than 1 above. shards_start_offset=0, # Number of the first shard to decode. num_decodes=1, force_decode_length=False, display_decoded_images=False, # Multi-problem decoding task id. multiproblem_task_id=-1, # Used for video decoding. frames_per_second=10, skip_eos_postprocess=False, # Creates a blue/red border covering border_percent of the frame. border_percent=2, # Maximum number of videos displayed. # Total number of videos are max_display_outputs * num_decodes max_display_outputs=10, # Used in computation of VGG feature based video metrics. # Set this to be the path to a trained VGG ckpt to output # useful metrics. vgg_ckpt_path=None, # Used for MLPerf compliance logging. mlperf_decode_step=0.0, mlperf_threshold=25.0, mlperf_success=False) hp.parse(overrides) return hp def log_decode_results(inputs, outputs, problem_name, prediction_idx, inputs_vocab, targets_vocab, targets=None, save_images=False, output_dir=None, identity_output=False, log_results=True): """Log inference results.""" # TODO(lukaszkaiser) refactor this into feature_encoder is_video = "video" in problem_name or "gym" in problem_name if is_video: def fix_and_save_video(vid, prefix): save_path_template = os.path.join( output_dir, "%s_%s_%05d_{:05d}.png" % (problem_name, prefix, prediction_idx)) # this is only required for predictions if vid.shape[-1] == 1: vid = np.squeeze(vid, axis=-1) save_video(vid, save_path_template) tf.logging.info("Saving video: {}".format(prediction_idx)) fix_and_save_video(inputs, "inputs") fix_and_save_video(outputs, "outputs") fix_and_save_video(targets, "targets") is_image = "image" in problem_name is_text2class = isinstance(registry.problem(problem_name), text_problems.Text2ClassProblem) skip_eos_postprocess = is_image or is_text2class decoded_inputs = None if is_image and save_images: save_path = os.path.join( output_dir, "%s_prediction_%d.jpg" % (problem_name, prediction_idx)) show_and_save_image(inputs / 255., save_path) elif inputs is not None and inputs_vocab: if identity_output: decoded_inputs = " ".join(map(str, inputs.flatten())) else: decoded_inputs = inputs_vocab.decode(_save_until_eos( inputs, skip_eos_postprocess)) if log_results and not is_video: tf.logging.info("Inference results INPUT: %s" % decoded_inputs) decoded_targets = None decoded_outputs = None if identity_output: decoded_outputs = " ".join(map(str, outputs.flatten())) if targets is not None: decoded_targets = " ".join(map(str, targets.flatten())) else: decoded_outputs = targets_vocab.decode(_save_until_eos( outputs, skip_eos_postprocess)) if targets is not None and log_results: decoded_targets = targets_vocab.decode(_save_until_eos( targets, skip_eos_postprocess)) if log_results and not is_video: tf.logging.info("Inference results OUTPUT: %s" % decoded_outputs) if targets is not None and log_results and not is_video: tf.logging.info("Inference results TARGET: %s" % decoded_targets) return decoded_inputs, decoded_outputs, decoded_targets def decode_from_dataset(estimator, problem_name, hparams, decode_hp, decode_to_file=None, dataset_split=None, checkpoint_path=None): """Perform decoding from dataset.""" tf.logging.info("Performing local inference from dataset for %s.", str(problem_name)) # We assume that worker_id corresponds to shard number. shard = decode_hp.shard_id if decode_hp.shards > 1 else None # Setup decode output directory for any artifacts that may be written out output_dir = os.path.join(estimator.model_dir, "decode") tf.gfile.MakeDirs(output_dir) # If decode_hp.batch_size is specified, use a fixed batch size if decode_hp.batch_size: hparams.batch_size = decode_hp.batch_size hparams.use_fixed_batch_size = True dataset_kwargs = { "shard": shard, "dataset_split": dataset_split, "max_records": decode_hp.num_samples } # Build the inference input function problem = hparams.problem infer_input_fn = problem.make_estimator_input_fn( tf.estimator.ModeKeys.PREDICT, hparams, dataset_kwargs=dataset_kwargs) predictions, output_dirs = [], [] for decode_id in range(decode_hp.num_decodes): tf.logging.info("Decoding {}".format(decode_id)) # Create decode directory if not in-memory decoding. if not decode_hp.decode_in_memory: output_dir = os.path.join(estimator.model_dir, "decode_%05d" % decode_id) tf.gfile.MakeDirs(output_dir) output_dirs.append(output_dir) result = decode_once(estimator, problem_name, hparams, infer_input_fn, decode_hp, decode_to_file, output_dir, log_results=not decode_hp.decode_in_memory, checkpoint_path=checkpoint_path) if decode_hp.decode_in_memory: output_dirs = [output_dir] predictions.append(result) if decode_hp.decode_to_file: decode_hp.decode_to_file = _decode_filename( decode_hp.decode_to_file, problem_name, decode_hp) run_postdecode_hooks(DecodeHookArgs( estimator=estimator, problem=problem, output_dirs=output_dirs, hparams=hparams, decode_hparams=decode_hp, predictions=predictions ), dataset_split) return predictions def decode_once(estimator, problem_name, hparams, infer_input_fn, decode_hp, decode_to_file, output_dir, log_results=True, checkpoint_path=None): """Decodes once.""" # Get the predictions as an iterable predictions = estimator.predict(infer_input_fn, checkpoint_path=checkpoint_path) if not log_results: return list(predictions) # Prepare output file writers if decode_to_file passed decode_to_file = decode_to_file or decode_hp.decode_to_file if decode_to_file: output_filepath = _decode_filename(decode_to_file, problem_name, decode_hp) parts = output_filepath.split(".") parts[-1] = "targets" target_filepath = ".".join(parts) parts[-1] = "inputs" input_filepath = ".".join(parts) output_file = tf.gfile.Open(output_filepath, "w") target_file = tf.gfile.Open(target_filepath, "w") input_file = tf.gfile.Open(input_filepath, "w") problem_hparams = hparams.problem_hparams # Inputs vocabulary is set to targets if there are no inputs in the problem, # e.g., for language models where the inputs are just a prefix of targets. has_input = "inputs" in problem_hparams.vocabulary inputs_vocab_key = "inputs" if has_input else "targets" inputs_vocab = problem_hparams.vocabulary[inputs_vocab_key] targets_vocab = problem_hparams.vocabulary["targets"] num_eval_samples = 0 for num_predictions, prediction in enumerate(predictions): num_eval_samples += 1 num_predictions += 1 inputs = prediction.get("inputs") targets = prediction.get("targets") outputs = prediction.get("outputs") # Log predictions decoded_outputs = [] decoded_scores = [] if decode_hp.return_beams: output_beams = np.split(outputs, decode_hp.beam_size, axis=0) scores = None if "scores" in prediction: scores = np.split(prediction["scores"], decode_hp.beam_size, axis=0) for i, beam in enumerate(output_beams): tf.logging.info("BEAM %d:" % i) score = scores and scores[i] decoded = log_decode_results( inputs, beam, problem_name, num_predictions, inputs_vocab, targets_vocab, save_images=decode_hp.save_images, output_dir=output_dir, identity_output=decode_hp.identity_output, targets=targets, log_results=decode_hp.log_results) decoded_outputs.append(decoded) if decode_hp.write_beam_scores: decoded_scores.append(score) else: decoded = log_decode_results( inputs, outputs, problem_name, num_predictions, inputs_vocab, targets_vocab, save_images=decode_hp.save_images, output_dir=output_dir, identity_output=decode_hp.identity_output, targets=targets, log_results=decode_hp.log_results) decoded_outputs.append(decoded) # Write out predictions if decode_to_file passed if decode_to_file: for i, (d_input, d_output, d_target) in enumerate(decoded_outputs): # Skip if all padding if d_input and re.match("^({})+$".format(text_encoder.PAD), d_input): continue beam_score_str = "" if decode_hp.write_beam_scores: beam_score_str = "\t%.2f" % decoded_scores[i] output_file.write(str(d_output) + beam_score_str + decode_hp.delimiter) target_file.write(str(d_target) + decode_hp.delimiter) input_file.write(str(d_input) + decode_hp.delimiter) if (decode_hp.num_samples >= 0 and num_predictions >= decode_hp.num_samples): break mlperf_log.transformer_print(key=mlperf_log.EVAL_SIZE, value=num_eval_samples, hparams=hparams) if decode_to_file: output_file.close() target_file.close() input_file.close() def decode_from_file(estimator, filename, hparams, decode_hp, decode_to_file=None, checkpoint_path=None): """Compute predictions on entries in filename and write them out.""" if not decode_hp.batch_size: decode_hp.batch_size = 32 tf.logging.info( "decode_hp.batch_size not specified; default=%d" % decode_hp.batch_size) # Inputs vocabulary is set to targets if there are no inputs in the problem, # e.g., for language models where the inputs are just a prefix of targets. p_hp = hparams.problem_hparams has_input = "inputs" in p_hp.vocabulary inputs_vocab_key = "inputs" if has_input else "targets" inputs_vocab = p_hp.vocabulary[inputs_vocab_key] targets_vocab = p_hp.vocabulary["targets"] problem_name = FLAGS.problem filename = _add_shard_to_filename(filename, decode_hp) tf.logging.info("Performing decoding from file (%s)." % filename) sorted_inputs, sorted_keys = _get_sorted_inputs(filename, decode_hp.delimiter) num_decode_batches = (len(sorted_inputs) - 1) // decode_hp.batch_size + 1 if estimator.config.use_tpu: length = getattr(hparams, "length", hparams.max_length) batch_ids = [] for line in sorted_inputs: ids = inputs_vocab.encode(line.strip()) + [1] if len(ids) < length: ids.extend([0] * (length - len(ids))) else: ids = ids[:length] batch_ids.append(ids) np_ids = np.array(batch_ids, dtype=np.int32) def input_fn(params): batch_size = params["batch_size"] dataset = tf.data.Dataset.from_tensor_slices({"inputs": np_ids}) dataset = dataset.map( lambda ex: {"inputs": tf.reshape(ex["inputs"], (length, 1, 1))}) dataset = dataset.batch(batch_size) return dataset else: def input_fn(): input_gen = _decode_batch_input_fn( num_decode_batches, sorted_inputs, inputs_vocab, decode_hp.batch_size, decode_hp.max_input_size, task_id=decode_hp.multiproblem_task_id) gen_fn = make_input_fn_from_generator(input_gen) example = gen_fn() return _decode_input_tensor_to_features_dict(example, hparams) decodes = [] result_iter = estimator.predict(input_fn, checkpoint_path=checkpoint_path) start_time = time.time() total_time_per_step = 0 total_cnt = 0 def timer(gen): while True: try: start_time = time.time() item = next(gen) elapsed_time = time.time() - start_time yield elapsed_time, item except StopIteration: break for elapsed_time, result in timer(result_iter): if decode_hp.return_beams: beam_decodes = [] beam_scores = [] output_beams = np.split(result["outputs"], decode_hp.beam_size, axis=0) scores = None if "scores" in result: if np.isscalar(result["scores"]): result["scores"] = result["scores"].reshape(1) scores = np.split(result["scores"], decode_hp.beam_size, axis=0) for k, beam in enumerate(output_beams): tf.logging.info("BEAM %d:" % k) score = scores and scores[k] _, decoded_outputs, _ = log_decode_results( result["inputs"], beam, problem_name, None, inputs_vocab, targets_vocab, log_results=decode_hp.log_results) beam_decodes.append(decoded_outputs) if decode_hp.write_beam_scores: beam_scores.append(score) if decode_hp.write_beam_scores: decodes.append("\t".join([ "\t".join([d, "%.2f" % s]) for d, s in zip(beam_decodes, beam_scores) ])) else: decodes.append("\t".join(beam_decodes)) else: _, decoded_outputs, _ = log_decode_results( result["inputs"], result["outputs"], problem_name, None, inputs_vocab, targets_vocab, log_results=decode_hp.log_results) decodes.append(decoded_outputs) total_time_per_step += elapsed_time total_cnt += result["outputs"].shape[-1] tf.logging.info("Elapsed Time: %5.5f" % (time.time() - start_time)) tf.logging.info("Averaged Single Token Generation Time: %5.7f " "(time %5.7f count %d)" % (total_time_per_step / total_cnt, total_time_per_step, total_cnt)) # If decode_to_file was provided use it as the output filename without change # (except for adding shard_id if using more shards for decoding). # Otherwise, use the input filename plus model, hp, problem, beam, alpha. decode_filename = decode_to_file if decode_to_file else filename if not decode_to_file: decode_filename = _decode_filename(decode_filename, problem_name, decode_hp) else: decode_filename = _add_shard_to_filename(decode_filename, decode_hp) tf.logging.info("Writing decodes into %s" % decode_filename) outfile = tf.gfile.Open(decode_filename, "w") for index in range(len(sorted_inputs)): outfile.write("%s%s" % (decodes[sorted_keys[index]], decode_hp.delimiter)) outfile.flush() outfile.close() output_dir = os.path.join(estimator.model_dir, "decode") tf.gfile.MakeDirs(output_dir) run_postdecode_hooks(DecodeHookArgs( estimator=estimator, problem=hparams.problem, output_dirs=[output_dir], hparams=hparams, decode_hparams=decode_hp, predictions=list(result_iter) ), None) def _add_shard_to_filename(filename, decode_hp): if decode_hp.shards > 1: shard_id = decode_hp.shard_id + decode_hp.shards_start_offset filename = filename + ("%.3d" % shard_id) return filename def _decode_filename(base_filename, problem_name, decode_hp): """Generates decode filename. Args: base_filename: A string, base of the decode filename. problem_name: A string, name of the problem. decode_hp: HParams for decoding. Returns: A string, produced decode filename. """ if decode_hp.shards > 1: base_filename = _add_shard_to_filename(base_filename, decode_hp) if ("beam{beam}.alpha{alpha}.decodes".format( beam=str(decode_hp.beam_size), alpha=str(decode_hp.alpha)) in base_filename): return base_filename else: return ( "{base}.{model}.{hp}.{problem}.beam{beam}.alpha{alpha}.decodes".format( base=base_filename, model=FLAGS.model, hp=FLAGS.hparams_set, problem=problem_name, beam=str(decode_hp.beam_size), alpha=str(decode_hp.alpha))) def make_input_fn_from_generator(gen): """Use py_func to yield elements from the given generator.""" first_ex = six.next(gen) flattened = tf.contrib.framework.nest.flatten(first_ex) types = [t.dtype for t in flattened] shapes = [[None] * len(t.shape) for t in flattened] first_ex_list = [first_ex] def py_func(): if first_ex_list: example = first_ex_list.pop() else: example = six.next(gen) return tf.contrib.framework.nest.flatten(example) def input_fn(): flat_example = tf.py_func(py_func, [], types) _ = [t.set_shape(shape) for t, shape in zip(flat_example, shapes)] example = tf.contrib.framework.nest.pack_sequence_as(first_ex, flat_example) return example return input_fn def decode_interactively(estimator, hparams, decode_hp, checkpoint_path=None): """Interactive decoding.""" is_image = "image" in hparams.problem.name is_text2class = isinstance(hparams.problem, text_problems.Text2ClassProblem) skip_eos_postprocess = ( is_image or is_text2class or decode_hp.skip_eos_postprocess) def input_fn(): gen_fn = make_input_fn_from_generator( _interactive_input_fn(hparams, decode_hp)) example = gen_fn() example = _interactive_input_tensor_to_features_dict(example, hparams) return example result_iter = estimator.predict(input_fn, checkpoint_path=checkpoint_path) for result in result_iter: targets_vocab = hparams.problem_hparams.vocabulary["targets"] if decode_hp.return_beams: beams = np.split(result["outputs"], decode_hp.beam_size, axis=0) scores = None if "scores" in result: if np.isscalar(result["scores"]): result["scores"] = result["scores"].reshape(1) scores = np.split(result["scores"], decode_hp.beam_size, axis=0) for k, beam in enumerate(beams): tf.logging.info("BEAM %d:" % k) beam_string = targets_vocab.decode(_save_until_eos( beam, skip_eos_postprocess)) if scores is not None: tf.logging.info("\"%s\"\tScore:%f" % (beam_string, scores[k])) else: tf.logging.info("\"%s\"" % beam_string) else: if decode_hp.identity_output: tf.logging.info(" ".join(map(str, result["outputs"].flatten()))) else: tf.logging.info( targets_vocab.decode(_save_until_eos( result["outputs"], skip_eos_postprocess))) def _decode_batch_input_fn(num_decode_batches, sorted_inputs, vocabulary, batch_size, max_input_size, task_id=-1): """Generator to produce batches of inputs.""" tf.logging.info(" batch %d" % num_decode_batches) for b in range(num_decode_batches): tf.logging.info("Decoding batch %d" % b) batch_length = 0 batch_inputs = [] for inputs in sorted_inputs[b * batch_size:(b + 1) * batch_size]: input_ids = vocabulary.encode(inputs) if max_input_size > 0: # Subtract 1 for the EOS_ID. input_ids = input_ids[:max_input_size - 1] final_id = text_encoder.EOS_ID if task_id < 0 else task_id input_ids.append(final_id) batch_inputs.append(input_ids) if len(input_ids) > batch_length: batch_length = len(input_ids) final_batch_inputs = [] for input_ids in batch_inputs: assert len(input_ids) <= batch_length x = input_ids + [0] * (batch_length - len(input_ids)) final_batch_inputs.append(x) yield { "inputs": np.array(final_batch_inputs).astype(np.int32), } def _interactive_input_fn(hparams, decode_hp): """Generator that reads from the terminal and yields "interactive inputs". Due to temporary limitations in tf.learn, if we don't want to reload the whole graph, then we are stuck encoding all of the input as one fixed-size numpy array. We yield int32 arrays with shape [const_array_size]. The format is: [num_samples, decode_length, len(input ids), <input ids>, <padding>] Args: hparams: model hparams decode_hp: decode hparams Yields: numpy arrays Raises: Exception: when `input_type` is invalid. """ num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1 decode_length = decode_hp.extra_length input_type = "text" p_hparams = hparams.problem_hparams has_input = "inputs" in p_hparams.modality vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"] # This should be longer than the longest input. const_array_size = 10000 # Import readline if available for command line editing and recall. try: import readline # pylint: disable=g-import-not-at-top,unused-variable except ImportError: pass while True: prompt = ("INTERACTIVE MODE num_samples=%d decode_length=%d \n" " it=<input_type> ('text' or 'image' or 'label', default: " "text)\n" " ns=<num_samples> (changes number of samples, default: 1)\n" " dl=<decode_length> (changes decode length, default: 100)\n" " <%s> (decode)\n" " q (quit)\n" ">" % (num_samples, decode_length, "source_string" if has_input else "target_prefix")) input_string = input(prompt) if input_string == "q": return elif input_string[:3] == "ns=": num_samples = int(input_string[3:]) elif input_string[:3] == "dl=": decode_length = int(input_string[3:]) elif input_string[:3] == "it=": input_type = input_string[3:] else: if input_type == "text": input_ids = vocabulary.encode(input_string) if has_input: input_ids.append(text_encoder.EOS_ID) x = [num_samples, decode_length, len(input_ids)] + input_ids assert len(x) < const_array_size x += [0] * (const_array_size - len(x)) features = { "inputs": np.array(x).astype(np.int32), } elif input_type == "image": input_path = input_string img = vocabulary.encode(input_path) features = { "inputs": img.astype(np.int32), } elif input_type == "label": input_ids = [int(input_string)] x = [num_samples, decode_length, len(input_ids)] + input_ids features = { "inputs": np.array(x).astype(np.int32), } else: raise Exception("Unsupported input type.") for k, v in six.iteritems( problem_lib.problem_hparams_to_features(p_hparams)): features[k] = np.array(v).astype(np.int32) yield features def save_video(video, save_path_template): """Save frames of the videos into files.""" try: from PIL import Image # pylint: disable=g-import-not-at-top except ImportError as e: tf.logging.warning( "Showing and saving an image requires PIL library to be " "installed: %s", e) raise NotImplementedError("Image display and save not implemented.") for i, frame in enumerate(video): save_path = save_path_template.format(i) with tf.gfile.Open(save_path, "wb") as sp: Image.fromarray(np.uint8(frame)).save(sp) def show_and_save_image(img, save_path): """Shows an image using matplotlib and saves it.""" try: import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top except ImportError as e: tf.logging.warning( "Showing and saving an image requires matplotlib to be " "installed: %s", e) raise NotImplementedError("Image display and save not implemented.") plt.imshow(img) with tf.gfile.Open(save_path, "wb") as sp: plt.savefig(sp) def _get_sorted_inputs(filename, delimiter="\n"): """Returning inputs sorted according to decreasing length. This causes inputs of similar lengths to be processed in the same batch, facilitating early stopping for short sequences. Longer sequences are sorted first so that if you're going to get OOMs, you'll see it in the first batch. Args: filename: path to file with inputs, 1 per line. delimiter: str, delimits records in the file. Returns: a sorted list of inputs """ tf.logging.info("Getting sorted inputs") with tf.gfile.Open(filename) as f: text = f.read() records = text.split(delimiter) inputs = [record.strip() for record in records] # Strip the last empty line. if not inputs[-1]: inputs.pop() input_lens = [(i, -len(line.split())) for i, line in enumerate(inputs)] sorted_input_lens = sorted(input_lens, key=operator.itemgetter(1)) # We'll need the keys to rearrange the inputs back into their original order sorted_keys = {} sorted_inputs = [] for i, (index, _) in enumerate(sorted_input_lens): sorted_inputs.append(inputs[index]) sorted_keys[index] = i return sorted_inputs, sorted_keys def _save_until_eos(ids, skip=False): """Strips everything after the first <EOS> token, which is normally 1.""" ids = ids.flatten() if skip: return ids try: index = list(ids).index(text_encoder.EOS_ID) return ids[0:index] except ValueError: # No EOS_ID: return the array as-is. return ids def _interactive_input_tensor_to_features_dict(feature_map, hparams): """Convert the interactive input format (see above) to a dictionary. Args: feature_map: dict with inputs. hparams: model hyperparameters Returns: a features dictionary, as expected by the decoder. """ inputs = tf.convert_to_tensor(feature_map["inputs"]) input_is_image = False if len(inputs.get_shape()) < 3 else True x = inputs if input_is_image: x = tf.image.resize_images(x, [299, 299]) x = tf.reshape(x, [1, 299, 299, -1]) x = tf.to_int32(x) else: # Remove the batch dimension. num_samples = x[0] length = x[2] x = tf.slice(x, [3], tf.to_int32([length])) x = tf.reshape(x, [1, -1, 1, 1]) # Transform into a batch of size num_samples to get that many random # decodes. x = tf.tile(x, tf.to_int32([num_samples, 1, 1, 1])) p_hparams = hparams.problem_hparams input_space_id = tf.constant(p_hparams.input_space_id) target_space_id = tf.constant(p_hparams.target_space_id) features = {} features["input_space_id"] = input_space_id features["target_space_id"] = target_space_id features["decode_length"] = ( IMAGE_DECODE_LENGTH if input_is_image else inputs[1]) features["inputs"] = x return features def _decode_input_tensor_to_features_dict(feature_map, hparams): """Convert the interactive input format (see above) to a dictionary. Args: feature_map: dict with inputs. hparams: model hyperparameters Returns: a features dictionary, as expected by the decoder. """ inputs = tf.convert_to_tensor(feature_map["inputs"]) input_is_image = False x = inputs p_hparams = hparams.problem_hparams # Add a third empty dimension x = tf.expand_dims(x, axis=[2]) x = tf.to_int32(x) input_space_id = tf.constant(p_hparams.input_space_id) target_space_id = tf.constant(p_hparams.target_space_id) features = {} features["input_space_id"] = input_space_id features["target_space_id"] = target_space_id features["decode_length"] = ( IMAGE_DECODE_LENGTH if input_is_image else tf.shape(x)[1] + 50) features["inputs"] = x return features def get_step_from_ckpt_path(path): return int(os.path.basename(path).split("-")[1]) def latest_checkpoint_step(ckpt_dir): ckpt = tf.train.get_checkpoint_state(ckpt_dir) if not ckpt: return None path = ckpt.model_checkpoint_path return get_step_from_ckpt_path(path) class DecodeHookArgs(collections.namedtuple( "DecodeHookArgs", ["estimator", "problem", "output_dirs", "hparams", "decode_hparams", "predictions"])): pass def run_postdecode_hooks(decode_hook_args, dataset_split): """Run hooks after decodes have run.""" hooks = decode_hook_args.problem.decode_hooks if not hooks: return global_step = latest_checkpoint_step(decode_hook_args.estimator.model_dir) if global_step is None: tf.logging.info( "Skipping decode hooks because no checkpoint yet available.") return tf.logging.info("Running decode hooks.") parent_dir = os.path.join(decode_hook_args.output_dirs[0], os.pardir) child_dir = decode_hook_args.decode_hparams.summaries_log_dir if dataset_split is not None: child_dir += "_{}".format(dataset_split) final_dir = os.path.join(parent_dir, child_dir) summary_writer = tf.summary.FileWriter(final_dir) for hook in hooks: # Isolate each hook in case it creates TF ops with tf.Graph().as_default(): summaries = hook(decode_hook_args) if summaries: summary = tf.Summary(value=list(summaries)) summary_writer.add_summary(summary, global_step) summary_writer.close() tf.logging.info("Decode hooks done.")

34.877619

80

0.669786

51e31291379372af5e260295e3d4c305fad978a1

1,179

py

Python

tests/test_galaxy_importer.py

alikins/galaxy-importer-1

f9c7aebd826250dd47b022388d45ff5d33b91068

[ "Apache-2.0" ]

null

tests/test_galaxy_importer.py

alikins/galaxy-importer-1

f9c7aebd826250dd47b022388d45ff5d33b91068

[ "Apache-2.0" ]

null

tests/test_galaxy_importer.py

alikins/galaxy-importer-1

f9c7aebd826250dd47b022388d45ff5d33b91068

[ "Apache-2.0" ]

null

# (c) 2012-2019, Ansible by Red Hat # # This file is part of Ansible Galaxy # # Ansible Galaxy is free software: you can redistribute it and/or modify # it under the terms of the Apache License as published by # the Apache Software Foundation, either version 2 of the License, or # (at your option) any later version. # # Ansible Galaxy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Apache License for more details. # # You should have received a copy of the Apache License # along with Galaxy. If not, see <http://www.apache.org/licenses/>. import pytest from galaxy_importer import main def test_parser(): parser = main.parse_args(['path/to/my_file.tar.gz']) assert parser.file == 'path/to/my_file.tar.gz' assert not parser.print_result parser = main.parse_args(['my_file.tar.gz', '--print-result']) assert parser.file == 'my_file.tar.gz' assert parser.print_result # SystemExit with missing required positional file argument with pytest.raises(SystemExit): main.parse_args(['--print-result'])

33.685714

72

0.732824

ab526aea26860fa082d9cd66287c90e3508a39bf

19,527

py

Python

test/world_test.py

Beliaar/bGrease

24a0e3f2ed9ba57c8e3e83e16d16409ca2551c44

[ "MIT" ]

1

2018-07-17T03:53:43.000Z

test/world_test.py

Beliaar/bGrease

24a0e3f2ed9ba57c8e3e83e16d16409ca2551c44

[ "MIT" ]

null

test/world_test.py

Beliaar/bGrease

24a0e3f2ed9ba57c8e3e83e16d16409ca2551c44

[ "MIT" ]

null

import unittest class TestComponent(dict): world = None runtime = 0 def __init__(self): self.entities = set() def set_world(self, world): self.world = world def add(self, entity, data=None): self[entity] = data self.entities.add(entity) def step(self, dt): self.runtime += dt class TestSystem(object): runtime = 0 world = None order = 0 def set_world(self, world): self.world = world def step(self, dt): self.runtime += dt TestSystem.order += 1 self.order = TestSystem.order class TestSystemInjector(TestSystem): def __init__(self, name, system): self.injected = False self.name = name self.system = system def step(self, dt): TestSystem.step(self, dt) if not self.injected: setattr(self.world.systems, self.name, self.system) self.injected = True class TestRenderer(object): world = None drawn = False order = 0 def draw(self): self.drawn = True TestRenderer.order += 1 self.order = TestRenderer.order def set_world(self, world): self.world = world class TestGL(object): matrix_reset = False cleared = False GL_DEPTH_BUFFER_BIT = 1 GL_COLOR_BUFFER_BIT = 2 def glClear(self, bits): self.cleared = bits def glLoadIdentity(self): self.matrix_reset = True class TestClock(object): def __init__(self, time_function=None): self.scheduled = [] self.time_func = time_function self.ticks = 0 def schedule_interval(self, what, interval): self.scheduled.append((what, interval)) def schedule(self, what): self.scheduled.append((what, None)) def unschedule(self, what): for i in range(len(self.scheduled)): if self.scheduled[i][0] == what: del self.scheduled[i] return def tick(self, poll=True): self.ticks += 1 class TestModeManager(object): def __init__(self): self.handlers = [] self.event_dispatcher = self def push_handlers(self, handler): self.handlers.append(handler) def remove_handlers(self, handler): self.handlers.remove(handler) class WorldTestCase(unittest.TestCase): def test_defaults(self): from bGrease import World world = World(clock_factory=TestClock) self.assertEqual(world.step_rate, 60) self.assertFalse(world.active) self.assertTrue(world.running) self.assertEqual(world.time, 0) self.assertTrue((world.step, 1.0/60) in world.clock.scheduled) def test_overrides(self): from bGrease import World world = World(step_rate=30, clock_factory=TestClock) self.assertEqual(world.step_rate, 30) self.assertTrue((world.step, 1.0/30) in world.clock.scheduled) def test_create_entities_in_world(self): from bGrease import World, Entity world = World() self.assertFalse(world.entities) e1 = Entity(world) e2 = Entity(world) self.assertEqual(len(world.entities), 2) self.assertTrue(e1 in world.entities) self.assertTrue(e1.world is world) self.assertTrue(e2 in world.entities) self.assertTrue(e2.world is world) self.assertNotEqual(e1, e2) def test_worlds_disjoint(self): from bGrease import World, Entity world1 = World() world2 = World() self.assertTrue(world1 is not world2) e1 = Entity(world1) e2 = Entity(world2) self.assertEqual(len(world1.entities), 1) self.assertEqual(len(world2.entities), 1) self.assertTrue(e1.world is world1) self.assertTrue(e2.world is world2) self.assertTrue(e1 in world1.entities) self.assertFalse(e2 in world1.entities) self.assertFalse(e1 in world2.entities) self.assertTrue(e2 in world2.entities) self.assertNotEqual(e1, e2) def test_remove_entity(self): from bGrease import World, Entity world = World() comp1 = world.components.one = TestComponent() comp2 = world.components.two = TestComponent() comp3 = world.components.three = TestComponent() entity = Entity(world) comp1.add(entity) comp2.add(entity) self.assertTrue(entity in world.entities) self.assertTrue(entity in comp1) self.assertTrue(entity in comp2) self.assertFalse(entity in comp3) world.entities.remove(entity) self.assertFalse(entity in world.entities) self.assertFalse(entity in comp1) self.assertFalse(entity in comp2) self.assertFalse(entity in comp3) self.assertRaises(KeyError, world.entities.remove, entity) def test_discard_entity(self): from bGrease import World, Entity world = World() comp1 = world.components.one = TestComponent() comp2 = world.components.two = TestComponent() comp3 = world.components.three = TestComponent() entity = Entity(world) comp1.add(entity) comp2.add(entity) self.assertTrue(entity in world.entities) self.assertTrue(entity in comp1) self.assertTrue(entity in comp2) self.assertFalse(entity in comp3) world.entities.discard(entity) self.assertFalse(entity in world.entities) self.assertFalse(entity in comp1) self.assertFalse(entity in comp2) self.assertFalse(entity in comp3) world.entities.discard(entity) self.assertFalse(entity in world.entities) self.assertFalse(entity in comp1) self.assertFalse(entity in comp2) self.assertFalse(entity in comp3) def test_entity_extent_component_access(self): from bGrease import World, Entity from bGrease.entity import ComponentEntitySet world = World() comp = world.components.test = TestComponent() e1 = Entity(world) e2 = Entity(world) comp.add(e1) comp.add(e2) extent = world[Entity] comp_set = extent.test self.assertEqual(comp_set, set([e1, e2])) self.assertRaises(AttributeError, getattr, extent, "hummina") def test_entity_extent_membership_simple(self): from bGrease import World, Entity class MyEntity(Entity): pass class Another(Entity): pass world = World() self.assertFalse(world.entities) extent = world[MyEntity] self.assertFalse(extent.entities) entity1 = MyEntity(world) self.assertTrue(entity1 in extent.entities) entity2 = MyEntity(world) self.assertTrue(entity2 in extent.entities) world.entities.remove(entity2) self.assertTrue(entity1 in extent.entities) self.assertFalse(entity2 in extent.entities) entity3 = Another(world) self.assertFalse(entity3 in extent.entities) self.assertTrue(entity3 in world[Another].entities) def test_entity_superclass_extents(self): from bGrease import World, Entity class Superentity(Entity): pass class Subentity(Superentity): pass class SubSubentity(Subentity): pass class Another(Entity): pass world = World() super_extent = world[Superentity] super = Superentity(world) sub = Subentity(world) subsub = SubSubentity(world) another = Another(world) self.assertTrue(super in super_extent.entities) self.assertTrue(sub in super_extent.entities) self.assertTrue(subsub in super_extent.entities) self.assertFalse(another in super_extent.entities) sub_extent = world[Subentity] self.assertFalse(super in sub_extent.entities) self.assertTrue(sub in sub_extent.entities) self.assertTrue(subsub in sub_extent.entities) self.assertFalse(another in sub_extent.entities) subsub_extent = world[SubSubentity] self.assertFalse(super in subsub_extent.entities) self.assertFalse(sub in subsub_extent.entities) self.assertTrue(subsub in subsub_extent.entities) self.assertFalse(another in subsub_extent.entities) another_extent = world[Another] self.assertFalse(super in another_extent.entities) self.assertFalse(sub in another_extent.entities) self.assertFalse(subsub in another_extent.entities) self.assertTrue(another in another_extent.entities) world.entities.remove(subsub) self.assertFalse(subsub in super_extent.entities) self.assertFalse(subsub in sub_extent.entities) self.assertFalse(subsub in subsub_extent.entities) self.assertFalse(subsub in another_extent.entities) def test_union_extent(self): from bGrease import World, Entity class Entity1(Entity): pass class Entity2(Entity1): pass class Entity3(Entity): pass world = World() entities = [Entity1(world), Entity2(world), Entity2(world), Entity3(world)] union_extent_1_2 = world[Entity1, Entity2] self.assertEqual(union_extent_1_2.entities, set(entities[:-1])) union_extent_2_3 = world[Entity2, Entity3] self.assertEqual(union_extent_2_3.entities, set(entities[1:])) union_extent_1_3 = world[Entity1, Entity3] self.assertEqual(union_extent_1_3.entities, set(entities)) def test_full_extent(self): from bGrease import World, Entity class Entity1(Entity): pass class Entity2(Entity1): pass class Entity3(Entity): pass world = World() full_extent = world[...] self.assertEqual(world.entities, full_extent.entities) entities = set([Entity1(world), Entity2(world), Entity3(world), Entity1(world)]) self.assertEqual(world.entities, entities) self.assertEqual(full_extent.entities, entities) self.assertEqual(world[...].entities, entities) def test_configure_components(self): from bGrease import World comp1 = TestComponent() comp2 = TestComponent() comp3 = TestComponent() world = World() self.assertEqual(len(world.components), 0) world.components.one = comp1 world.components.two = comp2 world.components.three = comp3 self.assertEqual(list(world.components), [comp1, comp2, comp3]) self.assertTrue(comp1.world is world) self.assertTrue(comp2.world is world) self.assertTrue(comp3.world is world) def test_set_components(self): from bGrease import World comp1 = TestComponent() comp2 = TestComponent() comp3 = TestComponent() world = World() self.assertFalse(world.systems) self.assertFalse(world.components) self.assertRaises(AttributeError, getattr, world, 'foobar') world.components.foobar = comp1 self.assertTrue(world.components.foobar is comp1) self.assertTrue(comp1.world is world) self.assertEqual(len(world.components), 1) self.assertRaises(AttributeError, getattr, world, 'spam') world.components.spam = comp2 self.assertTrue(world.components.spam is comp2) self.assertTrue(comp2.world is world) self.assertEqual(len(world.components), 2) self.assertRaises(AttributeError, getattr, world, 'foobar') world.components.foobar = comp3 self.assertTrue(world.components.foobar is comp3) self.assertTrue(comp3.world is world) self.assertEqual(len(world.components), 2) self.assertEqual(list(world.components), [comp3, comp2]) def test_del_component(self): from bGrease import World world = World() comp1 = world.components.one = TestComponent() comp2 = world.components.two = TestComponent() comp3 = world.components.three = TestComponent() self.assertEqual(list(world.components), [comp1, comp2, comp3]) del world.components.two self.assertEqual(list(world.components), [comp1, comp3]) del world.components.one self.assertEqual(list(world.components), [comp3]) self.assertRaises(AttributeError, delattr, world, 'one') def test_step_components(self): from bGrease import World, Entity world = World() comp1 = world.components.one = TestComponent() comp2 = world.components.two = TestComponent() entity = Entity(world) self.assertTrue(comp1.runtime == comp2.runtime == 0, comp1.runtime) world.step(0.05) self.assertEqual(comp1.runtime, 0.05) self.assertEqual(comp2.runtime, 0.05) world.step(0.06) self.assertEqual(comp1.runtime, 0.11) self.assertEqual(comp2.runtime, 0.11) def test_join_components(self): from bGrease import World, Entity world = World() comp1 = world.components.foo = TestComponent() comp2 = world.components.bar = TestComponent() comp3 = world.components.baz = TestComponent() entity = Entity(world) for i in range(20): entity = object() comp1.add(entity, i) if i < 5: comp2.add(entity, i * 10) if i < 3: comp3.add(entity, i * 100) self.assertEqual(sorted(world.components.join('baz', 'bar', 'foo')), [ (0, 0, 0), (100, 10, 1), (200, 20, 2)]) self.assertEqual(sorted(world.components.join('foo', 'bar')), [ (0, 0), (1, 10), (2, 20), (3, 30), (4, 40)]) self.assertEqual(sorted(world.components.join('baz')), [ (0,), (100,), (200,)]) def test_illegal_part_name(self): from bGrease import World from bGrease.component import ComponentError world = World() self.assertRaises(ComponentError, setattr, world.components, 'entities', TestComponent()) self.assertRaises(ComponentError, setattr, world.systems, 'entities', TestSystem()) self.assertRaises(ComponentError, setattr, world.renderers, 'entities', TestRenderer()) self.assertRaises(ComponentError, setattr, world.components, '_reserved', TestComponent()) self.assertRaises(ComponentError, setattr, world.systems, '_reserved', TestSystem()) self.assertRaises(ComponentError, setattr, world.renderers, '_reserved', TestRenderer()) self.assertRaises(AttributeError, setattr, world.components, 'insert', TestComponent()) self.assertRaises(AttributeError, setattr, world.systems, 'insert', TestSystem()) self.assertRaises(AttributeError, setattr, world.renderers, 'insert', TestRenderer()) def test_add_systems(self): from bGrease import World world = World() self.assertFalse(world.systems) sys1 = world.systems.one = TestSystem() sys2 = world.systems.two = TestSystem() sys3 = world.systems.three = TestSystem() self.assertEqual(list(world.systems), [sys1, sys2, sys3]) self.assertTrue(world.systems.one is sys1) self.assertTrue(world.systems.two is sys2) self.assertTrue(world.systems.three is sys3) self.assertTrue(sys1.world is world) self.assertTrue(sys2.world is world) self.assertTrue(sys3.world is world) def test_del_systems(self): from bGrease import World world = World() sys1 = world.systems.one = TestSystem() sys2 = world.systems.two = TestSystem() sys3 = world.systems.three = TestSystem() self.assertEqual(list(world.systems), [sys1, sys2, sys3]) del world.systems.two self.assertEqual(list(world.systems), [sys1, sys3]) del world.systems.one self.assertEqual(list(world.systems), [sys3]) self.assertRaises(AttributeError, delattr, world, 'one') def test_insert_system(self): from bGrease import World world = World() sys1 = world.systems.sys1 = TestSystem() sys2 = world.systems.sys2 = TestSystem() sys3 = world.systems.sys3 = TestSystem() inserted = TestSystem() world.systems.insert('inserted', inserted, before='sys2') self.assertEqual(list(world.systems), [sys1, inserted, sys2, sys3]) self.assertTrue(world.systems.inserted is inserted) another = TestSystem() world.systems.insert('another', another, before=world.systems.sys3) self.assertTrue(world.systems.another is another) self.assertEqual(list(world.systems), [sys1, inserted, sys2, another, sys3]) onemore = TestSystem() world.systems.insert('onemore', onemore, index=1) self.assertEqual(list(world.systems), [sys1, onemore, inserted, sys2, another, sys3]) self.assertTrue(world.systems.onemore is onemore) def test_system_step_order(self): from bGrease import World world = World() sys1 = world.systems.one = TestSystem() sys3 = world.systems.three = TestSystem() sys2 = TestSystem() world.systems.insert('two', sys2, index=1) self.assertEqual(len(world.systems), 3) self.assertTrue(sys1.runtime == sys2.runtime == sys3.runtime == 0) self.assertTrue(sys1.order == sys2.order == sys3.order == TestSystem.order) world.step(0.13) self.assertTrue(sys1.runtime == sys2.runtime == sys3.runtime == 0.13) start = sys1.order self.assertEqual(sys2.order, start + 1) self.assertEqual(sys3.order, start + 2) def test_add_system_during_run(self): from bGrease import World world = World() sys1 = world.systems.sys1 = TestSystem() to_inject = TestSystem() injector = world.systems.injector = TestSystemInjector('injected', to_inject) self.assertEqual(len(world.systems), 2) self.assertTrue(sys1.runtime == to_inject.runtime == injector.runtime == 0) self.assertFalse(injector.injected) world.step(0.1) self.assertEqual(len(world.systems), 3) self.assertEqual(sys1.runtime, 0.1) self.assertEqual(injector.runtime, 0.1) self.assertEqual(to_inject.runtime, 0) self.assertTrue(injector.injected) world.step(0.1) self.assertEqual(len(world.systems), 3) self.assertEqual(sys1.runtime, 0.2) self.assertEqual(injector.runtime, 0.2) self.assertEqual(to_inject.runtime, 0.1) def test_activate(self): from bGrease import World world = World(master_clock=TestClock()) sys1 = world.systems.one = TestSystem() sys2 = world.systems.two = TestSystem() manager = TestModeManager() self.assertFalse(world.active) world.activate(manager) self.assertTrue(world.manager is manager, world.manager) self.assertTrue(world.active) self.assertTrue((world.tick, None) in world.master_clock.scheduled) self.assertTrue(sys1 in manager.handlers) self.assertTrue(sys2 in manager.handlers) return world, manager def test_deactivate(self): world, manager = self.test_activate() sys1, sys2 = world.systems world.deactivate(manager) self.assertFalse(world.active) self.assertFalse((world.tick, None) in world.master_clock.scheduled) self.assertFalse(sys1 in manager.handlers) self.assertFalse(sys2 in manager.handlers) def test_tick_increments_world_time(self): from bGrease import World world = World(clock_factory=TestClock) self.assertEqual(world.time, 0) self.assertEqual(world.clock.ticks, 0) self.assertEqual(world.clock.time_func(), world.time) dt = 1.0/30.0 world.tick(dt) self.assertAlmostEqual(world.time, dt) self.assertEqual(world.clock.time_func(), world.time) self.assertEqual(world.clock.ticks, 1) world.tick(dt) self.assertAlmostEqual(world.time, dt*2) self.assertEqual(world.clock.time_func(), world.time) self.assertEqual(world.clock.ticks, 2) def test_running(self): from bGrease import World world = World() self.assertTrue(world.running) self.assertEqual(world.time, 0) dt = 1.0/30.0 world.tick(dt) self.assertAlmostEqual(world.time, dt) world.running = False world.tick(dt) world.tick(dt) self.assertAlmostEqual(world.time, dt) world.running = True world.tick(dt) self.assertAlmostEqual(world.time, dt*2) def test_step_max_dt(self): from bGrease import World world = World() sys1 = world.systems.sys = TestSystem() comp1 = world.components.foo = TestComponent() world.step(10000) self.assertEqual(comp1.runtime, 10.0 / world.step_rate) self.assertEqual(sys1.runtime, 10.0 / world.step_rate) def test_set_renderers(self): from bGrease import World world = World() self.assertEqual(tuple(world.renderers), ()) renderer1 = world.renderers.one = TestRenderer() renderer2 = world.renderers.two = TestRenderer() renderer3 = world.renderers.three = object() # arbitrary objects can be renderers self.assertEqual(tuple(world.renderers), (renderer1, renderer2, renderer3)) # objects with a set_world() method should have it called when set self.assertTrue(renderer1.world is world) self.assertTrue(renderer2.world is world) def test_on_draw(self): from bGrease import World world = World() renderer1 = world.renderers.one = TestRenderer() renderer2 = world.renderers.two = TestRenderer() gl = TestGL() self.assertFalse(gl.cleared) self.assertFalse(gl.matrix_reset) self.assertFalse(renderer1.drawn) self.assertFalse(renderer2.drawn) world.on_draw(gl=gl) self.assertTrue(gl.cleared) self.assertTrue(gl.matrix_reset) self.assertTrue(renderer1.drawn) self.assertTrue(renderer2.drawn) start = renderer1.order self.assertEqual(renderer2.order, start + 1) if __name__ == '__main__': unittest.main()

32.383085

87

0.741537

6cc3ba613294da0f2a44a84dddcf1f39faa367ca

28,316

py

Python

conans/client/cmd/uploader.py

rweickelt/conan

2e5b21e82618d471c16e8ad6b8872d80c03fe609

[ "MIT" ]

null

conans/client/cmd/uploader.py

rweickelt/conan

2e5b21e82618d471c16e8ad6b8872d80c03fe609

[ "MIT" ]

null

conans/client/cmd/uploader.py

rweickelt/conan

2e5b21e82618d471c16e8ad6b8872d80c03fe609

[ "MIT" ]

null

import os import stat import tarfile import time from collections import defaultdict from tqdm import tqdm from conans.client.remote_manager import is_package_snapshot_complete from conans.client.source import complete_recipe_sources from conans.errors import ConanException, NotFoundException from conans.model.manifest import gather_files, FileTreeManifest from conans.model.ref import ConanFileReference, PackageReference, check_valid_ref from conans.paths import (CONAN_MANIFEST, CONANFILE, EXPORT_SOURCES_TGZ_NAME, EXPORT_TGZ_NAME, PACKAGE_TGZ_NAME, CONANINFO) from conans.search.search import search_packages, search_recipes from conans.util.files import (load, clean_dirty, is_dirty, gzopen_without_timestamps, set_dirty) from conans.util.log import logger from conans.util.tracer import (log_recipe_upload, log_compressed_files, log_package_upload) UPLOAD_POLICY_FORCE = "force-upload" UPLOAD_POLICY_NO_OVERWRITE = "no-overwrite" UPLOAD_POLICY_NO_OVERWRITE_RECIPE = "no-overwrite-recipe" UPLOAD_POLICY_SKIP = "skip-upload" class CmdUpload(object): """ This class is responsible for uploading packages to remotes. The flow is: - Collect all the data from the local cache: - Collect the refs that matches the given pattern _collect_refs_to_upload - Collect for every ref all the binaries IDs that has to be uploaded "_collect_packages_to_upload". This may discard binaries that do not belong to the current RREV The collection of this does the interactivity (ask user if yes/no), the errors (don't upload packages with policy=build_always, and computing the full REVISIONS for every that has to be uploaded. No remote API calls are done in this step, everything is local - Execute the upload. For every ref: - Upload the recipe of the ref: "_upload_recipe" - If not FORCE, check the date "_check_recipe_date", i.e. if there are changes, do not allow uploading if the remote date is newer than the local cache one - Retrieve the sources (exports_sources), if they are not cached, and uploading to a different remote. "complete_recipe_sources" - Gather files and create 2 .tgz (exports, exports_sources) with "_compress_recipe_files" - Decide which files have to be uploaded and deleted from the server based on the different with the remote snapshot "_recipe_files_to_upload" This can raise if upload policy is not overwrite - Execute the real transfer "remote_manager.upload_recipe()" - For every package_id of every ref: "_upload_package" - Gather files and create package.tgz. "_compress_package_files" - (Optional) Do the integrity check of the package - Decide which files to upload and delete from server: "_package_files_to_upload". Can raise if policy is NOT overwrite - Do the actual upload All the REVISIONS are local defined, not retrieved from servers This requires calling to the remote API methods: - get_recipe_sources() to get the export_sources if they are missing - get_recipe_snapshot() to do the diff and know what files to upload - get_package_snapshot() to do the diff and know what files to upload - get_recipe_manifest() to check the date and raise if policy requires - get_package_manifest() to raise if policy!=force and manifests change """ def __init__(self, cache, user_io, remote_manager, loader, hook_manager): self._cache = cache self._user_io = user_io self._remote_manager = remote_manager self._loader = loader self._hook_manager = hook_manager def upload(self, reference_or_pattern, remotes, upload_recorder, package_id=None, all_packages=None, confirm=False, retry=None, retry_wait=None, integrity_check=False, policy=None, query=None): t1 = time.time() refs, confirm = self._collects_refs_to_upload(package_id, reference_or_pattern, confirm) refs_by_remote = self._collect_packages_to_upload(refs, confirm, remotes, all_packages, query, package_id) # Do the job for remote, refs in refs_by_remote.items(): self._user_io.out.info("Uploading to remote '{}':".format(remote.name)) for (ref, conanfile, prefs) in refs: self._upload_ref(conanfile, ref, prefs, retry, retry_wait, integrity_check, policy, remote, upload_recorder, remotes) logger.debug("UPLOAD: Time manager upload: %f" % (time.time() - t1)) def _collects_refs_to_upload(self, package_id, reference_or_pattern, confirm): """ validate inputs and compute the refs (without revisions) to be uploaded """ if package_id and not check_valid_ref(reference_or_pattern, allow_pattern=False): raise ConanException("-p parameter only allowed with a valid recipe reference, " "not with a pattern") if package_id or check_valid_ref(reference_or_pattern, allow_pattern=False): # Upload package ref = ConanFileReference.loads(reference_or_pattern) refs = [ref, ] confirm = True else: refs = search_recipes(self._cache, reference_or_pattern) if not refs: raise NotFoundException(("No packages found matching pattern '%s'" % reference_or_pattern)) return refs, confirm def _collect_packages_to_upload(self, refs, confirm, remotes, all_packages, query, package_id): """ compute the references with revisions and the package_ids to be uploaded """ # Group recipes by remote refs_by_remote = defaultdict(list) for ref in refs: metadata = self._cache.package_layout(ref).load_metadata() ref = ref.copy_with_rev(metadata.recipe.revision) remote = remotes.selected if remote: ref_remote = remote else: ref_remote = metadata.recipe.remote ref_remote = remotes.get_remote(ref_remote) upload = True if not confirm: msg = "Are you sure you want to upload '%s' to '%s'?" % (str(ref), ref_remote.name) upload = self._user_io.request_boolean(msg) if upload: try: conanfile_path = self._cache.package_layout(ref).conanfile() conanfile = self._loader.load_class(conanfile_path) except NotFoundException: raise NotFoundException(("There is no local conanfile exported as %s" % str(ref))) # TODO: This search of binary packages has to be improved, more robust # So only real packages are retrieved if all_packages or query: if all_packages: query = None # better to do a search, that will retrieve real packages with ConanInfo # Not only "package_id" folders that could be empty package_layout = self._cache.package_layout(ref.copy_clear_rev()) packages = search_packages(package_layout, query) packages_ids = list(packages.keys()) elif package_id: packages_ids = [package_id, ] else: packages_ids = [] if packages_ids: if conanfile.build_policy == "always": raise ConanException("Conanfile '%s' has build_policy='always', " "no packages can be uploaded" % str(ref)) prefs = [] # Gather all the complete PREFS with PREV for package_id in packages_ids: if package_id not in metadata.packages: raise ConanException("Binary package %s:%s not found" % (str(ref), package_id)) # Filter packages that don't match the recipe revision if self._cache.config.revisions_enabled and ref.revision: rec_rev = metadata.packages[package_id].recipe_revision if ref.revision != rec_rev: self._user_io.out.warn("Skipping package '%s', it doesn't belong to the" " current recipe revision" % package_id) continue package_revision = metadata.packages[package_id].revision assert package_revision is not None, "PREV cannot be None to upload" prefs.append(PackageReference(ref, package_id, package_revision)) refs_by_remote[ref_remote].append((ref, conanfile, prefs)) return refs_by_remote def _upload_ref(self, conanfile, ref, prefs, retry, retry_wait, integrity_check, policy, recipe_remote, upload_recorder, remotes): """ Uploads the recipes and binaries identified by ref """ assert (ref.revision is not None), "Cannot upload a recipe without RREV" conanfile_path = self._cache.package_layout(ref).conanfile() # FIXME: I think it makes no sense to specify a remote to "pre_upload" # FIXME: because the recipe can have one and the package a different one self._hook_manager.execute("pre_upload", conanfile_path=conanfile_path, reference=ref, remote=recipe_remote) self._user_io.out.info("Uploading %s to remote '%s'" % (str(ref), recipe_remote.name)) self._upload_recipe(ref, conanfile, retry, retry_wait, policy, recipe_remote, remotes) upload_recorder.add_recipe(ref, recipe_remote.name, recipe_remote.url) # Now the binaries if prefs: total = len(prefs) for index, pref in enumerate(prefs): p_remote = recipe_remote msg = ("Uploading package %d/%d: %s to '%s'" % (index+1, total, str(pref.id), p_remote.name)) self._user_io.out.info(msg) self._upload_package(pref, retry, retry_wait, integrity_check, policy, p_remote) upload_recorder.add_package(pref, p_remote.name, p_remote.url) # FIXME: I think it makes no sense to specify a remote to "post_upload" # FIXME: because the recipe can have one and the package a different one self._hook_manager.execute("post_upload", conanfile_path=conanfile_path, reference=ref, remote=recipe_remote) def _upload_recipe(self, ref, conanfile, retry, retry_wait, policy, remote, remotes): current_remote_name = self._cache.package_layout(ref).load_metadata().recipe.remote if remote.name != current_remote_name: complete_recipe_sources(self._remote_manager, self._cache, conanfile, ref, remotes) conanfile_path = self._cache.package_layout(ref).conanfile() self._hook_manager.execute("pre_upload_recipe", conanfile_path=conanfile_path, reference=ref, remote=remote) t1 = time.time() the_files = self._compress_recipe_files(ref) local_manifest = FileTreeManifest.loads(load(the_files["conanmanifest.txt"])) remote_manifest = None if policy != UPLOAD_POLICY_FORCE: remote_manifest = self._check_recipe_date(ref, remote, local_manifest) if policy == UPLOAD_POLICY_SKIP: return ref files_to_upload, deleted = self._recipe_files_to_upload(ref, policy, the_files, remote, remote_manifest, local_manifest) if files_to_upload or deleted: self._remote_manager.upload_recipe(ref, files_to_upload, deleted, remote, retry, retry_wait) self._upload_recipe_end_msg(ref, remote) else: self._user_io.out.info("Recipe is up to date, upload skipped") duration = time.time() - t1 log_recipe_upload(ref, duration, the_files, remote.name) self._hook_manager.execute("post_upload_recipe", conanfile_path=conanfile_path, reference=ref, remote=remote) # The recipe wasn't in the registry or it has changed the revision field only if not current_remote_name: with self._cache.package_layout(ref).update_metadata() as metadata: metadata.recipe.remote = remote.name return ref def _upload_package(self, pref, retry=None, retry_wait=None, integrity_check=False, policy=None, p_remote=None): assert (pref.revision is not None), "Cannot upload a package without PREV" assert (pref.ref.revision is not None), "Cannot upload a package without RREV" conanfile_path = self._cache.package_layout(pref.ref).conanfile() self._hook_manager.execute("pre_upload_package", conanfile_path=conanfile_path, reference=pref.ref, package_id=pref.id, remote=p_remote) t1 = time.time() the_files = self._compress_package_files(pref, integrity_check) if policy == UPLOAD_POLICY_SKIP: return None files_to_upload, deleted = self._package_files_to_upload(pref, policy, the_files, p_remote) if files_to_upload or deleted: self._remote_manager.upload_package(pref, files_to_upload, deleted, p_remote, retry, retry_wait) logger.debug("UPLOAD: Time upload package: %f" % (time.time() - t1)) else: self._user_io.out.info("Package is up to date, upload skipped") duration = time.time() - t1 log_package_upload(pref, duration, the_files, p_remote) self._hook_manager.execute("post_upload_package", conanfile_path=conanfile_path, reference=pref.ref, package_id=pref.id, remote=p_remote) logger.debug("UPLOAD: Time uploader upload_package: %f" % (time.time() - t1)) metadata = self._cache.package_layout(pref.ref).load_metadata() cur_package_remote = metadata.packages[pref.id].remote if not cur_package_remote and policy != UPLOAD_POLICY_SKIP: with self._cache.package_layout(pref.ref).update_metadata() as metadata: metadata.packages[pref.id].remote = p_remote.name return pref def _compress_recipe_files(self, ref): export_folder = self._cache.package_layout(ref).export() for f in (EXPORT_TGZ_NAME, EXPORT_SOURCES_TGZ_NAME): tgz_path = os.path.join(export_folder, f) if is_dirty(tgz_path): self._user_io.out.warn("%s: Removing %s, marked as dirty" % (str(ref), f)) os.remove(tgz_path) clean_dirty(tgz_path) files, symlinks = gather_files(export_folder) if CONANFILE not in files or CONAN_MANIFEST not in files: raise ConanException("Cannot upload corrupted recipe '%s'" % str(ref)) export_src_folder = self._cache.package_layout(ref).export_sources() src_files, src_symlinks = gather_files(export_src_folder) the_files = _compress_recipe_files(files, symlinks, src_files, src_symlinks, export_folder, self._user_io.out) return the_files def _compress_package_files(self, pref, integrity_check): t1 = time.time() # existing package, will use short paths if defined package_folder = self._cache.package_layout(pref.ref, short_paths=None).package(pref) if is_dirty(package_folder): raise ConanException("Package %s is corrupted, aborting upload.\n" "Remove it with 'conan remove %s -p=%s'" % (pref, pref.ref, pref.id)) tgz_path = os.path.join(package_folder, PACKAGE_TGZ_NAME) if is_dirty(tgz_path): self._user_io.out.warn("%s: Removing %s, marked as dirty" % (str(pref), PACKAGE_TGZ_NAME)) os.remove(tgz_path) clean_dirty(tgz_path) # Get all the files in that directory files, symlinks = gather_files(package_folder) if CONANINFO not in files or CONAN_MANIFEST not in files: logger.error("Missing info or manifest in uploading files: %s" % (str(files))) raise ConanException("Cannot upload corrupted package '%s'" % str(pref)) logger.debug("UPLOAD: Time remote_manager build_files_set : %f" % (time.time() - t1)) if integrity_check: self._package_integrity_check(pref, files, package_folder) logger.debug("UPLOAD: Time remote_manager check package integrity : %f" % (time.time() - t1)) the_files = _compress_package_files(files, symlinks, package_folder, self._user_io.out) return the_files def _recipe_files_to_upload(self, ref, policy, the_files, remote, remote_manifest, local_manifest): self._remote_manager.check_credentials(remote) remote_snapshot = self._remote_manager.get_recipe_snapshot(ref, remote) files_to_upload = {filename.replace("\\", "/"): path for filename, path in the_files.items()} if not remote_snapshot: return files_to_upload, set() deleted = set(remote_snapshot).difference(the_files) if policy != UPLOAD_POLICY_FORCE: if remote_manifest is None: # This is the weird scenario, we have a snapshot but don't have a manifest. # Can be due to concurrency issues, so we can try retrieve it now try: remote_manifest, _ = self._remote_manager.get_recipe_manifest(ref, remote) except NotFoundException: # This is weird, the manifest still not there, better upload everything self._user_io.out.warn("The remote recipe doesn't have the 'conanmanifest.txt' " "file and will be uploaded: '{}'".format(ref)) return files_to_upload, deleted if remote_manifest == local_manifest: return None, None if policy in (UPLOAD_POLICY_NO_OVERWRITE, UPLOAD_POLICY_NO_OVERWRITE_RECIPE): raise ConanException("Local recipe is different from the remote recipe. " "Forbidden overwrite.") return files_to_upload, deleted def _package_files_to_upload(self, pref, policy, the_files, remote): self._remote_manager.check_credentials(remote) remote_snapshot = self._remote_manager.get_package_snapshot(pref, remote) if remote_snapshot and policy != UPLOAD_POLICY_FORCE: if not is_package_snapshot_complete(remote_snapshot): return the_files, set([]) remote_manifest, _ = self._remote_manager.get_package_manifest(pref, remote) local_manifest = FileTreeManifest.loads(load(the_files["conanmanifest.txt"])) if remote_manifest == local_manifest: return None, None if policy == UPLOAD_POLICY_NO_OVERWRITE: raise ConanException("Local package is different from the remote package. Forbidden" " overwrite.") deleted = set(remote_snapshot).difference(the_files) return the_files, deleted def _upload_recipe_end_msg(self, ref, remote): msg = "Uploaded conan recipe '%s' to '%s'" % (str(ref), remote.name) url = remote.url.replace("https://api.bintray.com/conan", "https://bintray.com") msg += ": %s" % url self._user_io.out.writeln("") self._user_io.out.info(msg) def _package_integrity_check(self, pref, files, package_folder): # If package has been modified remove tgz to regenerate it self._user_io.out.rewrite_line("Checking package integrity...") # short_paths = None is enough if there exist short_paths layout = self._cache.package_layout(pref.ref, short_paths=None) read_manifest, expected_manifest = layout.package_manifests(pref) if read_manifest != expected_manifest: self._user_io.out.writeln("") diff = read_manifest.difference(expected_manifest) for fname, (h1, h2) in diff.items(): self._user_io.out.warn("Mismatched checksum '%s' (manifest: %s, file: %s)" % (fname, h1, h2)) if PACKAGE_TGZ_NAME in files: try: tgz_path = os.path.join(package_folder, PACKAGE_TGZ_NAME) os.unlink(tgz_path) except Exception: pass error_msg = os.linesep.join("Mismatched checksum '%s' (manifest: %s, file: %s)" % (fname, h1, h2) for fname, (h1, h2) in diff.items()) logger.error("Manifests doesn't match!\n%s" % error_msg) raise ConanException("Cannot upload corrupted package '%s'" % str(pref)) else: self._user_io.out.rewrite_line("Package integrity OK!") self._user_io.out.writeln("") def _check_recipe_date(self, ref, remote, local_manifest): try: remote_recipe_manifest, ref = self._remote_manager.get_recipe_manifest(ref, remote) except NotFoundException: return # First time uploading this package if (remote_recipe_manifest != local_manifest and remote_recipe_manifest.time > local_manifest.time): self._print_manifest_information(remote_recipe_manifest, local_manifest, ref, remote) raise ConanException("Remote recipe is newer than local recipe: " "\n Remote date: %s\n Local date: %s" % (remote_recipe_manifest.time, local_manifest.time)) return remote_recipe_manifest def _print_manifest_information(self, remote_recipe_manifest, local_manifest, ref, remote): try: self._user_io.out.info("\n%s" % ("-"*40)) self._user_io.out.info("Remote manifest:") self._user_io.out.info(remote_recipe_manifest) self._user_io.out.info("Local manifest:") self._user_io.out.info(local_manifest) difference = remote_recipe_manifest.difference(local_manifest) if "conanfile.py" in difference: contents = load(self._cache.package_layout(ref).conanfile()) endlines = "\\r\\n" if "\r\n" in contents else "\\n" self._user_io.out.info("Local 'conanfile.py' using '%s' line-ends" % endlines) remote_contents = self._remote_manager.get_recipe_path(ref, path="conanfile.py", remote=remote) endlines = "\\r\\n" if "\r\n" in remote_contents else "\\n" self._user_io.out.info("Remote 'conanfile.py' using '%s' line-ends" % endlines) self._user_io.out.info("\n%s" % ("-"*40)) except Exception as e: self._user_io.out.info("Error printing information about the diff: %s" % str(e)) def _compress_recipe_files(files, symlinks, src_files, src_symlinks, dest_folder, output): # This is the minimum recipe result = {CONANFILE: files.pop(CONANFILE), CONAN_MANIFEST: files.pop(CONAN_MANIFEST)} export_tgz_path = files.pop(EXPORT_TGZ_NAME, None) sources_tgz_path = files.pop(EXPORT_SOURCES_TGZ_NAME, None) def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg): if tgz_path: result[tgz_name] = tgz_path elif tgz_files: if output and not output.is_terminal: output.writeln(msg) tgz_path = compress_files(tgz_files, tgz_symlinks, tgz_name, dest_folder, output) result[tgz_name] = tgz_path add_tgz(EXPORT_TGZ_NAME, export_tgz_path, files, symlinks, "Compressing recipe...") add_tgz(EXPORT_SOURCES_TGZ_NAME, sources_tgz_path, src_files, src_symlinks, "Compressing recipe sources...") return result def _compress_package_files(files, symlinks, dest_folder, output): tgz_path = files.get(PACKAGE_TGZ_NAME) if not tgz_path: if output and not output.is_terminal: output.writeln("Compressing package...") tgz_files = {f: path for f, path in files.items() if f not in [CONANINFO, CONAN_MANIFEST]} tgz_path = compress_files(tgz_files, symlinks, PACKAGE_TGZ_NAME, dest_folder, output) return {PACKAGE_TGZ_NAME: tgz_path, CONANINFO: files[CONANINFO], CONAN_MANIFEST: files[CONAN_MANIFEST]} def compress_files(files, symlinks, name, dest_dir, output=None): t1 = time.time() # FIXME, better write to disk sequentially and not keep tgz contents in memory tgz_path = os.path.join(dest_dir, name) set_dirty(tgz_path) with open(tgz_path, "wb") as tgz_handle: # tgz_contents = BytesIO() tgz = gzopen_without_timestamps(name, mode="w", fileobj=tgz_handle) for filename, dest in sorted(symlinks.items()): info = tarfile.TarInfo(name=filename) info.type = tarfile.SYMTYPE info.linkname = dest info.size = 0 # A symlink shouldn't have size tgz.addfile(tarinfo=info) mask = ~(stat.S_IWOTH | stat.S_IWGRP) i_file = 0 n_files = len(files) last_progress = None if output and n_files > 1 and not output.is_terminal: output.write("[") elif output and n_files > 1 and output.is_terminal: progress_bar = tqdm(total=len(files), desc="Compressing %s" % name, unit="files", leave=True, dynamic_ncols=False, ascii=True, file=output) for filename, abs_path in sorted(files.items()): info = tarfile.TarInfo(name=filename) info.size = os.stat(abs_path).st_size info.mode = os.stat(abs_path).st_mode & mask if os.path.islink(abs_path): info.type = tarfile.SYMTYPE info.size = 0 # A symlink shouldn't have size info.linkname = os.readlink(abs_path) # @UndefinedVariable tgz.addfile(tarinfo=info) else: with open(abs_path, 'rb') as file_handler: tgz.addfile(tarinfo=info, fileobj=file_handler) if output and n_files > 1: i_file = i_file + 1 units = min(50, int(50 * i_file / n_files)) if last_progress != units: # Avoid screen refresh if nothing has change if not output.is_terminal: output.write('=' * (units - (last_progress or 0))) last_progress = units if output.is_terminal: progress_bar.update() if output and n_files > 1: if output.is_terminal: progress_bar.close() else: output.writeln("]") tgz.close() clean_dirty(tgz_path) duration = time.time() - t1 log_compressed_files(files, duration, tgz_path) return tgz_path

50.474153

100

0.615165

870ac5230ae4d3b7880dfbae2a9e3a967ca3ad9d

2,009

py

Python

encoders/rnn/plotobjective.py

SvenBollweg/eqnet

76bc51fdff8bef1b898820329e1ddf212323d0b0

[ "BSD-3-Clause" ]

35

2016-12-11T12:14:48.000Z

2022-03-28T14:20:27.000Z

encoders/rnn/plotobjective.py

SvenBollweg/eqnet

76bc51fdff8bef1b898820329e1ddf212323d0b0

[ "BSD-3-Clause" ]

null

encoders/rnn/plotobjective.py

SvenBollweg/eqnet

76bc51fdff8bef1b898820329e1ddf212323d0b0

[ "BSD-3-Clause" ]

12

2016-12-16T07:11:42.000Z

2020-04-11T02:53:03.000Z

import pickle import matplotlib.cm as cm import matplotlib.pyplot as plt import numpy as np from encoders.rnn.siameseencoder import RecursiveNNSiameseEncoder if __name__ == '__main__': import sys import os if len(sys.argv) != 2: print("Usage <FileName>") sys.exit(-1) hyperparameters = dict(log_learning_rate=-1, rmsprop_rho=.98, momentum=0.8, minibatch_size=10, memory_size=32, grad_clip=10, log_init_scale_embedding=-1, dropout_rate=0, dissimilar_margin=.1) dataset = sys.argv[1] dset_name = os.path.basename(dataset) assert dset_name.endswith('.json.gz') dset_name = dset_name[:-len('.json.gz')] data_dump_name = 'datadump-' + dset_name + '.pkl' if not os.path.exists(data_dump_name): all_params = dict(hyperparameters) lm = RecursiveNNSiameseEncoder(dataset, hyperparameters) X, Y, Z = lm.scan_objective(dataset, 'Add') with open(data_dump_name, 'wb') as f: pickle.dump((X, Y, Z), f, pickle.HIGHEST_PROTOCOL) else: with open(data_dump_name, 'rb') as f: X, Y, Z = pickle.load(f) # plt.figure() # CS = plt.contour(X, Y, np.log(-Z + 1e-20), levels=[-20, -15, -10, -5, -2, -1, -0, 1, 2, 3, 4]) # plt.clabel(CS, inline=1, fontsize=10) Z_norm = np.log(-Z + 1e-20) im = plt.imshow(-np.clip(Z, -2, 0), interpolation='gaussian', origin='lower', cmap=cm.gray, extent=(0, 1, 0, 1)) levels = [-2, -1, 0, 1, 2, 3, 3.95, 4, 4.025, 4.04, 5, 6, 7, 8] CS = plt.contour(Z_norm, levels, origin='lower', linewidths=2, extent=(0, 1, 0, 1)) plt.xlabel("add_weight") plt.ylabel("subtract_weight") plt.title('Objective Values') plt.grid() plt.show()

34.050847

100

0.537083

18bfd50e9c3ed87efe906eadbbe8db807fe12e29

8,294

py

Python

huaweicloud-sdk-gaussdb/huaweicloudsdkgaussdb/v3/model/list_gauss_my_sql_error_log_request.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

64

2020-06-12T07:05:07.000Z

2022-03-30T03:32:50.000Z

huaweicloud-sdk-gaussdb/huaweicloudsdkgaussdb/v3/model/list_gauss_my_sql_error_log_request.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

11

2020-07-06T07:56:54.000Z

2022-01-11T11:14:40.000Z

huaweicloud-sdk-gaussdb/huaweicloudsdkgaussdb/v3/model/list_gauss_my_sql_error_log_request.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

24

2020-06-08T11:42:13.000Z

2022-03-04T06:44:08.000Z

# coding: utf-8 import re import six from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization class ListGaussMySqlErrorLogRequest: """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'x_language': 'str', 'instance_id': 'str', 'start_date': 'str', 'end_date': 'str', 'offset': 'int', 'limit': 'int', 'level': 'str', 'node_id': 'str' } attribute_map = { 'x_language': 'X-Language', 'instance_id': 'instance_id', 'start_date': 'start_date', 'end_date': 'end_date', 'offset': 'offset', 'limit': 'limit', 'level': 'level', 'node_id': 'node_id' } def __init__(self, x_language=None, instance_id=None, start_date=None, end_date=None, offset=None, limit=None, level=None, node_id=None): """ListGaussMySqlErrorLogRequest - a model defined in huaweicloud sdk""" self._x_language = None self._instance_id = None self._start_date = None self._end_date = None self._offset = None self._limit = None self._level = None self._node_id = None self.discriminator = None if x_language is not None: self.x_language = x_language self.instance_id = instance_id self.start_date = start_date self.end_date = end_date if offset is not None: self.offset = offset if limit is not None: self.limit = limit if level is not None: self.level = level if node_id is not None: self.node_id = node_id @property def x_language(self): """Gets the x_language of this ListGaussMySqlErrorLogRequest. 语言 :return: The x_language of this ListGaussMySqlErrorLogRequest. :rtype: str """ return self._x_language @x_language.setter def x_language(self, x_language): """Sets the x_language of this ListGaussMySqlErrorLogRequest. 语言 :param x_language: The x_language of this ListGaussMySqlErrorLogRequest. :type: str """ self._x_language = x_language @property def instance_id(self): """Gets the instance_id of this ListGaussMySqlErrorLogRequest. 实例ID :return: The instance_id of this ListGaussMySqlErrorLogRequest. :rtype: str """ return self._instance_id @instance_id.setter def instance_id(self, instance_id): """Sets the instance_id of this ListGaussMySqlErrorLogRequest. 实例ID :param instance_id: The instance_id of this ListGaussMySqlErrorLogRequest. :type: str """ self._instance_id = instance_id @property def start_date(self): """Gets the start_date of this ListGaussMySqlErrorLogRequest. 开始时间，格式为“yyyy-mm-ddThh:mm:ssZ”。其中，T指某个时间的开始；Z指时区偏移量，例如北京时间偏移显示为+0800。 :return: The start_date of this ListGaussMySqlErrorLogRequest. :rtype: str """ return self._start_date @start_date.setter def start_date(self, start_date): """Sets the start_date of this ListGaussMySqlErrorLogRequest. 开始时间，格式为“yyyy-mm-ddThh:mm:ssZ”。其中，T指某个时间的开始；Z指时区偏移量，例如北京时间偏移显示为+0800。 :param start_date: The start_date of this ListGaussMySqlErrorLogRequest. :type: str """ self._start_date = start_date @property def end_date(self): """Gets the end_date of this ListGaussMySqlErrorLogRequest. 结束时间，格式为“yyyy-mm-ddThh:mm:ssZ”。其中，T指某个时间的开始；Z指时区偏移量，例如北京时间偏移显示为+0800。 :return: The end_date of this ListGaussMySqlErrorLogRequest. :rtype: str """ return self._end_date @end_date.setter def end_date(self, end_date): """Sets the end_date of this ListGaussMySqlErrorLogRequest. 结束时间，格式为“yyyy-mm-ddThh:mm:ssZ”。其中，T指某个时间的开始；Z指时区偏移量，例如北京时间偏移显示为+0800。 :param end_date: The end_date of this ListGaussMySqlErrorLogRequest. :type: str """ self._end_date = end_date @property def offset(self): """Gets the offset of this ListGaussMySqlErrorLogRequest. 索引位置，偏移量。从第一条数据偏移offset条数据后开始查询，默认为0（偏移0条数据，表示从第一条数据开始查询），必须为数字，不能为负数 :return: The offset of this ListGaussMySqlErrorLogRequest. :rtype: int """ return self._offset @offset.setter def offset(self, offset): """Sets the offset of this ListGaussMySqlErrorLogRequest. 索引位置，偏移量。从第一条数据偏移offset条数据后开始查询，默认为0（偏移0条数据，表示从第一条数据开始查询），必须为数字，不能为负数 :param offset: The offset of this ListGaussMySqlErrorLogRequest. :type: int """ self._offset = offset @property def limit(self): """Gets the limit of this ListGaussMySqlErrorLogRequest. 查询记录数。默认为100，不能为负数，最小值为1，最大值为100 :return: The limit of this ListGaussMySqlErrorLogRequest. :rtype: int """ return self._limit @limit.setter def limit(self, limit): """Sets the limit of this ListGaussMySqlErrorLogRequest. 查询记录数。默认为100，不能为负数，最小值为1，最大值为100 :param limit: The limit of this ListGaussMySqlErrorLogRequest. :type: int """ self._limit = limit @property def level(self): """Gets the level of this ListGaussMySqlErrorLogRequest. 日志级别 :return: The level of this ListGaussMySqlErrorLogRequest. :rtype: str """ return self._level @level.setter def level(self, level): """Sets the level of this ListGaussMySqlErrorLogRequest. 日志级别 :param level: The level of this ListGaussMySqlErrorLogRequest. :type: str """ self._level = level @property def node_id(self): """Gets the node_id of this ListGaussMySqlErrorLogRequest. 节点ID :return: The node_id of this ListGaussMySqlErrorLogRequest. :rtype: str """ return self._node_id @node_id.setter def node_id(self, node_id): """Sets the node_id of this ListGaussMySqlErrorLogRequest. 节点ID :param node_id: The node_id of this ListGaussMySqlErrorLogRequest. :type: str """ self._node_id = node_id def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" import simplejson as json if six.PY2: import sys reload(sys) sys.setdefaultencoding("utf-8") return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) def __repr__(self): """For `print`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, ListGaussMySqlErrorLogRequest): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

27.554817

141

0.59742

577b07b2fc2988f83160120578688d4975662914

44,233

py

Python

src/transformers/models/squeezebert/modeling_squeezebert.py

hardianlawi/transformers

d45fc7da3d43ff29ca597f5ffa8cf3151d705013

[ "Apache-2.0" ]

2

2021-08-20T21:22:37.000Z

2021-11-17T10:53:34.000Z

src/transformers/models/squeezebert/modeling_squeezebert.py

xiankaigit/transformers

61f64262692ac7dc90e2e0bdeb7e79d9cd607a66

[ "Apache-2.0" ]

null

src/transformers/models/squeezebert/modeling_squeezebert.py

xiankaigit/transformers

61f64262692ac7dc90e2e0bdeb7e79d9cd607a66

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch SqueezeBert model. """ import math import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import logging from .configuration_squeezebert import SqueezeBertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "squeezebert/squeezebert-uncased" _CONFIG_FOR_DOC = "SqueezeBertConfig" _TOKENIZER_FOR_DOC = "SqueezeBertTokenizer" SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "squeezebert/squeezebert-uncased", "squeezebert/squeezebert-mnli", "squeezebert/squeezebert-mnli-headless", ] class SqueezeBertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + position_embeddings + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class MatMulWrapper(nn.Module): """ Wrapper for torch.matmul(). This makes flop-counting easier to implement. Note that if you directly call torch.matmul() in your code, the flop counter will typically ignore the flops of the matmul. """ def __init__(self): super().__init__() def forward(self, mat1, mat2): """ :param inputs: two torch tensors :return: matmul of these tensors Here are the typical dimensions found in BERT (the B is optional) mat1.shape: [B, <optional extra dims>, M, K] mat2.shape: [B, <optional extra dims>, K, N] output shape: [B, <optional extra dims>, M, N] """ return torch.matmul(mat1, mat2) class SqueezeBertLayerNorm(nn.LayerNorm): """ This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension. N = batch C = channels W = sequence length """ def __init__(self, hidden_size, eps=1e-12): nn.LayerNorm.__init__(self, normalized_shape=hidden_size, eps=eps) # instantiates self.{weight, bias, eps} def forward(self, x): x = x.permute(0, 2, 1) x = nn.LayerNorm.forward(self, x) return x.permute(0, 2, 1) class ConvDropoutLayerNorm(nn.Module): """ ConvDropoutLayerNorm: Conv, Dropout, LayerNorm """ def __init__(self, cin, cout, groups, dropout_prob): super().__init__() self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups) self.layernorm = SqueezeBertLayerNorm(cout) self.dropout = nn.Dropout(dropout_prob) def forward(self, hidden_states, input_tensor): x = self.conv1d(hidden_states) x = self.dropout(x) x = x + input_tensor x = self.layernorm(x) return x class ConvActivation(nn.Module): """ ConvActivation: Conv, Activation """ def __init__(self, cin, cout, groups, act): super().__init__() self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups) self.act = ACT2FN[act] def forward(self, x): output = self.conv1d(x) return self.act(output) class SqueezeBertSelfAttention(nn.Module): def __init__(self, config, cin, q_groups=1, k_groups=1, v_groups=1): """ config = used for some things; ignored for others (work in progress...) cin = input channels = output channels groups = number of groups to use in conv1d layers """ super().__init__() if cin % config.num_attention_heads != 0: raise ValueError( f"cin ({cin}) is not a multiple of the number of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(cin / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=q_groups) self.key = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=k_groups) self.value = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=v_groups) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.softmax = nn.Softmax(dim=-1) self.matmul_qk = MatMulWrapper() self.matmul_qkv = MatMulWrapper() def transpose_for_scores(self, x): """ - input: [N, C, W] - output: [N, C1, W, C2] where C1 is the head index, and C2 is one head's contents """ new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1]) # [N, C1, C2, W] x = x.view(*new_x_shape) return x.permute(0, 1, 3, 2) # [N, C1, C2, W] --> [N, C1, W, C2] def transpose_key_for_scores(self, x): """ - input: [N, C, W] - output: [N, C1, C2, W] where C1 is the head index, and C2 is one head's contents """ new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1]) # [N, C1, C2, W] x = x.view(*new_x_shape) # no `permute` needed return x def transpose_output(self, x): """ - input: [N, C1, W, C2] - output: [N, C, W] """ x = x.permute(0, 1, 3, 2).contiguous() # [N, C1, C2, W] new_x_shape = (x.size()[0], self.all_head_size, x.size()[3]) # [N, C, W] x = x.view(*new_x_shape) return x def forward(self, hidden_states, attention_mask, output_attentions): """ expects hidden_states in [N, C, W] data layout. The attention_mask data layout is [N, W], and it does not need to be transposed. """ mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_key_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_score = self.matmul_qk(query_layer, key_layer) attention_score = attention_score / math.sqrt(self.attention_head_size) # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_score = attention_score + attention_mask # Normalize the attention scores to probabilities. attention_probs = self.softmax(attention_score) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = self.matmul_qkv(attention_probs, value_layer) context_layer = self.transpose_output(context_layer) result = {"context_layer": context_layer} if output_attentions: result["attention_score"] = attention_score return result class SqueezeBertModule(nn.Module): def __init__(self, config): """ - hidden_size = input chans = output chans for Q, K, V (they are all the same ... for now) = output chans for the module - intermediate_size = output chans for intermediate layer - groups = number of groups for all layers in the BertModule. (eventually we could change the interface to allow different groups for different layers) """ super().__init__() c0 = config.hidden_size c1 = config.hidden_size c2 = config.intermediate_size c3 = config.hidden_size self.attention = SqueezeBertSelfAttention( config=config, cin=c0, q_groups=config.q_groups, k_groups=config.k_groups, v_groups=config.v_groups ) self.post_attention = ConvDropoutLayerNorm( cin=c0, cout=c1, groups=config.post_attention_groups, dropout_prob=config.hidden_dropout_prob ) self.intermediate = ConvActivation(cin=c1, cout=c2, groups=config.intermediate_groups, act=config.hidden_act) self.output = ConvDropoutLayerNorm( cin=c2, cout=c3, groups=config.output_groups, dropout_prob=config.hidden_dropout_prob ) def forward(self, hidden_states, attention_mask, output_attentions): att = self.attention(hidden_states, attention_mask, output_attentions) attention_output = att["context_layer"] post_attention_output = self.post_attention(attention_output, hidden_states) intermediate_output = self.intermediate(post_attention_output) layer_output = self.output(intermediate_output, post_attention_output) output_dict = {"feature_map": layer_output} if output_attentions: output_dict["attention_score"] = att["attention_score"] return output_dict class SqueezeBertEncoder(nn.Module): def __init__(self, config): super().__init__() assert config.embedding_size == config.hidden_size, ( "If you want embedding_size != intermediate hidden_size, " "please insert a Conv1d layer to adjust the number of channels " "before the first SqueezeBertModule." ) self.layers = nn.ModuleList(SqueezeBertModule(config) for _ in range(config.num_hidden_layers)) def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): if head_mask is None: head_mask_is_all_none = True elif head_mask.count(None) == len(head_mask): head_mask_is_all_none = True else: head_mask_is_all_none = False assert head_mask_is_all_none is True, "head_mask is not yet supported in the SqueezeBert implementation." # [batch_size, sequence_length, hidden_size] --> [batch_size, hidden_size, sequence_length] hidden_states = hidden_states.permute(0, 2, 1) all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for layer in self.layers: if output_hidden_states: hidden_states = hidden_states.permute(0, 2, 1) all_hidden_states += (hidden_states,) hidden_states = hidden_states.permute(0, 2, 1) layer_output = layer.forward(hidden_states, attention_mask, output_attentions) hidden_states = layer_output["feature_map"] if output_attentions: all_attentions += (layer_output["attention_score"],) # [batch_size, hidden_size, sequence_length] --> [batch_size, sequence_length, hidden_size] hidden_states = hidden_states.permute(0, 2, 1) if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class SqueezeBertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class SqueezeBertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class SqueezeBertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = SqueezeBertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class SqueezeBertOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = SqueezeBertLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class SqueezeBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = SqueezeBertConfig base_model_prefix = "transformer" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, SqueezeBertLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) SQUEEZEBERT_START_DOCSTRING = r""" The SqueezeBERT model was proposed in `SqueezeBERT: What can computer vision teach NLP about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. For best results finetuning SqueezeBERT on text classification tasks, it is recommended to use the `squeezebert/squeezebert-mnli-headless` checkpoint as a starting point. Parameters: config (:class:`~transformers.SqueezeBertConfig`): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights. Hierarchy:: Internal class hierarchy: SqueezeBertModel SqueezeBertEncoder SqueezeBertModule SqueezeBertSelfAttention ConvActivation ConvDropoutLayerNorm Data layouts:: Input data is in [batch, sequence_length, hidden_size] format. Data inside the encoder is in [batch, hidden_size, sequence_length] format. But, if :obj:`output_hidden_states == True`, the data from inside the encoder is returned in [batch, sequence_length, hidden_size] format. The final output of the encoder is in [batch, sequence_length, hidden_size] format. """ SQUEEZEBERT_INPUTS_DOCSTRING = r""" Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`~transformers.SqueezeBertTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for details. `What are input IDs? <../glossary.html#input-ids>`__ attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`): Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. `What are attention masks? <../glossary.html#attention-mask>`__ token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, 1]``: - 0 corresponds to a `sentence A` token, - 1 corresponds to a `sentence B` token. `What are token type IDs? <../glossary.html#token-type-ids>`_ position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, config.max_position_embeddings - 1]``. `What are position IDs? <../glossary.html#position-ids>`_ head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert :obj:`input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (:obj:`bool`, `optional`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. """ @add_start_docstrings( "The bare SqueezeBERT Model transformer outputting raw hidden-states without any specific head on top.", SQUEEZEBERT_START_DOCSTRING, ) class SqueezeBertModel(SqueezeBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.embeddings = SqueezeBertEmbeddings(config) self.encoder = SqueezeBertEncoder(config) self.pooler = SqueezeBertPooler(config) self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, new_embeddings): self.embeddings.word_embeddings = new_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings("""SqueezeBERT Model with a `language modeling` head on top. """, SQUEEZEBERT_START_DOCSTRING) class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel): _keys_to_ignore_on_load_missing = [r"predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.transformer = SqueezeBertModel(config) self.cls = SqueezeBertOnlyMLMHead(config) self.init_weights() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ SqueezeBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, SQUEEZEBERT_START_DOCSTRING, ) class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.transformer = SqueezeBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, self.config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ SqueezeBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, SQUEEZEBERT_START_DOCSTRING, ) class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.transformer = SqueezeBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) self.init_weights() @add_start_docstrings_to_model_forward( SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the multiple choice classification loss. Indices should be in ``[0, ..., num_choices-1]`` where `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ SqueezeBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, SQUEEZEBERT_START_DOCSTRING, ) class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = SqueezeBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels - 1]``. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ SqueezeBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, SQUEEZEBERT_START_DOCSTRING, ) class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = SqueezeBertModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

40.138838

122

0.666267

b67168618e4b00ae00b3c5ffc45d1903ae242ab5

1,731

py

Python

mitorch/models/seresnext.py

shonohs/shtorch_models

afc20dbc9fd272a74ca08ec460401721b1476e72

[ "MIT" ]

3

2020-06-26T11:30:33.000Z

2021-01-23T07:42:25.000Z

mitorch/models/seresnext.py

shonohs/shtorch_models

afc20dbc9fd272a74ca08ec460401721b1476e72

[ "MIT" ]

null

mitorch/models/seresnext.py

shonohs/shtorch_models

afc20dbc9fd272a74ca08ec460401721b1476e72

[ "MIT" ]

2

2020-04-22T16:19:28.000Z

2020-06-11T09:06:37.000Z

"""SE-ResNext""" from .resnext import ResNext from .modules import SEBlock class SEResNext(ResNext): class BasicBlock(ResNext.BasicBlock): def __init__(self, in_channels, out_channels, cardinality, stride=1, reduction_ratio=16): super().__init__(in_channels, out_channels, cardinality, stride) self.se = SEBlock(out_channels, reduction_ratio) def forward(self, input): x = self.conv0(input) x = self.conv1(x) x = self.conv2(x) x = self.se(x) x = self.add(x, self.conv_shortcut(input) if self.conv_shortcut else input) return self.activation(x) def __init__(self, num_blocks=[3, 4, 6, 3], cardinality=32, bottleneck_width=4, reduction_ratio=16): self.reduction_ratio = reduction_ratio super().__init__(num_blocks, cardinality, bottleneck_width) def _make_stage(self, in_channels, out_channels, num_blocks, first_block_stride, cardinality, index): blocks = [(f'block{index}_0', SEResNext.BasicBlock(in_channels, out_channels, cardinality, first_block_stride, self.reduction_ratio))] for i in range(num_blocks - 1): blocks.append((f'block{index}_{i+1}', SEResNext.BasicBlock(out_channels, out_channels, cardinality, reduction_ratio=self.reduction_ratio))) return blocks class SEResNext14(SEResNext): def __init__(self): super().__init__([1, 1, 1, 1]) class SEResNext26(SEResNext): def __init__(self): super().__init__([2, 2, 2, 2]) class SEResNext50(SEResNext): def __init__(self): super().__init__([3, 4, 6, 3]) class SEResNext101(SEResNext): def __init__(self): super().__init__([3, 4, 23, 3])

34.62

151

0.665511

f3a32afb076b27faec70a0b5dde715655a3b480f

7,072

py

Python

deploy.py

bzhaoopenstack/labkeeper

fecda8a306fd3f1dea3f66606ac8bd962981d2b0

[ "Apache-2.0" ]

4

2019-04-02T03:49:13.000Z

2022-01-22T14:57:33.000Z

deploy.py

bzhaoopenstack/labkeeper

fecda8a306fd3f1dea3f66606ac8bd962981d2b0

[ "Apache-2.0" ]

451

2019-03-25T07:27:52.000Z

2021-07-26T01:26:43.000Z

deploy.py

bzhaoopenstack/labkeeper

fecda8a306fd3f1dea3f66606ac8bd962981d2b0

[ "Apache-2.0" ]

14

2018-09-28T18:45:12.000Z

2022-01-22T14:57:22.000Z

#!/usr/bin/env python import datetime import json import os import subprocess import sys import argparse from prettytable import PrettyTable import requests def add_cli_args(): parser = argparse.ArgumentParser() parser.add_argument('type', choices=["allinone", "allinone-ha", "openlab", "openlab-ha"], help='OpenLab deployment type', ) parser.add_argument('--action', choices=["deploy", "new-slave", "new-zookeeper", "switch-role", "show-graph", "show-ip", "list-change", "upgrade", "upgrade-complete"], default="deploy", help="The action that labkeeper supports. Default " "value is 'deploy'.\n" "'deploy': create a new OpenLab CI environment.\n" "'new-slave': create new slave nodes base on " "current master nodes.\n" "'switch-role': switch the master/slave role of " "current deployment. This will re-config the " "existing OpenLab environment.\n" "'show-graph': show the hosts graph of ansible " "inventory. It can be used with --with-vars to " "show more detail.\n" "'show-ip': show hosts ip address.\n" "'list-change': show zuul and nodepool code " "change during last month.\n" "'upgrade': upgrade zuul and nodepool to the " "newest master branch.\n" "'upgrade-complete: after checking upgraded " "environment, if everything works well, run this " "action to complete upgrade. Otherwise all nodes" "will keep being in maintaining status\n '") parser.add_argument('-u', '--user', help='the Ansible remote user performing deployment, ' 'default is "ubuntu" configured in ansible.cfg', ) parser.add_argument('-e', '--extra-vars', metavar="<key=value>", action='append', default=[], help='extra vars passed to Ansible command', ) parser.add_argument('--host-ip', metavar="<key=value>", action='append', help='override new ip address for current inventory, ' 'this argument need to use with key-value pairs ' 'combined with "=", the key must be the host name' ' defined in the inventory yaml files, e.g. ' 'zuul01, nodepool02, allinone01.', ) parser.add_argument('--with-vars', action='store_true', help='show the hosts graph of ansible inventory with ' 'vars. It must be used together with --action ' 'show-graph.' ) return parser def list_changes(project, days): from_day = (datetime.date.today() - datetime.timedelta(days=days)).strftime('%Y-%m-%d') response = requests.get( 'https://review.opendev.org/changes/?q=project:' + project + '+status:merged+after:' + from_day) changes = json.loads( response.content.decode("utf-8")[5:].replace('\n', '')) pt = PrettyTable([project, 'change_id'], caching=False) pt.align = 'l' for change in changes: pt.add_row([change['subject'], change['change_id']]) print(pt.get_string(sortby=project)) def main(): parsed_args = add_cli_args().parse_args() os.environ['OL_TYPE'] = parsed_args.type cmd = [] if parsed_args.action == 'deploy': cmd = ['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/site.yaml'] elif parsed_args.action == 'new-slave': cmd = ['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/site.yaml', '-l', '*-slave'] elif parsed_args.action == 'new-zookeeper': cmd = ['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/conf-new-zookeeper.yaml'] elif parsed_args.action == 'switch-role': os.environ['OL_SWITCH_MASTER_SLAVE'] = True cmd = ['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/switch_role.yaml'] elif parsed_args.action == 'show-graph': cmd = ['ansible-inventory', '-i', 'inventory/inventory.py', '--graph'] if parsed_args.with_vars: cmd.append('--vars') elif parsed_args.action == 'show-ip': cmd = ['python', 'inventory/inventory.py', '--show-ip'] elif parsed_args.action == 'list-change': list_changes('zuul/zuul', 31) list_changes('zuul/nodepool', 31) elif parsed_args.action == 'upgrade': if parsed_args.type != 'openlab-ha': print("upgrade action only support openlab-ha deployment.") exit(1) cmd = ['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/upgrade-ha-deployment.yaml'] elif parsed_args.action == 'upgrade-complete': if parsed_args.type != 'openlab-ha': print("upgrade-complete action only support openlab-ha deployment.") exit(1) cmd = ['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/upgrade-complete-ha-deployment.yaml'] if parsed_args.host_ip: specified_ips = dict([(d.partition('=')[::2]) for d in parsed_args.new_ip]) for host, ip in specified_ips.items(): os.environ['OL_%s_IP' % host.upper()] = ip if parsed_args.user and cmd[0] == 'ansible-playbook': cmd += ['-u', parsed_args.user] if parsed_args.extra_vars: cmd += ['-e', ' '.join(parsed_args.extra_vars)] ol_env_msg = '\n'.join(['%s=%s' % (k, os.environ[k]) for k in os.environ if k.startswith('OL_')]) if cmd: print("OpenLab deployment ENV:\n%s" % ol_env_msg) print('Ansible command:\n%s' % ' '.join(cmd)) print("*" * 100) subprocess.call(cmd) if (parsed_args.action == 'new-slave' or parsed_args.action == 'new-zookeeper'): print("Don't forget to restart zuul and nodepool by hand.") if parsed_args.action == 'new-slave': subprocess.call(['ansible-playbook', '-i', 'inventory/inventory.py', 'playbooks/conf-cluster.yaml']) if __name__ == '__main__': sys.exit(main())

45.333333

91

0.516544

5f0aac252d83b7bf4266e7dab89093ddcdd55348

279

py

Python

etcampy/api.py

Yuki-Mori/etcampy

0af30327420b981d32b601b0346e4e4646094d67

[ "MIT" ]

null

etcampy/api.py

Yuki-Mori/etcampy

0af30327420b981d32b601b0346e4e4646094d67

[ "MIT" ]

null

etcampy/api.py

Yuki-Mori/etcampy

0af30327420b981d32b601b0346e4e4646094d67

[ "MIT" ]

null

from .exception import CameraCannotOpenError import cv2 def save_image(path, camera=0): cap = cv2.VideoCapture(camera) if not cap.isOpened: raise CameraCannotOpenError("Camera cannot open!") _, frame = cap.read() cv2.imwrite(path,frame) cap.release()

27.9

58

0.706093

b8c98745cafcf29a4da0932ed7f63cf1d2c7a56c

4,763

py

Python

models.py

sulabhkatiyar/Spatial_Att

675d0d51b131f277f764cb9604d0f9cd559c5484

[ "Apache-2.0" ]

null

models.py

sulabhkatiyar/Spatial_Att

675d0d51b131f277f764cb9604d0f9cd559c5484

[ "Apache-2.0" ]

null

models.py

sulabhkatiyar/Spatial_Att

675d0d51b131f277f764cb9604d0f9cd559c5484

[ "Apache-2.0" ]

null

import torch from torch import nn import torchvision import pretrainedmodels import json from tqdm import tqdm import numpy as np device = torch.device("cuda" if torch.cuda.is_available() else "cpu") encoder_choice = 1 class Encoder(nn.Module): def __init__(self, encoded_image_size=8): super(Encoder, self).__init__() self.enc_image_size = encoded_image_size if encoder_choice==1: vgg16 = torchvision.models.vgg16(pretrained = True) self.features_nopool = nn.Sequential(*list(vgg16.features.children())[:-1]) self.features_pool = list(vgg16.features.children())[-1] self.adaptive_pool = nn.AdaptiveAvgPool2d((encoded_image_size, encoded_image_size)) self.fine_tune() def forward(self, images): global encoder_choice if encoder_choice==1: x = self.features_nopool(images) x_pool = self.features_pool(x) return x_pool.permute(0,2,3,1) out = self.adaptive_pool(out) out = out.permute(0, 2, 3, 1) return out class DecoderWithAttention_choice(nn.Module): def __init__(self, embed_dim, decoder_dim, vocab_size, encoder_dim=2048, dropout=0.5): super(DecoderWithAttention_choice, self).__init__() self.encoder_dim = encoder_dim self.embed_dim = embed_dim self.decoder_dim = decoder_dim self.vocab_size = vocab_size self.dropout = dropout self.num_feats = 64 self.embedding = nn.Embedding(vocab_size, self.embed_dim) self.dropout = nn.Dropout(p=self.dropout) self.decode_step = nn.LSTMCell(embed_dim + decoder_dim, decoder_dim, bias=True) self.init_h = nn.Linear(encoder_dim, decoder_dim) self.init_c = nn.Linear(encoder_dim, decoder_dim) self.get_global_features = nn.Linear(encoder_dim, decoder_dim) self.image_feat_small = nn.Linear(encoder_dim, decoder_dim, bias = False) self.w_v = nn.Linear(decoder_dim, self.num_feats, bias = False) self.w_g = nn.Linear(decoder_dim, self.num_feats, bias = False) self.w_h_t = nn.Linear(self.num_feats, 1, bias = False) self.tanh = torch.nn.Tanh() self.softmax = nn.Softmax(dim=1) self.fc = nn.Linear(decoder_dim + decoder_dim, vocab_size) self.init_weights() def init_weights(self): self.embedding.weight.data.uniform_(-0.1, 0.1) self.fc.bias.data.fill_(0) self.fc.weight.data.uniform_(-0.1, 0.1) def get_global_image(self, encoder_out): mean_encoder_out = encoder_out.mean(dim=1) img = self.get_global_features(mean_encoder_out) return img def forward(self, encoder_out, encoded_captions, caption_lengths): batch_size = encoder_out.size(0) encoder_dim = encoder_out.size(-1) vocab_size = self.vocab_size encoder_out = encoder_out.view(batch_size, -1, encoder_dim) num_pixels = encoder_out.size(1) assert(self.num_feats == num_pixels) caption_lengths, sort_ind = caption_lengths.squeeze(1).sort(dim=0, descending=True) encoder_out = encoder_out[sort_ind] encoded_captions = encoded_captions[sort_ind] embeddings = self.embedding(encoded_captions) global_img = self.get_global_image(encoder_out) h, c = torch.zeros_like(global_img), torch.zeros_like(global_img) encoder_out_small = self.image_feat_small(encoder_out) decode_lengths = (caption_lengths - 1).tolist() predictions = torch.zeros(batch_size, max(decode_lengths), vocab_size).to(device) predictions_reverse = torch.zeros(batch_size, max(decode_lengths), vocab_size).to(device) for t in range(max(decode_lengths)): batch_size_t = sum([l > t for l in decode_lengths]) h, c = self.decode_step(torch.cat([embeddings[:batch_size_t, t, :], global_img[:batch_size_t]], dim = 1),(h[:batch_size_t], c[:batch_size_t])) h_new = self.w_g(h).unsqueeze(-1) alpha = self.softmax(self.w_h_t(self.tanh(self.w_v(encoder_out_small[:batch_size_t]) + torch.matmul(h_new, torch.ones(batch_size_t, 1, num_pixels).to(device)))).squeeze(2)) context_vector = (encoder_out_small[:batch_size_t] * alpha.unsqueeze(2)).sum(dim=1) preds = self.fc(self.dropout(torch.cat([h, context_vector], dim =1))) # (batch_size_t, vocab_size) predictions[:batch_size_t, t, :] = preds return predictions, encoded_captions, decode_lengths, sort_ind

41.780702

184

0.643502

8edd1ace5b2de8fcd6b2a01d0707dfc96192d652

200

py

Python

aws/container/app.py

m-li/lambda_cluster

1481ecf83cd4be16c1c09a77301689e6e1ce61fb

[ "MIT" ]

null

aws/container/app.py

m-li/lambda_cluster

1481ecf83cd4be16c1c09a77301689e6e1ce61fb

[ "MIT" ]

null

aws/container/app.py

m-li/lambda_cluster

1481ecf83cd4be16c1c09a77301689e6e1ce61fb

[ "MIT" ]

null

import cloudpickle def handler(event, context): func = cloudpickle.loads(event["func"].encode('latin1')) args = event["args"] r = func(*args) return {"statusCode": 200, "results": r}

25

60

0.645

0cf3e3e954d4f1264adc139d4133a9412a794d03

3,592

py

Python

tests/components/zha/test_fan.py

jasperro/core

26d7b2164e8a971506790ae5af06f31abdf278b5

[ "Apache-2.0" ]

4

2016-12-23T10:36:36.000Z

2021-04-22T12:38:16.000Z

tests/components/zha/test_fan.py

jasperro/core

26d7b2164e8a971506790ae5af06f31abdf278b5

[ "Apache-2.0" ]

9

2022-01-27T06:32:10.000Z

2022-03-31T07:07:51.000Z

tests/components/zha/test_fan.py

jasperro/core

26d7b2164e8a971506790ae5af06f31abdf278b5

[ "Apache-2.0" ]

1

2020-03-07T10:43:50.000Z

"""Test zha fan.""" from unittest.mock import call import pytest import zigpy.zcl.clusters.hvac as hvac import zigpy.zcl.foundation as zcl_f from homeassistant.components import fan from homeassistant.components.fan import ATTR_SPEED, DOMAIN, SERVICE_SET_SPEED from homeassistant.const import ( ATTR_ENTITY_ID, SERVICE_TURN_OFF, SERVICE_TURN_ON, STATE_OFF, STATE_ON, STATE_UNAVAILABLE, ) from .common import ( async_enable_traffic, async_test_rejoin, find_entity_id, make_attribute, make_zcl_header, ) @pytest.fixture def zigpy_device(zigpy_device_mock): """Device tracker zigpy device.""" endpoints = { 1: {"in_clusters": [hvac.Fan.cluster_id], "out_clusters": [], "device_type": 0} } return zigpy_device_mock(endpoints) async def test_fan(hass, zha_device_joined_restored, zigpy_device): """Test zha fan platform.""" zha_device = await zha_device_joined_restored(zigpy_device) cluster = zigpy_device.endpoints.get(1).fan entity_id = await find_entity_id(DOMAIN, zha_device, hass) assert entity_id is not None # test that the fan was created and that it is unavailable assert hass.states.get(entity_id).state == STATE_UNAVAILABLE # allow traffic to flow through the gateway and device await async_enable_traffic(hass, [zha_device]) # test that the state has changed from unavailable to off assert hass.states.get(entity_id).state == STATE_OFF # turn on at fan attr = make_attribute(0, 1) hdr = make_zcl_header(zcl_f.Command.Report_Attributes) cluster.handle_message(hdr, [[attr]]) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_ON # turn off at fan attr.value.value = 0 cluster.handle_message(hdr, [[attr]]) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_OFF # turn on from HA cluster.write_attributes.reset_mock() await async_turn_on(hass, entity_id) assert len(cluster.write_attributes.mock_calls) == 1 assert cluster.write_attributes.call_args == call({"fan_mode": 2}) # turn off from HA cluster.write_attributes.reset_mock() await async_turn_off(hass, entity_id) assert len(cluster.write_attributes.mock_calls) == 1 assert cluster.write_attributes.call_args == call({"fan_mode": 0}) # change speed from HA cluster.write_attributes.reset_mock() await async_set_speed(hass, entity_id, speed=fan.SPEED_HIGH) assert len(cluster.write_attributes.mock_calls) == 1 assert cluster.write_attributes.call_args == call({"fan_mode": 3}) # test adding new fan to the network and HA await async_test_rejoin(hass, zigpy_device, [cluster], (1,)) async def async_turn_on(hass, entity_id, speed=None): """Turn fan on.""" data = { key: value for key, value in [(ATTR_ENTITY_ID, entity_id), (ATTR_SPEED, speed)] if value is not None } await hass.services.async_call(DOMAIN, SERVICE_TURN_ON, data, blocking=True) async def async_turn_off(hass, entity_id): """Turn fan off.""" data = {ATTR_ENTITY_ID: entity_id} if entity_id else {} await hass.services.async_call(DOMAIN, SERVICE_TURN_OFF, data, blocking=True) async def async_set_speed(hass, entity_id, speed=None): """Set speed for specified fan.""" data = { key: value for key, value in [(ATTR_ENTITY_ID, entity_id), (ATTR_SPEED, speed)] if value is not None } await hass.services.async_call(DOMAIN, SERVICE_SET_SPEED, data, blocking=True)

30.965517

87

0.714087

231c2c22193f731f1d6524f942d2be3a6ab52e63

7,870

py

Python

pokemonlib/GameClasses/PokemonClass.py

Lapinux/pokemonlib

b7e2e056fdd9988ba8d38a159b8e6a0ecf17e090

[ "MIT" ]

null

pokemonlib/GameClasses/PokemonClass.py

Lapinux/pokemonlib

b7e2e056fdd9988ba8d38a159b8e6a0ecf17e090

[ "MIT" ]

null

pokemonlib/GameClasses/PokemonClass.py

Lapinux/pokemonlib

b7e2e056fdd9988ba8d38a159b8e6a0ecf17e090

[ "MIT" ]

null

import os # for finding the relative path to pokemon_data import json # for parsing pokemon_data json files import random # for generating IV or other random values import logging # for printing data from pokemonlib.misc import Exeptions # for initialisation exceptions logging.basicConfig() logging.root.setLevel(logging.NOTSET) logging.basicConfig(level=logging.NOTSET) logPokemon = logging.getLogger("Main.PokemonClass") logPokemon.setLevel(logging.DEBUG) class Pokemon(object): # noinspection PyTypeChecker def __init__(self, pokemon_id=1): """ Pokemon is the base class of all pokemon objects. It stores the data and stats of the pokemons. It also generates the base stats of a pokemon basing on it's id. Usage: p = Pokemon(pokemon_id) Result: 'Pokemon detected as ´" + self.name + "´ has finished initializing.' """ # Pokemon ID to recognize what pokemon it is even if self.name is changed (For Example Zoroarks # illusions or Dittos transformation) self.__id = pokemon_id pokemon = [] path = os.getcwd() # Gets the relative path from main.py # Decides if using \ for windows or / for mac and linux in the path if os.name == "nt": path += "\\pokemonlib\\pokemon_data\\pokemon_properties\\" else: path += "/pokemonlib/pokemon_data/pokemon_properties/" # Search in pokemon_data for the data of the pokemon depending on the ID for filename in os.listdir(path): if filename != "pokemon_" + str(self.__id) + ".json": continue else: f = open(path + filename, "r") pokemon = json.load(f) f.close() break # When There are no properties in the list raise an error if pokemon == []: raise Exeptions.IdNotReferenced("The pokemon_data with id " + str(self.__id) + " was not found in the directory ´" + path + "´. Make sure it is in a file named pokemon_(id of the pokemon_data).json") # Sets all the base pokemon data to object variables. If any needed information is missing # raise an error try: stats = pokemon["stats"] self.name = pokemon["name"] self._hp = stats["hp"] self._defph = stats["defense"] self._defsp = stats["special-defense"] self._attkph = stats["attack"] self._attksp = stats["special-defense"] self._speed = stats["speed"] self._lv = 1 self._types = pokemon["types"] self._attacks = {1: ["Thunderbolt", 20, True], 2: ["Poop", 40, True], 3: ["Earthquake", 40, True], 4: ["Scratch", 40, True]} self._iv = random.randint(0, 31) except KeyError: raise Exeptions.InvalidPokemonData("Some needed data is missing in ´" + path + filename + "´. Please " + "ensure that the name, base stats, attacks and the type are included." + "") logPokemon.info("Pokemon detected as ´" + pokemon["name"] + "´ has finished initializing.") def getstats(self): """ Gets all the stats of the pokemon and return them in form of a Dictionary. Usage: getstats() Returns: {'hp', 'def', 'defsp', 'attk', 'attksp', 'attacks'=[]} """ return {"hp": self._hp, "def": self._defph, "defsp": self._defsp, "attk": self._attkph, "attksp": self._attksp, "speed": self._speed, "attacks": self._attacks} class Team: def __init__(self, pokemon1, pokemon2=None, pokemon3=None, pokemon4=None, pokemon5=None, pokemon6=None): templist = [pokemon1, pokemon2, pokemon3, pokemon4, pokemon5, pokemon6] self.__pokemonlist = [] for pokemon in templist: if pokemon is not None: # Check if this argument was passed if isinstance(pokemon, Pokemon): # Basic check self.__pokemonlist.append(pokemon) else: raise Exeptions.NotAPokemon("Argument was passed that wasn't an instance of pokemon class") def pop(self): pass def add(self): pass def transfer(self, BoxInstance): pass class Box(Team): def __init__(self, *args, **kwargs): templist = [] self.__pokemonlist = [] if "MaxPokemon" in kwargs: self.MaxPokemon = kwargs["MaxPokemon"] else: self.MaxPokemon = 50 for _ in range(self.MaxPokemon): templist.append(None) for pkmn in args: length = 0 for p in templist: if p is not None: length += 1 logPokemon.debug(str(length)) logPokemon.debug(templist) logPokemon.debug(len(templist)) templist[length] = pkmn for pos, pkmn in kwargs.items(): if pos == "MaxPokemon": continue try: pos = [int(s) for s in pos.split() if s.isdigit()][0] templist[pos] = pkmn except IndexError: logPokemon.info("An Unvalid keyword arg was given. Handling it as a normal arg.") length = 0 for p in templist: if p is not None: length += 1 logPokemon.debug(str(length)) logPokemon.debug(templist) logPokemon.debug(len(templist)) templist[length] = pkmn if len(templist)-1 > self.MaxPokemon: raise ValueError("You entered contradictory values: More pokemons than the max capacity of the box") for pokemon in templist: if pokemon is not None: # Check if this argument was passed if isinstance(pokemon, Pokemon): # Basic check self.__pokemonlist.append(pokemon) else: raise Exeptions.NotAPokemon("Argument was passed that wasn't an instance of pokemon class") else: self.__pokemonlist.append(pokemon) def __len__(self): length = 0 for p in self.__pokemonlist: if p is not None: length +=1 return length def __str__(self): string = "" for pokemon in self.__pokemonlist: if pokemon is not None: string += pokemon.name + " " return string def transfer(self, TeamInstance): pass def testPokemonClass(loggerInstance=logPokemon): """ A function that inits an object of the Pokemon Class to see if it works. :param loggerInstance: :return: """ loggerInstance.info("Testing Pokemon Class:") num = int(input("Give the id of a Pokemon to pass to the pokemon class")) # Get id to pass to the __init__ # function from Pokemon Class if type(num) is int: num_poke = 0 path = os.getcwd() # Gets the relative path from main.py # Decides if using \ for windows or / for mac and linux in the path if os.name == "nt": path += "pokemonlib\\pokemon_data\\pokemon_properties\\" else: path += "pokemonlib/pokemon_data/pokemon_properties/" # Count the pokemon IDs for _ in os.listdir(path): num_poke += 1 if num not in range(0, num_poke): raise Exeptions.IdNotReferenced("The id you entered is incorrect.") else: raise TypeError("Please enter an Integer.") pkmn = Pokemon(num) loggerInstance.info(pkmn.getstats()) return True

37.47619

119

0.569759