body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
@commands.command(name='hoogle', brief='search hoogle')
async def hoogle(self, ctx, *query: str):
'Searches Hoggle and returns first two options\n Click title to see full search'
url = f"https://hoogle.haskell.org?mode=json&hoogle={'+'.join(query)}&start=1&count=1"
(result, error) = (await get_json(u... | -1,525,945,134,972,282,000 | Searches Hoggle and returns first two options
Click title to see full search | extensions/api.py | hoogle | JoseFilipeFerreira/JBB.py | python | @commands.command(name='hoogle', brief='search hoogle')
async def hoogle(self, ctx, *query: str):
'Searches Hoggle and returns first two options\n Click title to see full search'
url = f"https://hoogle.haskell.org?mode=json&hoogle={'+'.join(query)}&start=1&count=1"
(result, error) = (await get_json(u... |
def generateRequestData(self, offset, timestamp, chunkSize, isGroupConversation=False):
'Generate the data for the POST request.\n :return: the generated data\n '
ids_type = ('thread_fbids' if isGroupConversation else 'user_ids')
dataForm = {'messages[{}][{}][offset]'.format(ids_type, self._c... | -2,812,959,289,043,096,600 | Generate the data for the POST request.
:return: the generated data | src/util/conversationScraper.py | generateRequestData | 5agado/conversation-analyzer | python | def generateRequestData(self, offset, timestamp, chunkSize, isGroupConversation=False):
'Generate the data for the POST request.\n :return: the generated data\n '
ids_type = ('thread_fbids' if isGroupConversation else 'user_ids')
dataForm = {'messages[{}][{}][offset]'.format(ids_type, self._c... |
def executeRequest(self, requestData):
'Executes the POST request and retrieves the correspondent response content.\n Request headers are generated here\n :return: the response content\n '
headers = {'Host': 'www.facebook.com', 'Origin': 'https://www.facebook.com', 'Referer': 'https://www.f... | -2,882,766,584,559,465,500 | Executes the POST request and retrieves the correspondent response content.
Request headers are generated here
:return: the response content | src/util/conversationScraper.py | executeRequest | 5agado/conversation-analyzer | python | def executeRequest(self, requestData):
'Executes the POST request and retrieves the correspondent response content.\n Request headers are generated here\n :return: the response content\n '
headers = {'Host': 'www.facebook.com', 'Origin': 'https://www.facebook.com', 'Referer': 'https://www.f... |
def scrapeConversation(self, merge, offset, timestampOffset, chunkSize, limit, isGroupConversation):
'Retrieves conversation messages and stores them in a JSON file\n If merge is specified, the new messages will be merged with the previous version of the conversation, if present\n '
if merge:
... | -4,032,054,314,809,956,000 | Retrieves conversation messages and stores them in a JSON file
If merge is specified, the new messages will be merged with the previous version of the conversation, if present | src/util/conversationScraper.py | scrapeConversation | 5agado/conversation-analyzer | python | def scrapeConversation(self, merge, offset, timestampOffset, chunkSize, limit, isGroupConversation):
'Retrieves conversation messages and stores them in a JSON file\n If merge is specified, the new messages will be merged with the previous version of the conversation, if present\n '
if merge:
... |
def check_num_list(prompt: str, max_length: int=0, min_length: int=0) -> List[float]:
'Function to check if users input is a number, splitting number\n by spaces and checking that the correct amount of numbers are\n entered, returning them in a list'
while True:
try:
num = input(prompt... | -5,256,720,275,051,967,000 | Function to check if users input is a number, splitting number
by spaces and checking that the correct amount of numbers are
entered, returning them in a list | 150-Challenges/Challenges 27 - 34/Challenge 33.py | check_num_list | DGrifferty/Python | python | def check_num_list(prompt: str, max_length: int=0, min_length: int=0) -> List[float]:
'Function to check if users input is a number, splitting number\n by spaces and checking that the correct amount of numbers are\n entered, returning them in a list'
while True:
try:
num = input(prompt... |
def get_imdb(name):
'Get an imdb (image database) by name.'
if (name not in __sets):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]() | -3,263,413,934,054,098,000 | Get an imdb (image database) by name. | lib/datasets/factory.py | get_imdb | wangvation/torch-mobilenet | python | def get_imdb(name):
if (name not in __sets):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]() |
def list_imdbs():
'List all registered imdbs.'
return list(__sets.keys()) | 4,693,669,182,354,276,000 | List all registered imdbs. | lib/datasets/factory.py | list_imdbs | wangvation/torch-mobilenet | python | def list_imdbs():
return list(__sets.keys()) |
def main(*args):
'\n Process command line arguments and invoke bot.\n\n If args is an empty list, sys.argv is used.\n\n @param args: command line arguments\n @type args: str\n '
filename = 'fb2w.nt.gz'
for arg in pywikibot.handle_args(args):
if arg.startswith('-filename'):
... | 8,901,002,085,312,635,000 | Process command line arguments and invoke bot.
If args is an empty list, sys.argv is used.
@param args: command line arguments
@type args: str | scripts/freebasemappingupload.py | main | 5j9/pywikibot-core | python | def main(*args):
'\n Process command line arguments and invoke bot.\n\n If args is an empty list, sys.argv is used.\n\n @param args: command line arguments\n @type args: str\n '
filename = 'fb2w.nt.gz'
for arg in pywikibot.handle_args(args):
if arg.startswith('-filename'):
... |
def __init__(self, filename):
'Initializer.'
self.repo = pywikibot.Site('wikidata', 'wikidata').data_repository()
self.filename = filename
if (not os.path.exists(self.filename)):
pywikibot.output(('Cannot find %s. Try providing the absolute path.' % self.filename))
sys.exit(1) | 8,991,422,563,458,918,000 | Initializer. | scripts/freebasemappingupload.py | __init__ | 5j9/pywikibot-core | python | def __init__(self, filename):
self.repo = pywikibot.Site('wikidata', 'wikidata').data_repository()
self.filename = filename
if (not os.path.exists(self.filename)):
pywikibot.output(('Cannot find %s. Try providing the absolute path.' % self.filename))
sys.exit(1) |
def run(self):
'Run the bot.'
self.claim = pywikibot.Claim(self.repo, 'P646')
self.statedin = pywikibot.Claim(self.repo, 'P248')
freebasedumpitem = pywikibot.ItemPage(self.repo, 'Q15241312')
self.statedin.setTarget(freebasedumpitem)
self.dateofpub = pywikibot.Claim(self.repo, 'P577')
oct28 =... | -7,101,836,632,625,595,000 | Run the bot. | scripts/freebasemappingupload.py | run | 5j9/pywikibot-core | python | def run(self):
self.claim = pywikibot.Claim(self.repo, 'P646')
self.statedin = pywikibot.Claim(self.repo, 'P248')
freebasedumpitem = pywikibot.ItemPage(self.repo, 'Q15241312')
self.statedin.setTarget(freebasedumpitem)
self.dateofpub = pywikibot.Claim(self.repo, 'P577')
oct28 = pywikibot.WbT... |
def processLine(self, line):
'Process a single line.'
if ((not line) or line.startswith('#')):
return
(mid, sameas, qid, dot) = line.split()
if (sameas != '<https://www.w3.org/2002/07/owl#sameAs>'):
return
if (dot != '.'):
return
if (not mid.startswith('<https://rdf.freeb... | 3,031,414,387,276,538,400 | Process a single line. | scripts/freebasemappingupload.py | processLine | 5j9/pywikibot-core | python | def processLine(self, line):
if ((not line) or line.startswith('#')):
return
(mid, sameas, qid, dot) = line.split()
if (sameas != '<https://www.w3.org/2002/07/owl#sameAs>'):
return
if (dot != '.'):
return
if (not mid.startswith('<https://rdf.freebase.com/ns/m')):
... |
def _parse_decl_specs_simple(self, outer: str, typed: bool) -> ASTDeclSpecsSimple:
'Just parse the simple ones.'
storage = None
threadLocal = None
inline = None
virtual = None
explicit = None
constexpr = None
volatile = None
const = None
friend = None
attrs = []
while 1:
... | 489,535,564,035,447,300 | Just parse the simple ones. | sphinx/domains/cpp.py | _parse_decl_specs_simple | begolu2/sphinx | python | def _parse_decl_specs_simple(self, outer: str, typed: bool) -> ASTDeclSpecsSimple:
storage = None
threadLocal = None
inline = None
virtual = None
explicit = None
constexpr = None
volatile = None
const = None
friend = None
attrs = []
while 1:
self.skip_ws()
... |
def _parse_type(self, named: Union[(bool, str)], outer: str=None) -> ASTType:
"\n named=False|'maybe'|True: 'maybe' is e.g., for function objects which\n doesn't need to name the arguments\n\n outer == operatorCast: annoying case, we should not take the params\n "
if outer:
i... | -6,564,301,317,631,929,000 | named=False|'maybe'|True: 'maybe' is e.g., for function objects which
doesn't need to name the arguments
outer == operatorCast: annoying case, we should not take the params | sphinx/domains/cpp.py | _parse_type | begolu2/sphinx | python | def _parse_type(self, named: Union[(bool, str)], outer: str=None) -> ASTType:
"\n named=False|'maybe'|True: 'maybe' is e.g., for function objects which\n doesn't need to name the arguments\n\n outer == operatorCast: annoying case, we should not take the params\n "
if outer:
i... |
def run(self) -> List[Node]:
"\n On purpose this doesn't call the ObjectDescription version, but is based on it.\n Each alias signature may expand into multiple real signatures (an overload set).\n The code is therefore based on the ObjectDescription version.\n "
if (':' in self.name... | -4,870,467,569,921,633,000 | On purpose this doesn't call the ObjectDescription version, but is based on it.
Each alias signature may expand into multiple real signatures (an overload set).
The code is therefore based on the ObjectDescription version. | sphinx/domains/cpp.py | run | begolu2/sphinx | python | def run(self) -> List[Node]:
"\n On purpose this doesn't call the ObjectDescription version, but is based on it.\n Each alias signature may expand into multiple real signatures (an overload set).\n The code is therefore based on the ObjectDescription version.\n "
if (':' in self.name... |
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, dynamic_tags_json: Optional[pulumi.Input[str]]=None, is_push_enabled: Optional[pulumi.Input[bool]]=None, kind: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, resource_group_name: Optional[pulumi.Input[str]... | -8,120,773,970,255,479,000 | Push settings for the App.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] dynamic_tags_json: Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint... | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | __init__ | pulumi-bot/pulumi-azure-native | python | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, dynamic_tags_json: Optional[pulumi.Input[str]]=None, is_push_enabled: Optional[pulumi.Input[bool]]=None, kind: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, resource_group_name: Optional[pulumi.Input[str]... |
@staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'WebAppSitePushSettingsSlot':
"\n Get an existing WebAppSitePushSettingsSlot resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n ... | -3,682,211,527,570,844,700 | Get an existing WebAppSitePushSettingsSlot resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOption... | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | get | pulumi-bot/pulumi-azure-native | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'WebAppSitePushSettingsSlot':
"\n Get an existing WebAppSitePushSettingsSlot resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n ... |
@property
@pulumi.getter(name='dynamicTagsJson')
def dynamic_tags_json(self) -> pulumi.Output[Optional[str]]:
'\n Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint.\n '
return pulumi.get(self, 'dynamic_tags_json') | 2,910,655,291,979,059,000 | Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | dynamic_tags_json | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='dynamicTagsJson')
def dynamic_tags_json(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'dynamic_tags_json') |
@property
@pulumi.getter(name='isPushEnabled')
def is_push_enabled(self) -> pulumi.Output[bool]:
'\n Gets or sets a flag indicating whether the Push endpoint is enabled.\n '
return pulumi.get(self, 'is_push_enabled') | 4,037,824,550,830,096,400 | Gets or sets a flag indicating whether the Push endpoint is enabled. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | is_push_enabled | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='isPushEnabled')
def is_push_enabled(self) -> pulumi.Output[bool]:
'\n \n '
return pulumi.get(self, 'is_push_enabled') |
@property
@pulumi.getter
def kind(self) -> pulumi.Output[Optional[str]]:
'\n Kind of resource.\n '
return pulumi.get(self, 'kind') | -1,425,049,396,835,993,600 | Kind of resource. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | kind | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def kind(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'kind') |
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n Resource Name.\n '
return pulumi.get(self, 'name') | 1,193,115,514,403,237,400 | Resource Name. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | name | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') |
@property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n The system metadata relating to this resource.\n '
return pulumi.get(self, 'system_data') | -7,169,214,494,930,004,000 | The system metadata relating to this resource. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | system_data | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='systemData')
def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']:
'\n \n '
return pulumi.get(self, 'system_data') |
@property
@pulumi.getter(name='tagWhitelistJson')
def tag_whitelist_json(self) -> pulumi.Output[Optional[str]]:
'\n Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint.\n '
return pulumi.get(self, 'tag_whitelist_json') | -212,308,369,731,080,420 | Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | tag_whitelist_json | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='tagWhitelistJson')
def tag_whitelist_json(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'tag_whitelist_json') |
@property
@pulumi.getter(name='tagsRequiringAuth')
def tags_requiring_auth(self) -> pulumi.Output[Optional[str]]:
"\n Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.\n Tags can consist of alphanumeric characters and th... | -2,422,842,160,071,476,000 | Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.
Tags can consist of alphanumeric characters and the following:
'_', '@', '#', '.', ':', '-'.
Validation should be performed at the PushRequestHandler. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | tags_requiring_auth | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter(name='tagsRequiringAuth')
def tags_requiring_auth(self) -> pulumi.Output[Optional[str]]:
"\n Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.\n Tags can consist of alphanumeric characters and th... |
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n Resource type.\n '
return pulumi.get(self, 'type') | 2,132,950,812,122,862,800 | Resource type. | sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py | type | pulumi-bot/pulumi-azure-native | python | @property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'type') |
def _define_structure(self):
'\n Define the main sizers building to build this application.\n '
self.main_sizer = wx.BoxSizer(wx.VERTICAL)
self.box_source = wx.StaticBox(self, (- 1), str('Kiessig Thickness Calculator'))
self.boxsizer_source = wx.StaticBoxSizer(self.box_source, wx.VERTICAL)... | 4,143,718,605,268,381,000 | Define the main sizers building to build this application. | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _define_structure | andyfaff/sasview | python | def _define_structure(self):
'\n \n '
self.main_sizer = wx.BoxSizer(wx.VERTICAL)
self.box_source = wx.StaticBox(self, (- 1), str('Kiessig Thickness Calculator'))
self.boxsizer_source = wx.StaticBoxSizer(self.box_source, wx.VERTICAL)
self.dq_name_sizer = wx.BoxSizer(wx.HORIZONTAL)
s... |
def _layout_dq_name(self):
'\n Fill the sizer containing dq name\n '
dq_value = str(self.kiessig.get_deltaq())
dq_unit_txt = wx.StaticText(self, (- 1), '[1/A]')
dq_name_txt = wx.StaticText(self, (- 1), 'Kiessig Fringe Width (Delta Q): ')
self.dq_name_tcl = InputTextCtrl(self, (- 1), si... | 1,429,371,075,089,788,000 | Fill the sizer containing dq name | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _layout_dq_name | andyfaff/sasview | python | def _layout_dq_name(self):
'\n \n '
dq_value = str(self.kiessig.get_deltaq())
dq_unit_txt = wx.StaticText(self, (- 1), '[1/A]')
dq_name_txt = wx.StaticText(self, (- 1), 'Kiessig Fringe Width (Delta Q): ')
self.dq_name_tcl = InputTextCtrl(self, (- 1), size=(_BOX_WIDTH, (- 1)))
dq_hi... |
def _layout_thickness_size(self):
'\n Fill the sizer containing thickness information\n '
thick_unit = (('[' + self.kiessig.get_thickness_unit()) + ']')
thickness_size_txt = wx.StaticText(self, (- 1), 'Thickness (or Diameter): ')
self.thickness_size_tcl = OutputTextCtrl(self, (- 1), size=(... | 3,358,032,383,524,434,400 | Fill the sizer containing thickness information | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _layout_thickness_size | andyfaff/sasview | python | def _layout_thickness_size(self):
'\n \n '
thick_unit = (('[' + self.kiessig.get_thickness_unit()) + ']')
thickness_size_txt = wx.StaticText(self, (- 1), 'Thickness (or Diameter): ')
self.thickness_size_tcl = OutputTextCtrl(self, (- 1), size=(_BOX_WIDTH, (- 1)))
thickness_size_hint = '... |
def _layout_hint(self):
'\n Fill the sizer containing hint \n '
hint_msg = 'This tool is to approximately estimate '
hint_msg += 'the thickness of a layer'
hint_msg += ' or the diameter of particles\n '
hint_msg += 'from the Kiessig fringe width in SAS/NR data.'
hint_msg += ''
... | -8,116,110,328,013,586,000 | Fill the sizer containing hint | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _layout_hint | andyfaff/sasview | python | def _layout_hint(self):
'\n \n '
hint_msg = 'This tool is to approximately estimate '
hint_msg += 'the thickness of a layer'
hint_msg += ' or the diameter of particles\n '
hint_msg += 'from the Kiessig fringe width in SAS/NR data.'
hint_msg +=
self.hint_txt = wx.StaticText(se... |
def _layout_button(self):
'\n Do the layout for the button widgets\n '
id = wx.NewId()
self.bt_help = wx.Button(self, id, 'HELP')
self.bt_help.Bind(wx.EVT_BUTTON, self.on_help)
self.bt_help.SetToolTipString('Help using the Kiessig fringe calculator.')
self.bt_close = wx.Button(self... | -6,821,566,193,316,706,000 | Do the layout for the button widgets | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _layout_button | andyfaff/sasview | python | def _layout_button(self):
'\n \n '
id = wx.NewId()
self.bt_help = wx.Button(self, id, 'HELP')
self.bt_help.Bind(wx.EVT_BUTTON, self.on_help)
self.bt_help.SetToolTipString('Help using the Kiessig fringe calculator.')
self.bt_close = wx.Button(self, wx.ID_CANCEL, 'Close')
self.bt... |
def _do_layout(self):
'\n Draw window content\n '
self._define_structure()
self._layout_dq_name()
self._layout_thickness_size()
self._layout_hint()
self._layout_button()
self.boxsizer_source.AddMany([(self.dq_name_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5), (self.thic... | -3,224,533,211,146,210,300 | Draw window content | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _do_layout | andyfaff/sasview | python | def _do_layout(self):
'\n \n '
self._define_structure()
self._layout_dq_name()
self._layout_thickness_size()
self._layout_hint()
self._layout_button()
self.boxsizer_source.AddMany([(self.dq_name_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5), (self.thickness_size_sizer, 0... |
def on_help(self, event):
'\n Bring up the Kiessig fringe calculator Documentation whenever\n the HELP button is clicked.\n Calls DocumentationWindow with the path of the location within the\n documentation tree (after /doc/ ....". Note that when using old\n versions of Wx (befor... | 6,930,186,287,438,744,000 | Bring up the Kiessig fringe calculator Documentation whenever
the HELP button is clicked.
Calls DocumentationWindow with the path of the location within the
documentation tree (after /doc/ ....". Note that when using old
versions of Wx (before 2.9) and thus not the release version of
installers, th... | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | on_help | andyfaff/sasview | python | def on_help(self, event):
'\n Bring up the Kiessig fringe calculator Documentation whenever\n the HELP button is clicked.\n Calls DocumentationWindow with the path of the location within the\n documentation tree (after /doc/ ....". Note that when using old\n versions of Wx (befor... |
def on_close(self, event):
'\n close the window containing this panel\n '
self.parent.Close()
if (event is not None):
event.Skip() | -8,261,816,066,434,704,000 | close the window containing this panel | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | on_close | andyfaff/sasview | python | def on_close(self, event):
'\n \n '
self.parent.Close()
if (event is not None):
event.Skip() |
def on_compute(self, event):
'\n Execute the computation of thickness\n '
if (event is not None):
event.Skip()
dq = self.dq_name_tcl.GetValue()
self.kiessig.set_deltaq(dq)
output = self.kiessig.compute_thickness()
thickness = self.format_number(output)
self.thickness_si... | 4,238,333,682,032,974,300 | Execute the computation of thickness | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | on_compute | andyfaff/sasview | python | def on_compute(self, event):
'\n \n '
if (event is not None):
event.Skip()
dq = self.dq_name_tcl.GetValue()
self.kiessig.set_deltaq(dq)
output = self.kiessig.compute_thickness()
thickness = self.format_number(output)
self.thickness_size_tcl.SetValue(str(thickness)) |
def format_number(self, value=None):
'\n Return a float in a standardized, human-readable formatted string\n '
try:
value = float(value)
except:
output = None
return output
output = ('%-7.4g' % value)
return output.lstrip().rstrip() | 3,595,117,808,034,005,000 | Return a float in a standardized, human-readable formatted string | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | format_number | andyfaff/sasview | python | def format_number(self, value=None):
'\n \n '
try:
value = float(value)
except:
output = None
return output
output = ('%-7.4g' % value)
return output.lstrip().rstrip() |
def _onparamEnter(self, event=None):
'\n On Text_enter_callback, perform compute\n '
self.on_compute(event) | -6,448,870,365,049,814,000 | On Text_enter_callback, perform compute | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | _onparamEnter | andyfaff/sasview | python | def _onparamEnter(self, event=None):
'\n \n '
self.on_compute(event) |
def on_close(self, event):
'\n Close event\n '
if (self.manager is not None):
self.manager.kiessig_frame = None
self.Destroy() | -8,310,142,565,720,282,000 | Close event | src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py | on_close | andyfaff/sasview | python | def on_close(self, event):
'\n \n '
if (self.manager is not None):
self.manager.kiessig_frame = None
self.Destroy() |
@classmethod
def find_project_root_directory(cls, current_directory: Optional[Path]) -> Optional[Path]:
"\n Given a directory (with ``None`` implying the current directory) assumed to be at or under this project's root,\n find the project root directory.\n This implementation attempts to find a... | -2,530,477,784,102,690,000 | Given a directory (with ``None`` implying the current directory) assumed to be at or under this project's root,
find the project root directory.
This implementation attempts to find a directory having both a ``.git/`` child directory and a ``.env`` file.
Parameters
----------
current_directory
Returns
-------
Optional[... | github_archive/test/test_get_github.py | find_project_root_directory | hellkite500/github_archive | python | @classmethod
def find_project_root_directory(cls, current_directory: Optional[Path]) -> Optional[Path]:
"\n Given a directory (with ``None`` implying the current directory) assumed to be at or under this project's root,\n find the project root directory.\n This implementation attempts to find a... |
@classmethod
def load_token(cls):
"\n Read an API token from a configuration file, if none found, use '' for no auth\n "
token = ''
root_dir = cls.find_project_root_directory(None)
if (not root_dir):
return token
config_file = (root_dir / 'config.yaml')
if config_file.e... | 7,484,951,432,618,107,000 | Read an API token from a configuration file, if none found, use '' for no auth | github_archive/test/test_get_github.py | load_token | hellkite500/github_archive | python | @classmethod
def load_token(cls):
"\n \n "
token =
root_dir = cls.find_project_root_directory(None)
if (not root_dir):
return token
config_file = (root_dir / 'config.yaml')
if config_file.exists():
with open(config_file) as file:
config = yaml.load(... |
def test_get_repo_meta(self):
'\n Test the archive_repo function to ensure all meta data is properly captured\n '
meta = get_repo_meta(self.repo, self.time, TestGetGithub._current_dir)
self.assertIsNotNone(meta)
self.assertTrue(len(meta), 6)
pattern = '{repo}_{name}_{time}.json'.fo... | 8,121,341,174,717,497,000 | Test the archive_repo function to ensure all meta data is properly captured | github_archive/test/test_get_github.py | test_get_repo_meta | hellkite500/github_archive | python | def test_get_repo_meta(self):
'\n \n '
meta = get_repo_meta(self.repo, self.time, TestGetGithub._current_dir)
self.assertIsNotNone(meta)
self.assertTrue(len(meta), 6)
pattern = '{repo}_{name}_{time}.json'.format(repo=self.repo_string, name='{name}', time=self.time)
self.assertE... |
def test_clone_and_archive(self):
'\n Test the clone functionality\n '
self.assertFalse(self.repo.has_wiki)
clone_url = self.repo.clone_url
archive_name = clone_and_archive(self.repo_string, clone_url, self.time, TestGetGithub._current_dir, [])
name = '{repo}_github_archive_{time}.... | -257,225,097,966,887,840 | Test the clone functionality | github_archive/test/test_get_github.py | test_clone_and_archive | hellkite500/github_archive | python | def test_clone_and_archive(self):
'\n \n '
self.assertFalse(self.repo.has_wiki)
clone_url = self.repo.clone_url
archive_name = clone_and_archive(self.repo_string, clone_url, self.time, TestGetGithub._current_dir, [])
name = '{repo}_github_archive_{time}.tar.gz'.format(repo=self.rep... |
def test_clone_and_archive_1(self):
'\n Test cloning a repo with a wiki\n '
self.assertTrue(self.wiki_repo.has_wiki)
wiki_url = (self.wiki_repo.clone_url[:(- 3)] + 'wiki.git')
clone_url = self.wiki_repo.clone_url
archive_name = clone_and_archive(self.repo_w_wiki, clone_url, self.ti... | -7,822,465,264,797,826,000 | Test cloning a repo with a wiki | github_archive/test/test_get_github.py | test_clone_and_archive_1 | hellkite500/github_archive | python | def test_clone_and_archive_1(self):
'\n \n '
self.assertTrue(self.wiki_repo.has_wiki)
wiki_url = (self.wiki_repo.clone_url[:(- 3)] + 'wiki.git')
clone_url = self.wiki_repo.clone_url
archive_name = clone_and_archive(self.repo_w_wiki, clone_url, self.time, TestGetGithub._current_dir,... |
def policy_v0():
'Autoaugment policy that was used in AutoAugment Detection Paper.'
policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6... | 8,460,943,867,710,150,000 | Autoaugment policy that was used in AutoAugment Detection Paper. | efficientdet/aug/autoaugment.py | policy_v0 | datawowio/automl | python | def policy_v0():
policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('Color', 1.0, 6)]]
return p... |
def policy_v1():
'Autoaugment policy that was used in AutoAugment Detection Paper.'
policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6... | -8,715,538,783,788,513,000 | Autoaugment policy that was used in AutoAugment Detection Paper. | efficientdet/aug/autoaugment.py | policy_v1 | datawowio/automl | python | def policy_v1():
policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('Color', 1.0, 6)], [('Color', 0... |
def policy_vtest():
'Autoaugment test policy for debugging.'
policy = [[('TranslateX_BBox', 1.0, 4), ('Equalize', 1.0, 10)]]
return policy | -9,018,532,416,153,881,000 | Autoaugment test policy for debugging. | efficientdet/aug/autoaugment.py | policy_vtest | datawowio/automl | python | def policy_vtest():
policy = [[('TranslateX_BBox', 1.0, 4), ('Equalize', 1.0, 10)]]
return policy |
def policy_v2():
'Additional policy that performs well on object detection.'
policy = [[('Color', 0.0, 6), ('Cutout', 0.6, 8), ('Sharpness', 0.4, 8)], [('Rotate_BBox', 0.4, 8), ('Sharpness', 0.4, 2), ('Rotate_BBox', 0.8, 10)], [('TranslateY_BBox', 1.0, 8), ('AutoContrast', 0.8, 2)], [('AutoContrast', 0.4, 6), (... | 8,499,406,954,455,301,000 | Additional policy that performs well on object detection. | efficientdet/aug/autoaugment.py | policy_v2 | datawowio/automl | python | def policy_v2():
policy = [[('Color', 0.0, 6), ('Cutout', 0.6, 8), ('Sharpness', 0.4, 8)], [('Rotate_BBox', 0.4, 8), ('Sharpness', 0.4, 2), ('Rotate_BBox', 0.8, 10)], [('TranslateY_BBox', 1.0, 8), ('AutoContrast', 0.8, 2)], [('AutoContrast', 0.4, 6), ('ShearX_BBox', 0.8, 8), ('Brightness', 0.0, 10)], [('Solari... |
def policy_v3():
'"Additional policy that performs well on object detection.'
policy = [[('Posterize', 0.8, 2), ('TranslateX_BBox', 1.0, 8)], [('BBox_Cutout', 0.2, 10), ('Sharpness', 1.0, 8)], [('Rotate_BBox', 0.6, 8), ('Rotate_BBox', 0.8, 10)], [('Equalize', 0.8, 10), ('AutoContrast', 0.2, 10)], [('SolarizeAdd... | -2,631,217,006,608,270,000 | "Additional policy that performs well on object detection. | efficientdet/aug/autoaugment.py | policy_v3 | datawowio/automl | python | def policy_v3():
policy = [[('Posterize', 0.8, 2), ('TranslateX_BBox', 1.0, 8)], [('BBox_Cutout', 0.2, 10), ('Sharpness', 1.0, 8)], [('Rotate_BBox', 0.6, 8), ('Rotate_BBox', 0.8, 10)], [('Equalize', 0.8, 10), ('AutoContrast', 0.2, 10)], [('SolarizeAdd', 0.2, 2), ('TranslateY_BBox', 0.2, 8)], [('Sharpness', 0.0... |
def blend(image1, image2, factor):
'Blend image1 and image2 using \'factor\'.\n\n Factor can be above 0.0. A value of 0.0 means only image1 is used.\n A value of 1.0 means only image2 is used. A value between 0.0 and\n 1.0 means we linearly interpolate the pixel values between the two\n images. A value great... | -7,071,893,540,884,844,000 | Blend image1 and image2 using 'factor'.
Factor can be above 0.0. A value of 0.0 means only image1 is used.
A value of 1.0 means only image2 is used. A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images. A value greater than 1.0 "extrapolates" the difference
between the t... | efficientdet/aug/autoaugment.py | blend | datawowio/automl | python | def blend(image1, image2, factor):
'Blend image1 and image2 using \'factor\'.\n\n Factor can be above 0.0. A value of 0.0 means only image1 is used.\n A value of 1.0 means only image2 is used. A value between 0.0 and\n 1.0 means we linearly interpolate the pixel values between the two\n images. A value great... |
def cutout(image, pad_size, replace=0):
'Apply cutout (https://arxiv.org/abs/1708.04552) to image.\n\n This operation applies a (2*pad_size x 2*pad_size) mask of zeros to\n a random location within `img`. The pixel values filled in will be of the\n value `replace`. The located where the mask will be applied is r... | -4,239,343,247,505,422,000 | Apply cutout (https://arxiv.org/abs/1708.04552) to image.
This operation applies a (2*pad_size x 2*pad_size) mask of zeros to
a random location within `img`. The pixel values filled in will be of the
value `replace`. The located where the mask will be applied is randomly
chosen uniformly over the whole image.
Args:
... | efficientdet/aug/autoaugment.py | cutout | datawowio/automl | python | def cutout(image, pad_size, replace=0):
'Apply cutout (https://arxiv.org/abs/1708.04552) to image.\n\n This operation applies a (2*pad_size x 2*pad_size) mask of zeros to\n a random location within `img`. The pixel values filled in will be of the\n value `replace`. The located where the mask will be applied is r... |
def color(image, factor):
'Equivalent of PIL Color.'
degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))
return blend(degenerate, image, factor) | 2,872,861,326,192,433,000 | Equivalent of PIL Color. | efficientdet/aug/autoaugment.py | color | datawowio/automl | python | def color(image, factor):
degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))
return blend(degenerate, image, factor) |
def contrast(image, factor):
'Equivalent of PIL Contrast.'
degenerate = tf.image.rgb_to_grayscale(image)
degenerate = tf.cast(degenerate, tf.int32)
mean = tf.reduce_mean(tf.cast(degenerate, tf.float32))
degenerate = (tf.ones_like(degenerate, dtype=tf.float32) * mean)
degenerate = tf.clip_by_valu... | 3,150,907,722,058,286,000 | Equivalent of PIL Contrast. | efficientdet/aug/autoaugment.py | contrast | datawowio/automl | python | def contrast(image, factor):
degenerate = tf.image.rgb_to_grayscale(image)
degenerate = tf.cast(degenerate, tf.int32)
mean = tf.reduce_mean(tf.cast(degenerate, tf.float32))
degenerate = (tf.ones_like(degenerate, dtype=tf.float32) * mean)
degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
... |
def brightness(image, factor):
'Equivalent of PIL Brightness.'
degenerate = tf.zeros_like(image)
return blend(degenerate, image, factor) | -5,514,793,971,791,669,000 | Equivalent of PIL Brightness. | efficientdet/aug/autoaugment.py | brightness | datawowio/automl | python | def brightness(image, factor):
degenerate = tf.zeros_like(image)
return blend(degenerate, image, factor) |
def posterize(image, bits):
'Equivalent of PIL Posterize.'
shift = (8 - bits)
return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift) | -7,653,707,230,299,955,000 | Equivalent of PIL Posterize. | efficientdet/aug/autoaugment.py | posterize | datawowio/automl | python | def posterize(image, bits):
shift = (8 - bits)
return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift) |
def rotate(image, degrees, replace):
'Rotates the image by degrees either clockwise or counterclockwise.\n\n Args:\n image: An image Tensor of type uint8.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n... | 9,033,878,547,422,465,000 | Rotates the image by degrees either clockwise or counterclockwise.
Args:
image: An image Tensor of type uint8.
degrees: Float, a scalar angle in degrees to rotate all images by. If
degrees is positive the image will be rotated clockwise otherwise it will
be rotated counterclockwise.
replace: A one or thr... | efficientdet/aug/autoaugment.py | rotate | datawowio/automl | python | def rotate(image, degrees, replace):
'Rotates the image by degrees either clockwise or counterclockwise.\n\n Args:\n image: An image Tensor of type uint8.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n... |
def random_shift_bbox(image, bbox, pixel_scaling, replace, new_min_bbox_coords=None):
'Move the bbox and the image content to a slightly new random location.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normali... | -5,648,054,145,612,244,000 | Move the bbox and the image content to a slightly new random location.
Args:
image: 3D uint8 Tensor.
bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
of type float that represents the normalized coordinates between 0 and 1.
The potential values for the new min corner of the bbox will be bet... | efficientdet/aug/autoaugment.py | random_shift_bbox | datawowio/automl | python | def random_shift_bbox(image, bbox, pixel_scaling, replace, new_min_bbox_coords=None):
'Move the bbox and the image content to a slightly new random location.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normali... |
def _clip_bbox(min_y, min_x, max_y, max_x):
'Clip bounding box coordinates between 0 and 1.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type float between 0 and 1.\n ... | -5,820,401,531,858,483,000 | Clip bounding box coordinates between 0 and 1.
Args:
min_y: Normalized bbox coordinate of type float between 0 and 1.
min_x: Normalized bbox coordinate of type float between 0 and 1.
max_y: Normalized bbox coordinate of type float between 0 and 1.
max_x: Normalized bbox coordinate of type float between 0 and 1... | efficientdet/aug/autoaugment.py | _clip_bbox | datawowio/automl | python | def _clip_bbox(min_y, min_x, max_y, max_x):
'Clip bounding box coordinates between 0 and 1.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type float between 0 and 1.\n ... |
def _check_bbox_area(min_y, min_x, max_y, max_x, delta=0.05):
'Adjusts bbox coordinates to make sure the area is > 0.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type ... | 68,563,721,215,404,140 | Adjusts bbox coordinates to make sure the area is > 0.
Args:
min_y: Normalized bbox coordinate of type float between 0 and 1.
min_x: Normalized bbox coordinate of type float between 0 and 1.
max_y: Normalized bbox coordinate of type float between 0 and 1.
max_x: Normalized bbox coordinate of type float between... | efficientdet/aug/autoaugment.py | _check_bbox_area | datawowio/automl | python | def _check_bbox_area(min_y, min_x, max_y, max_x, delta=0.05):
'Adjusts bbox coordinates to make sure the area is > 0.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type ... |
def _scale_bbox_only_op_probability(prob):
'Reduce the probability of the bbox-only operation.\n\n Probability is reduced so that we do not distort the content of too many\n bounding boxes that are close to each other. The value of 3.0 was a chosen\n hyper parameter when designing the autoaugment algorithm that ... | 8,180,426,613,846,989,000 | Reduce the probability of the bbox-only operation.
Probability is reduced so that we do not distort the content of too many
bounding boxes that are close to each other. The value of 3.0 was a chosen
hyper parameter when designing the autoaugment algorithm that we found
empirically to work well.
Args:
prob: Float th... | efficientdet/aug/autoaugment.py | _scale_bbox_only_op_probability | datawowio/automl | python | def _scale_bbox_only_op_probability(prob):
'Reduce the probability of the bbox-only operation.\n\n Probability is reduced so that we do not distort the content of too many\n bounding boxes that are close to each other. The value of 3.0 was a chosen\n hyper parameter when designing the autoaugment algorithm that ... |
def _apply_bbox_augmentation(image, bbox, augmentation_func, *args):
'Applies augmentation_func to the subsection of image indicated by bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates ... | 5,033,668,537,318,786,000 | Applies augmentation_func to the subsection of image indicated by bbox.
Args:
image: 3D uint8 Tensor.
bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
of type float that represents the normalized coordinates between 0 and 1.
augmentation_func: Augmentation function that will be applied to the... | efficientdet/aug/autoaugment.py | _apply_bbox_augmentation | datawowio/automl | python | def _apply_bbox_augmentation(image, bbox, augmentation_func, *args):
'Applies augmentation_func to the subsection of image indicated by bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates ... |
def _concat_bbox(bbox, bboxes):
'Helper function that concats bbox to bboxes along the first dimension.'
bboxes_sum_check = tf.reduce_sum(bboxes)
bbox = tf.expand_dims(bbox, 0)
bboxes = tf.cond(tf.equal(bboxes_sum_check, (- 4.0)), (lambda : bbox), (lambda : tf.concat([bboxes, bbox], 0)))
return bbox... | 9,145,103,630,591,172,000 | Helper function that concats bbox to bboxes along the first dimension. | efficientdet/aug/autoaugment.py | _concat_bbox | datawowio/automl | python | def _concat_bbox(bbox, bboxes):
bboxes_sum_check = tf.reduce_sum(bboxes)
bbox = tf.expand_dims(bbox, 0)
bboxes = tf.cond(tf.equal(bboxes_sum_check, (- 4.0)), (lambda : bbox), (lambda : tf.concat([bboxes, bbox], 0)))
return bboxes |
def _apply_bbox_augmentation_wrapper(image, bbox, new_bboxes, prob, augmentation_func, func_changes_bbox, *args):
'Applies _apply_bbox_augmentation with probability prob.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represent... | -6,801,094,433,672,039,000 | Applies _apply_bbox_augmentation with probability prob.
Args:
image: 3D uint8 Tensor.
bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
of type float that represents the normalized coordinates between 0 and 1.
new_bboxes: 2D Tensor that is a list of the bboxes in the image after they
have ... | efficientdet/aug/autoaugment.py | _apply_bbox_augmentation_wrapper | datawowio/automl | python | def _apply_bbox_augmentation_wrapper(image, bbox, new_bboxes, prob, augmentation_func, func_changes_bbox, *args):
'Applies _apply_bbox_augmentation with probability prob.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represent... |
def _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *args):
'Applies aug_func to the image for each bbox in bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) ... | 102,916,792,991,740,450 | Applies aug_func to the image for each bbox in bboxes.
Args:
image: 3D uint8 Tensor.
bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox
has 4 elements (min_y, min_x, max_y, max_x) of type float.
prob: Float that is the probability of applying aug_func to a specific
bounding box within... | efficientdet/aug/autoaugment.py | _apply_multi_bbox_augmentation | datawowio/automl | python | def _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *args):
'Applies aug_func to the image for each bbox in bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) ... |
def _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, aug_func, func_changes_bbox, *args):
'Checks to be sure num bboxes > 0 before calling inner function.'
num_bboxes = tf.shape(bboxes)[0]
(image, bboxes) = tf.cond(tf.equal(num_bboxes, 0), (lambda : (image, bboxes)), (lambda : _apply_multi_bbox_a... | -2,794,229,445,988,702,000 | Checks to be sure num bboxes > 0 before calling inner function. | efficientdet/aug/autoaugment.py | _apply_multi_bbox_augmentation_wrapper | datawowio/automl | python | def _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, aug_func, func_changes_bbox, *args):
num_bboxes = tf.shape(bboxes)[0]
(image, bboxes) = tf.cond(tf.equal(num_bboxes, 0), (lambda : (image, bboxes)), (lambda : _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *ar... |
def rotate_only_bboxes(image, bboxes, prob, degrees, replace):
'Apply rotate to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, rotate, func_changes_bbox, degrees, rep... | 9,211,649,917,027,160,000 | Apply rotate to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | rotate_only_bboxes | datawowio/automl | python | def rotate_only_bboxes(image, bboxes, prob, degrees, replace):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, rotate, func_changes_bbox, degrees, replace) |
def shear_x_only_bboxes(image, bboxes, prob, level, replace):
'Apply shear_x to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_x, func_changes_bbox, level, repl... | 5,920,012,366,805,690,000 | Apply shear_x to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | shear_x_only_bboxes | datawowio/automl | python | def shear_x_only_bboxes(image, bboxes, prob, level, replace):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_x, func_changes_bbox, level, replace) |
def shear_y_only_bboxes(image, bboxes, prob, level, replace):
'Apply shear_y to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_y, func_changes_bbox, level, repl... | 7,922,629,001,002,101,000 | Apply shear_y to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | shear_y_only_bboxes | datawowio/automl | python | def shear_y_only_bboxes(image, bboxes, prob, level, replace):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_y, func_changes_bbox, level, replace) |
def translate_x_only_bboxes(image, bboxes, prob, pixels, replace):
'Apply translate_x to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_x, func_changes_bbox... | -460,229,343,365,831,800 | Apply translate_x to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | translate_x_only_bboxes | datawowio/automl | python | def translate_x_only_bboxes(image, bboxes, prob, pixels, replace):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_x, func_changes_bbox, pixels, replace) |
def translate_y_only_bboxes(image, bboxes, prob, pixels, replace):
'Apply translate_y to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_y, func_changes_bbox... | -888,010,208,539,744,100 | Apply translate_y to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | translate_y_only_bboxes | datawowio/automl | python | def translate_y_only_bboxes(image, bboxes, prob, pixels, replace):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_y, func_changes_bbox, pixels, replace) |
def flip_only_bboxes(image, bboxes, prob):
'Apply flip_lr to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, tf.image.flip_left_right, func_changes_bbox) | -5,039,183,936,218,054,000 | Apply flip_lr to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | flip_only_bboxes | datawowio/automl | python | def flip_only_bboxes(image, bboxes, prob):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, tf.image.flip_left_right, func_changes_bbox) |
def solarize_only_bboxes(image, bboxes, prob, threshold):
'Apply solarize to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, solarize, func_changes_bbox, threshold) | -7,773,348,117,398,861,000 | Apply solarize to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | solarize_only_bboxes | datawowio/automl | python | def solarize_only_bboxes(image, bboxes, prob, threshold):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, solarize, func_changes_bbox, threshold) |
def equalize_only_bboxes(image, bboxes, prob):
'Apply equalize to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, equalize, func_changes_bbox) | -6,543,237,160,596,510,000 | Apply equalize to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | equalize_only_bboxes | datawowio/automl | python | def equalize_only_bboxes(image, bboxes, prob):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, equalize, func_changes_bbox) |
def cutout_only_bboxes(image, bboxes, prob, pad_size, replace):
'Apply cutout to each bbox in the image with probability prob.'
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, cutout, func_changes_bbox, pad_size, r... | -2,471,233,948,783,666,000 | Apply cutout to each bbox in the image with probability prob. | efficientdet/aug/autoaugment.py | cutout_only_bboxes | datawowio/automl | python | def cutout_only_bboxes(image, bboxes, prob, pad_size, replace):
func_changes_bbox = False
prob = _scale_bbox_only_op_probability(prob)
return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, cutout, func_changes_bbox, pad_size, replace) |
def _rotate_bbox(bbox, image_height, image_width, degrees):
'Rotates the bbox coordinated by degrees.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n im... | 6,211,981,566,521,351,000 | Rotates the bbox coordinated by degrees.
Args:
bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
of type float that represents the normalized coordinates between 0 and 1.
image_height: Int, height of the image.
image_width: Int, height of the image.
degrees: Float, a scalar angle in degrees ... | efficientdet/aug/autoaugment.py | _rotate_bbox | datawowio/automl | python | def _rotate_bbox(bbox, image_height, image_width, degrees):
'Rotates the bbox coordinated by degrees.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n im... |
def rotate_with_bboxes(image, bboxes, degrees, replace):
'Equivalent of PIL Rotate that rotates the image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float.\n degrees: Floa... | -3,447,364,672,112,616,400 | Equivalent of PIL Rotate that rotates the image and bbox.
Args:
image: 3D uint8 Tensor.
bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox
has 4 elements (min_y, min_x, max_y, max_x) of type float.
degrees: Float, a scalar angle in degrees to rotate all images by. If
degrees is positi... | efficientdet/aug/autoaugment.py | rotate_with_bboxes | datawowio/automl | python | def rotate_with_bboxes(image, bboxes, degrees, replace):
'Equivalent of PIL Rotate that rotates the image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float.\n degrees: Floa... |
def translate_x(image, pixels, replace):
'Equivalent of PIL Translate in X dimension.'
image = image_ops.translate(wrap(image), [(- pixels), 0])
return unwrap(image, replace) | -3,514,625,783,606,751,000 | Equivalent of PIL Translate in X dimension. | efficientdet/aug/autoaugment.py | translate_x | datawowio/automl | python | def translate_x(image, pixels, replace):
image = image_ops.translate(wrap(image), [(- pixels), 0])
return unwrap(image, replace) |
def translate_y(image, pixels, replace):
'Equivalent of PIL Translate in Y dimension.'
image = image_ops.translate(wrap(image), [0, (- pixels)])
return unwrap(image, replace) | -3,311,760,775,496,658,400 | Equivalent of PIL Translate in Y dimension. | efficientdet/aug/autoaugment.py | translate_y | datawowio/automl | python | def translate_y(image, pixels, replace):
image = image_ops.translate(wrap(image), [0, (- pixels)])
return unwrap(image, replace) |
def _shift_bbox(bbox, image_height, image_width, pixels, shift_horizontal):
'Shifts the bbox coordinates by pixels.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the ... | -6,072,056,995,610,296,000 | Shifts the bbox coordinates by pixels.
Args:
bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
of type float that represents the normalized coordinates between 0 and 1.
image_height: Int, height of the image.
image_width: Int, width of the image.
pixels: An int. How many pixels to shift the ... | efficientdet/aug/autoaugment.py | _shift_bbox | datawowio/automl | python | def _shift_bbox(bbox, image_height, image_width, pixels, shift_horizontal):
'Shifts the bbox coordinates by pixels.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the ... |
def translate_bbox(image, bboxes, pixels, replace, shift_horizontal):
'Equivalent of PIL Translate in X/Y dimension that shifts image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of ty... | -1,837,703,549,154,977,800 | Equivalent of PIL Translate in X/Y dimension that shifts image and bbox.
Args:
image: 3D uint8 Tensor.
bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox
has 4 elements (min_y, min_x, max_y, max_x) of type float with values
between [0, 1].
pixels: An int. How many pixels to shift the ... | efficientdet/aug/autoaugment.py | translate_bbox | datawowio/automl | python | def translate_bbox(image, bboxes, pixels, replace, shift_horizontal):
'Equivalent of PIL Translate in X/Y dimension that shifts image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of ty... |
def shear_x(image, level, replace):
'Equivalent of PIL Shearing in X dimension.'
image = image_ops.transform(wrap(image), [1.0, level, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
return unwrap(image, replace) | -6,123,410,436,435,485,000 | Equivalent of PIL Shearing in X dimension. | efficientdet/aug/autoaugment.py | shear_x | datawowio/automl | python | def shear_x(image, level, replace):
image = image_ops.transform(wrap(image), [1.0, level, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
return unwrap(image, replace) |
def shear_y(image, level, replace):
'Equivalent of PIL Shearing in Y dimension.'
image = image_ops.transform(wrap(image), [1.0, 0.0, 0.0, level, 1.0, 0.0, 0.0, 0.0])
return unwrap(image, replace) | -7,747,551,437,542,087,000 | Equivalent of PIL Shearing in Y dimension. | efficientdet/aug/autoaugment.py | shear_y | datawowio/automl | python | def shear_y(image, level, replace):
image = image_ops.transform(wrap(image), [1.0, 0.0, 0.0, level, 1.0, 0.0, 0.0, 0.0])
return unwrap(image, replace) |
def _shear_bbox(bbox, image_height, image_width, level, shear_horizontal):
'Shifts the bbox according to how the image was sheared.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int... | -6,425,782,020,536,974,000 | Shifts the bbox according to how the image was sheared.
Args:
bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
of type float that represents the normalized coordinates between 0 and 1.
image_height: Int, height of the image.
image_width: Int, height of the image.
level: Float. How much to s... | efficientdet/aug/autoaugment.py | _shear_bbox | datawowio/automl | python | def _shear_bbox(bbox, image_height, image_width, level, shear_horizontal):
'Shifts the bbox according to how the image was sheared.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int... |
def shear_with_bboxes(image, bboxes, level, replace, shear_horizontal):
'Applies Shear Transformation to the image and shifts the bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type flo... | -8,922,601,394,061,101,000 | Applies Shear Transformation to the image and shifts the bboxes.
Args:
image: 3D uint8 Tensor.
bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox
has 4 elements (min_y, min_x, max_y, max_x) of type float with values
between [0, 1].
level: Float. How much to shear the image. This value... | efficientdet/aug/autoaugment.py | shear_with_bboxes | datawowio/automl | python | def shear_with_bboxes(image, bboxes, level, replace, shear_horizontal):
'Applies Shear Transformation to the image and shifts the bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type flo... |
def autocontrast(image):
'Implements Autocontrast function from PIL using TF ops.\n\n Args:\n image: A 3D uint8 tensor.\n\n Returns:\n The image after it has had autocontrast applied to it and will be of type\n uint8.\n '
def scale_channel(image):
'Scale the 2D image using the autocontrast ... | 303,571,217,608,186,900 | Implements Autocontrast function from PIL using TF ops.
Args:
image: A 3D uint8 tensor.
Returns:
The image after it has had autocontrast applied to it and will be of type
uint8. | efficientdet/aug/autoaugment.py | autocontrast | datawowio/automl | python | def autocontrast(image):
'Implements Autocontrast function from PIL using TF ops.\n\n Args:\n image: A 3D uint8 tensor.\n\n Returns:\n The image after it has had autocontrast applied to it and will be of type\n uint8.\n '
def scale_channel(image):
'Scale the 2D image using the autocontrast ... |
def sharpness(image, factor):
'Implements Sharpness function from PIL using TF ops.'
orig_image = image
image = tf.cast(image, tf.float32)
image = tf.expand_dims(image, 0)
kernel = (tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0)
kernel = tf.tile(kern... | -1,232,677,706,748,177,200 | Implements Sharpness function from PIL using TF ops. | efficientdet/aug/autoaugment.py | sharpness | datawowio/automl | python | def sharpness(image, factor):
orig_image = image
image = tf.cast(image, tf.float32)
image = tf.expand_dims(image, 0)
kernel = (tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0)
kernel = tf.tile(kernel, [1, 1, 3, 1])
strides = [1, 1, 1, 1]
with ... |
def equalize(image):
'Implements Equalize function from PIL using TF ops.'
def scale_channel(im, c):
'Scale the data in the channel to implement equalize.'
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equ... | -4,360,158,787,895,066,600 | Implements Equalize function from PIL using TF ops. | efficientdet/aug/autoaugment.py | equalize | datawowio/automl | python | def equalize(image):
def scale_channel(im, c):
'Scale the data in the channel to implement equalize.'
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.g... |
def wrap(image):
"Returns 'image' with an extra channel set to all 1s."
shape = tf.shape(image)
extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype)
extended = tf.concat([image, extended_channel], 2)
return extended | -2,054,740,842,410,237,000 | Returns 'image' with an extra channel set to all 1s. | efficientdet/aug/autoaugment.py | wrap | datawowio/automl | python | def wrap(image):
shape = tf.shape(image)
extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype)
extended = tf.concat([image, extended_channel], 2)
return extended |
def unwrap(image, replace):
"Unwraps an image produced by wrap.\n\n Where there is a 0 in the last channel for every spatial position,\n the rest of the three channels in that spatial dimension are grayed\n (set to 128). Operations like translate and shear on a wrapped\n Tensor will leave 0s in empty locations... | 6,263,443,170,076,591,000 | Unwraps an image produced by wrap.
Where there is a 0 in the last channel for every spatial position,
the rest of the three channels in that spatial dimension are grayed
(set to 128). Operations like translate and shear on a wrapped
Tensor will leave 0s in empty locations. Some transformations look
at the intensity ... | efficientdet/aug/autoaugment.py | unwrap | datawowio/automl | python | def unwrap(image, replace):
"Unwraps an image produced by wrap.\n\n Where there is a 0 in the last channel for every spatial position,\n the rest of the three channels in that spatial dimension are grayed\n (set to 128). Operations like translate and shear on a wrapped\n Tensor will leave 0s in empty locations... |
def _cutout_inside_bbox(image, bbox, pad_fraction):
'Generates cutout mask and the mean pixel value of the bbox.\n\n First a location is randomly chosen within the image as the center where the\n cutout mask will be applied. Note this can be towards the boundaries of the\n image, so the full cutout mask may not ... | -8,068,290,216,123,599,000 | Generates cutout mask and the mean pixel value of the bbox.
First a location is randomly chosen within the image as the center where the
cutout mask will be applied. Note this can be towards the boundaries of the
image, so the full cutout mask may not be applied.
Args:
image: 3D uint8 Tensor.
bbox: 1D Tensor that... | efficientdet/aug/autoaugment.py | _cutout_inside_bbox | datawowio/automl | python | def _cutout_inside_bbox(image, bbox, pad_fraction):
'Generates cutout mask and the mean pixel value of the bbox.\n\n First a location is randomly chosen within the image as the center where the\n cutout mask will be applied. Note this can be towards the boundaries of the\n image, so the full cutout mask may not ... |
def bbox_cutout(image, bboxes, pad_fraction, replace_with_mean):
'Applies cutout to the image according to bbox information.\n\n This is a cutout variant that using bbox information to make more informed\n decisions on where to place the cutout mask.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor ... | 3,325,208,541,111,828,000 | Applies cutout to the image according to bbox information.
This is a cutout variant that using bbox information to make more informed
decisions on where to place the cutout mask.
Args:
image: 3D uint8 Tensor.
bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox
has 4 elements (min_y, min_x, ... | efficientdet/aug/autoaugment.py | bbox_cutout | datawowio/automl | python | def bbox_cutout(image, bboxes, pad_fraction, replace_with_mean):
'Applies cutout to the image according to bbox information.\n\n This is a cutout variant that using bbox information to make more informed\n decisions on where to place the cutout mask.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor ... |
def _randomly_negate_tensor(tensor):
'With 50% prob turn the tensor negative.'
should_flip = tf.cast(tf.floor((tf.random_uniform([]) + 0.5)), tf.bool)
final_tensor = tf.cond(should_flip, (lambda : tensor), (lambda : (- tensor)))
return final_tensor | 7,650,776,648,965,303,000 | With 50% prob turn the tensor negative. | efficientdet/aug/autoaugment.py | _randomly_negate_tensor | datawowio/automl | python | def _randomly_negate_tensor(tensor):
should_flip = tf.cast(tf.floor((tf.random_uniform([]) + 0.5)), tf.bool)
final_tensor = tf.cond(should_flip, (lambda : tensor), (lambda : (- tensor)))
return final_tensor |
def _shrink_level_to_arg(level):
'Converts level to ratio by which we shrink the image content.'
if (level == 0):
return (1.0,)
level = ((2.0 / (_MAX_LEVEL / level)) + 0.9)
return (level,) | 9,177,563,595,245,319,000 | Converts level to ratio by which we shrink the image content. | efficientdet/aug/autoaugment.py | _shrink_level_to_arg | datawowio/automl | python | def _shrink_level_to_arg(level):
if (level == 0):
return (1.0,)
level = ((2.0 / (_MAX_LEVEL / level)) + 0.9)
return (level,) |
def bbox_wrapper(func):
'Adds a bboxes function argument to func and returns unchanged bboxes.'
def wrapper(images, bboxes, *args, **kwargs):
return (func(images, *args, **kwargs), bboxes)
return wrapper | 7,986,334,951,161,487,000 | Adds a bboxes function argument to func and returns unchanged bboxes. | efficientdet/aug/autoaugment.py | bbox_wrapper | datawowio/automl | python | def bbox_wrapper(func):
def wrapper(images, bboxes, *args, **kwargs):
return (func(images, *args, **kwargs), bboxes)
return wrapper |
def _parse_policy_info(name, prob, level, replace_value, augmentation_hparams):
'Return the function that corresponds to `name` and update `level` param.'
func = NAME_TO_FUNC[name]
args = level_to_arg(augmentation_hparams)[name](level)
if ('prob' in inspect.getfullargspec(func)[0]):
args = tuple... | 1,299,395,002,175,213,300 | Return the function that corresponds to `name` and update `level` param. | efficientdet/aug/autoaugment.py | _parse_policy_info | datawowio/automl | python | def _parse_policy_info(name, prob, level, replace_value, augmentation_hparams):
func = NAME_TO_FUNC[name]
args = level_to_arg(augmentation_hparams)[name](level)
if ('prob' in inspect.getfullargspec(func)[0]):
args = tuple(([prob] + list(args)))
if ('replace' in inspect.getfullargspec(func)[... |
def _apply_func_with_prob(func, image, args, prob, bboxes):
'Apply `func` to image w/ `args` as input with probability `prob`.'
assert isinstance(args, tuple)
assert ('bboxes' == inspect.getfullargspec(func)[0][1])
if ('prob' in inspect.getfullargspec(func)[0]):
prob = 1.0
should_apply_op = ... | -935,833,796,455,203,700 | Apply `func` to image w/ `args` as input with probability `prob`. | efficientdet/aug/autoaugment.py | _apply_func_with_prob | datawowio/automl | python | def _apply_func_with_prob(func, image, args, prob, bboxes):
assert isinstance(args, tuple)
assert ('bboxes' == inspect.getfullargspec(func)[0][1])
if ('prob' in inspect.getfullargspec(func)[0]):
prob = 1.0
should_apply_op = tf.cast(tf.floor((tf.random_uniform([], dtype=tf.float32) + prob)),... |
def select_and_apply_random_policy(policies, image, bboxes):
'Select a random policy from `policies` and apply it to `image`.'
policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32)
for (i, policy) in enumerate(policies):
(image, bboxes) = tf.cond(tf.equal(i, policy_to_select... | -2,167,437,932,029,373,700 | Select a random policy from `policies` and apply it to `image`. | efficientdet/aug/autoaugment.py | select_and_apply_random_policy | datawowio/automl | python | def select_and_apply_random_policy(policies, image, bboxes):
policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32)
for (i, policy) in enumerate(policies):
(image, bboxes) = tf.cond(tf.equal(i, policy_to_select), (lambda selected_policy=policy: selected_policy(image, bboxes)... |
def build_and_apply_nas_policy(policies, image, bboxes, augmentation_hparams):
'Build a policy from the given policies passed in and apply to image.\n\n Args:\n policies: list of lists of tuples in the form `(func, prob, level)`, `func`\n is a string name of the augmentation function, `prob` is the probabi... | -5,391,226,156,041,492,000 | Build a policy from the given policies passed in and apply to image.
Args:
policies: list of lists of tuples in the form `(func, prob, level)`, `func`
is a string name of the augmentation function, `prob` is the probability
of applying the `func` operation, `level` is the input argument for
`func`.
ima... | efficientdet/aug/autoaugment.py | build_and_apply_nas_policy | datawowio/automl | python | def build_and_apply_nas_policy(policies, image, bboxes, augmentation_hparams):
'Build a policy from the given policies passed in and apply to image.\n\n Args:\n policies: list of lists of tuples in the form `(func, prob, level)`, `func`\n is a string name of the augmentation function, `prob` is the probabi... |
@tf.autograph.experimental.do_not_convert
def distort_image_with_autoaugment(image, bboxes, augmentation_name):
'Applies the AutoAugment policy to `image` and `bboxes`.\n\n Args:\n image: `Tensor` of shape [height, width, 3] representing an image.\n bboxes: `Tensor` of shape [N, 4] representing ground truth ... | 6,681,142,781,319,894,000 | Applies the AutoAugment policy to `image` and `bboxes`.
Args:
image: `Tensor` of shape [height, width, 3] representing an image.
bboxes: `Tensor` of shape [N, 4] representing ground truth boxes that are
normalized between [0, 1].
augmentation_name: The name of the AutoAugment policy to use. The available
... | efficientdet/aug/autoaugment.py | distort_image_with_autoaugment | datawowio/automl | python | @tf.autograph.experimental.do_not_convert
def distort_image_with_autoaugment(image, bboxes, augmentation_name):
'Applies the AutoAugment policy to `image` and `bboxes`.\n\n Args:\n image: `Tensor` of shape [height, width, 3] representing an image.\n bboxes: `Tensor` of shape [N, 4] representing ground truth ... |
def distort_image_with_randaugment(image, bboxes, num_layers, magnitude):
'Applies the RandAugment to `image` and `bboxes`.'
replace_value = [128, 128, 128]
tf.logging.info('Using RandAugment.')
augmentation_hparams = hparams_config.Config(dict(cutout_max_pad_fraction=0.75, cutout_bbox_replace_with_mean... | 6,479,833,031,204,732,000 | Applies the RandAugment to `image` and `bboxes`. | efficientdet/aug/autoaugment.py | distort_image_with_randaugment | datawowio/automl | python | def distort_image_with_randaugment(image, bboxes, num_layers, magnitude):
replace_value = [128, 128, 128]
tf.logging.info('Using RandAugment.')
augmentation_hparams = hparams_config.Config(dict(cutout_max_pad_fraction=0.75, cutout_bbox_replace_with_mean=False, cutout_const=100, translate_const=250, cut... |
def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor, image_):
'Applies mask to bbox region in image then adds content_tensor to it.'
mask = tf.pad(mask, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=1)
content_tensor = tf.p... | -1,229,225,343,436,366,000 | Applies mask to bbox region in image then adds content_tensor to it. | efficientdet/aug/autoaugment.py | mask_and_add_image | datawowio/automl | python | def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor, image_):
mask = tf.pad(mask, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=1)
content_tensor = tf.pad(content_tensor, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ... |
def scale_channel(image):
'Scale the 2D image using the autocontrast rule.'
lo = tf.to_float(tf.reduce_min(image))
hi = tf.to_float(tf.reduce_max(image))
def scale_values(im):
scale = (255.0 / (hi - lo))
offset = ((- lo) * scale)
im = ((tf.to_float(im) * scale) + offset)
... | 1,234,125,559,131,667,000 | Scale the 2D image using the autocontrast rule. | efficientdet/aug/autoaugment.py | scale_channel | datawowio/automl | python | def scale_channel(image):
lo = tf.to_float(tf.reduce_min(image))
hi = tf.to_float(tf.reduce_max(image))
def scale_values(im):
scale = (255.0 / (hi - lo))
offset = ((- lo) * scale)
im = ((tf.to_float(im) * scale) + offset)
im = tf.clip_by_value(im, 0.0, 255.0)
re... |
def scale_channel(im, c):
'Scale the data in the channel to implement equalize.'
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)])
step = ((tf.red... | 6,353,019,026,156,998,000 | Scale the data in the channel to implement equalize. | efficientdet/aug/autoaugment.py | scale_channel | datawowio/automl | python | def scale_channel(im, c):
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)])
step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255)... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.