body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
1605276693eb9051c872eef4a87d03c4e37b722287653a36765a8ee550e79efa | def get_one_page(self, url):
'\n 请求url返回响应结果\n :param url:\n :return:\n '
try:
response = requests.get(url, headers=self.generate_random_ua)
if (response.status_code == 200):
return response.text
except Exception as e:
print('连接糗事百科失败,错误原因', e)
return None | 请求url返回响应结果
:param url:
:return: | qiushibaike/qiushibaike.py | get_one_page | jumploop/Python3_WebSpider | 1 | python | def get_one_page(self, url):
'\n 请求url返回响应结果\n :param url:\n :return:\n '
try:
response = requests.get(url, headers=self.generate_random_ua)
if (response.status_code == 200):
return response.text
except Exception as e:
print('连接糗事百科失败,错误原因', e)
return None | def get_one_page(self, url):
'\n 请求url返回响应结果\n :param url:\n :return:\n '
try:
response = requests.get(url, headers=self.generate_random_ua)
if (response.status_code == 200):
return response.text
except Exception as e:
print('连接糗事百科失败,错误原因', e)
return None<|docstring|>请求url返回响应结果
:param url:
:return:<|endoftext|> |
468cd31f5ee4a6c35065727db9860d81356424e4143e3bd354a23a79ac70c5e0 | @staticmethod
def parse_one_page(contents):
'\n 解析页面数据，提取数据\n :param content:\n :return:\n '
html = etree.HTML(contents)
items = html.xpath('//div[contains(@id,"qiushi_tag")]')
pageStories = []
for item in items:
author = item.xpath('.//div[@class="author clearfix"]/a[2]/h2/text()')[0].strip()
age = item.xpath('.//div[@class="author clearfix"]/div/text()')[0]
content = item.xpath('.//a[1]/div[@class="content"]/span[1]/text()')
content = '\n'.join([n.strip() for n in content])
funny_number = item.xpath('.//div[@class="stats"]/span[1]/i/text()')[0]
comments_number = item.xpath('.//div[@class="stats"]/span[2]/a/i/text()')[0]
pageStories.append([author, age, content, funny_number, comments_number])
return pageStories | 解析页面数据，提取数据
:param content:
:return: | qiushibaike/qiushibaike.py | parse_one_page | jumploop/Python3_WebSpider | 1 | python | @staticmethod
def parse_one_page(contents):
'\n 解析页面数据，提取数据\n :param content:\n :return:\n '
html = etree.HTML(contents)
items = html.xpath('//div[contains(@id,"qiushi_tag")]')
pageStories = []
for item in items:
author = item.xpath('.//div[@class="author clearfix"]/a[2]/h2/text()')[0].strip()
age = item.xpath('.//div[@class="author clearfix"]/div/text()')[0]
content = item.xpath('.//a[1]/div[@class="content"]/span[1]/text()')
content = '\n'.join([n.strip() for n in content])
funny_number = item.xpath('.//div[@class="stats"]/span[1]/i/text()')[0]
comments_number = item.xpath('.//div[@class="stats"]/span[2]/a/i/text()')[0]
pageStories.append([author, age, content, funny_number, comments_number])
return pageStories | @staticmethod
def parse_one_page(contents):
'\n 解析页面数据，提取数据\n :param content:\n :return:\n '
html = etree.HTML(contents)
items = html.xpath('//div[contains(@id,"qiushi_tag")]')
pageStories = []
for item in items:
author = item.xpath('.//div[@class="author clearfix"]/a[2]/h2/text()')[0].strip()
age = item.xpath('.//div[@class="author clearfix"]/div/text()')[0]
content = item.xpath('.//a[1]/div[@class="content"]/span[1]/text()')
content = '\n'.join([n.strip() for n in content])
funny_number = item.xpath('.//div[@class="stats"]/span[1]/i/text()')[0]
comments_number = item.xpath('.//div[@class="stats"]/span[2]/a/i/text()')[0]
pageStories.append([author, age, content, funny_number, comments_number])
return pageStories<|docstring|>解析页面数据，提取数据
:param content:
:return:<|endoftext|> |
6cb20982d733841adaf6b674acdecba8584ef057abf9023a35a7f2d404ba0af7 | def write_to_file_by_csv(self, content):
'\n 将数据写入文件\n :param content:\n :return:\n '
with open('result.csv', 'w', newline='', encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(self.fieldnames)
writer.writerows(content) | 将数据写入文件
:param content:
:return: | qiushibaike/qiushibaike.py | write_to_file_by_csv | jumploop/Python3_WebSpider | 1 | python | def write_to_file_by_csv(self, content):
'\n 将数据写入文件\n :param content:\n :return:\n '
with open('result.csv', 'w', newline=, encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(self.fieldnames)
writer.writerows(content) | def write_to_file_by_csv(self, content):
'\n 将数据写入文件\n :param content:\n :return:\n '
with open('result.csv', 'w', newline=, encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(self.fieldnames)
writer.writerows(content)<|docstring|>将数据写入文件
:param content:
:return:<|endoftext|> |
cf20d2b6a3cccc8ed009f1248e2365f6f3e9c4025bdf0e8ecb5e2b6592c261b9 | def write_to_file_by_pandas(self, content):
'\n 通过pandas模块将数据写入文件\n :param content:\n :return:\n '
content = [line for line in content]
df = pd.DataFrame(content, columns=self.fieldnames)
df.to_excel('results.xlsx', index=False) | 通过pandas模块将数据写入文件
:param content:
:return: | qiushibaike/qiushibaike.py | write_to_file_by_pandas | jumploop/Python3_WebSpider | 1 | python | def write_to_file_by_pandas(self, content):
'\n 通过pandas模块将数据写入文件\n :param content:\n :return:\n '
content = [line for line in content]
df = pd.DataFrame(content, columns=self.fieldnames)
df.to_excel('results.xlsx', index=False) | def write_to_file_by_pandas(self, content):
'\n 通过pandas模块将数据写入文件\n :param content:\n :return:\n '
content = [line for line in content]
df = pd.DataFrame(content, columns=self.fieldnames)
df.to_excel('results.xlsx', index=False)<|docstring|>通过pandas模块将数据写入文件
:param content:
:return:<|endoftext|> |
28d90bb2a77dabc51a68a5e3dc43eca233bdd510da58f9b3165133aa47900909 | def run(self):
'\n 主方法\n :return:\n '
results = []
urls = [self.url.format(i) for i in range(1, 14)]
for url in urls:
time.sleep(random.randint(1, 3))
content = self.get_one_page(url)
item = self.parse_one_page(content)
print(item)
results.extend(item)
self.write_to_file_by_csv(results)
self.write_to_file_by_pandas(results) | 主方法
:return: | qiushibaike/qiushibaike.py | run | jumploop/Python3_WebSpider | 1 | python | def run(self):
'\n 主方法\n :return:\n '
results = []
urls = [self.url.format(i) for i in range(1, 14)]
for url in urls:
time.sleep(random.randint(1, 3))
content = self.get_one_page(url)
item = self.parse_one_page(content)
print(item)
results.extend(item)
self.write_to_file_by_csv(results)
self.write_to_file_by_pandas(results) | def run(self):
'\n 主方法\n :return:\n '
results = []
urls = [self.url.format(i) for i in range(1, 14)]
for url in urls:
time.sleep(random.randint(1, 3))
content = self.get_one_page(url)
item = self.parse_one_page(content)
print(item)
results.extend(item)
self.write_to_file_by_csv(results)
self.write_to_file_by_pandas(results)<|docstring|>主方法
:return:<|endoftext|> |
91aa9b031d9f2d991b1349c39e66ba11ab59dbc73314dbef523223f38a16d1a1 | def load_moonshot_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
moonshot_labeled = pinot.data.moonshot()
moonshot_unlabeled = pinot.data.datasets.UnlabeledDataset().from_txt((os.path.dirname(utils.__file__) + '/moonshot_activity_synthetic.txt'), unlabeled_size, seed=seed)()
np.random.seed(seed)
moonshot_labeled.extend(moonshot_unlabeled)
np.random.shuffle(moonshot_labeled)
return moonshot_labeled | Parameters
----------
unlabeled_size :
(Default value = 0.1)
seed :
(Default value = 2666)
Returns
------- | pinot/data/unlabeled_datasets.py | load_moonshot_semi_supervised | choderalab/pinot | 13 | python | def load_moonshot_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
moonshot_labeled = pinot.data.moonshot()
moonshot_unlabeled = pinot.data.datasets.UnlabeledDataset().from_txt((os.path.dirname(utils.__file__) + '/moonshot_activity_synthetic.txt'), unlabeled_size, seed=seed)()
np.random.seed(seed)
moonshot_labeled.extend(moonshot_unlabeled)
np.random.shuffle(moonshot_labeled)
return moonshot_labeled | def load_moonshot_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
moonshot_labeled = pinot.data.moonshot()
moonshot_unlabeled = pinot.data.datasets.UnlabeledDataset().from_txt((os.path.dirname(utils.__file__) + '/moonshot_activity_synthetic.txt'), unlabeled_size, seed=seed)()
np.random.seed(seed)
moonshot_labeled.extend(moonshot_unlabeled)
np.random.shuffle(moonshot_labeled)
return moonshot_labeled<|docstring|>Parameters
----------
unlabeled_size :
(Default value = 0.1)
seed :
(Default value = 2666)
Returns
-------<|endoftext|> |
8558f5e8303d32e0afc20e3495ed675c450c4dc2fbd254bcad2dc1023e2f4e72 | def load_esol_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
esol_labeled = pinot.data.esol()
esol_unlabeled = utils.load_unlabeled_data((os.path.dirname(utils.__file__) + '/esol_synthetic_smiles.txt'), unlabeled_size, seed=seed)()
np.random.seed(seed)
esol_labeled.extend(esol_unlabeled)
np.random.shuffle(esol_labeled)
return esol_labeled | Parameters
----------
unlabeled_size :
(Default value = 0.1)
seed :
(Default value = 2666)
Returns
------- | pinot/data/unlabeled_datasets.py | load_esol_semi_supervised | choderalab/pinot | 13 | python | def load_esol_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
esol_labeled = pinot.data.esol()
esol_unlabeled = utils.load_unlabeled_data((os.path.dirname(utils.__file__) + '/esol_synthetic_smiles.txt'), unlabeled_size, seed=seed)()
np.random.seed(seed)
esol_labeled.extend(esol_unlabeled)
np.random.shuffle(esol_labeled)
return esol_labeled | def load_esol_semi_supervised(unlabeled_size=0.1, seed=2666):
'\n\n Parameters\n ----------\n unlabeled_size :\n (Default value = 0.1)\n seed :\n (Default value = 2666)\n\n Returns\n -------\n\n '
esol_labeled = pinot.data.esol()
esol_unlabeled = utils.load_unlabeled_data((os.path.dirname(utils.__file__) + '/esol_synthetic_smiles.txt'), unlabeled_size, seed=seed)()
np.random.seed(seed)
esol_labeled.extend(esol_unlabeled)
np.random.shuffle(esol_labeled)
return esol_labeled<|docstring|>Parameters
----------
unlabeled_size :
(Default value = 0.1)
seed :
(Default value = 2666)
Returns
-------<|endoftext|> |
47d2dc790cfec4096f40b408ab2e50cfb62bab2b95e8fd1a5e3b770bdda4765c | def version(filename):
'Extract the version number from the dictionary file name.'
match = dict_version_re.match(filename)
if (match is None):
message.warning('Found a dictionary with a malformed name: {}'.format(filename))
return None
return tuple((int(n) for n in match.group('version').split('-'))) | Extract the version number from the dictionary file name. | luminos/browser/webengine/Spell.py | version | linuxaddict89/luminos | 0 | python | def version(filename):
match = dict_version_re.match(filename)
if (match is None):
message.warning('Found a dictionary with a malformed name: {}'.format(filename))
return None
return tuple((int(n) for n in match.group('version').split('-'))) | def version(filename):
match = dict_version_re.match(filename)
if (match is None):
message.warning('Found a dictionary with a malformed name: {}'.format(filename))
return None
return tuple((int(n) for n in match.group('version').split('-')))<|docstring|>Extract the version number from the dictionary file name.<|endoftext|> |
4e25247a13c08076166afe07a363400cf83a4b7380fed74433a0c991ff1d9027 | def dictionary_dir(old=False):
"Return the path (str) to the QtWebEngine's dictionaries directory."
if (qtutils.version_check('5.10', compiled=False) and (not old)):
datapath = standarddir.data()
else:
datapath = QLibraryInfo.location(QLibraryInfo.DataPath)
return os.path.join(datapath, 'qtwebengine_dictionaries') | Return the path (str) to the QtWebEngine's dictionaries directory. | luminos/browser/webengine/Spell.py | dictionary_dir | linuxaddict89/luminos | 0 | python | def dictionary_dir(old=False):
if (qtutils.version_check('5.10', compiled=False) and (not old)):
datapath = standarddir.data()
else:
datapath = QLibraryInfo.location(QLibraryInfo.DataPath)
return os.path.join(datapath, 'qtwebengine_dictionaries') | def dictionary_dir(old=False):
if (qtutils.version_check('5.10', compiled=False) and (not old)):
datapath = standarddir.data()
else:
datapath = QLibraryInfo.location(QLibraryInfo.DataPath)
return os.path.join(datapath, 'qtwebengine_dictionaries')<|docstring|>Return the path (str) to the QtWebEngine's dictionaries directory.<|endoftext|> |
06699badca172b0cebd13d6c159964e8a6a2d992c914c5a2201a034c8eba2da8 | def local_files(code):
'Return all installed dictionaries for the given code.\n\n The returned dictionaries are sorted by version, therefore the latest will\n be the first element. The list will be empty if no dictionaries are found.\n '
pathname = os.path.join(dictionary_dir(), '{}*.bdic'.format(code))
matching_dicts = glob.glob(pathname)
versioned_dicts = []
for matching_dict in matching_dicts:
parsed_version = version(matching_dict)
if (parsed_version is not None):
filename = os.path.basename(matching_dict)
log.config.debug('Found file for dict {}: {}'.format(code, filename))
versioned_dicts.append((parsed_version, filename))
return [filename for (version, filename) in sorted(versioned_dicts, reverse=True)] | Return all installed dictionaries for the given code.
The returned dictionaries are sorted by version, therefore the latest will
be the first element. The list will be empty if no dictionaries are found. | luminos/browser/webengine/Spell.py | local_files | linuxaddict89/luminos | 0 | python | def local_files(code):
'Return all installed dictionaries for the given code.\n\n The returned dictionaries are sorted by version, therefore the latest will\n be the first element. The list will be empty if no dictionaries are found.\n '
pathname = os.path.join(dictionary_dir(), '{}*.bdic'.format(code))
matching_dicts = glob.glob(pathname)
versioned_dicts = []
for matching_dict in matching_dicts:
parsed_version = version(matching_dict)
if (parsed_version is not None):
filename = os.path.basename(matching_dict)
log.config.debug('Found file for dict {}: {}'.format(code, filename))
versioned_dicts.append((parsed_version, filename))
return [filename for (version, filename) in sorted(versioned_dicts, reverse=True)] | def local_files(code):
'Return all installed dictionaries for the given code.\n\n The returned dictionaries are sorted by version, therefore the latest will\n be the first element. The list will be empty if no dictionaries are found.\n '
pathname = os.path.join(dictionary_dir(), '{}*.bdic'.format(code))
matching_dicts = glob.glob(pathname)
versioned_dicts = []
for matching_dict in matching_dicts:
parsed_version = version(matching_dict)
if (parsed_version is not None):
filename = os.path.basename(matching_dict)
log.config.debug('Found file for dict {}: {}'.format(code, filename))
versioned_dicts.append((parsed_version, filename))
return [filename for (version, filename) in sorted(versioned_dicts, reverse=True)]<|docstring|>Return all installed dictionaries for the given code.
The returned dictionaries are sorted by version, therefore the latest will
be the first element. The list will be empty if no dictionaries are found.<|endoftext|> |
2d2792aeaf637ce674081958e01b045c74ce5269f6005e5c96b58ae0ad00d193 | def local_filename(code):
'Return the newest installed dictionary for the given code.\n\n Return the filename of the installed dictionary with the highest version\n number or None if the dictionary is not installed.\n '
all_installed = local_files(code)
return (os.path.splitext(all_installed[0])[0] if all_installed else None) | Return the newest installed dictionary for the given code.
Return the filename of the installed dictionary with the highest version
number or None if the dictionary is not installed. | luminos/browser/webengine/Spell.py | local_filename | linuxaddict89/luminos | 0 | python | def local_filename(code):
'Return the newest installed dictionary for the given code.\n\n Return the filename of the installed dictionary with the highest version\n number or None if the dictionary is not installed.\n '
all_installed = local_files(code)
return (os.path.splitext(all_installed[0])[0] if all_installed else None) | def local_filename(code):
'Return the newest installed dictionary for the given code.\n\n Return the filename of the installed dictionary with the highest version\n number or None if the dictionary is not installed.\n '
all_installed = local_files(code)
return (os.path.splitext(all_installed[0])[0] if all_installed else None)<|docstring|>Return the newest installed dictionary for the given code.
Return the filename of the installed dictionary with the highest version
number or None if the dictionary is not installed.<|endoftext|> |
4386382a6a7715683cc7cda7d185d020555d69add1b3d183570d30e4e77048d6 | def init():
'Initialize the dictionary path if supported.'
if qtutils.version_check('5.10', compiled=False):
new_dir = dictionary_dir()
old_dir = dictionary_dir(old=True)
os.environ['QTWEBENGINE_DICTIONARIES_PATH'] = new_dir
try:
if (os.path.exists(old_dir) and (not os.path.exists(new_dir))):
shutil.copytree(old_dir, new_dir)
except OSError:
log.misc.exception('Failed to copy old dictionaries') | Initialize the dictionary path if supported. | luminos/browser/webengine/Spell.py | init | linuxaddict89/luminos | 0 | python | def init():
if qtutils.version_check('5.10', compiled=False):
new_dir = dictionary_dir()
old_dir = dictionary_dir(old=True)
os.environ['QTWEBENGINE_DICTIONARIES_PATH'] = new_dir
try:
if (os.path.exists(old_dir) and (not os.path.exists(new_dir))):
shutil.copytree(old_dir, new_dir)
except OSError:
log.misc.exception('Failed to copy old dictionaries') | def init():
if qtutils.version_check('5.10', compiled=False):
new_dir = dictionary_dir()
old_dir = dictionary_dir(old=True)
os.environ['QTWEBENGINE_DICTIONARIES_PATH'] = new_dir
try:
if (os.path.exists(old_dir) and (not os.path.exists(new_dir))):
shutil.copytree(old_dir, new_dir)
except OSError:
log.misc.exception('Failed to copy old dictionaries')<|docstring|>Initialize the dictionary path if supported.<|endoftext|> |
cc06da47a12d635c870c86ea64d9de9bac629bc7b649a4bea7e5c8b7be89802d | def customer_image_file_path(instance, file_name):
'Generate file path for new customer image'
ext = file_name.split('.')[(- 1)]
file_name = f'{uuid.uuid4()}.{ext}'
return os.path.join('images/', file_name) | Generate file path for new customer image | billing_shop/apps/clients/models/clients.py | customer_image_file_path | sandoval19/build_crew | 0 | python | def customer_image_file_path(instance, file_name):
ext = file_name.split('.')[(- 1)]
file_name = f'{uuid.uuid4()}.{ext}'
return os.path.join('images/', file_name) | def customer_image_file_path(instance, file_name):
ext = file_name.split('.')[(- 1)]
file_name = f'{uuid.uuid4()}.{ext}'
return os.path.join('images/', file_name)<|docstring|>Generate file path for new customer image<|endoftext|> |
0428aa6039ba9cb7173e6d7d9ffc88d41299ddbe8115f6774d5162b057a95259 | def generate_unique_anonymous_username():
'\n Generate an unique username for a player. Check in database if the username already exists.\n TODO: check in db if a user with the generated username already exists\n '
unique_id = get_random_string(length=10)
new_username = ('u_%s' % unique_id)
return new_username | Generate an unique username for a player. Check in database if the username already exists.
TODO: check in db if a user with the generated username already exists | web/utils.py | generate_unique_anonymous_username | NejcZupec/tictactoe | 1 | python | def generate_unique_anonymous_username():
'\n Generate an unique username for a player. Check in database if the username already exists.\n TODO: check in db if a user with the generated username already exists\n '
unique_id = get_random_string(length=10)
new_username = ('u_%s' % unique_id)
return new_username | def generate_unique_anonymous_username():
'\n Generate an unique username for a player. Check in database if the username already exists.\n TODO: check in db if a user with the generated username already exists\n '
unique_id = get_random_string(length=10)
new_username = ('u_%s' % unique_id)
return new_username<|docstring|>Generate an unique username for a player. Check in database if the username already exists.
TODO: check in db if a user with the generated username already exists<|endoftext|> |
5212bc37b7af3f6478baf91708147f5f4e972b7868316ad29e720144910d0f48 | def create_new_game(p1_type, p2_type):
'\n Generate two random players and create a new Game instance.\n '
player1 = Player.objects.create(username=generate_unique_anonymous_username(), type=p1_type)
player2 = Player.objects.create(username=generate_unique_anonymous_username(), type=p2_type)
return Game.objects.create(player1=player1, player2=player2) | Generate two random players and create a new Game instance. | web/utils.py | create_new_game | NejcZupec/tictactoe | 1 | python | def create_new_game(p1_type, p2_type):
'\n \n '
player1 = Player.objects.create(username=generate_unique_anonymous_username(), type=p1_type)
player2 = Player.objects.create(username=generate_unique_anonymous_username(), type=p2_type)
return Game.objects.create(player1=player1, player2=player2) | def create_new_game(p1_type, p2_type):
'\n \n '
player1 = Player.objects.create(username=generate_unique_anonymous_username(), type=p1_type)
player2 = Player.objects.create(username=generate_unique_anonymous_username(), type=p2_type)
return Game.objects.create(player1=player1, player2=player2)<|docstring|>Generate two random players and create a new Game instance.<|endoftext|> |
f4f7acfacc3270e66c237768148fcad3edc883902e78fe9af47ac151817f3410 | def download_content(url, dst, proxy=None, verbose=True):
"\n\n Download web content.\n\n Parameters\n ----------\n url: str\n Content url.\n\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, indicating\n that the env variable https_proxy will be searched. In\n case of not found, the proxy will be set to empty.\n\n verbose: bool\n Controls whether logging should be done on console.\n\n "
import sys
import shutil
import requests
if verbose:
requests.packages.urllib3.add_stderr_logger()
if verbose:
print('Downloading from {}'.format(url))
if (not proxy):
try:
proxy = dict(https=os.environ['https_proxy'])
except:
print('https proxy not used.')
__res = requests.get(url, verify=False, stream=True, proxies=None)
__res.raw.decode_content = True
with open(dst, 'wb') as __f:
(__l, __t) = (0, __res.headers.get('content-length'))
for __chunk in __res.iter_content(chunk_size=4096):
if __chunk:
__f.write(__chunk)
__l += len(__chunk)
if verbose:
sys.stdout.write('Progress: {}/{}\r'.format(__l, __t))
print() | Download web content.
Parameters
----------
url: str
Content url.
dst: str
Destination for file saving.
proxy: dict
Dictionary with 'https' as key and a string indicating
the https proxy as value. Defaults to None, indicating
that the env variable https_proxy will be searched. In
case of not found, the proxy will be set to empty.
verbose: bool
Controls whether logging should be done on console. | lib/utils/utils.py | download_content | jonathanzjl/cam-vision | 0 | python | def download_content(url, dst, proxy=None, verbose=True):
"\n\n Download web content.\n\n Parameters\n ----------\n url: str\n Content url.\n\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, indicating\n that the env variable https_proxy will be searched. In\n case of not found, the proxy will be set to empty.\n\n verbose: bool\n Controls whether logging should be done on console.\n\n "
import sys
import shutil
import requests
if verbose:
requests.packages.urllib3.add_stderr_logger()
if verbose:
print('Downloading from {}'.format(url))
if (not proxy):
try:
proxy = dict(https=os.environ['https_proxy'])
except:
print('https proxy not used.')
__res = requests.get(url, verify=False, stream=True, proxies=None)
__res.raw.decode_content = True
with open(dst, 'wb') as __f:
(__l, __t) = (0, __res.headers.get('content-length'))
for __chunk in __res.iter_content(chunk_size=4096):
if __chunk:
__f.write(__chunk)
__l += len(__chunk)
if verbose:
sys.stdout.write('Progress: {}/{}\r'.format(__l, __t))
print() | def download_content(url, dst, proxy=None, verbose=True):
"\n\n Download web content.\n\n Parameters\n ----------\n url: str\n Content url.\n\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, indicating\n that the env variable https_proxy will be searched. In\n case of not found, the proxy will be set to empty.\n\n verbose: bool\n Controls whether logging should be done on console.\n\n "
import sys
import shutil
import requests
if verbose:
requests.packages.urllib3.add_stderr_logger()
if verbose:
print('Downloading from {}'.format(url))
if (not proxy):
try:
proxy = dict(https=os.environ['https_proxy'])
except:
print('https proxy not used.')
__res = requests.get(url, verify=False, stream=True, proxies=None)
__res.raw.decode_content = True
with open(dst, 'wb') as __f:
(__l, __t) = (0, __res.headers.get('content-length'))
for __chunk in __res.iter_content(chunk_size=4096):
if __chunk:
__f.write(__chunk)
__l += len(__chunk)
if verbose:
sys.stdout.write('Progress: {}/{}\r'.format(__l, __t))
print()<|docstring|>Download web content.
Parameters
----------
url: str
Content url.
dst: str
Destination for file saving.
proxy: dict
Dictionary with 'https' as key and a string indicating
the https proxy as value. Defaults to None, indicating
that the env variable https_proxy will be searched. In
case of not found, the proxy will be set to empty.
verbose: bool
Controls whether logging should be done on console.<|endoftext|> |
11286e6b7b205b27dccb87e3e2d2031580bb0e893071a29a28e7b1ce4b757a67 | def download_yoolov3tiny_weights(dst, proxy=None, verbose=True):
"\n\n Download YOLOv3-Tiny weight file from official darknet\n website.\n\n Parameters\n ----------\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, indicating\n that the env variable https_proxy will be searched. In\n case of not found, the proxy will be set to empty.\n\n verbose: bool\n Controls whether logging should be done on console.\n\n "
__url = 'https://pjreddie.com/media/files/yolov3-tiny.weights'
download_content(__url, dst, proxy, verbose) | Download YOLOv3-Tiny weight file from official darknet
website.
Parameters
----------
dst: str
Destination for file saving.
proxy: dict
Dictionary with 'https' as key and a string indicating
the https proxy as value. Defaults to None, indicating
that the env variable https_proxy will be searched. In
case of not found, the proxy will be set to empty.
verbose: bool
Controls whether logging should be done on console. | lib/utils/utils.py | download_yoolov3tiny_weights | jonathanzjl/cam-vision | 0 | python | def download_yoolov3tiny_weights(dst, proxy=None, verbose=True):
"\n\n Download YOLOv3-Tiny weight file from official darknet\n website.\n\n Parameters\n ----------\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, indicating\n that the env variable https_proxy will be searched. In\n case of not found, the proxy will be set to empty.\n\n verbose: bool\n Controls whether logging should be done on console.\n\n "
__url = 'https://pjreddie.com/media/files/yolov3-tiny.weights'
download_content(__url, dst, proxy, verbose) | def download_yoolov3tiny_weights(dst, proxy=None, verbose=True):
"\n\n Download YOLOv3-Tiny weight file from official darknet\n website.\n\n Parameters\n ----------\n dst: str\n Destination for file saving.\n\n proxy: dict\n Dictionary with 'https' as key and a string indicating\n the https proxy as value. Defaults to None, indicating\n that the env variable https_proxy will be searched. In\n case of not found, the proxy will be set to empty.\n\n verbose: bool\n Controls whether logging should be done on console.\n\n "
__url = 'https://pjreddie.com/media/files/yolov3-tiny.weights'
download_content(__url, dst, proxy, verbose)<|docstring|>Download YOLOv3-Tiny weight file from official darknet
website.
Parameters
----------
dst: str
Destination for file saving.
proxy: dict
Dictionary with 'https' as key and a string indicating
the https proxy as value. Defaults to None, indicating
that the env variable https_proxy will be searched. In
case of not found, the proxy will be set to empty.
verbose: bool
Controls whether logging should be done on console.<|endoftext|> |
b23020a4a5323bac9d34b65f69c299dd28e196bee8ffce1b50cf72f298e474fb | def print_mat(mat, width=10, prec=4):
'\n A nice printer for floating point\n matrices.\n\n Parameters\n ----------\n mat: 2D matrix\n An input 2D matrix to print.\n\n width: int\n Minimum width for each element to print.\n\n prec: int\n Floating point precision for each element\n to print.\n\n '
for __indx in range(mat.shape[0]):
__str = ('{:{width}.{prec}f} ' * mat.shape[1])
print(__str.format(*mat[(__indx, :)], width=width, prec=prec)) | A nice printer for floating point
matrices.
Parameters
----------
mat: 2D matrix
An input 2D matrix to print.
width: int
Minimum width for each element to print.
prec: int
Floating point precision for each element
to print. | lib/utils/utils.py | print_mat | jonathanzjl/cam-vision | 0 | python | def print_mat(mat, width=10, prec=4):
'\n A nice printer for floating point\n matrices.\n\n Parameters\n ----------\n mat: 2D matrix\n An input 2D matrix to print.\n\n width: int\n Minimum width for each element to print.\n\n prec: int\n Floating point precision for each element\n to print.\n\n '
for __indx in range(mat.shape[0]):
__str = ('{:{width}.{prec}f} ' * mat.shape[1])
print(__str.format(*mat[(__indx, :)], width=width, prec=prec)) | def print_mat(mat, width=10, prec=4):
'\n A nice printer for floating point\n matrices.\n\n Parameters\n ----------\n mat: 2D matrix\n An input 2D matrix to print.\n\n width: int\n Minimum width for each element to print.\n\n prec: int\n Floating point precision for each element\n to print.\n\n '
for __indx in range(mat.shape[0]):
__str = ('{:{width}.{prec}f} ' * mat.shape[1])
print(__str.format(*mat[(__indx, :)], width=width, prec=prec))<|docstring|>A nice printer for floating point
matrices.
Parameters
----------
mat: 2D matrix
An input 2D matrix to print.
width: int
Minimum width for each element to print.
prec: int
Floating point precision for each element
to print.<|endoftext|> |
f146be34658f0bb4f992c8fa2b82cb087fe395fbeeed361e7f95969654315f44 | def read_txt_as_strs(txt_path, strip=' ', cmnt=None):
'\n\n Read a txt file. Each line will be treated\n as a string.\n\n Empty lines will be skipped. Spaces will be\n automatically stripped.\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file.\n\n strip: bool\n Character(s) stripped from the beginning and\n the end of each line. Defaults to whitespace.\n Use `None` to indicate no-op.\n\n cmnt: str\n Comment character. If a line (after stripping,\n if strip character(s) is not `None`) starts with\n `cmnt` will not be read.\n\n Returns\n ----------\n list\n A list of strings, where each string represents\n a line in the txt file.\n\n '
__lines = []
with open(txt_path, 'r') as __f:
for __l in __f.readlines():
if (strip is not None):
__l = __l.strip().strip(strip)
if (not __l):
continue
if (cmnt is not None):
if __l.startswith(cmnt):
continue
__lines.append(__l.strip('\n'))
return __lines | Read a txt file. Each line will be treated
as a string.
Empty lines will be skipped. Spaces will be
automatically stripped.
Parameters
----------
txt_path: str
Path to the txt file.
strip: bool
Character(s) stripped from the beginning and
the end of each line. Defaults to whitespace.
Use `None` to indicate no-op.
cmnt: str
Comment character. If a line (after stripping,
if strip character(s) is not `None`) starts with
`cmnt` will not be read.
Returns
----------
list
A list of strings, where each string represents
a line in the txt file. | lib/utils/utils.py | read_txt_as_strs | jonathanzjl/cam-vision | 0 | python | def read_txt_as_strs(txt_path, strip=' ', cmnt=None):
'\n\n Read a txt file. Each line will be treated\n as a string.\n\n Empty lines will be skipped. Spaces will be\n automatically stripped.\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file.\n\n strip: bool\n Character(s) stripped from the beginning and\n the end of each line. Defaults to whitespace.\n Use `None` to indicate no-op.\n\n cmnt: str\n Comment character. If a line (after stripping,\n if strip character(s) is not `None`) starts with\n `cmnt` will not be read.\n\n Returns\n ----------\n list\n A list of strings, where each string represents\n a line in the txt file.\n\n '
__lines = []
with open(txt_path, 'r') as __f:
for __l in __f.readlines():
if (strip is not None):
__l = __l.strip().strip(strip)
if (not __l):
continue
if (cmnt is not None):
if __l.startswith(cmnt):
continue
__lines.append(__l.strip('\n'))
return __lines | def read_txt_as_strs(txt_path, strip=' ', cmnt=None):
'\n\n Read a txt file. Each line will be treated\n as a string.\n\n Empty lines will be skipped. Spaces will be\n automatically stripped.\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file.\n\n strip: bool\n Character(s) stripped from the beginning and\n the end of each line. Defaults to whitespace.\n Use `None` to indicate no-op.\n\n cmnt: str\n Comment character. If a line (after stripping,\n if strip character(s) is not `None`) starts with\n `cmnt` will not be read.\n\n Returns\n ----------\n list\n A list of strings, where each string represents\n a line in the txt file.\n\n '
__lines = []
with open(txt_path, 'r') as __f:
for __l in __f.readlines():
if (strip is not None):
__l = __l.strip().strip(strip)
if (not __l):
continue
if (cmnt is not None):
if __l.startswith(cmnt):
continue
__lines.append(__l.strip('\n'))
return __lines<|docstring|>Read a txt file. Each line will be treated
as a string.
Empty lines will be skipped. Spaces will be
automatically stripped.
Parameters
----------
txt_path: str
Path to the txt file.
strip: bool
Character(s) stripped from the beginning and
the end of each line. Defaults to whitespace.
Use `None` to indicate no-op.
cmnt: str
Comment character. If a line (after stripping,
if strip character(s) is not `None`) starts with
`cmnt` will not be read.
Returns
----------
list
A list of strings, where each string represents
a line in the txt file.<|endoftext|> |
dd49f3fe44b580c35b0d9d171b7347d5b675d4644a388ae414f1136addfc4a04 | def load_img(img_path, target_size, normalize=True):
'\n\n Load image for TF prediction mode.\n\n Parameters\n ----------\n img_path: str\n Path to image file.\n\n target_size: int\n Target square size for image resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n ----------\n np.ndarray\n Tensor with rank 4, to be used for\n TF model prediction.\n\n '
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
if (target_size is not None):
img = cv2.resize(img, dsize=(target_size, target_size))
if normalize:
img = (img.astype(np.float32) / 255.0)
return np.expand_dims(img.astype(np.float32), axis=0) | Load image for TF prediction mode.
Parameters
----------
img_path: str
Path to image file.
target_size: int
Target square size for image resizing.
normalize: bool
Whether input image should be divided by 255.
Returns
----------
np.ndarray
Tensor with rank 4, to be used for
TF model prediction. | lib/utils/utils.py | load_img | jonathanzjl/cam-vision | 0 | python | def load_img(img_path, target_size, normalize=True):
'\n\n Load image for TF prediction mode.\n\n Parameters\n ----------\n img_path: str\n Path to image file.\n\n target_size: int\n Target square size for image resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n ----------\n np.ndarray\n Tensor with rank 4, to be used for\n TF model prediction.\n\n '
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
if (target_size is not None):
img = cv2.resize(img, dsize=(target_size, target_size))
if normalize:
img = (img.astype(np.float32) / 255.0)
return np.expand_dims(img.astype(np.float32), axis=0) | def load_img(img_path, target_size, normalize=True):
'\n\n Load image for TF prediction mode.\n\n Parameters\n ----------\n img_path: str\n Path to image file.\n\n target_size: int\n Target square size for image resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n ----------\n np.ndarray\n Tensor with rank 4, to be used for\n TF model prediction.\n\n '
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
if (target_size is not None):
img = cv2.resize(img, dsize=(target_size, target_size))
if normalize:
img = (img.astype(np.float32) / 255.0)
return np.expand_dims(img.astype(np.float32), axis=0)<|docstring|>Load image for TF prediction mode.
Parameters
----------
img_path: str
Path to image file.
target_size: int
Target square size for image resizing.
normalize: bool
Whether input image should be divided by 255.
Returns
----------
np.ndarray
Tensor with rank 4, to be used for
TF model prediction.<|endoftext|> |
18933248c5606bef95d8395c9a715b9f622c51cb0cddae7e84b3bad1b4750726 | def make_predict_inp(img, target_size=None, normalize=True, permute_br=True, letter_box=None, to_channel_first=False):
'\n\n Transform an image for prediction mode. Pixel\n values will be rescaled to between 0 and 1.\n\n Parameters\n ----------\n img: np.ndarray\n An input image array. Assumed to be RGB image.\n\n target_size: int\n Target square size for image resizing. Defaults\n to None, i.e. no resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n permute_br: bool\n Whether permutation of the Blue and Red\n channels should be performed. This is generally\n needed when the input image is read using OpenCV,\n since OpenCV uses BGR ordering, while most neural\n networks assumes input to be RGB ordering. Defaults\n to True.\n\n letter_box: tuple or None\n Side length and fill value of the square box when\n performing letter box transformation of the image.\n Default to None indicating no letter box transformation.\n\n to_channel_first: bool\n When set to true, the input image will be\n converted to `channel_first` ordering. Defaults to\n False.\n\n Returns\n ----------\n np.ndarray\n Tensor with rank 4, to be used for\n model prediction.\n\n '
if target_size:
img = cv2.resize(img, dsize=(target_size, target_size))
if normalize:
img = (img.astype(np.float32) / 255.0)
if permute_br:
(img[(:, :, 0)], img[(:, :, 2)]) = (img[(:, :, 2)], img[(:, :, 0)].copy())
if letter_box:
(img, shift, ratio) = letterbox_image(img, letter_box[0], fill=letter_box[1], normalize=False)
img = np.expand_dims(img.astype(np.float32), axis=0)
if to_channel_first:
img = img.transpose(0, 3, 1, 2)
if letter_box:
return (img, shift, ratio)
return img | Transform an image for prediction mode. Pixel
values will be rescaled to between 0 and 1.
Parameters
----------
img: np.ndarray
An input image array. Assumed to be RGB image.
target_size: int
Target square size for image resizing. Defaults
to None, i.e. no resizing.
normalize: bool
Whether input image should be divided by 255.
permute_br: bool
Whether permutation of the Blue and Red
channels should be performed. This is generally
needed when the input image is read using OpenCV,
since OpenCV uses BGR ordering, while most neural
networks assumes input to be RGB ordering. Defaults
to True.
letter_box: tuple or None
Side length and fill value of the square box when
performing letter box transformation of the image.
Default to None indicating no letter box transformation.
to_channel_first: bool
When set to true, the input image will be
converted to `channel_first` ordering. Defaults to
False.
Returns
----------
np.ndarray
Tensor with rank 4, to be used for
model prediction. | lib/utils/utils.py | make_predict_inp | jonathanzjl/cam-vision | 0 | python | def make_predict_inp(img, target_size=None, normalize=True, permute_br=True, letter_box=None, to_channel_first=False):
'\n\n Transform an image for prediction mode. Pixel\n values will be rescaled to between 0 and 1.\n\n Parameters\n ----------\n img: np.ndarray\n An input image array. Assumed to be RGB image.\n\n target_size: int\n Target square size for image resizing. Defaults\n to None, i.e. no resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n permute_br: bool\n Whether permutation of the Blue and Red\n channels should be performed. This is generally\n needed when the input image is read using OpenCV,\n since OpenCV uses BGR ordering, while most neural\n networks assumes input to be RGB ordering. Defaults\n to True.\n\n letter_box: tuple or None\n Side length and fill value of the square box when\n performing letter box transformation of the image.\n Default to None indicating no letter box transformation.\n\n to_channel_first: bool\n When set to true, the input image will be\n converted to `channel_first` ordering. Defaults to\n False.\n\n Returns\n ----------\n np.ndarray\n Tensor with rank 4, to be used for\n model prediction.\n\n '
if target_size:
img = cv2.resize(img, dsize=(target_size, target_size))
if normalize:
img = (img.astype(np.float32) / 255.0)
if permute_br:
(img[(:, :, 0)], img[(:, :, 2)]) = (img[(:, :, 2)], img[(:, :, 0)].copy())
if letter_box:
(img, shift, ratio) = letterbox_image(img, letter_box[0], fill=letter_box[1], normalize=False)
img = np.expand_dims(img.astype(np.float32), axis=0)
if to_channel_first:
img = img.transpose(0, 3, 1, 2)
if letter_box:
return (img, shift, ratio)
return img | def make_predict_inp(img, target_size=None, normalize=True, permute_br=True, letter_box=None, to_channel_first=False):
'\n\n Transform an image for prediction mode. Pixel\n values will be rescaled to between 0 and 1.\n\n Parameters\n ----------\n img: np.ndarray\n An input image array. Assumed to be RGB image.\n\n target_size: int\n Target square size for image resizing. Defaults\n to None, i.e. no resizing.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n permute_br: bool\n Whether permutation of the Blue and Red\n channels should be performed. This is generally\n needed when the input image is read using OpenCV,\n since OpenCV uses BGR ordering, while most neural\n networks assumes input to be RGB ordering. Defaults\n to True.\n\n letter_box: tuple or None\n Side length and fill value of the square box when\n performing letter box transformation of the image.\n Default to None indicating no letter box transformation.\n\n to_channel_first: bool\n When set to true, the input image will be\n converted to `channel_first` ordering. Defaults to\n False.\n\n Returns\n ----------\n np.ndarray\n Tensor with rank 4, to be used for\n model prediction.\n\n '
if target_size:
img = cv2.resize(img, dsize=(target_size, target_size))
if normalize:
img = (img.astype(np.float32) / 255.0)
if permute_br:
(img[(:, :, 0)], img[(:, :, 2)]) = (img[(:, :, 2)], img[(:, :, 0)].copy())
if letter_box:
(img, shift, ratio) = letterbox_image(img, letter_box[0], fill=letter_box[1], normalize=False)
img = np.expand_dims(img.astype(np.float32), axis=0)
if to_channel_first:
img = img.transpose(0, 3, 1, 2)
if letter_box:
return (img, shift, ratio)
return img<|docstring|>Transform an image for prediction mode. Pixel
values will be rescaled to between 0 and 1.
Parameters
----------
img: np.ndarray
An input image array. Assumed to be RGB image.
target_size: int
Target square size for image resizing. Defaults
to None, i.e. no resizing.
normalize: bool
Whether input image should be divided by 255.
permute_br: bool
Whether permutation of the Blue and Red
channels should be performed. This is generally
needed when the input image is read using OpenCV,
since OpenCV uses BGR ordering, while most neural
networks assumes input to be RGB ordering. Defaults
to True.
letter_box: tuple or None
Side length and fill value of the square box when
performing letter box transformation of the image.
Default to None indicating no letter box transformation.
to_channel_first: bool
When set to true, the input image will be
converted to `channel_first` ordering. Defaults to
False.
Returns
----------
np.ndarray
Tensor with rank 4, to be used for
model prediction.<|endoftext|> |
7929c0099a2e5f39faee7a38162674eec8bdb8ae6214da16f802f8f6991b5ca1 | def predict_top(model, img, top_classes, label_dict):
'\n\n Run prediction on input image and get\n prediction scores and class indices for\n `top_classes` classes.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n img: np.ndarray\n Input image in form of 4D tensor.\n\n top_classes: int\n Number of top classes for prediction.\n\n label_dict: dict\n Dictionary with keys the prediction indices\n (int) and values the corresponding class\n labels (str).\n\n Returns\n ----------\n tuple\n Tuple with 3 elements:\n - a list of predicted class labels descending\n order,\n - a list with corresponding prediction\n scores.\n\n '
scores = np.squeeze(model.predict(img))
top_indx = np.argsort(scores)[(- top_classes):]
top_scrs = scores[top_indx]
top_labs = [label_dict[indx] for indx in top_indx]
return (list(reversed(top_labs)), list(reversed(top_scrs))) | Run prediction on input image and get
prediction scores and class indices for
`top_classes` classes.
Parameters
----------
model: tf.keras.models.Model
A keras model.
img: np.ndarray
Input image in form of 4D tensor.
top_classes: int
Number of top classes for prediction.
label_dict: dict
Dictionary with keys the prediction indices
(int) and values the corresponding class
labels (str).
Returns
----------
tuple
Tuple with 3 elements:
- a list of predicted class labels descending
order,
- a list with corresponding prediction
scores. | lib/utils/utils.py | predict_top | jonathanzjl/cam-vision | 0 | python | def predict_top(model, img, top_classes, label_dict):
'\n\n Run prediction on input image and get\n prediction scores and class indices for\n `top_classes` classes.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n img: np.ndarray\n Input image in form of 4D tensor.\n\n top_classes: int\n Number of top classes for prediction.\n\n label_dict: dict\n Dictionary with keys the prediction indices\n (int) and values the corresponding class\n labels (str).\n\n Returns\n ----------\n tuple\n Tuple with 3 elements:\n - a list of predicted class labels descending\n order,\n - a list with corresponding prediction\n scores.\n\n '
scores = np.squeeze(model.predict(img))
top_indx = np.argsort(scores)[(- top_classes):]
top_scrs = scores[top_indx]
top_labs = [label_dict[indx] for indx in top_indx]
return (list(reversed(top_labs)), list(reversed(top_scrs))) | def predict_top(model, img, top_classes, label_dict):
'\n\n Run prediction on input image and get\n prediction scores and class indices for\n `top_classes` classes.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n img: np.ndarray\n Input image in form of 4D tensor.\n\n top_classes: int\n Number of top classes for prediction.\n\n label_dict: dict\n Dictionary with keys the prediction indices\n (int) and values the corresponding class\n labels (str).\n\n Returns\n ----------\n tuple\n Tuple with 3 elements:\n - a list of predicted class labels descending\n order,\n - a list with corresponding prediction\n scores.\n\n '
scores = np.squeeze(model.predict(img))
top_indx = np.argsort(scores)[(- top_classes):]
top_scrs = scores[top_indx]
top_labs = [label_dict[indx] for indx in top_indx]
return (list(reversed(top_labs)), list(reversed(top_scrs)))<|docstring|>Run prediction on input image and get
prediction scores and class indices for
`top_classes` classes.
Parameters
----------
model: tf.keras.models.Model
A keras model.
img: np.ndarray
Input image in form of 4D tensor.
top_classes: int
Number of top classes for prediction.
label_dict: dict
Dictionary with keys the prediction indices
(int) and values the corresponding class
labels (str).
Returns
----------
tuple
Tuple with 3 elements:
- a list of predicted class labels descending
order,
- a list with corresponding prediction
scores.<|endoftext|> |
d9af481db73556e58e514f0602304e500207388520da0fe3dd04aa54d44cf799 | def get_imagenet_dict(txt_path):
'\n\n Make ImageNet ground truth dict.\n The ground truth dictionay maps\n a class index to its label.\n\n The .txt file can be found at:\n https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file with ImageNet\n class index-to-label mappings.\n\n Returns\n ----------\n dict\n A dictionay with class indices as\n keys and their corresponding ImageNet\n labels as values.\n\n '
imagenet_dict = {}
with open(txt_path, 'r') as f:
for __l in f.readlines():
(key, val) = __l.split(':')
__v = val.strip()[:(- 1)].replace("'", '')
imagenet_dict[int(key)] = __v
return imagenet_dict | Make ImageNet ground truth dict.
The ground truth dictionay maps
a class index to its label.
The .txt file can be found at:
https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
Parameters
----------
txt_path: str
Path to the txt file with ImageNet
class index-to-label mappings.
Returns
----------
dict
A dictionay with class indices as
keys and their corresponding ImageNet
labels as values. | lib/utils/utils.py | get_imagenet_dict | jonathanzjl/cam-vision | 0 | python | def get_imagenet_dict(txt_path):
'\n\n Make ImageNet ground truth dict.\n The ground truth dictionay maps\n a class index to its label.\n\n The .txt file can be found at:\n https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file with ImageNet\n class index-to-label mappings.\n\n Returns\n ----------\n dict\n A dictionay with class indices as\n keys and their corresponding ImageNet\n labels as values.\n\n '
imagenet_dict = {}
with open(txt_path, 'r') as f:
for __l in f.readlines():
(key, val) = __l.split(':')
__v = val.strip()[:(- 1)].replace("'", )
imagenet_dict[int(key)] = __v
return imagenet_dict | def get_imagenet_dict(txt_path):
'\n\n Make ImageNet ground truth dict.\n The ground truth dictionay maps\n a class index to its label.\n\n The .txt file can be found at:\n https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n\n Parameters\n ----------\n txt_path: str\n Path to the txt file with ImageNet\n class index-to-label mappings.\n\n Returns\n ----------\n dict\n A dictionay with class indices as\n keys and their corresponding ImageNet\n labels as values.\n\n '
imagenet_dict = {}
with open(txt_path, 'r') as f:
for __l in f.readlines():
(key, val) = __l.split(':')
__v = val.strip()[:(- 1)].replace("'", )
imagenet_dict[int(key)] = __v
return imagenet_dict<|docstring|>Make ImageNet ground truth dict.
The ground truth dictionay maps
a class index to its label.
The .txt file can be found at:
https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
Parameters
----------
txt_path: str
Path to the txt file with ImageNet
class index-to-label mappings.
Returns
----------
dict
A dictionay with class indices as
keys and their corresponding ImageNet
labels as values.<|endoftext|> |
d3df50c856658bed26a06cbf0d67e06c509e0d678ee063c5b30b26907792790c | def classify_frame(model, frame, target_size, top_classes, label_dict, normalize=True, permute_br=True, to_channel_first=False, verbose=True):
'\n\n Run classification on input frame.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n frame: np.ndarray\n An input image frame.\n\n target_size: int\n Target square image size for resizing.\n None indicates no resizing.\n\n top_classes: int\n Number of top classes for prediction.\n\n label_dict: dict\n Dictionary with keys the prediction indices\n (int) and values the corresponding class\n labels (str).\n\n normalize: bool\n Whether input image should be divided by 255.\n\n permute_br: bool\n Whether permutation of the Blue and Red\n channels should be performed. This is generally\n needed when the input image is read using OpenCV,\n since OpenCV uses BGR ordering, while most neural\n networks assumes input to be RGB ordering. Defaults\n to True.\n\n to_channel_first: bool\n When set to true, the input image will be\n converted to `channel_first` ordering. Defaults to\n False.\n\n verbose: bool\n Controls console print verbosity. Defaults\n to True.\n\n Returns\n ----------\n tuple\n Tuple with 3 elements:\n - a list of predicted class labels with\n prediction scores in descending order,\n - a list with corresponding prediction\n scores.\n\n '
inp = make_predict_inp(frame, target_size, normalize, permute_br, to_channel_first)
(top_labs, top_scrs) = predict_top(model, inp, top_classes, label_dict)
if verbose:
print('\nframe size: {} x {}'.format(inp.shape[1], inp.shape[2]))
print('top {} predictions: '.format(top_classes))
for __c in range(top_classes):
print('{:25s}: {:.3f}'.format(top_labs[__c][:min(len(top_labs[__c]), 20)], top_scrs[__c]))
return (top_labs, top_scrs) | Run classification on input frame.
Parameters
----------
model: tf.keras.models.Model
A keras model.
frame: np.ndarray
An input image frame.
target_size: int
Target square image size for resizing.
None indicates no resizing.
top_classes: int
Number of top classes for prediction.
label_dict: dict
Dictionary with keys the prediction indices
(int) and values the corresponding class
labels (str).
normalize: bool
Whether input image should be divided by 255.
permute_br: bool
Whether permutation of the Blue and Red
channels should be performed. This is generally
needed when the input image is read using OpenCV,
since OpenCV uses BGR ordering, while most neural
networks assumes input to be RGB ordering. Defaults
to True.
to_channel_first: bool
When set to true, the input image will be
converted to `channel_first` ordering. Defaults to
False.
verbose: bool
Controls console print verbosity. Defaults
to True.
Returns
----------
tuple
Tuple with 3 elements:
- a list of predicted class labels with
prediction scores in descending order,
- a list with corresponding prediction
scores. | lib/utils/utils.py | classify_frame | jonathanzjl/cam-vision | 0 | python | def classify_frame(model, frame, target_size, top_classes, label_dict, normalize=True, permute_br=True, to_channel_first=False, verbose=True):
'\n\n Run classification on input frame.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n frame: np.ndarray\n An input image frame.\n\n target_size: int\n Target square image size for resizing.\n None indicates no resizing.\n\n top_classes: int\n Number of top classes for prediction.\n\n label_dict: dict\n Dictionary with keys the prediction indices\n (int) and values the corresponding class\n labels (str).\n\n normalize: bool\n Whether input image should be divided by 255.\n\n permute_br: bool\n Whether permutation of the Blue and Red\n channels should be performed. This is generally\n needed when the input image is read using OpenCV,\n since OpenCV uses BGR ordering, while most neural\n networks assumes input to be RGB ordering. Defaults\n to True.\n\n to_channel_first: bool\n When set to true, the input image will be\n converted to `channel_first` ordering. Defaults to\n False.\n\n verbose: bool\n Controls console print verbosity. Defaults\n to True.\n\n Returns\n ----------\n tuple\n Tuple with 3 elements:\n - a list of predicted class labels with\n prediction scores in descending order,\n - a list with corresponding prediction\n scores.\n\n '
inp = make_predict_inp(frame, target_size, normalize, permute_br, to_channel_first)
(top_labs, top_scrs) = predict_top(model, inp, top_classes, label_dict)
if verbose:
print('\nframe size: {} x {}'.format(inp.shape[1], inp.shape[2]))
print('top {} predictions: '.format(top_classes))
for __c in range(top_classes):
print('{:25s}: {:.3f}'.format(top_labs[__c][:min(len(top_labs[__c]), 20)], top_scrs[__c]))
return (top_labs, top_scrs) | def classify_frame(model, frame, target_size, top_classes, label_dict, normalize=True, permute_br=True, to_channel_first=False, verbose=True):
'\n\n Run classification on input frame.\n\n Parameters\n ----------\n model: tf.keras.models.Model\n A keras model.\n\n frame: np.ndarray\n An input image frame.\n\n target_size: int\n Target square image size for resizing.\n None indicates no resizing.\n\n top_classes: int\n Number of top classes for prediction.\n\n label_dict: dict\n Dictionary with keys the prediction indices\n (int) and values the corresponding class\n labels (str).\n\n normalize: bool\n Whether input image should be divided by 255.\n\n permute_br: bool\n Whether permutation of the Blue and Red\n channels should be performed. This is generally\n needed when the input image is read using OpenCV,\n since OpenCV uses BGR ordering, while most neural\n networks assumes input to be RGB ordering. Defaults\n to True.\n\n to_channel_first: bool\n When set to true, the input image will be\n converted to `channel_first` ordering. Defaults to\n False.\n\n verbose: bool\n Controls console print verbosity. Defaults\n to True.\n\n Returns\n ----------\n tuple\n Tuple with 3 elements:\n - a list of predicted class labels with\n prediction scores in descending order,\n - a list with corresponding prediction\n scores.\n\n '
inp = make_predict_inp(frame, target_size, normalize, permute_br, to_channel_first)
(top_labs, top_scrs) = predict_top(model, inp, top_classes, label_dict)
if verbose:
print('\nframe size: {} x {}'.format(inp.shape[1], inp.shape[2]))
print('top {} predictions: '.format(top_classes))
for __c in range(top_classes):
print('{:25s}: {:.3f}'.format(top_labs[__c][:min(len(top_labs[__c]), 20)], top_scrs[__c]))
return (top_labs, top_scrs)<|docstring|>Run classification on input frame.
Parameters
----------
model: tf.keras.models.Model
A keras model.
frame: np.ndarray
An input image frame.
target_size: int
Target square image size for resizing.
None indicates no resizing.
top_classes: int
Number of top classes for prediction.
label_dict: dict
Dictionary with keys the prediction indices
(int) and values the corresponding class
labels (str).
normalize: bool
Whether input image should be divided by 255.
permute_br: bool
Whether permutation of the Blue and Red
channels should be performed. This is generally
needed when the input image is read using OpenCV,
since OpenCV uses BGR ordering, while most neural
networks assumes input to be RGB ordering. Defaults
to True.
to_channel_first: bool
When set to true, the input image will be
converted to `channel_first` ordering. Defaults to
False.
verbose: bool
Controls console print verbosity. Defaults
to True.
Returns
----------
tuple
Tuple with 3 elements:
- a list of predicted class labels with
prediction scores in descending order,
- a list with corresponding prediction
scores.<|endoftext|> |
23ec06474e3a5bd772c46a2928c76cd9c78a6bf91f329568bac3ba1be090b7e4 | def load_dkn_weights(w_path, dtype, skip_bytes=20):
'\n\n Load Darknet weight file.\n\n Parameters\n ----------\n w_path: str\n Path to the weight file.\n\n dtype: str or datatype\n Data type of stored weights.\n\n skip_bytes: int\n Number of bytes to skip. Darknet weight\n file starts with 5 x int32 (20 bytes) header\n elements.\n\n Returns\n ----------\n np.array\n Weight array.\n\n '
with open(w_path, 'rb') as __wf:
__wf.seek(skip_bytes, 0)
__content = np.fromfile(__wf, dtype)
return __content | Load Darknet weight file.
Parameters
----------
w_path: str
Path to the weight file.
dtype: str or datatype
Data type of stored weights.
skip_bytes: int
Number of bytes to skip. Darknet weight
file starts with 5 x int32 (20 bytes) header
elements.
Returns
----------
np.array
Weight array. | lib/utils/utils.py | load_dkn_weights | jonathanzjl/cam-vision | 0 | python | def load_dkn_weights(w_path, dtype, skip_bytes=20):
'\n\n Load Darknet weight file.\n\n Parameters\n ----------\n w_path: str\n Path to the weight file.\n\n dtype: str or datatype\n Data type of stored weights.\n\n skip_bytes: int\n Number of bytes to skip. Darknet weight\n file starts with 5 x int32 (20 bytes) header\n elements.\n\n Returns\n ----------\n np.array\n Weight array.\n\n '
with open(w_path, 'rb') as __wf:
__wf.seek(skip_bytes, 0)
__content = np.fromfile(__wf, dtype)
return __content | def load_dkn_weights(w_path, dtype, skip_bytes=20):
'\n\n Load Darknet weight file.\n\n Parameters\n ----------\n w_path: str\n Path to the weight file.\n\n dtype: str or datatype\n Data type of stored weights.\n\n skip_bytes: int\n Number of bytes to skip. Darknet weight\n file starts with 5 x int32 (20 bytes) header\n elements.\n\n Returns\n ----------\n np.array\n Weight array.\n\n '
with open(w_path, 'rb') as __wf:
__wf.seek(skip_bytes, 0)
__content = np.fromfile(__wf, dtype)
return __content<|docstring|>Load Darknet weight file.
Parameters
----------
w_path: str
Path to the weight file.
dtype: str or datatype
Data type of stored weights.
skip_bytes: int
Number of bytes to skip. Darknet weight
file starts with 5 x int32 (20 bytes) header
elements.
Returns
----------
np.array
Weight array.<|endoftext|> |
64a02305751c0e0c51ff32c729fcf1b30505d35c7dd193f0df6fbf5d567ef6a1 | def load_img_folder(folder, ext, permute_br=True, normalize=True, loader=None):
'\n\n Load all images inside given folder.\n\n Parameters\n ----------\n folder: str\n Absolute folder to image folder.\n\n ext: str\n Image file extension. Must be recognizable by\n OpenCV.\n\n permute_br: bool\n Whether blue and red channel permutation should\n be performed.\n\n normalize: bool\n Indicating whether the image pixel value should\n be divided by 255.0.\n\n loader: function\n An image loader. Defaults to None, in which\n OpenCV `imread` is used.\n\n Returns\n ----------\n tuple(list)\n The image (np.array) list and path list.\n\n '
__imgs = []
__plst = glob.glob(os.path.join(folder, '*.{}'.format(ext)))
for __p in __plst:
if loader:
__img = loader(__p)
else:
__img = cv2.imread(__p, cv2.IMREAD_UNCHANGED)
if normalize:
__img = (__img / 255.0)
if permute_br:
(__img[(:, :, 0)], __img[(:, :, 2)]) = (__img[(:, :, 2)], __img[(:, :, 0)].copy())
__imgs.append(__img)
return (__imgs, __plst) | Load all images inside given folder.
Parameters
----------
folder: str
Absolute folder to image folder.
ext: str
Image file extension. Must be recognizable by
OpenCV.
permute_br: bool
Whether blue and red channel permutation should
be performed.
normalize: bool
Indicating whether the image pixel value should
be divided by 255.0.
loader: function
An image loader. Defaults to None, in which
OpenCV `imread` is used.
Returns
----------
tuple(list)
The image (np.array) list and path list. | lib/utils/utils.py | load_img_folder | jonathanzjl/cam-vision | 0 | python | def load_img_folder(folder, ext, permute_br=True, normalize=True, loader=None):
'\n\n Load all images inside given folder.\n\n Parameters\n ----------\n folder: str\n Absolute folder to image folder.\n\n ext: str\n Image file extension. Must be recognizable by\n OpenCV.\n\n permute_br: bool\n Whether blue and red channel permutation should\n be performed.\n\n normalize: bool\n Indicating whether the image pixel value should\n be divided by 255.0.\n\n loader: function\n An image loader. Defaults to None, in which\n OpenCV `imread` is used.\n\n Returns\n ----------\n tuple(list)\n The image (np.array) list and path list.\n\n '
__imgs = []
__plst = glob.glob(os.path.join(folder, '*.{}'.format(ext)))
for __p in __plst:
if loader:
__img = loader(__p)
else:
__img = cv2.imread(__p, cv2.IMREAD_UNCHANGED)
if normalize:
__img = (__img / 255.0)
if permute_br:
(__img[(:, :, 0)], __img[(:, :, 2)]) = (__img[(:, :, 2)], __img[(:, :, 0)].copy())
__imgs.append(__img)
return (__imgs, __plst) | def load_img_folder(folder, ext, permute_br=True, normalize=True, loader=None):
'\n\n Load all images inside given folder.\n\n Parameters\n ----------\n folder: str\n Absolute folder to image folder.\n\n ext: str\n Image file extension. Must be recognizable by\n OpenCV.\n\n permute_br: bool\n Whether blue and red channel permutation should\n be performed.\n\n normalize: bool\n Indicating whether the image pixel value should\n be divided by 255.0.\n\n loader: function\n An image loader. Defaults to None, in which\n OpenCV `imread` is used.\n\n Returns\n ----------\n tuple(list)\n The image (np.array) list and path list.\n\n '
__imgs = []
__plst = glob.glob(os.path.join(folder, '*.{}'.format(ext)))
for __p in __plst:
if loader:
__img = loader(__p)
else:
__img = cv2.imread(__p, cv2.IMREAD_UNCHANGED)
if normalize:
__img = (__img / 255.0)
if permute_br:
(__img[(:, :, 0)], __img[(:, :, 2)]) = (__img[(:, :, 2)], __img[(:, :, 0)].copy())
__imgs.append(__img)
return (__imgs, __plst)<|docstring|>Load all images inside given folder.
Parameters
----------
folder: str
Absolute folder to image folder.
ext: str
Image file extension. Must be recognizable by
OpenCV.
permute_br: bool
Whether blue and red channel permutation should
be performed.
normalize: bool
Indicating whether the image pixel value should
be divided by 255.0.
loader: function
An image loader. Defaults to None, in which
OpenCV `imread` is used.
Returns
----------
tuple(list)
The image (np.array) list and path list.<|endoftext|> |
41f829b3f8895c13ac6b09a3a89e584075004983f9c3e52f6712f52d830d42e8 | def letterbox_image(img, frame_size, fill=0.5, normalize=True):
'\n\n Letter box an input image.\n\n Image will be centered into a squared frame,\n where the longer side of the image is resized\n to the frame size and the shorter side is resized\n by keepng the same aspect ratio.\n\n Parameters\n ----------\n img: np.array\n The input image. Assumed to be rank-3, channel-last.\n\n frame_size: int\n Size of the square frame.\n\n fill: float\n Value used to fill empty border. Defaults to 0.5.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n ----------\n tuple\n 1. The letter boxed image.\n 2. Horizontal and vertical shift in number of pixels\n with respect to square frame size.\n 3. The resize ratio: box size/ original longer side.\n\n '
if normalize:
img = (img.astype(np.float32) / 255.0)
__lindx = np.argmax(img.shape[:2])
__ratio = (frame_size / img.shape[:2][__lindx])
__rsize = (np.array(img.shape[:2]) * __ratio).astype(np.int32)
__shift = (np.array(([frame_size] * 2), dtype=np.int32) - __rsize)
__shift = (__shift / 2).astype(np.int32)
__rsimg = torch.nn.functional.interpolate(torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0), size=(__rsize[0], __rsize[1]), mode='bilinear', align_corners=True)
__rsimg = __rsimg.squeeze().permute(1, 2, 0)
__ltbox = (np.ones((frame_size, frame_size, img.shape[2])) * fill)
__ltbox[(__shift[0]:(__shift[0] + __rsize[0]), __shift[1]:(__shift[1] + __rsize[1]), :)] = __rsimg
return (__ltbox.astype(np.float32), np.flip(__shift, axis=0).copy(), __ratio) | Letter box an input image.
Image will be centered into a squared frame,
where the longer side of the image is resized
to the frame size and the shorter side is resized
by keepng the same aspect ratio.
Parameters
----------
img: np.array
The input image. Assumed to be rank-3, channel-last.
frame_size: int
Size of the square frame.
fill: float
Value used to fill empty border. Defaults to 0.5.
normalize: bool
Whether input image should be divided by 255.
Returns
----------
tuple
1. The letter boxed image.
2. Horizontal and vertical shift in number of pixels
with respect to square frame size.
3. The resize ratio: box size/ original longer side. | lib/utils/utils.py | letterbox_image | jonathanzjl/cam-vision | 0 | python | def letterbox_image(img, frame_size, fill=0.5, normalize=True):
'\n\n Letter box an input image.\n\n Image will be centered into a squared frame,\n where the longer side of the image is resized\n to the frame size and the shorter side is resized\n by keepng the same aspect ratio.\n\n Parameters\n ----------\n img: np.array\n The input image. Assumed to be rank-3, channel-last.\n\n frame_size: int\n Size of the square frame.\n\n fill: float\n Value used to fill empty border. Defaults to 0.5.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n ----------\n tuple\n 1. The letter boxed image.\n 2. Horizontal and vertical shift in number of pixels\n with respect to square frame size.\n 3. The resize ratio: box size/ original longer side.\n\n '
if normalize:
img = (img.astype(np.float32) / 255.0)
__lindx = np.argmax(img.shape[:2])
__ratio = (frame_size / img.shape[:2][__lindx])
__rsize = (np.array(img.shape[:2]) * __ratio).astype(np.int32)
__shift = (np.array(([frame_size] * 2), dtype=np.int32) - __rsize)
__shift = (__shift / 2).astype(np.int32)
__rsimg = torch.nn.functional.interpolate(torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0), size=(__rsize[0], __rsize[1]), mode='bilinear', align_corners=True)
__rsimg = __rsimg.squeeze().permute(1, 2, 0)
__ltbox = (np.ones((frame_size, frame_size, img.shape[2])) * fill)
__ltbox[(__shift[0]:(__shift[0] + __rsize[0]), __shift[1]:(__shift[1] + __rsize[1]), :)] = __rsimg
return (__ltbox.astype(np.float32), np.flip(__shift, axis=0).copy(), __ratio) | def letterbox_image(img, frame_size, fill=0.5, normalize=True):
'\n\n Letter box an input image.\n\n Image will be centered into a squared frame,\n where the longer side of the image is resized\n to the frame size and the shorter side is resized\n by keepng the same aspect ratio.\n\n Parameters\n ----------\n img: np.array\n The input image. Assumed to be rank-3, channel-last.\n\n frame_size: int\n Size of the square frame.\n\n fill: float\n Value used to fill empty border. Defaults to 0.5.\n\n normalize: bool\n Whether input image should be divided by 255.\n\n Returns\n ----------\n tuple\n 1. The letter boxed image.\n 2. Horizontal and vertical shift in number of pixels\n with respect to square frame size.\n 3. The resize ratio: box size/ original longer side.\n\n '
if normalize:
img = (img.astype(np.float32) / 255.0)
__lindx = np.argmax(img.shape[:2])
__ratio = (frame_size / img.shape[:2][__lindx])
__rsize = (np.array(img.shape[:2]) * __ratio).astype(np.int32)
__shift = (np.array(([frame_size] * 2), dtype=np.int32) - __rsize)
__shift = (__shift / 2).astype(np.int32)
__rsimg = torch.nn.functional.interpolate(torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0), size=(__rsize[0], __rsize[1]), mode='bilinear', align_corners=True)
__rsimg = __rsimg.squeeze().permute(1, 2, 0)
__ltbox = (np.ones((frame_size, frame_size, img.shape[2])) * fill)
__ltbox[(__shift[0]:(__shift[0] + __rsize[0]), __shift[1]:(__shift[1] + __rsize[1]), :)] = __rsimg
return (__ltbox.astype(np.float32), np.flip(__shift, axis=0).copy(), __ratio)<|docstring|>Letter box an input image.
Image will be centered into a squared frame,
where the longer side of the image is resized
to the frame size and the shorter side is resized
by keepng the same aspect ratio.
Parameters
----------
img: np.array
The input image. Assumed to be rank-3, channel-last.
frame_size: int
Size of the square frame.
fill: float
Value used to fill empty border. Defaults to 0.5.
normalize: bool
Whether input image should be divided by 255.
Returns
----------
tuple
1. The letter boxed image.
2. Horizontal and vertical shift in number of pixels
with respect to square frame size.
3. The resize ratio: box size/ original longer side.<|endoftext|> |
f21f16227c86cfe4135f94a19a1444caa454cadcac7b0f9cc19ee8477098175a | def correct_bboxes(dets, shift, ratio):
'\n\n Correct bounding box centers and scales\n to match original input image before\n letter boxing.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n shift: np.array\n Horizontal and vertical shift in number of pixels\n with respect to square frame size.\n\n ratio\n The resize ratio: box size/ original longer side.\n\n '
dets[(:2, :)] -= torch.from_numpy(shift).float().view(2, 1)
dets[(:4, :)] /= ratio | Correct bounding box centers and scales
to match original input image before
letter boxing.
Parameters
----------
dets: torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. max proba = max class proba * objectness score.
6. class index of the corresponding max proba.
shift: np.array
Horizontal and vertical shift in number of pixels
with respect to square frame size.
ratio
The resize ratio: box size/ original longer side. | lib/utils/utils.py | correct_bboxes | jonathanzjl/cam-vision | 0 | python | def correct_bboxes(dets, shift, ratio):
'\n\n Correct bounding box centers and scales\n to match original input image before\n letter boxing.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n shift: np.array\n Horizontal and vertical shift in number of pixels\n with respect to square frame size.\n\n ratio\n The resize ratio: box size/ original longer side.\n\n '
dets[(:2, :)] -= torch.from_numpy(shift).float().view(2, 1)
dets[(:4, :)] /= ratio | def correct_bboxes(dets, shift, ratio):
'\n\n Correct bounding box centers and scales\n to match original input image before\n letter boxing.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n shift: np.array\n Horizontal and vertical shift in number of pixels\n with respect to square frame size.\n\n ratio\n The resize ratio: box size/ original longer side.\n\n '
dets[(:2, :)] -= torch.from_numpy(shift).float().view(2, 1)
dets[(:4, :)] /= ratio<|docstring|>Correct bounding box centers and scales
to match original input image before
letter boxing.
Parameters
----------
dets: torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. max proba = max class proba * objectness score.
6. class index of the corresponding max proba.
shift: np.array
Horizontal and vertical shift in number of pixels
with respect to square frame size.
ratio
The resize ratio: box size/ original longer side.<|endoftext|> |
a1edcf48f0951e192bbb833f8169f0b9d12c562e5c90b0992a09b5f42e5b188e | def nms(dets, nms_thresh):
'\n\n Do non-maximum suppression.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n nms_thresh: float\n NMS threshold.\n\n Returns\n ----------\n torch.tensor\n New bounding box attributed with boxes having\n high IOU with the top prediction sppressed.\n\n '
__book = {}
(__, __ord) = torch.sort(dets[(4, :)], descending=True)
(__dets, __cls) = (dets[(:, __ord)], set(dets[((- 1), :)]))
for (__i, __c) in enumerate(__dets[((- 1), :)]):
if (int(__c) not in __book):
__book[int(__c)] = __i
else:
__iou = compute_iou(__dets[(:4, __i)], __dets[(:4, __book[int(__c)])])
if (__iou > nms_thresh):
__dets[(4, __i)] = (- 1)
return __dets[(:, (__dets[(4, :)] >= 0))] | Do non-maximum suppression.
Parameters
----------
dets: torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. max proba = max class proba * objectness score.
6. class index of the corresponding max proba.
nms_thresh: float
NMS threshold.
Returns
----------
torch.tensor
New bounding box attributed with boxes having
high IOU with the top prediction sppressed. | lib/utils/utils.py | nms | jonathanzjl/cam-vision | 0 | python | def nms(dets, nms_thresh):
'\n\n Do non-maximum suppression.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n nms_thresh: float\n NMS threshold.\n\n Returns\n ----------\n torch.tensor\n New bounding box attributed with boxes having\n high IOU with the top prediction sppressed.\n\n '
__book = {}
(__, __ord) = torch.sort(dets[(4, :)], descending=True)
(__dets, __cls) = (dets[(:, __ord)], set(dets[((- 1), :)]))
for (__i, __c) in enumerate(__dets[((- 1), :)]):
if (int(__c) not in __book):
__book[int(__c)] = __i
else:
__iou = compute_iou(__dets[(:4, __i)], __dets[(:4, __book[int(__c)])])
if (__iou > nms_thresh):
__dets[(4, __i)] = (- 1)
return __dets[(:, (__dets[(4, :)] >= 0))] | def nms(dets, nms_thresh):
'\n\n Do non-maximum suppression.\n\n Parameters\n ----------\n dets: torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n nms_thresh: float\n NMS threshold.\n\n Returns\n ----------\n torch.tensor\n New bounding box attributed with boxes having\n high IOU with the top prediction sppressed.\n\n '
__book = {}
(__, __ord) = torch.sort(dets[(4, :)], descending=True)
(__dets, __cls) = (dets[(:, __ord)], set(dets[((- 1), :)]))
for (__i, __c) in enumerate(__dets[((- 1), :)]):
if (int(__c) not in __book):
__book[int(__c)] = __i
else:
__iou = compute_iou(__dets[(:4, __i)], __dets[(:4, __book[int(__c)])])
if (__iou > nms_thresh):
__dets[(4, __i)] = (- 1)
return __dets[(:, (__dets[(4, :)] >= 0))]<|docstring|>Do non-maximum suppression.
Parameters
----------
dets: torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. max proba = max class proba * objectness score.
6. class index of the corresponding max proba.
nms_thresh: float
NMS threshold.
Returns
----------
torch.tensor
New bounding box attributed with boxes having
high IOU with the top prediction sppressed.<|endoftext|> |
0209748ba636f48d0ddb946a50617106be0d99db3e375be6b91dd352b0ee0296 | def compute_iou(lhs, rhs):
'\n\n Compute the intersection over union of two\n bounding boxes.\n\n Parameters\n ----------\n lhs: torch.tensor\n Bounding box 1.\n\n rhs: torch.tensor\n Bounding box 2.\n\n Returns\n ----------\n float\n The intersection over union.\n\n '
__beg = np.array([max(lhs[0], rhs[0]), max(lhs[1], rhs[1])])
__end = np.array([min((lhs[0] + lhs[2]), (rhs[0] + rhs[2])), min((lhs[1] + lhs[3]), (rhs[1] + rhs[3]))])
__num = np.prod((__end - __beg))
if (__num <= 0):
return 0
__den = (((lhs[2] * lhs[3]) + (rhs[2] * rhs[3])) - __num)
return (__num / __den) | Compute the intersection over union of two
bounding boxes.
Parameters
----------
lhs: torch.tensor
Bounding box 1.
rhs: torch.tensor
Bounding box 2.
Returns
----------
float
The intersection over union. | lib/utils/utils.py | compute_iou | jonathanzjl/cam-vision | 0 | python | def compute_iou(lhs, rhs):
'\n\n Compute the intersection over union of two\n bounding boxes.\n\n Parameters\n ----------\n lhs: torch.tensor\n Bounding box 1.\n\n rhs: torch.tensor\n Bounding box 2.\n\n Returns\n ----------\n float\n The intersection over union.\n\n '
__beg = np.array([max(lhs[0], rhs[0]), max(lhs[1], rhs[1])])
__end = np.array([min((lhs[0] + lhs[2]), (rhs[0] + rhs[2])), min((lhs[1] + lhs[3]), (rhs[1] + rhs[3]))])
__num = np.prod((__end - __beg))
if (__num <= 0):
return 0
__den = (((lhs[2] * lhs[3]) + (rhs[2] * rhs[3])) - __num)
return (__num / __den) | def compute_iou(lhs, rhs):
'\n\n Compute the intersection over union of two\n bounding boxes.\n\n Parameters\n ----------\n lhs: torch.tensor\n Bounding box 1.\n\n rhs: torch.tensor\n Bounding box 2.\n\n Returns\n ----------\n float\n The intersection over union.\n\n '
__beg = np.array([max(lhs[0], rhs[0]), max(lhs[1], rhs[1])])
__end = np.array([min((lhs[0] + lhs[2]), (rhs[0] + rhs[2])), min((lhs[1] + lhs[3]), (rhs[1] + rhs[3]))])
__num = np.prod((__end - __beg))
if (__num <= 0):
return 0
__den = (((lhs[2] * lhs[3]) + (rhs[2] * rhs[3])) - __num)
return (__num / __den)<|docstring|>Compute the intersection over union of two
bounding boxes.
Parameters
----------
lhs: torch.tensor
Bounding box 1.
rhs: torch.tensor
Bounding box 2.
Returns
----------
float
The intersection over union.<|endoftext|> |
b1f48b117ee01bbe0a48569ab83d99e7c203e000292cd8edb35299612acf7caa | def detect_frame(model, frame, obj_thresh=0.5, nms_thresh=None, box_correction=None):
'\n\n Detect objects in a frame.\n\n Parameters\n ----------\n model: YOLO\n The YOLO detector model.\n\n frame: torch.tensor\n The input frame as a torch rank-4 tensor.\n\n obj_thresh: float\n Threshold on objectiveness and class\n probabilities.\n\n nms_thresh: float\n Threshold on IOU used during nms.\n\n box_correction: tuple or None\n A tuple of (shift, ratio) parameters used to\n perform letter box correction to bring the\n centers and scale of the bounding boxes to\n match the original image before letter boxing.\n This need to be set if bounding boxes are plotted\n into the original image before letter boxing.\n Defaults to None, indicating no correction.\n\n Returns\n ----------\n torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n '
__detections = model(frame)
__boxes = []
for __d in __detections:
__p = __d.permute(0, 2, 1, 3).contiguous().view(__d.shape[2], (- 1))
(__mprb, __midx) = torch.max(__p[(5:, :)], dim=0)
__p[(4, :)] *= __mprb
__b = torch.cat([__p[(:5, :)], __midx.type(torch.FloatTensor).unsqueeze(0)], 0)
__b = __b[(:, (__b[(4, :)] > obj_thresh))]
if __b.numel():
__b[(:2, :)] -= (__b[(2:4, :)] / 2.0)
__boxes.append(__b)
if (len(__boxes) == 0):
return None
__dets = torch.cat(__boxes, dim=1)
if nms_thresh:
__dets = nms(__dets, nms_thresh)
if box_correction:
correct_bboxes(__dets, *box_correction)
return __dets | Detect objects in a frame.
Parameters
----------
model: YOLO
The YOLO detector model.
frame: torch.tensor
The input frame as a torch rank-4 tensor.
obj_thresh: float
Threshold on objectiveness and class
probabilities.
nms_thresh: float
Threshold on IOU used during nms.
box_correction: tuple or None
A tuple of (shift, ratio) parameters used to
perform letter box correction to bring the
centers and scale of the bounding boxes to
match the original image before letter boxing.
This need to be set if bounding boxes are plotted
into the original image before letter boxing.
Defaults to None, indicating no correction.
Returns
----------
torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. max proba = max class proba * objectness score.
6. class index of the corresponding max proba. | lib/utils/utils.py | detect_frame | jonathanzjl/cam-vision | 0 | python | def detect_frame(model, frame, obj_thresh=0.5, nms_thresh=None, box_correction=None):
'\n\n Detect objects in a frame.\n\n Parameters\n ----------\n model: YOLO\n The YOLO detector model.\n\n frame: torch.tensor\n The input frame as a torch rank-4 tensor.\n\n obj_thresh: float\n Threshold on objectiveness and class\n probabilities.\n\n nms_thresh: float\n Threshold on IOU used during nms.\n\n box_correction: tuple or None\n A tuple of (shift, ratio) parameters used to\n perform letter box correction to bring the\n centers and scale of the bounding boxes to\n match the original image before letter boxing.\n This need to be set if bounding boxes are plotted\n into the original image before letter boxing.\n Defaults to None, indicating no correction.\n\n Returns\n ----------\n torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n '
__detections = model(frame)
__boxes = []
for __d in __detections:
__p = __d.permute(0, 2, 1, 3).contiguous().view(__d.shape[2], (- 1))
(__mprb, __midx) = torch.max(__p[(5:, :)], dim=0)
__p[(4, :)] *= __mprb
__b = torch.cat([__p[(:5, :)], __midx.type(torch.FloatTensor).unsqueeze(0)], 0)
__b = __b[(:, (__b[(4, :)] > obj_thresh))]
if __b.numel():
__b[(:2, :)] -= (__b[(2:4, :)] / 2.0)
__boxes.append(__b)
if (len(__boxes) == 0):
return None
__dets = torch.cat(__boxes, dim=1)
if nms_thresh:
__dets = nms(__dets, nms_thresh)
if box_correction:
correct_bboxes(__dets, *box_correction)
return __dets | def detect_frame(model, frame, obj_thresh=0.5, nms_thresh=None, box_correction=None):
'\n\n Detect objects in a frame.\n\n Parameters\n ----------\n model: YOLO\n The YOLO detector model.\n\n frame: torch.tensor\n The input frame as a torch rank-4 tensor.\n\n obj_thresh: float\n Threshold on objectiveness and class\n probabilities.\n\n nms_thresh: float\n Threshold on IOU used during nms.\n\n box_correction: tuple or None\n A tuple of (shift, ratio) parameters used to\n perform letter box correction to bring the\n centers and scale of the bounding boxes to\n match the original image before letter boxing.\n This need to be set if bounding boxes are plotted\n into the original image before letter boxing.\n Defaults to None, indicating no correction.\n\n Returns\n ----------\n torch.tensor\n A rank-2 tensor, where each col is a size-6\n vector representing a detection bounding box.\n The meaning of each element in the vector is\n as follows:\n 1. bbox begin point x coordinate.\n 2. bbox begin point y coordinate.\n 3. bbox width.\n 4. bbox height.\n 5. max proba = max class proba * objectness score.\n 6. class index of the corresponding max proba.\n\n '
__detections = model(frame)
__boxes = []
for __d in __detections:
__p = __d.permute(0, 2, 1, 3).contiguous().view(__d.shape[2], (- 1))
(__mprb, __midx) = torch.max(__p[(5:, :)], dim=0)
__p[(4, :)] *= __mprb
__b = torch.cat([__p[(:5, :)], __midx.type(torch.FloatTensor).unsqueeze(0)], 0)
__b = __b[(:, (__b[(4, :)] > obj_thresh))]
if __b.numel():
__b[(:2, :)] -= (__b[(2:4, :)] / 2.0)
__boxes.append(__b)
if (len(__boxes) == 0):
return None
__dets = torch.cat(__boxes, dim=1)
if nms_thresh:
__dets = nms(__dets, nms_thresh)
if box_correction:
correct_bboxes(__dets, *box_correction)
return __dets<|docstring|>Detect objects in a frame.
Parameters
----------
model: YOLO
The YOLO detector model.
frame: torch.tensor
The input frame as a torch rank-4 tensor.
obj_thresh: float
Threshold on objectiveness and class
probabilities.
nms_thresh: float
Threshold on IOU used during nms.
box_correction: tuple or None
A tuple of (shift, ratio) parameters used to
perform letter box correction to bring the
centers and scale of the bounding boxes to
match the original image before letter boxing.
This need to be set if bounding boxes are plotted
into the original image before letter boxing.
Defaults to None, indicating no correction.
Returns
----------
torch.tensor
A rank-2 tensor, where each col is a size-6
vector representing a detection bounding box.
The meaning of each element in the vector is
as follows:
1. bbox begin point x coordinate.
2. bbox begin point y coordinate.
3. bbox width.
4. bbox height.
5. max proba = max class proba * objectness score.
6. class index of the corresponding max proba.<|endoftext|> |
a3e59ea570463414ef9d26de334c41b1393057cbc4ae9cb2d5c363677e776e26 | @nb.njit('uint64(uint8, uint8, uint8)')
def separate_n_nb(packed, n, chunk_bits):
'\n A relatively inefficient generalization of the "separate bits"\n step of Morton encoding. Assuming that each of the `n` coordinates\n has `chunk_bits` bits, we can "space out" each bit of each coordinate\n `n` spaces at a time.\n\n >>> for i in range(8):\n ... print(i,\n ... format(separate_n_nb(i, 3, 3), \'#012b\'),\n ... format(separate_n_nb(i, 3, 3) << 1, \'#012b\'),\n ... format(separate_n_nb(i, 3, 3) << 2, \'#012b\'))\n 0 0b0000000000 0b0000000000 0b0000000000\n 1 0b0000000001 0b0000000010 0b0000000100\n 2 0b0000001000 0b0000010000 0b0000100000\n 3 0b0000001001 0b0000010010 0b0000100100\n 4 0b0001000000 0b0010000000 0b0100000000\n 5 0b0001000001 0b0010000010 0b0100000100\n 6 0b0001001000 0b0010010000 0b0100100000\n 7 0b0001001001 0b0010010010 0b0100100100\n\n :param packed: packed tensor\n :param n: number of components that we will eventually want to Morton code\n :param chunk_bits: the number of bits that represent each coordinate\n :return: spaced-out bit representation, ready to be interleaved\n '
a = nb.uint64(packed)
a = (a & nb.uint64(255))
x = 0
for i in range(chunk_bits):
bit_to_set = (nb.uint64(1) << nb.uint64((i * n)))
x |= ((a << nb.uint64(((n - 1) * i))) & bit_to_set)
return x | A relatively inefficient generalization of the "separate bits"
step of Morton encoding. Assuming that each of the `n` coordinates
has `chunk_bits` bits, we can "space out" each bit of each coordinate
`n` spaces at a time.
>>> for i in range(8):
... print(i,
... format(separate_n_nb(i, 3, 3), '#012b'),
... format(separate_n_nb(i, 3, 3) << 1, '#012b'),
... format(separate_n_nb(i, 3, 3) << 2, '#012b'))
0 0b0000000000 0b0000000000 0b0000000000
1 0b0000000001 0b0000000010 0b0000000100
2 0b0000001000 0b0000010000 0b0000100000
3 0b0000001001 0b0000010010 0b0000100100
4 0b0001000000 0b0010000000 0b0100000000
5 0b0001000001 0b0010000010 0b0100000100
6 0b0001001000 0b0010010000 0b0100100000
7 0b0001001001 0b0010010010 0b0100100100
:param packed: packed tensor
:param n: number of components that we will eventually want to Morton code
:param chunk_bits: the number of bits that represent each coordinate
:return: spaced-out bit representation, ready to be interleaved | morton.py | separate_n_nb | AnimatedRNG/lsc | 0 | python | @nb.njit('uint64(uint8, uint8, uint8)')
def separate_n_nb(packed, n, chunk_bits):
'\n A relatively inefficient generalization of the "separate bits"\n step of Morton encoding. Assuming that each of the `n` coordinates\n has `chunk_bits` bits, we can "space out" each bit of each coordinate\n `n` spaces at a time.\n\n >>> for i in range(8):\n ... print(i,\n ... format(separate_n_nb(i, 3, 3), \'#012b\'),\n ... format(separate_n_nb(i, 3, 3) << 1, \'#012b\'),\n ... format(separate_n_nb(i, 3, 3) << 2, \'#012b\'))\n 0 0b0000000000 0b0000000000 0b0000000000\n 1 0b0000000001 0b0000000010 0b0000000100\n 2 0b0000001000 0b0000010000 0b0000100000\n 3 0b0000001001 0b0000010010 0b0000100100\n 4 0b0001000000 0b0010000000 0b0100000000\n 5 0b0001000001 0b0010000010 0b0100000100\n 6 0b0001001000 0b0010010000 0b0100100000\n 7 0b0001001001 0b0010010010 0b0100100100\n\n :param packed: packed tensor\n :param n: number of components that we will eventually want to Morton code\n :param chunk_bits: the number of bits that represent each coordinate\n :return: spaced-out bit representation, ready to be interleaved\n '
a = nb.uint64(packed)
a = (a & nb.uint64(255))
x = 0
for i in range(chunk_bits):
bit_to_set = (nb.uint64(1) << nb.uint64((i * n)))
x |= ((a << nb.uint64(((n - 1) * i))) & bit_to_set)
return x | @nb.njit('uint64(uint8, uint8, uint8)')
def separate_n_nb(packed, n, chunk_bits):
'\n A relatively inefficient generalization of the "separate bits"\n step of Morton encoding. Assuming that each of the `n` coordinates\n has `chunk_bits` bits, we can "space out" each bit of each coordinate\n `n` spaces at a time.\n\n >>> for i in range(8):\n ... print(i,\n ... format(separate_n_nb(i, 3, 3), \'#012b\'),\n ... format(separate_n_nb(i, 3, 3) << 1, \'#012b\'),\n ... format(separate_n_nb(i, 3, 3) << 2, \'#012b\'))\n 0 0b0000000000 0b0000000000 0b0000000000\n 1 0b0000000001 0b0000000010 0b0000000100\n 2 0b0000001000 0b0000010000 0b0000100000\n 3 0b0000001001 0b0000010010 0b0000100100\n 4 0b0001000000 0b0010000000 0b0100000000\n 5 0b0001000001 0b0010000010 0b0100000100\n 6 0b0001001000 0b0010010000 0b0100100000\n 7 0b0001001001 0b0010010010 0b0100100100\n\n :param packed: packed tensor\n :param n: number of components that we will eventually want to Morton code\n :param chunk_bits: the number of bits that represent each coordinate\n :return: spaced-out bit representation, ready to be interleaved\n '
a = nb.uint64(packed)
a = (a & nb.uint64(255))
x = 0
for i in range(chunk_bits):
bit_to_set = (nb.uint64(1) << nb.uint64((i * n)))
x |= ((a << nb.uint64(((n - 1) * i))) & bit_to_set)
return x<|docstring|>A relatively inefficient generalization of the "separate bits"
step of Morton encoding. Assuming that each of the `n` coordinates
has `chunk_bits` bits, we can "space out" each bit of each coordinate
`n` spaces at a time.
>>> for i in range(8):
... print(i,
... format(separate_n_nb(i, 3, 3), '#012b'),
... format(separate_n_nb(i, 3, 3) << 1, '#012b'),
... format(separate_n_nb(i, 3, 3) << 2, '#012b'))
0 0b0000000000 0b0000000000 0b0000000000
1 0b0000000001 0b0000000010 0b0000000100
2 0b0000001000 0b0000010000 0b0000100000
3 0b0000001001 0b0000010010 0b0000100100
4 0b0001000000 0b0010000000 0b0100000000
5 0b0001000001 0b0010000010 0b0100000100
6 0b0001001000 0b0010010000 0b0100100000
7 0b0001001001 0b0010010010 0b0100100100
:param packed: packed tensor
:param n: number of components that we will eventually want to Morton code
:param chunk_bits: the number of bits that represent each coordinate
:return: spaced-out bit representation, ready to be interleaved<|endoftext|> |
3434840cac354ebbc3ee887e2e90976fb692dea23839f49078cd42ad38207d9a | @nb.njit('uint64(uint8[:], uint8)')
def encode_single_coord(coord, chunk_bits):
'\n Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that\n the size of each dimension is 0..2^{chunk_bits}\n\n >>> morton_offsets = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... morton_offsets.add(encode_single_coord(\n ... np.array([i, j], dtype=np.uint8),\n ... 4))\n >>> morton_offsets == {i for i in range(256)}\n True\n\n Here we demonstrate that there is mapping from coordinates in a 16x16 square\n to the numbers 0..255\n\n :param coord: coordinate to encode, numba array of type uint8, size <= 8\n :param chunk_bits: coordinate dimensions\n :return: Morton-coded offset of type uint64\n '
assert (coord.shape[0] <= 8)
x = nb.uint64(0)
for i in range(coord.shape[0]):
x += (separate_n_nb(coord[i], coord.shape[0], chunk_bits) << i)
return x | Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that
the size of each dimension is 0..2^{chunk_bits}
>>> morton_offsets = set()
>>> for i in range(16):
... for j in range(16):
... morton_offsets.add(encode_single_coord(
... np.array([i, j], dtype=np.uint8),
... 4))
>>> morton_offsets == {i for i in range(256)}
True
Here we demonstrate that there is mapping from coordinates in a 16x16 square
to the numbers 0..255
:param coord: coordinate to encode, numba array of type uint8, size <= 8
:param chunk_bits: coordinate dimensions
:return: Morton-coded offset of type uint64 | morton.py | encode_single_coord | AnimatedRNG/lsc | 0 | python | @nb.njit('uint64(uint8[:], uint8)')
def encode_single_coord(coord, chunk_bits):
'\n Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that\n the size of each dimension is 0..2^{chunk_bits}\n\n >>> morton_offsets = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... morton_offsets.add(encode_single_coord(\n ... np.array([i, j], dtype=np.uint8),\n ... 4))\n >>> morton_offsets == {i for i in range(256)}\n True\n\n Here we demonstrate that there is mapping from coordinates in a 16x16 square\n to the numbers 0..255\n\n :param coord: coordinate to encode, numba array of type uint8, size <= 8\n :param chunk_bits: coordinate dimensions\n :return: Morton-coded offset of type uint64\n '
assert (coord.shape[0] <= 8)
x = nb.uint64(0)
for i in range(coord.shape[0]):
x += (separate_n_nb(coord[i], coord.shape[0], chunk_bits) << i)
return x | @nb.njit('uint64(uint8[:], uint8)')
def encode_single_coord(coord, chunk_bits):
'\n Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that\n the size of each dimension is 0..2^{chunk_bits}\n\n >>> morton_offsets = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... morton_offsets.add(encode_single_coord(\n ... np.array([i, j], dtype=np.uint8),\n ... 4))\n >>> morton_offsets == {i for i in range(256)}\n True\n\n Here we demonstrate that there is mapping from coordinates in a 16x16 square\n to the numbers 0..255\n\n :param coord: coordinate to encode, numba array of type uint8, size <= 8\n :param chunk_bits: coordinate dimensions\n :return: Morton-coded offset of type uint64\n '
assert (coord.shape[0] <= 8)
x = nb.uint64(0)
for i in range(coord.shape[0]):
x += (separate_n_nb(coord[i], coord.shape[0], chunk_bits) << i)
return x<|docstring|>Encodes a coordinate in ℝⁿ in ℝ¹ using Morton ordering, assuming that
the size of each dimension is 0..2^{chunk_bits}
>>> morton_offsets = set()
>>> for i in range(16):
... for j in range(16):
... morton_offsets.add(encode_single_coord(
... np.array([i, j], dtype=np.uint8),
... 4))
>>> morton_offsets == {i for i in range(256)}
True
Here we demonstrate that there is mapping from coordinates in a 16x16 square
to the numbers 0..255
:param coord: coordinate to encode, numba array of type uint8, size <= 8
:param chunk_bits: coordinate dimensions
:return: Morton-coded offset of type uint64<|endoftext|> |
67cdc69d9e657bf849bbbeee41f035524ee55804420ba176448cb7ea326cddf9 | @nb.njit('uint8[:](uint64, uint8, uint8)')
def decode_single_coord(offset, n, chunk_bits):
'\n The reverse of the Morton encode function above\n\n >>> verify_decode = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... coord = np.array([i, j], dtype=np.uint8)\n ... encoded = encode_single_coord(coord, 4)\n ... decoded = decode_single_coord(encoded, 2, 4)\n ... verify_decode.add(np.array_equal(coord, decoded))\n >>> all(v for v in verify_decode)\n True\n\n :param offset: morton encoded offset\n :param n: dimensionality of coordinates\n :param chunk_bits: size of the coordinate dimensions\n '
coord = np.zeros(n, dtype=np.uint8)
for i in range(n):
coord[i] = pack_n_nb((offset >> i), n, chunk_bits)
return coord | The reverse of the Morton encode function above
>>> verify_decode = set()
>>> for i in range(16):
... for j in range(16):
... coord = np.array([i, j], dtype=np.uint8)
... encoded = encode_single_coord(coord, 4)
... decoded = decode_single_coord(encoded, 2, 4)
... verify_decode.add(np.array_equal(coord, decoded))
>>> all(v for v in verify_decode)
True
:param offset: morton encoded offset
:param n: dimensionality of coordinates
:param chunk_bits: size of the coordinate dimensions | morton.py | decode_single_coord | AnimatedRNG/lsc | 0 | python | @nb.njit('uint8[:](uint64, uint8, uint8)')
def decode_single_coord(offset, n, chunk_bits):
'\n The reverse of the Morton encode function above\n\n >>> verify_decode = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... coord = np.array([i, j], dtype=np.uint8)\n ... encoded = encode_single_coord(coord, 4)\n ... decoded = decode_single_coord(encoded, 2, 4)\n ... verify_decode.add(np.array_equal(coord, decoded))\n >>> all(v for v in verify_decode)\n True\n\n :param offset: morton encoded offset\n :param n: dimensionality of coordinates\n :param chunk_bits: size of the coordinate dimensions\n '
coord = np.zeros(n, dtype=np.uint8)
for i in range(n):
coord[i] = pack_n_nb((offset >> i), n, chunk_bits)
return coord | @nb.njit('uint8[:](uint64, uint8, uint8)')
def decode_single_coord(offset, n, chunk_bits):
'\n The reverse of the Morton encode function above\n\n >>> verify_decode = set()\n >>> for i in range(16):\n ... for j in range(16):\n ... coord = np.array([i, j], dtype=np.uint8)\n ... encoded = encode_single_coord(coord, 4)\n ... decoded = decode_single_coord(encoded, 2, 4)\n ... verify_decode.add(np.array_equal(coord, decoded))\n >>> all(v for v in verify_decode)\n True\n\n :param offset: morton encoded offset\n :param n: dimensionality of coordinates\n :param chunk_bits: size of the coordinate dimensions\n '
coord = np.zeros(n, dtype=np.uint8)
for i in range(n):
coord[i] = pack_n_nb((offset >> i), n, chunk_bits)
return coord<|docstring|>The reverse of the Morton encode function above
>>> verify_decode = set()
>>> for i in range(16):
... for j in range(16):
... coord = np.array([i, j], dtype=np.uint8)
... encoded = encode_single_coord(coord, 4)
... decoded = decode_single_coord(encoded, 2, 4)
... verify_decode.add(np.array_equal(coord, decoded))
>>> all(v for v in verify_decode)
True
:param offset: morton encoded offset
:param n: dimensionality of coordinates
:param chunk_bits: size of the coordinate dimensions<|endoftext|> |
29960a3fc043eca1d7f7e320721d2385051c7618be9ef089c7d2e2f4d66c1cca | @nb.njit
def morton_encode_nb(coords):
'\n >>> x, y = np.arange(8), np.arange(8)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing=\'ij\')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)\n\n For the sake of clarity, let\'s inspect these values\n\n >>> with np.printoptions(formatter={\'float\': lambda x: "{0:0.3f}".format(x)}):\n ... print(inp)\n [[[0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000]\n [1.000 1.414 2.236 3.162 4.123 5.099 6.083 7.071]\n [2.000 2.236 2.828 3.606 4.472 5.385 6.325 7.280]\n [3.000 3.162 3.606 4.243 5.000 5.831 6.708 7.616]\n [4.000 4.123 4.472 5.000 5.657 6.403 7.211 8.062]\n [5.000 5.099 5.385 5.831 6.403 7.071 7.810 8.602]\n [6.000 6.083 6.325 6.708 7.211 7.810 8.485 9.220]\n [7.000 7.071 7.280 7.616 8.062 8.602 9.220 9.899]]]\n\n We rearrange them according to the Morton encoding, and then reshape\n the resulting array into the same dimensions as the chunks. You can see\n that (for the most part) groups similar frequency ranges close to each\n other (TODO: perhaps this isn\'t the best visualization...)\n\n >>> with np.printoptions(formatter={\'float\': lambda x: "{0:0.3f}".format(x)}):\n ... print(morton_encode_nb(inp)[0].reshape(8, 8))\n [[0.000 1.000 1.000 1.414 2.000 3.000 2.236 3.162]\n [2.000 2.236 3.000 3.162 2.828 3.606 3.606 4.243]\n [4.000 5.000 4.123 5.099 6.000 7.000 6.083 7.071]\n [4.472 5.385 5.000 5.831 6.325 7.280 6.708 7.616]\n [4.000 4.123 5.000 5.099 4.472 5.000 5.385 5.831]\n [6.000 6.083 7.000 7.071 6.325 6.708 7.280 7.616]\n [5.657 6.403 6.403 7.071 7.211 8.062 7.810 8.602]\n [7.211 7.810 8.062 8.602 8.485 9.220 9.220 9.899]]\n\n :param coords: coords is [BS, chunk1, chunk2, chunk3...]. All chunks\n must have the same size!\n :return: rearranged array of size [BS, product of chunk sizes]\n '
assert (len(coords.shape) <= 9)
n = (len(coords.shape) - 1)
bs = coords.shape[0]
chunk_size = nb.uint8(coords.shape[n])
total_chunk_size = nb.int64((chunk_size ** n))
chunk_bits = log2i((chunk_size - 1))
ind = np.zeros(n, dtype=nb.uint8)
output = np.zeros((bs, total_chunk_size), dtype=coords.dtype)
for (index, x) in np.ndenumerate(coords):
for i in range(n):
ind[i] = np.uint8(index[(1 + i)])
morton_offset = encode_single_coord(ind, chunk_bits)
output[(index[0], morton_offset)] = x
return output | >>> x, y = np.arange(8), np.arange(8)
>>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
>>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)
For the sake of clarity, let's inspect these values
>>> with np.printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)}):
... print(inp)
[[[0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000]
[1.000 1.414 2.236 3.162 4.123 5.099 6.083 7.071]
[2.000 2.236 2.828 3.606 4.472 5.385 6.325 7.280]
[3.000 3.162 3.606 4.243 5.000 5.831 6.708 7.616]
[4.000 4.123 4.472 5.000 5.657 6.403 7.211 8.062]
[5.000 5.099 5.385 5.831 6.403 7.071 7.810 8.602]
[6.000 6.083 6.325 6.708 7.211 7.810 8.485 9.220]
[7.000 7.071 7.280 7.616 8.062 8.602 9.220 9.899]]]
We rearrange them according to the Morton encoding, and then reshape
the resulting array into the same dimensions as the chunks. You can see
that (for the most part) groups similar frequency ranges close to each
other (TODO: perhaps this isn't the best visualization...)
>>> with np.printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)}):
... print(morton_encode_nb(inp)[0].reshape(8, 8))
[[0.000 1.000 1.000 1.414 2.000 3.000 2.236 3.162]
[2.000 2.236 3.000 3.162 2.828 3.606 3.606 4.243]
[4.000 5.000 4.123 5.099 6.000 7.000 6.083 7.071]
[4.472 5.385 5.000 5.831 6.325 7.280 6.708 7.616]
[4.000 4.123 5.000 5.099 4.472 5.000 5.385 5.831]
[6.000 6.083 7.000 7.071 6.325 6.708 7.280 7.616]
[5.657 6.403 6.403 7.071 7.211 8.062 7.810 8.602]
[7.211 7.810 8.062 8.602 8.485 9.220 9.220 9.899]]
:param coords: coords is [BS, chunk1, chunk2, chunk3...]. All chunks
must have the same size!
:return: rearranged array of size [BS, product of chunk sizes] | morton.py | morton_encode_nb | AnimatedRNG/lsc | 0 | python | @nb.njit
def morton_encode_nb(coords):
'\n >>> x, y = np.arange(8), np.arange(8)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing=\'ij\')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)\n\n For the sake of clarity, let\'s inspect these values\n\n >>> with np.printoptions(formatter={\'float\': lambda x: "{0:0.3f}".format(x)}):\n ... print(inp)\n [[[0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000]\n [1.000 1.414 2.236 3.162 4.123 5.099 6.083 7.071]\n [2.000 2.236 2.828 3.606 4.472 5.385 6.325 7.280]\n [3.000 3.162 3.606 4.243 5.000 5.831 6.708 7.616]\n [4.000 4.123 4.472 5.000 5.657 6.403 7.211 8.062]\n [5.000 5.099 5.385 5.831 6.403 7.071 7.810 8.602]\n [6.000 6.083 6.325 6.708 7.211 7.810 8.485 9.220]\n [7.000 7.071 7.280 7.616 8.062 8.602 9.220 9.899]]]\n\n We rearrange them according to the Morton encoding, and then reshape\n the resulting array into the same dimensions as the chunks. You can see\n that (for the most part) groups similar frequency ranges close to each\n other (TODO: perhaps this isn\'t the best visualization...)\n\n >>> with np.printoptions(formatter={\'float\': lambda x: "{0:0.3f}".format(x)}):\n ... print(morton_encode_nb(inp)[0].reshape(8, 8))\n [[0.000 1.000 1.000 1.414 2.000 3.000 2.236 3.162]\n [2.000 2.236 3.000 3.162 2.828 3.606 3.606 4.243]\n [4.000 5.000 4.123 5.099 6.000 7.000 6.083 7.071]\n [4.472 5.385 5.000 5.831 6.325 7.280 6.708 7.616]\n [4.000 4.123 5.000 5.099 4.472 5.000 5.385 5.831]\n [6.000 6.083 7.000 7.071 6.325 6.708 7.280 7.616]\n [5.657 6.403 6.403 7.071 7.211 8.062 7.810 8.602]\n [7.211 7.810 8.062 8.602 8.485 9.220 9.220 9.899]]\n\n :param coords: coords is [BS, chunk1, chunk2, chunk3...]. All chunks\n must have the same size!\n :return: rearranged array of size [BS, product of chunk sizes]\n '
assert (len(coords.shape) <= 9)
n = (len(coords.shape) - 1)
bs = coords.shape[0]
chunk_size = nb.uint8(coords.shape[n])
total_chunk_size = nb.int64((chunk_size ** n))
chunk_bits = log2i((chunk_size - 1))
ind = np.zeros(n, dtype=nb.uint8)
output = np.zeros((bs, total_chunk_size), dtype=coords.dtype)
for (index, x) in np.ndenumerate(coords):
for i in range(n):
ind[i] = np.uint8(index[(1 + i)])
morton_offset = encode_single_coord(ind, chunk_bits)
output[(index[0], morton_offset)] = x
return output | @nb.njit
def morton_encode_nb(coords):
'\n >>> x, y = np.arange(8), np.arange(8)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing=\'ij\')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)\n\n For the sake of clarity, let\'s inspect these values\n\n >>> with np.printoptions(formatter={\'float\': lambda x: "{0:0.3f}".format(x)}):\n ... print(inp)\n [[[0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000]\n [1.000 1.414 2.236 3.162 4.123 5.099 6.083 7.071]\n [2.000 2.236 2.828 3.606 4.472 5.385 6.325 7.280]\n [3.000 3.162 3.606 4.243 5.000 5.831 6.708 7.616]\n [4.000 4.123 4.472 5.000 5.657 6.403 7.211 8.062]\n [5.000 5.099 5.385 5.831 6.403 7.071 7.810 8.602]\n [6.000 6.083 6.325 6.708 7.211 7.810 8.485 9.220]\n [7.000 7.071 7.280 7.616 8.062 8.602 9.220 9.899]]]\n\n We rearrange them according to the Morton encoding, and then reshape\n the resulting array into the same dimensions as the chunks. You can see\n that (for the most part) groups similar frequency ranges close to each\n other (TODO: perhaps this isn\'t the best visualization...)\n\n >>> with np.printoptions(formatter={\'float\': lambda x: "{0:0.3f}".format(x)}):\n ... print(morton_encode_nb(inp)[0].reshape(8, 8))\n [[0.000 1.000 1.000 1.414 2.000 3.000 2.236 3.162]\n [2.000 2.236 3.000 3.162 2.828 3.606 3.606 4.243]\n [4.000 5.000 4.123 5.099 6.000 7.000 6.083 7.071]\n [4.472 5.385 5.000 5.831 6.325 7.280 6.708 7.616]\n [4.000 4.123 5.000 5.099 4.472 5.000 5.385 5.831]\n [6.000 6.083 7.000 7.071 6.325 6.708 7.280 7.616]\n [5.657 6.403 6.403 7.071 7.211 8.062 7.810 8.602]\n [7.211 7.810 8.062 8.602 8.485 9.220 9.220 9.899]]\n\n :param coords: coords is [BS, chunk1, chunk2, chunk3...]. All chunks\n must have the same size!\n :return: rearranged array of size [BS, product of chunk sizes]\n '
assert (len(coords.shape) <= 9)
n = (len(coords.shape) - 1)
bs = coords.shape[0]
chunk_size = nb.uint8(coords.shape[n])
total_chunk_size = nb.int64((chunk_size ** n))
chunk_bits = log2i((chunk_size - 1))
ind = np.zeros(n, dtype=nb.uint8)
output = np.zeros((bs, total_chunk_size), dtype=coords.dtype)
for (index, x) in np.ndenumerate(coords):
for i in range(n):
ind[i] = np.uint8(index[(1 + i)])
morton_offset = encode_single_coord(ind, chunk_bits)
output[(index[0], morton_offset)] = x
return output<|docstring|>>>> x, y = np.arange(8), np.arange(8)
>>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
>>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 8, 8)
For the sake of clarity, let's inspect these values
>>> with np.printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)}):
... print(inp)
[[[0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000]
[1.000 1.414 2.236 3.162 4.123 5.099 6.083 7.071]
[2.000 2.236 2.828 3.606 4.472 5.385 6.325 7.280]
[3.000 3.162 3.606 4.243 5.000 5.831 6.708 7.616]
[4.000 4.123 4.472 5.000 5.657 6.403 7.211 8.062]
[5.000 5.099 5.385 5.831 6.403 7.071 7.810 8.602]
[6.000 6.083 6.325 6.708 7.211 7.810 8.485 9.220]
[7.000 7.071 7.280 7.616 8.062 8.602 9.220 9.899]]]
We rearrange them according to the Morton encoding, and then reshape
the resulting array into the same dimensions as the chunks. You can see
that (for the most part) groups similar frequency ranges close to each
other (TODO: perhaps this isn't the best visualization...)
>>> with np.printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)}):
... print(morton_encode_nb(inp)[0].reshape(8, 8))
[[0.000 1.000 1.000 1.414 2.000 3.000 2.236 3.162]
[2.000 2.236 3.000 3.162 2.828 3.606 3.606 4.243]
[4.000 5.000 4.123 5.099 6.000 7.000 6.083 7.071]
[4.472 5.385 5.000 5.831 6.325 7.280 6.708 7.616]
[4.000 4.123 5.000 5.099 4.472 5.000 5.385 5.831]
[6.000 6.083 7.000 7.071 6.325 6.708 7.280 7.616]
[5.657 6.403 6.403 7.071 7.211 8.062 7.810 8.602]
[7.211 7.810 8.062 8.602 8.485 9.220 9.220 9.899]]
:param coords: coords is [BS, chunk1, chunk2, chunk3...]. All chunks
must have the same size!
:return: rearranged array of size [BS, product of chunk sizes]<|endoftext|> |
d0391f801e0deb333572ae383dc20fb46156af9baae3779f00a720700f840945 | @nb.njit
def morton_decode_nb(offsets, output):
"\n >>> x, y = np.arange(64), np.arange(64)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)\n >>> recon = np.zeros_like(inp)\n\n >>> morton_decode_nb(morton_encode_nb(inp), recon)\n >>> (inp - recon).max() < 1e-5\n True\n\n This function is basically the inverse of `morton_encode_nb`, converting\n from 1D offsets to ND coordinates. It stores the results in `output\n\n :param offsets: [BS, product of chunk sizes] encoded offsets\n :param output: output is [BS, chunk1, chunk2, chunk3...]. All chunks must\n have the same size!\n "
bs = offsets.shape[0]
assert (bs == output.shape[0])
n = (len(output.shape) - 1)
assert (n <= 8)
chunk_size = output.shape[1]
ind = np.zeros(n, dtype=nb.uint8)
chunk_bits = log2i((chunk_size - 1))
for (index, _) in np.ndenumerate(output):
for i in range(n):
ind[i] = np.uint8(index[(1 + i)])
morton_offset = encode_single_coord(ind, chunk_bits)
output[index] = offsets[(index[0], morton_offset)] | >>> x, y = np.arange(64), np.arange(64)
>>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
>>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)
>>> recon = np.zeros_like(inp)
>>> morton_decode_nb(morton_encode_nb(inp), recon)
>>> (inp - recon).max() < 1e-5
True
This function is basically the inverse of `morton_encode_nb`, converting
from 1D offsets to ND coordinates. It stores the results in `output
:param offsets: [BS, product of chunk sizes] encoded offsets
:param output: output is [BS, chunk1, chunk2, chunk3...]. All chunks must
have the same size! | morton.py | morton_decode_nb | AnimatedRNG/lsc | 0 | python | @nb.njit
def morton_decode_nb(offsets, output):
"\n >>> x, y = np.arange(64), np.arange(64)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)\n >>> recon = np.zeros_like(inp)\n\n >>> morton_decode_nb(morton_encode_nb(inp), recon)\n >>> (inp - recon).max() < 1e-5\n True\n\n This function is basically the inverse of `morton_encode_nb`, converting\n from 1D offsets to ND coordinates. It stores the results in `output\n\n :param offsets: [BS, product of chunk sizes] encoded offsets\n :param output: output is [BS, chunk1, chunk2, chunk3...]. All chunks must\n have the same size!\n "
bs = offsets.shape[0]
assert (bs == output.shape[0])
n = (len(output.shape) - 1)
assert (n <= 8)
chunk_size = output.shape[1]
ind = np.zeros(n, dtype=nb.uint8)
chunk_bits = log2i((chunk_size - 1))
for (index, _) in np.ndenumerate(output):
for i in range(n):
ind[i] = np.uint8(index[(1 + i)])
morton_offset = encode_single_coord(ind, chunk_bits)
output[index] = offsets[(index[0], morton_offset)] | @nb.njit
def morton_decode_nb(offsets, output):
"\n >>> x, y = np.arange(64), np.arange(64)\n >>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')\n >>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)\n >>> recon = np.zeros_like(inp)\n\n >>> morton_decode_nb(morton_encode_nb(inp), recon)\n >>> (inp - recon).max() < 1e-5\n True\n\n This function is basically the inverse of `morton_encode_nb`, converting\n from 1D offsets to ND coordinates. It stores the results in `output\n\n :param offsets: [BS, product of chunk sizes] encoded offsets\n :param output: output is [BS, chunk1, chunk2, chunk3...]. All chunks must\n have the same size!\n "
bs = offsets.shape[0]
assert (bs == output.shape[0])
n = (len(output.shape) - 1)
assert (n <= 8)
chunk_size = output.shape[1]
ind = np.zeros(n, dtype=nb.uint8)
chunk_bits = log2i((chunk_size - 1))
for (index, _) in np.ndenumerate(output):
for i in range(n):
ind[i] = np.uint8(index[(1 + i)])
morton_offset = encode_single_coord(ind, chunk_bits)
output[index] = offsets[(index[0], morton_offset)]<|docstring|>>>> x, y = np.arange(64), np.arange(64)
>>> xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
>>> inp = np.sqrt(xv ** 2 + yv ** 2).reshape(1, 64, 64)
>>> recon = np.zeros_like(inp)
>>> morton_decode_nb(morton_encode_nb(inp), recon)
>>> (inp - recon).max() < 1e-5
True
This function is basically the inverse of `morton_encode_nb`, converting
from 1D offsets to ND coordinates. It stores the results in `output
:param offsets: [BS, product of chunk sizes] encoded offsets
:param output: output is [BS, chunk1, chunk2, chunk3...]. All chunks must
have the same size!<|endoftext|> |
ce75354e5c403f7637dc56d58cfd7cf9cae0aa8f57268ea402b781a5d3308b62 | def detect_windows(self, images_windows):
'\n Do windowed detection over given images and windows. Windows are\n extracted then warped to the input dimensions of the net.\n\n Parameters\n ----------\n images_windows: (image filename, window list) iterable.\n context_crop: size of context border to crop in pixels.\n\n Returns\n -------\n detections: list of {filename: image filename, window: crop coordinates,\n predictions: prediction vector} dicts.\n '
window_inputs = []
for (image_fname, windows) in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
in_ = self.inputs[0]
caffe_in = np.zeros(((len(window_inputs), window_inputs[0].shape[2]) + self.blobs[in_].data.shape[2:]), dtype=np.float32)
for (ix, window_in) in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]]
detections = []
ix = 0
for (image_fname, windows) in images_windows:
for window in windows:
detections.append({'window': window, 'prediction': predictions[ix], 'filename': image_fname})
ix += 1
return detections | Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts. | python/caffe/detector.py | detect_windows | raytroop/caffe | 36,275 | python | def detect_windows(self, images_windows):
'\n Do windowed detection over given images and windows. Windows are\n extracted then warped to the input dimensions of the net.\n\n Parameters\n ----------\n images_windows: (image filename, window list) iterable.\n context_crop: size of context border to crop in pixels.\n\n Returns\n -------\n detections: list of {filename: image filename, window: crop coordinates,\n predictions: prediction vector} dicts.\n '
window_inputs = []
for (image_fname, windows) in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
in_ = self.inputs[0]
caffe_in = np.zeros(((len(window_inputs), window_inputs[0].shape[2]) + self.blobs[in_].data.shape[2:]), dtype=np.float32)
for (ix, window_in) in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]]
detections = []
ix = 0
for (image_fname, windows) in images_windows:
for window in windows:
detections.append({'window': window, 'prediction': predictions[ix], 'filename': image_fname})
ix += 1
return detections | def detect_windows(self, images_windows):
'\n Do windowed detection over given images and windows. Windows are\n extracted then warped to the input dimensions of the net.\n\n Parameters\n ----------\n images_windows: (image filename, window list) iterable.\n context_crop: size of context border to crop in pixels.\n\n Returns\n -------\n detections: list of {filename: image filename, window: crop coordinates,\n predictions: prediction vector} dicts.\n '
window_inputs = []
for (image_fname, windows) in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
in_ = self.inputs[0]
caffe_in = np.zeros(((len(window_inputs), window_inputs[0].shape[2]) + self.blobs[in_].data.shape[2:]), dtype=np.float32)
for (ix, window_in) in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]]
detections = []
ix = 0
for (image_fname, windows) in images_windows:
for window in windows:
detections.append({'window': window, 'prediction': predictions[ix], 'filename': image_fname})
ix += 1
return detections<|docstring|>Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.<|endoftext|> |
041edc0924a6af4db8ba04e76bc5a04d463f69d850a31241f9222ef1a6d5886f | def detect_selective_search(self, image_fnames):
'\n Do windowed detection over Selective Search proposals by extracting\n the crop and warping to the input dimensions of the net.\n\n Parameters\n ----------\n image_fnames: list\n\n Returns\n -------\n detections: list of {filename: image filename, window: crop coordinates,\n predictions: prediction vector} dicts.\n '
import selective_search_ijcv_with_python as selective_search
image_fnames = [os.path.abspath(f) for f in image_fnames]
windows_list = selective_search.get_windows(image_fnames, cmd='selective_search_rcnn')
return self.detect_windows(zip(image_fnames, windows_list)) | Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
Parameters
----------
image_fnames: list
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts. | python/caffe/detector.py | detect_selective_search | raytroop/caffe | 36,275 | python | def detect_selective_search(self, image_fnames):
'\n Do windowed detection over Selective Search proposals by extracting\n the crop and warping to the input dimensions of the net.\n\n Parameters\n ----------\n image_fnames: list\n\n Returns\n -------\n detections: list of {filename: image filename, window: crop coordinates,\n predictions: prediction vector} dicts.\n '
import selective_search_ijcv_with_python as selective_search
image_fnames = [os.path.abspath(f) for f in image_fnames]
windows_list = selective_search.get_windows(image_fnames, cmd='selective_search_rcnn')
return self.detect_windows(zip(image_fnames, windows_list)) | def detect_selective_search(self, image_fnames):
'\n Do windowed detection over Selective Search proposals by extracting\n the crop and warping to the input dimensions of the net.\n\n Parameters\n ----------\n image_fnames: list\n\n Returns\n -------\n detections: list of {filename: image filename, window: crop coordinates,\n predictions: prediction vector} dicts.\n '
import selective_search_ijcv_with_python as selective_search
image_fnames = [os.path.abspath(f) for f in image_fnames]
windows_list = selective_search.get_windows(image_fnames, cmd='selective_search_rcnn')
return self.detect_windows(zip(image_fnames, windows_list))<|docstring|>Do windowed detection over Selective Search proposals by extracting
the crop and warping to the input dimensions of the net.
Parameters
----------
image_fnames: list
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.<|endoftext|> |
5ac097739c4c85253b500f00ca85b36c4f9cd4e9d58eb6368d2803c0f3376405 | def crop(self, im, window):
'\n Crop a window from the image for detection. Include surrounding context\n according to the `context_pad` configuration.\n\n Parameters\n ----------\n im: H x W x K image ndarray to crop.\n window: bounding box coordinates as ymin, xmin, ymax, xmax.\n\n Returns\n -------\n crop: cropped window.\n '
crop = im[(window[0]:window[2], window[1]:window[3])]
if self.context_pad:
box = window.copy()
crop_size = self.blobs[self.inputs[0]].width
scale = (crop_size / ((1.0 * crop_size) - (self.context_pad * 2)))
half_h = (((box[2] - box[0]) + 1) / 2.0)
half_w = (((box[3] - box[1]) + 1) / 2.0)
center = ((box[0] + half_h), (box[1] + half_w))
scaled_dims = (scale * np.array(((- half_h), (- half_w), half_h, half_w)))
box = np.round((np.tile(center, 2) + scaled_dims))
full_h = ((box[2] - box[0]) + 1)
full_w = ((box[3] - box[1]) + 1)
scale_h = (crop_size / full_h)
scale_w = (crop_size / full_w)
pad_y = round((max(0, (- box[0])) * scale_h))
pad_x = round((max(0, (- box[1])) * scale_w))
(im_h, im_w) = im.shape[:2]
box = np.clip(box, 0.0, [im_h, im_w, im_h, im_w])
clip_h = ((box[2] - box[0]) + 1)
clip_w = ((box[3] - box[1]) + 1)
assert ((clip_h > 0) and (clip_w > 0))
crop_h = round((clip_h * scale_h))
crop_w = round((clip_w * scale_w))
if ((pad_y + crop_h) > crop_size):
crop_h = (crop_size - pad_y)
if ((pad_x + crop_w) > crop_size):
crop_w = (crop_size - pad_x)
context_crop = im[(box[0]:box[2], box[1]:box[3])]
context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
crop = (np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean)
crop[(pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w))] = context_crop
return crop | Crop a window from the image for detection. Include surrounding context
according to the `context_pad` configuration.
Parameters
----------
im: H x W x K image ndarray to crop.
window: bounding box coordinates as ymin, xmin, ymax, xmax.
Returns
-------
crop: cropped window. | python/caffe/detector.py | crop | raytroop/caffe | 36,275 | python | def crop(self, im, window):
'\n Crop a window from the image for detection. Include surrounding context\n according to the `context_pad` configuration.\n\n Parameters\n ----------\n im: H x W x K image ndarray to crop.\n window: bounding box coordinates as ymin, xmin, ymax, xmax.\n\n Returns\n -------\n crop: cropped window.\n '
crop = im[(window[0]:window[2], window[1]:window[3])]
if self.context_pad:
box = window.copy()
crop_size = self.blobs[self.inputs[0]].width
scale = (crop_size / ((1.0 * crop_size) - (self.context_pad * 2)))
half_h = (((box[2] - box[0]) + 1) / 2.0)
half_w = (((box[3] - box[1]) + 1) / 2.0)
center = ((box[0] + half_h), (box[1] + half_w))
scaled_dims = (scale * np.array(((- half_h), (- half_w), half_h, half_w)))
box = np.round((np.tile(center, 2) + scaled_dims))
full_h = ((box[2] - box[0]) + 1)
full_w = ((box[3] - box[1]) + 1)
scale_h = (crop_size / full_h)
scale_w = (crop_size / full_w)
pad_y = round((max(0, (- box[0])) * scale_h))
pad_x = round((max(0, (- box[1])) * scale_w))
(im_h, im_w) = im.shape[:2]
box = np.clip(box, 0.0, [im_h, im_w, im_h, im_w])
clip_h = ((box[2] - box[0]) + 1)
clip_w = ((box[3] - box[1]) + 1)
assert ((clip_h > 0) and (clip_w > 0))
crop_h = round((clip_h * scale_h))
crop_w = round((clip_w * scale_w))
if ((pad_y + crop_h) > crop_size):
crop_h = (crop_size - pad_y)
if ((pad_x + crop_w) > crop_size):
crop_w = (crop_size - pad_x)
context_crop = im[(box[0]:box[2], box[1]:box[3])]
context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
crop = (np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean)
crop[(pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w))] = context_crop
return crop | def crop(self, im, window):
'\n Crop a window from the image for detection. Include surrounding context\n according to the `context_pad` configuration.\n\n Parameters\n ----------\n im: H x W x K image ndarray to crop.\n window: bounding box coordinates as ymin, xmin, ymax, xmax.\n\n Returns\n -------\n crop: cropped window.\n '
crop = im[(window[0]:window[2], window[1]:window[3])]
if self.context_pad:
box = window.copy()
crop_size = self.blobs[self.inputs[0]].width
scale = (crop_size / ((1.0 * crop_size) - (self.context_pad * 2)))
half_h = (((box[2] - box[0]) + 1) / 2.0)
half_w = (((box[3] - box[1]) + 1) / 2.0)
center = ((box[0] + half_h), (box[1] + half_w))
scaled_dims = (scale * np.array(((- half_h), (- half_w), half_h, half_w)))
box = np.round((np.tile(center, 2) + scaled_dims))
full_h = ((box[2] - box[0]) + 1)
full_w = ((box[3] - box[1]) + 1)
scale_h = (crop_size / full_h)
scale_w = (crop_size / full_w)
pad_y = round((max(0, (- box[0])) * scale_h))
pad_x = round((max(0, (- box[1])) * scale_w))
(im_h, im_w) = im.shape[:2]
box = np.clip(box, 0.0, [im_h, im_w, im_h, im_w])
clip_h = ((box[2] - box[0]) + 1)
clip_w = ((box[3] - box[1]) + 1)
assert ((clip_h > 0) and (clip_w > 0))
crop_h = round((clip_h * scale_h))
crop_w = round((clip_w * scale_w))
if ((pad_y + crop_h) > crop_size):
crop_h = (crop_size - pad_y)
if ((pad_x + crop_w) > crop_size):
crop_w = (crop_size - pad_x)
context_crop = im[(box[0]:box[2], box[1]:box[3])]
context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
crop = (np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean)
crop[(pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w))] = context_crop
return crop<|docstring|>Crop a window from the image for detection. Include surrounding context
according to the `context_pad` configuration.
Parameters
----------
im: H x W x K image ndarray to crop.
window: bounding box coordinates as ymin, xmin, ymax, xmax.
Returns
-------
crop: cropped window.<|endoftext|> |
437017d115d6e4a198f32630da47d0a536bcd713011102ef050a297aee9ebb18 | def configure_crop(self, context_pad):
'\n Configure crop dimensions and amount of context for cropping.\n If context is included, make the special input mean for context padding.\n\n Parameters\n ----------\n context_pad : amount of context for cropping.\n '
in_ = self.inputs[0]
tpose = self.transformer.transpose[in_]
inv_tpose = [tpose[t] for t in tpose]
self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
self.context_pad = context_pad
if self.context_pad:
in_ = self.inputs[0]
transpose = self.transformer.transpose.get(in_)
channel_order = self.transformer.channel_swap.get(in_)
raw_scale = self.transformer.raw_scale.get(in_)
mean = self.transformer.mean.get(in_)
if (mean is not None):
inv_transpose = [transpose[t] for t in transpose]
crop_mean = mean.copy().transpose(inv_transpose)
if (channel_order is not None):
channel_order_inverse = [channel_order.index(i) for i in range(crop_mean.shape[2])]
crop_mean = crop_mean[(:, :, channel_order_inverse)]
if (raw_scale is not None):
crop_mean /= raw_scale
self.crop_mean = crop_mean
else:
self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32) | Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
Parameters
----------
context_pad : amount of context for cropping. | python/caffe/detector.py | configure_crop | raytroop/caffe | 36,275 | python | def configure_crop(self, context_pad):
'\n Configure crop dimensions and amount of context for cropping.\n If context is included, make the special input mean for context padding.\n\n Parameters\n ----------\n context_pad : amount of context for cropping.\n '
in_ = self.inputs[0]
tpose = self.transformer.transpose[in_]
inv_tpose = [tpose[t] for t in tpose]
self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
self.context_pad = context_pad
if self.context_pad:
in_ = self.inputs[0]
transpose = self.transformer.transpose.get(in_)
channel_order = self.transformer.channel_swap.get(in_)
raw_scale = self.transformer.raw_scale.get(in_)
mean = self.transformer.mean.get(in_)
if (mean is not None):
inv_transpose = [transpose[t] for t in transpose]
crop_mean = mean.copy().transpose(inv_transpose)
if (channel_order is not None):
channel_order_inverse = [channel_order.index(i) for i in range(crop_mean.shape[2])]
crop_mean = crop_mean[(:, :, channel_order_inverse)]
if (raw_scale is not None):
crop_mean /= raw_scale
self.crop_mean = crop_mean
else:
self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32) | def configure_crop(self, context_pad):
'\n Configure crop dimensions and amount of context for cropping.\n If context is included, make the special input mean for context padding.\n\n Parameters\n ----------\n context_pad : amount of context for cropping.\n '
in_ = self.inputs[0]
tpose = self.transformer.transpose[in_]
inv_tpose = [tpose[t] for t in tpose]
self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
self.context_pad = context_pad
if self.context_pad:
in_ = self.inputs[0]
transpose = self.transformer.transpose.get(in_)
channel_order = self.transformer.channel_swap.get(in_)
raw_scale = self.transformer.raw_scale.get(in_)
mean = self.transformer.mean.get(in_)
if (mean is not None):
inv_transpose = [transpose[t] for t in transpose]
crop_mean = mean.copy().transpose(inv_transpose)
if (channel_order is not None):
channel_order_inverse = [channel_order.index(i) for i in range(crop_mean.shape[2])]
crop_mean = crop_mean[(:, :, channel_order_inverse)]
if (raw_scale is not None):
crop_mean /= raw_scale
self.crop_mean = crop_mean
else:
self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)<|docstring|>Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
Parameters
----------
context_pad : amount of context for cropping.<|endoftext|> |
46ec2f9a685dae1253decf4df5e26d050322aa85bd16cbf66ddb99c27c7b6bae | def lon360to180(lon):
'\n\tConverts longitude values in the range [0,360]\n\tto longitude values in the range [-180,+180].\n\t'
lon = np.asanyarray(lon)
return (((lon + 180.0) % 360.0) - 180.0) | Converts longitude values in the range [0,360]
to longitude values in the range [-180,+180]. | calc_deriv/201e-calc_vortbdgt_daily.py | lon360to180 | apaloczy/AntarcticaVorticityBudget | 1 | python | def lon360to180(lon):
'\n\tConverts longitude values in the range [0,360]\n\tto longitude values in the range [-180,+180].\n\t'
lon = np.asanyarray(lon)
return (((lon + 180.0) % 360.0) - 180.0) | def lon360to180(lon):
'\n\tConverts longitude values in the range [0,360]\n\tto longitude values in the range [-180,+180].\n\t'
lon = np.asanyarray(lon)
return (((lon + 180.0) % 360.0) - 180.0)<|docstring|>Converts longitude values in the range [0,360]
to longitude values in the range [-180,+180].<|endoftext|> |
b817905d83e8a82d5d83302fac332f78b077bd5d64f357914ceb2ede15c79a0f | def test_post_now_application_nda_happy_path(self, test_client, db_session, auth_headers):
'Should return a new NoW NDA'
mine = MineFactory()
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
post_data = json.loads(post_resp.data.decode())
assert (post_resp.status_code == 201), post_resp.response
assert (post_data['messageid'] == APPLICATION_NDA_DATA['messageid'])
assert (post_data['application_nda_guid'] is not None)
assert (post_data['mine_guid'] == str(mine.mine_guid)) | Should return a new NoW NDA | services/core-api/tests/now_submissions/resources/test_application_nda_list_resource.py | test_post_now_application_nda_happy_path | bcgov/mds | 25 | python | def test_post_now_application_nda_happy_path(self, test_client, db_session, auth_headers):
mine = MineFactory()
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
post_data = json.loads(post_resp.data.decode())
assert (post_resp.status_code == 201), post_resp.response
assert (post_data['messageid'] == APPLICATION_NDA_DATA['messageid'])
assert (post_data['application_nda_guid'] is not None)
assert (post_data['mine_guid'] == str(mine.mine_guid)) | def test_post_now_application_nda_happy_path(self, test_client, db_session, auth_headers):
mine = MineFactory()
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
post_data = json.loads(post_resp.data.decode())
assert (post_resp.status_code == 201), post_resp.response
assert (post_data['messageid'] == APPLICATION_NDA_DATA['messageid'])
assert (post_data['application_nda_guid'] is not None)
assert (post_data['mine_guid'] == str(mine.mine_guid))<|docstring|>Should return a new NoW NDA<|endoftext|> |
965c4647735b377c138271416df52f8586dbbe80b5a2df9ac302e80ea6a2e1b4 | def test_post_now_application_messageid_in_use(self, test_client, db_session, auth_headers):
'Should return a 400 messageid in use for NDA'
mine = MineFactory()
application = NOWApplicationNDAFactory(mine=mine)
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
APPLICATION_NDA_DATA['messageid'] = application.messageid
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_code == 400), post_resp.response | Should return a 400 messageid in use for NDA | services/core-api/tests/now_submissions/resources/test_application_nda_list_resource.py | test_post_now_application_messageid_in_use | bcgov/mds | 25 | python | def test_post_now_application_messageid_in_use(self, test_client, db_session, auth_headers):
mine = MineFactory()
application = NOWApplicationNDAFactory(mine=mine)
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
APPLICATION_NDA_DATA['messageid'] = application.messageid
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_code == 400), post_resp.response | def test_post_now_application_messageid_in_use(self, test_client, db_session, auth_headers):
mine = MineFactory()
application = NOWApplicationNDAFactory(mine=mine)
APPLICATION_NDA_DATA['minenumber'] = mine.mine_no
APPLICATION_NDA_DATA['messageid'] = application.messageid
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_code == 400), post_resp.response<|docstring|>Should return a 400 messageid in use for NDA<|endoftext|> |
73f8caae406c9505f1728f318bb0c74924ce1e00fbe73e1350d38e9a94626086 | def test_post_now_application_no_mine_found(self, test_client, db_session, auth_headers):
'Should return a 400 mine not found for NDA'
APPLICATION_NDA_DATA['minenumber'] = '1234567'
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_code == 400), post_resp.response | Should return a 400 mine not found for NDA | services/core-api/tests/now_submissions/resources/test_application_nda_list_resource.py | test_post_now_application_no_mine_found | bcgov/mds | 25 | python | def test_post_now_application_no_mine_found(self, test_client, db_session, auth_headers):
APPLICATION_NDA_DATA['minenumber'] = '1234567'
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_code == 400), post_resp.response | def test_post_now_application_no_mine_found(self, test_client, db_session, auth_headers):
APPLICATION_NDA_DATA['minenumber'] = '1234567'
post_resp = test_client.post('/now-submissions/applications-nda', json=APPLICATION_NDA_DATA, headers=auth_headers['nros_vfcbc_auth_header'])
assert (post_resp.status_code == 400), post_resp.response<|docstring|>Should return a 400 mine not found for NDA<|endoftext|> |
17d509abfd8a4c010cb47859d3c55d7e544f55ccdac5a7a1b03841fb3c5602c5 | def minMutation(self, start, end, bank):
'\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n '
if ((len(start) < 1) or (len(end) < 1) or (len(bank) < 1) or (not (end in bank))):
return (- 1)
queue = []
queue.append((start, 0))
bankSet = set(bank)
while queue:
(current_mutation, current_level) = queue.pop(0)
if (current_mutation == end):
return current_level
for index in range(len(current_mutation)):
for char in 'ACGT':
mutation = ((current_mutation[:index] + char) + current_mutation[(index + 1):])
if (mutation in bankSet):
bankSet.remove(mutation)
queue.append((mutation, (current_level + 1)))
return (- 1) | :type start: str
:type end: str
:type bank: List[str]
:rtype: int | solutions/0433_MinimumGeneticMutation.py | minMutation | alexwawl/leetcode-solutions-javascript-python | 11 | python | def minMutation(self, start, end, bank):
'\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n '
if ((len(start) < 1) or (len(end) < 1) or (len(bank) < 1) or (not (end in bank))):
return (- 1)
queue = []
queue.append((start, 0))
bankSet = set(bank)
while queue:
(current_mutation, current_level) = queue.pop(0)
if (current_mutation == end):
return current_level
for index in range(len(current_mutation)):
for char in 'ACGT':
mutation = ((current_mutation[:index] + char) + current_mutation[(index + 1):])
if (mutation in bankSet):
bankSet.remove(mutation)
queue.append((mutation, (current_level + 1)))
return (- 1) | def minMutation(self, start, end, bank):
'\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n '
if ((len(start) < 1) or (len(end) < 1) or (len(bank) < 1) or (not (end in bank))):
return (- 1)
queue = []
queue.append((start, 0))
bankSet = set(bank)
while queue:
(current_mutation, current_level) = queue.pop(0)
if (current_mutation == end):
return current_level
for index in range(len(current_mutation)):
for char in 'ACGT':
mutation = ((current_mutation[:index] + char) + current_mutation[(index + 1):])
if (mutation in bankSet):
bankSet.remove(mutation)
queue.append((mutation, (current_level + 1)))
return (- 1)<|docstring|>:type start: str
:type end: str
:type bank: List[str]
:rtype: int<|endoftext|> |
66fbb5470b02a119e48d610656138fa9cc8b8874ab07b56e304947b65471f09c | def __init__(__self__, *, endpoint: str, name: str):
'\n :param str endpoint: Specifies the endpoint of the action.\n :param str name: Specifies the name of the action.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name) | :param str endpoint: Specifies the endpoint of the action.
:param str name: Specifies the name of the action. | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, endpoint: str, name: str):
'\n :param str endpoint: Specifies the endpoint of the action.\n :param str name: Specifies the name of the action.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name) | def __init__(__self__, *, endpoint: str, name: str):
'\n :param str endpoint: Specifies the endpoint of the action.\n :param str name: Specifies the name of the action.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name)<|docstring|>:param str endpoint: Specifies the endpoint of the action.
:param str name: Specifies the name of the action.<|endoftext|> |
5a5a08451321bd197fa93441b4f1c9585a3cb16c8d43c14ad57d79c1cc637afa | @property
@pulumi.getter
def endpoint(self) -> str:
'\n Specifies the endpoint of the action.\n '
return pulumi.get(self, 'endpoint') | Specifies the endpoint of the action. | sdk/python/pulumi_azure/core/outputs.py | endpoint | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint') | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint')<|docstring|>Specifies the endpoint of the action.<|endoftext|> |
b82bd907534ea5da88886ddb936d3d4816d562083a26e0ea6ef048fbcab3588e | @property
@pulumi.getter
def name(self) -> str:
'\n Specifies the name of the action.\n '
return pulumi.get(self, 'name') | Specifies the name of the action. | sdk/python/pulumi_azure/core/outputs.py | name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Specifies the name of the action.<|endoftext|> |
b466b6028bb8715d03acb93b8f0c7947eb5d997b6217517035af4c4399af3ac8 | def __init__(__self__, *, endpoint: str, name: str, routing_type: Optional[str]=None):
'\n :param str endpoint: Specifies the endpoint of the route definition.\n :param str name: Specifies the name of the route definition.\n :param str routing_type: The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name)
if (routing_type is not None):
pulumi.set(__self__, 'routing_type', routing_type) | :param str endpoint: Specifies the endpoint of the route definition.
:param str name: Specifies the name of the route definition.
:param str routing_type: The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`. | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, endpoint: str, name: str, routing_type: Optional[str]=None):
'\n :param str endpoint: Specifies the endpoint of the route definition.\n :param str name: Specifies the name of the route definition.\n :param str routing_type: The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name)
if (routing_type is not None):
pulumi.set(__self__, 'routing_type', routing_type) | def __init__(__self__, *, endpoint: str, name: str, routing_type: Optional[str]=None):
'\n :param str endpoint: Specifies the endpoint of the route definition.\n :param str name: Specifies the name of the route definition.\n :param str routing_type: The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.\n '
pulumi.set(__self__, 'endpoint', endpoint)
pulumi.set(__self__, 'name', name)
if (routing_type is not None):
pulumi.set(__self__, 'routing_type', routing_type)<|docstring|>:param str endpoint: Specifies the endpoint of the route definition.
:param str name: Specifies the name of the route definition.
:param str routing_type: The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.<|endoftext|> |
d1c203303027ff73f781fb0b8325df8cae3d8995a3ba4a43ab36de60cee18c8e | @property
@pulumi.getter
def endpoint(self) -> str:
'\n Specifies the endpoint of the route definition.\n '
return pulumi.get(self, 'endpoint') | Specifies the endpoint of the route definition. | sdk/python/pulumi_azure/core/outputs.py | endpoint | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint') | @property
@pulumi.getter
def endpoint(self) -> str:
'\n \n '
return pulumi.get(self, 'endpoint')<|docstring|>Specifies the endpoint of the route definition.<|endoftext|> |
6ec4fb712825d1316db353cbb5b59ad2e4ff9b712a4ca0eaacac8a150c8b666f | @property
@pulumi.getter
def name(self) -> str:
'\n Specifies the name of the route definition.\n '
return pulumi.get(self, 'name') | Specifies the name of the route definition. | sdk/python/pulumi_azure/core/outputs.py | name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Specifies the name of the route definition.<|endoftext|> |
94d7185fe5f40f1cdfe53a1c51f9c5539c0b50bf1778bab5b3e0de4e0fa78625 | @property
@pulumi.getter(name='routingType')
def routing_type(self) -> Optional[str]:
'\n The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.\n '
return pulumi.get(self, 'routing_type') | The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`. | sdk/python/pulumi_azure/core/outputs.py | routing_type | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='routingType')
def routing_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'routing_type') | @property
@pulumi.getter(name='routingType')
def routing_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'routing_type')<|docstring|>The routing type that is supported for the resource request. Valid values are `ResourceTypeRoutingProxy` or `ResourceTypeRoutingProxyCache`. This value defaults to `ResourceTypeRoutingProxy`.<|endoftext|> |
18cfd879e84778ba2c986f4f093f7b49bec1e296747a1819a30d981d2ed97bc3 | def __init__(__self__, *, specification: str):
'\n :param str specification: The endpoint where the validation specification is located.\n '
pulumi.set(__self__, 'specification', specification) | :param str specification: The endpoint where the validation specification is located. | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, specification: str):
'\n \n '
pulumi.set(__self__, 'specification', specification) | def __init__(__self__, *, specification: str):
'\n \n '
pulumi.set(__self__, 'specification', specification)<|docstring|>:param str specification: The endpoint where the validation specification is located.<|endoftext|> |
db11e8718f7a1d1d2839cbaf59153ad2a04895e3a2ae79e13ccebebf95700bc7 | @property
@pulumi.getter
def specification(self) -> str:
'\n The endpoint where the validation specification is located.\n '
return pulumi.get(self, 'specification') | The endpoint where the validation specification is located. | sdk/python/pulumi_azure/core/outputs.py | specification | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def specification(self) -> str:
'\n \n '
return pulumi.get(self, 'specification') | @property
@pulumi.getter
def specification(self) -> str:
'\n \n '
return pulumi.get(self, 'specification')<|docstring|>The endpoint where the validation specification is located.<|endoftext|> |
82c041f5be5d9a830a5e3eae2a9095bbc23958f72d90d1117d2908b067f74d24 | def __init__(__self__, *, id: str, location: str, name: str, tags: Mapping[(str, str)], type: str):
'\n :param str id: The ID of this Resource.\n :param str location: The Azure Region in which this Resource exists.\n :param str name: The name of the Resource.\n :param Mapping[str, str] tags: A map of tags assigned to this Resource.\n :param str type: The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).\n '
pulumi.set(__self__, 'id', id)
pulumi.set(__self__, 'location', location)
pulumi.set(__self__, 'name', name)
pulumi.set(__self__, 'tags', tags)
pulumi.set(__self__, 'type', type) | :param str id: The ID of this Resource.
:param str location: The Azure Region in which this Resource exists.
:param str name: The name of the Resource.
:param Mapping[str, str] tags: A map of tags assigned to this Resource.
:param str type: The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers). | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, id: str, location: str, name: str, tags: Mapping[(str, str)], type: str):
'\n :param str id: The ID of this Resource.\n :param str location: The Azure Region in which this Resource exists.\n :param str name: The name of the Resource.\n :param Mapping[str, str] tags: A map of tags assigned to this Resource.\n :param str type: The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).\n '
pulumi.set(__self__, 'id', id)
pulumi.set(__self__, 'location', location)
pulumi.set(__self__, 'name', name)
pulumi.set(__self__, 'tags', tags)
pulumi.set(__self__, 'type', type) | def __init__(__self__, *, id: str, location: str, name: str, tags: Mapping[(str, str)], type: str):
'\n :param str id: The ID of this Resource.\n :param str location: The Azure Region in which this Resource exists.\n :param str name: The name of the Resource.\n :param Mapping[str, str] tags: A map of tags assigned to this Resource.\n :param str type: The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).\n '
pulumi.set(__self__, 'id', id)
pulumi.set(__self__, 'location', location)
pulumi.set(__self__, 'name', name)
pulumi.set(__self__, 'tags', tags)
pulumi.set(__self__, 'type', type)<|docstring|>:param str id: The ID of this Resource.
:param str location: The Azure Region in which this Resource exists.
:param str name: The name of the Resource.
:param Mapping[str, str] tags: A map of tags assigned to this Resource.
:param str type: The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).<|endoftext|> |
6dfb29f8cbddc3b05598b0e1f884b1e8469c08070a4e2b6ec31c0e5f5e0b7372 | @property
@pulumi.getter
def id(self) -> str:
'\n The ID of this Resource.\n '
return pulumi.get(self, 'id') | The ID of this Resource. | sdk/python/pulumi_azure/core/outputs.py | id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def id(self) -> str:
'\n \n '
return pulumi.get(self, 'id') | @property
@pulumi.getter
def id(self) -> str:
'\n \n '
return pulumi.get(self, 'id')<|docstring|>The ID of this Resource.<|endoftext|> |
1834d17ce1d3a6f83ffcb95ecbcc063494db1f45806c980d1dfdeca998790183 | @property
@pulumi.getter
def location(self) -> str:
'\n The Azure Region in which this Resource exists.\n '
return pulumi.get(self, 'location') | The Azure Region in which this Resource exists. | sdk/python/pulumi_azure/core/outputs.py | location | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def location(self) -> str:
'\n \n '
return pulumi.get(self, 'location') | @property
@pulumi.getter
def location(self) -> str:
'\n \n '
return pulumi.get(self, 'location')<|docstring|>The Azure Region in which this Resource exists.<|endoftext|> |
ae4134ad03102542e3e8617953e789d012fc31a9a10f8f8273c04f8a3bec9985 | @property
@pulumi.getter
def name(self) -> str:
'\n The name of the Resource.\n '
return pulumi.get(self, 'name') | The name of the Resource. | sdk/python/pulumi_azure/core/outputs.py | name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> str:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>The name of the Resource.<|endoftext|> |
8c00abb590634528ba691e4e25844180fd0b3a84cbb82283654ba001b9ef1c0e | @property
@pulumi.getter
def tags(self) -> Mapping[(str, str)]:
'\n A map of tags assigned to this Resource.\n '
return pulumi.get(self, 'tags') | A map of tags assigned to this Resource. | sdk/python/pulumi_azure/core/outputs.py | tags | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def tags(self) -> Mapping[(str, str)]:
'\n \n '
return pulumi.get(self, 'tags') | @property
@pulumi.getter
def tags(self) -> Mapping[(str, str)]:
'\n \n '
return pulumi.get(self, 'tags')<|docstring|>A map of tags assigned to this Resource.<|endoftext|> |
0e7e73d12011bc9cb6972cb017ea8c537e7141d912019a508a688f09fc160f9b | @property
@pulumi.getter
def type(self) -> str:
'\n The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).\n '
return pulumi.get(self, 'type') | The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers). | sdk/python/pulumi_azure/core/outputs.py | type | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def type(self) -> str:
'\n \n '
return pulumi.get(self, 'type') | @property
@pulumi.getter
def type(self) -> str:
'\n \n '
return pulumi.get(self, 'type')<|docstring|>The Resource Type of the Resources you want to list (e.g. `Microsoft.Network/virtualNetworks`). A full list of available Resource Types can be found [here](https://docs.microsoft.com/en-us/azure/azure-resource-manager/azure-services-resource-providers).<|endoftext|> |
0ad40ffb6c17ddd502c137f7ec7d505c0b5f5f7953870b8c9abd70d30552bdf6 | def __init__(__self__, *, display_name: str, location_placement_id: str, quota_id: str, spending_limit: str, state: str, subscription_id: str, tenant_id: str):
'\n :param str display_name: The subscription display name.\n :param str location_placement_id: The subscription location placement ID.\n :param str quota_id: The subscription quota ID.\n :param str spending_limit: The subscription spending limit.\n :param str state: The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.\n :param str subscription_id: The subscription GUID.\n :param str tenant_id: The subscription tenant ID.\n '
pulumi.set(__self__, 'display_name', display_name)
pulumi.set(__self__, 'location_placement_id', location_placement_id)
pulumi.set(__self__, 'quota_id', quota_id)
pulumi.set(__self__, 'spending_limit', spending_limit)
pulumi.set(__self__, 'state', state)
pulumi.set(__self__, 'subscription_id', subscription_id)
pulumi.set(__self__, 'tenant_id', tenant_id) | :param str display_name: The subscription display name.
:param str location_placement_id: The subscription location placement ID.
:param str quota_id: The subscription quota ID.
:param str spending_limit: The subscription spending limit.
:param str state: The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.
:param str subscription_id: The subscription GUID.
:param str tenant_id: The subscription tenant ID. | sdk/python/pulumi_azure/core/outputs.py | __init__ | suresh198526/pulumi-azure | 0 | python | def __init__(__self__, *, display_name: str, location_placement_id: str, quota_id: str, spending_limit: str, state: str, subscription_id: str, tenant_id: str):
'\n :param str display_name: The subscription display name.\n :param str location_placement_id: The subscription location placement ID.\n :param str quota_id: The subscription quota ID.\n :param str spending_limit: The subscription spending limit.\n :param str state: The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.\n :param str subscription_id: The subscription GUID.\n :param str tenant_id: The subscription tenant ID.\n '
pulumi.set(__self__, 'display_name', display_name)
pulumi.set(__self__, 'location_placement_id', location_placement_id)
pulumi.set(__self__, 'quota_id', quota_id)
pulumi.set(__self__, 'spending_limit', spending_limit)
pulumi.set(__self__, 'state', state)
pulumi.set(__self__, 'subscription_id', subscription_id)
pulumi.set(__self__, 'tenant_id', tenant_id) | def __init__(__self__, *, display_name: str, location_placement_id: str, quota_id: str, spending_limit: str, state: str, subscription_id: str, tenant_id: str):
'\n :param str display_name: The subscription display name.\n :param str location_placement_id: The subscription location placement ID.\n :param str quota_id: The subscription quota ID.\n :param str spending_limit: The subscription spending limit.\n :param str state: The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.\n :param str subscription_id: The subscription GUID.\n :param str tenant_id: The subscription tenant ID.\n '
pulumi.set(__self__, 'display_name', display_name)
pulumi.set(__self__, 'location_placement_id', location_placement_id)
pulumi.set(__self__, 'quota_id', quota_id)
pulumi.set(__self__, 'spending_limit', spending_limit)
pulumi.set(__self__, 'state', state)
pulumi.set(__self__, 'subscription_id', subscription_id)
pulumi.set(__self__, 'tenant_id', tenant_id)<|docstring|>:param str display_name: The subscription display name.
:param str location_placement_id: The subscription location placement ID.
:param str quota_id: The subscription quota ID.
:param str spending_limit: The subscription spending limit.
:param str state: The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.
:param str subscription_id: The subscription GUID.
:param str tenant_id: The subscription tenant ID.<|endoftext|> |
6167b089fc26e32a33c4e49b58d082d31fe2ebc0b687d6e9855cd842adf4ad03 | @property
@pulumi.getter(name='displayName')
def display_name(self) -> str:
'\n The subscription display name.\n '
return pulumi.get(self, 'display_name') | The subscription display name. | sdk/python/pulumi_azure/core/outputs.py | display_name | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='displayName')
def display_name(self) -> str:
'\n \n '
return pulumi.get(self, 'display_name') | @property
@pulumi.getter(name='displayName')
def display_name(self) -> str:
'\n \n '
return pulumi.get(self, 'display_name')<|docstring|>The subscription display name.<|endoftext|> |
890a666c2af921e269e7df1917521c2ea09bafd983df0d88a20cfe9b6aebdbfb | @property
@pulumi.getter(name='locationPlacementId')
def location_placement_id(self) -> str:
'\n The subscription location placement ID.\n '
return pulumi.get(self, 'location_placement_id') | The subscription location placement ID. | sdk/python/pulumi_azure/core/outputs.py | location_placement_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='locationPlacementId')
def location_placement_id(self) -> str:
'\n \n '
return pulumi.get(self, 'location_placement_id') | @property
@pulumi.getter(name='locationPlacementId')
def location_placement_id(self) -> str:
'\n \n '
return pulumi.get(self, 'location_placement_id')<|docstring|>The subscription location placement ID.<|endoftext|> |
cb7a9fbfe34078649b021cd2a5c2f68265529b204603ffd9f6890b9dc942e129 | @property
@pulumi.getter(name='quotaId')
def quota_id(self) -> str:
'\n The subscription quota ID.\n '
return pulumi.get(self, 'quota_id') | The subscription quota ID. | sdk/python/pulumi_azure/core/outputs.py | quota_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='quotaId')
def quota_id(self) -> str:
'\n \n '
return pulumi.get(self, 'quota_id') | @property
@pulumi.getter(name='quotaId')
def quota_id(self) -> str:
'\n \n '
return pulumi.get(self, 'quota_id')<|docstring|>The subscription quota ID.<|endoftext|> |
0ee8b1fa3641bff0ff52034e073b4fee89ebffabbfaf1feefcedae50973bdfbc | @property
@pulumi.getter(name='spendingLimit')
def spending_limit(self) -> str:
'\n The subscription spending limit.\n '
return pulumi.get(self, 'spending_limit') | The subscription spending limit. | sdk/python/pulumi_azure/core/outputs.py | spending_limit | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='spendingLimit')
def spending_limit(self) -> str:
'\n \n '
return pulumi.get(self, 'spending_limit') | @property
@pulumi.getter(name='spendingLimit')
def spending_limit(self) -> str:
'\n \n '
return pulumi.get(self, 'spending_limit')<|docstring|>The subscription spending limit.<|endoftext|> |
4479ab8edb8d8cbfdea39e3206640c5ef0d794cbd9d05ac5f6c340966a3fcd4e | @property
@pulumi.getter
def state(self) -> str:
'\n The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.\n '
return pulumi.get(self, 'state') | The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted. | sdk/python/pulumi_azure/core/outputs.py | state | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter
def state(self) -> str:
'\n \n '
return pulumi.get(self, 'state') | @property
@pulumi.getter
def state(self) -> str:
'\n \n '
return pulumi.get(self, 'state')<|docstring|>The subscription state. Possible values are Enabled, Warned, PastDue, Disabled, and Deleted.<|endoftext|> |
458075aefcdf493f661e2d1848e2d1483f9ec9ca6f8316dddd656bead9588fc8 | @property
@pulumi.getter(name='subscriptionId')
def subscription_id(self) -> str:
'\n The subscription GUID.\n '
return pulumi.get(self, 'subscription_id') | The subscription GUID. | sdk/python/pulumi_azure/core/outputs.py | subscription_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='subscriptionId')
def subscription_id(self) -> str:
'\n \n '
return pulumi.get(self, 'subscription_id') | @property
@pulumi.getter(name='subscriptionId')
def subscription_id(self) -> str:
'\n \n '
return pulumi.get(self, 'subscription_id')<|docstring|>The subscription GUID.<|endoftext|> |
368362b34c317b35c366779ea4126012de0ac469bf6ee47bb4bc69bd73fe3c8e | @property
@pulumi.getter(name='tenantId')
def tenant_id(self) -> str:
'\n The subscription tenant ID.\n '
return pulumi.get(self, 'tenant_id') | The subscription tenant ID. | sdk/python/pulumi_azure/core/outputs.py | tenant_id | suresh198526/pulumi-azure | 0 | python | @property
@pulumi.getter(name='tenantId')
def tenant_id(self) -> str:
'\n \n '
return pulumi.get(self, 'tenant_id') | @property
@pulumi.getter(name='tenantId')
def tenant_id(self) -> str:
'\n \n '
return pulumi.get(self, 'tenant_id')<|docstring|>The subscription tenant ID.<|endoftext|> |
824f4f35619c6b82972bdb044f69deb9f22862f47dfc841ef116f22437883823 | def RoadnetPa(directed: bool=False, verbose: int=2, cache_path: str='graphs/networkrepository', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the roadNet-PA graph.\n\n The graph is automatically retrieved from the NetworkRepository repository. \n\n\t\n\n Parameters\n -------------------\n directed: bool = False,\n Wether to load the graph as directed or undirected.\n By default false.\n verbose: int = 2,\n Wether to show loading bars during the retrieval and building\n of the graph.\n cache_path: str = "graphs",\n Where to store the downloaded graphs.\n additional_graph_kwargs: Dict,\n Additional graph kwargs.\n\n Returns\n -----------------------\n Instace of roadNet-PA graph.\n\n\tReport\n\t---------------------\n\tAt the time of rendering these methods (please see datetime below), the graph\n\thad the following characteristics:\n\t\n\tDatetime: 2021-02-06 12:17:08.702691\n\t\n\tThe undirected graph roadNet-PA has 1088092 nodes and 1541898 unweighted\n\tedges, of which none are self-loops. The graph is extremely sparse as it\n\thas a density of 0.00000 and has 206 connected components, where the component\n\twith most nodes has 1087562 nodes and the component with the least nodes\n\thas 2 nodes. The graph median node degree is 3, the mean node degree is\n\t2.83, and the node degree mode is 3. The top 5 most central nodes are 859327\n\t(degree 9), 847933 (degree 9), 759554 (degree 9), 674503 (degree 9) and\n\t1046565 (degree 8).\n\t\n\n\tReferences\n\t---------------------\n\tPlease cite the following if you use the data:\n\t\n\t@inproceedings{nr,\n\t title = {The Network Data Repository with Interactive Graph Analytics and Visualization},\n\t author={Ryan A. Rossi and Nesreen K. Ahmed},\n\t booktitle = {AAAI},\n\t url={http://networkrepository.com},\n\t year={2015}\n\t}\n\t\n\n\tUsage example\n\t----------------------\n\tThe usage of this graph is relatively straightforward:\n\t\n\t.. code:: python\n\t\n\t # First import the function to retrieve the graph from the datasets\n\t from ensmallen_graph.datasets.networkrepository import RoadnetPa\n\t\n\t # Then load the graph\n\t graph = RoadnetPa()\n\t\n\t # Finally, you can do anything with it, for instance, compute its report:\n\t print(graph)\n\t\n\t # If you need to run a link prediction task with validation,\n\t # you can split the graph using a connected holdout as follows:\n\t train_graph, validation_graph = graph.connected_holdout(\n\t # You can use an 80/20 split the holdout, for example.\n\t train_size=0.8,\n\t # The random state is used to reproduce the holdout.\n\t random_state=42,\n\t # Wether to show a loading bar.\n\t verbose=True\n\t )\n\t\n\t # Remember that, if you need, you can enable the memory-time trade-offs:\n\t train_graph.enable(\n\t vector_sources=True,\n\t vector_destinations=True,\n\t vector_outbounds=True\n\t )\n\t\n\t # Consider using the methods made available in the Embiggen package\n\t # to run graph embedding or link prediction tasks.\n '
return AutomaticallyRetrievedGraph(graph_name='RoadnetPa', dataset='networkrepository', directed=directed, verbose=verbose, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs)() | Return new instance of the roadNet-PA graph.
The graph is automatically retrieved from the NetworkRepository repository.
Parameters
-------------------
directed: bool = False,
Wether to load the graph as directed or undirected.
By default false.
verbose: int = 2,
Wether to show loading bars during the retrieval and building
of the graph.
cache_path: str = "graphs",
Where to store the downloaded graphs.
additional_graph_kwargs: Dict,
Additional graph kwargs.
Returns
-----------------------
Instace of roadNet-PA graph.
Report
---------------------
At the time of rendering these methods (please see datetime below), the graph
had the following characteristics:
Datetime: 2021-02-06 12:17:08.702691
The undirected graph roadNet-PA has 1088092 nodes and 1541898 unweighted
edges, of which none are self-loops. The graph is extremely sparse as it
has a density of 0.00000 and has 206 connected components, where the component
with most nodes has 1087562 nodes and the component with the least nodes
has 2 nodes. The graph median node degree is 3, the mean node degree is
2.83, and the node degree mode is 3. The top 5 most central nodes are 859327
(degree 9), 847933 (degree 9), 759554 (degree 9), 674503 (degree 9) and
1046565 (degree 8).
References
---------------------
Please cite the following if you use the data:
@inproceedings{nr,
title = {The Network Data Repository with Interactive Graph Analytics and Visualization},
author={Ryan A. Rossi and Nesreen K. Ahmed},
booktitle = {AAAI},
url={http://networkrepository.com},
year={2015}
}
Usage example
----------------------
The usage of this graph is relatively straightforward:
.. code:: python
# First import the function to retrieve the graph from the datasets
from ensmallen_graph.datasets.networkrepository import RoadnetPa
# Then load the graph
graph = RoadnetPa()
# Finally, you can do anything with it, for instance, compute its report:
print(graph)
# If you need to run a link prediction task with validation,
# you can split the graph using a connected holdout as follows:
train_graph, validation_graph = graph.connected_holdout(
# You can use an 80/20 split the holdout, for example.
train_size=0.8,
# The random state is used to reproduce the holdout.
random_state=42,
# Wether to show a loading bar.
verbose=True
)
# Remember that, if you need, you can enable the memory-time trade-offs:
train_graph.enable(
vector_sources=True,
vector_destinations=True,
vector_outbounds=True
)
# Consider using the methods made available in the Embiggen package
# to run graph embedding or link prediction tasks. | bindings/python/ensmallen_graph/datasets/networkrepository/roadnetpa.py | RoadnetPa | caufieldjh/ensmallen_graph | 0 | python | def RoadnetPa(directed: bool=False, verbose: int=2, cache_path: str='graphs/networkrepository', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the roadNet-PA graph.\n\n The graph is automatically retrieved from the NetworkRepository repository. \n\n\t\n\n Parameters\n -------------------\n directed: bool = False,\n Wether to load the graph as directed or undirected.\n By default false.\n verbose: int = 2,\n Wether to show loading bars during the retrieval and building\n of the graph.\n cache_path: str = "graphs",\n Where to store the downloaded graphs.\n additional_graph_kwargs: Dict,\n Additional graph kwargs.\n\n Returns\n -----------------------\n Instace of roadNet-PA graph.\n\n\tReport\n\t---------------------\n\tAt the time of rendering these methods (please see datetime below), the graph\n\thad the following characteristics:\n\t\n\tDatetime: 2021-02-06 12:17:08.702691\n\t\n\tThe undirected graph roadNet-PA has 1088092 nodes and 1541898 unweighted\n\tedges, of which none are self-loops. The graph is extremely sparse as it\n\thas a density of 0.00000 and has 206 connected components, where the component\n\twith most nodes has 1087562 nodes and the component with the least nodes\n\thas 2 nodes. The graph median node degree is 3, the mean node degree is\n\t2.83, and the node degree mode is 3. The top 5 most central nodes are 859327\n\t(degree 9), 847933 (degree 9), 759554 (degree 9), 674503 (degree 9) and\n\t1046565 (degree 8).\n\t\n\n\tReferences\n\t---------------------\n\tPlease cite the following if you use the data:\n\t\n\t@inproceedings{nr,\n\t title = {The Network Data Repository with Interactive Graph Analytics and Visualization},\n\t author={Ryan A. Rossi and Nesreen K. Ahmed},\n\t booktitle = {AAAI},\n\t url={http://networkrepository.com},\n\t year={2015}\n\t}\n\t\n\n\tUsage example\n\t----------------------\n\tThe usage of this graph is relatively straightforward:\n\t\n\t.. code:: python\n\t\n\t # First import the function to retrieve the graph from the datasets\n\t from ensmallen_graph.datasets.networkrepository import RoadnetPa\n\t\n\t # Then load the graph\n\t graph = RoadnetPa()\n\t\n\t # Finally, you can do anything with it, for instance, compute its report:\n\t print(graph)\n\t\n\t # If you need to run a link prediction task with validation,\n\t # you can split the graph using a connected holdout as follows:\n\t train_graph, validation_graph = graph.connected_holdout(\n\t # You can use an 80/20 split the holdout, for example.\n\t train_size=0.8,\n\t # The random state is used to reproduce the holdout.\n\t random_state=42,\n\t # Wether to show a loading bar.\n\t verbose=True\n\t )\n\t\n\t # Remember that, if you need, you can enable the memory-time trade-offs:\n\t train_graph.enable(\n\t vector_sources=True,\n\t vector_destinations=True,\n\t vector_outbounds=True\n\t )\n\t\n\t # Consider using the methods made available in the Embiggen package\n\t # to run graph embedding or link prediction tasks.\n '
return AutomaticallyRetrievedGraph(graph_name='RoadnetPa', dataset='networkrepository', directed=directed, verbose=verbose, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs)() | def RoadnetPa(directed: bool=False, verbose: int=2, cache_path: str='graphs/networkrepository', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the roadNet-PA graph.\n\n The graph is automatically retrieved from the NetworkRepository repository. \n\n\t\n\n Parameters\n -------------------\n directed: bool = False,\n Wether to load the graph as directed or undirected.\n By default false.\n verbose: int = 2,\n Wether to show loading bars during the retrieval and building\n of the graph.\n cache_path: str = "graphs",\n Where to store the downloaded graphs.\n additional_graph_kwargs: Dict,\n Additional graph kwargs.\n\n Returns\n -----------------------\n Instace of roadNet-PA graph.\n\n\tReport\n\t---------------------\n\tAt the time of rendering these methods (please see datetime below), the graph\n\thad the following characteristics:\n\t\n\tDatetime: 2021-02-06 12:17:08.702691\n\t\n\tThe undirected graph roadNet-PA has 1088092 nodes and 1541898 unweighted\n\tedges, of which none are self-loops. The graph is extremely sparse as it\n\thas a density of 0.00000 and has 206 connected components, where the component\n\twith most nodes has 1087562 nodes and the component with the least nodes\n\thas 2 nodes. The graph median node degree is 3, the mean node degree is\n\t2.83, and the node degree mode is 3. The top 5 most central nodes are 859327\n\t(degree 9), 847933 (degree 9), 759554 (degree 9), 674503 (degree 9) and\n\t1046565 (degree 8).\n\t\n\n\tReferences\n\t---------------------\n\tPlease cite the following if you use the data:\n\t\n\t@inproceedings{nr,\n\t title = {The Network Data Repository with Interactive Graph Analytics and Visualization},\n\t author={Ryan A. Rossi and Nesreen K. Ahmed},\n\t booktitle = {AAAI},\n\t url={http://networkrepository.com},\n\t year={2015}\n\t}\n\t\n\n\tUsage example\n\t----------------------\n\tThe usage of this graph is relatively straightforward:\n\t\n\t.. code:: python\n\t\n\t # First import the function to retrieve the graph from the datasets\n\t from ensmallen_graph.datasets.networkrepository import RoadnetPa\n\t\n\t # Then load the graph\n\t graph = RoadnetPa()\n\t\n\t # Finally, you can do anything with it, for instance, compute its report:\n\t print(graph)\n\t\n\t # If you need to run a link prediction task with validation,\n\t # you can split the graph using a connected holdout as follows:\n\t train_graph, validation_graph = graph.connected_holdout(\n\t # You can use an 80/20 split the holdout, for example.\n\t train_size=0.8,\n\t # The random state is used to reproduce the holdout.\n\t random_state=42,\n\t # Wether to show a loading bar.\n\t verbose=True\n\t )\n\t\n\t # Remember that, if you need, you can enable the memory-time trade-offs:\n\t train_graph.enable(\n\t vector_sources=True,\n\t vector_destinations=True,\n\t vector_outbounds=True\n\t )\n\t\n\t # Consider using the methods made available in the Embiggen package\n\t # to run graph embedding or link prediction tasks.\n '
return AutomaticallyRetrievedGraph(graph_name='RoadnetPa', dataset='networkrepository', directed=directed, verbose=verbose, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs)()<|docstring|>Return new instance of the roadNet-PA graph.
The graph is automatically retrieved from the NetworkRepository repository.
Parameters
-------------------
directed: bool = False,
Wether to load the graph as directed or undirected.
By default false.
verbose: int = 2,
Wether to show loading bars during the retrieval and building
of the graph.
cache_path: str = "graphs",
Where to store the downloaded graphs.
additional_graph_kwargs: Dict,
Additional graph kwargs.
Returns
-----------------------
Instace of roadNet-PA graph.
Report
---------------------
At the time of rendering these methods (please see datetime below), the graph
had the following characteristics:
Datetime: 2021-02-06 12:17:08.702691
The undirected graph roadNet-PA has 1088092 nodes and 1541898 unweighted
edges, of which none are self-loops. The graph is extremely sparse as it
has a density of 0.00000 and has 206 connected components, where the component
with most nodes has 1087562 nodes and the component with the least nodes
has 2 nodes. The graph median node degree is 3, the mean node degree is
2.83, and the node degree mode is 3. The top 5 most central nodes are 859327
(degree 9), 847933 (degree 9), 759554 (degree 9), 674503 (degree 9) and
1046565 (degree 8).
References
---------------------
Please cite the following if you use the data:
@inproceedings{nr,
title = {The Network Data Repository with Interactive Graph Analytics and Visualization},
author={Ryan A. Rossi and Nesreen K. Ahmed},
booktitle = {AAAI},
url={http://networkrepository.com},
year={2015}
}
Usage example
----------------------
The usage of this graph is relatively straightforward:
.. code:: python
# First import the function to retrieve the graph from the datasets
from ensmallen_graph.datasets.networkrepository import RoadnetPa
# Then load the graph
graph = RoadnetPa()
# Finally, you can do anything with it, for instance, compute its report:
print(graph)
# If you need to run a link prediction task with validation,
# you can split the graph using a connected holdout as follows:
train_graph, validation_graph = graph.connected_holdout(
# You can use an 80/20 split the holdout, for example.
train_size=0.8,
# The random state is used to reproduce the holdout.
random_state=42,
# Wether to show a loading bar.
verbose=True
)
# Remember that, if you need, you can enable the memory-time trade-offs:
train_graph.enable(
vector_sources=True,
vector_destinations=True,
vector_outbounds=True
)
# Consider using the methods made available in the Embiggen package
# to run graph embedding or link prediction tasks.<|endoftext|> |
ed1f588a9117a99ce4783e5a70e015a7f89ea47c3d1a09b164428d394024c7d6 | def model_resnet50_keras(input_shape: tuple, classes: int, include_top=True, weights='imagenet') -> keras.Model:
'\n Keras Applicationsに用意されているResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_classes int:\n 分類するクラス数を指定する。\n\n Returns:\n keras.Model:\n ResNet50を返す。\n '
return keras.applications.resnet50.ResNet50(include_top=include_top, weights=weights, input_tensor=None, input_shape=((224, 224, 3) if (weights == 'imagenet') else input_shape), pooling=None, classes=(classes if (include_top and (not weights)) else 1000)) | Keras Applicationsに用意されているResNet50を読み込む。
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385
Args:
input_shape tuple:
入力の形状を指定する。
num_classes int:
分類するクラス数を指定する。
Returns:
keras.Model:
ResNet50を返す。 | models/resnet50.py | model_resnet50_keras | sugaok/my-deep-learning-base | 1 | python | def model_resnet50_keras(input_shape: tuple, classes: int, include_top=True, weights='imagenet') -> keras.Model:
'\n Keras Applicationsに用意されているResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_classes int:\n 分類するクラス数を指定する。\n\n Returns:\n keras.Model:\n ResNet50を返す。\n '
return keras.applications.resnet50.ResNet50(include_top=include_top, weights=weights, input_tensor=None, input_shape=((224, 224, 3) if (weights == 'imagenet') else input_shape), pooling=None, classes=(classes if (include_top and (not weights)) else 1000)) | def model_resnet50_keras(input_shape: tuple, classes: int, include_top=True, weights='imagenet') -> keras.Model:
'\n Keras Applicationsに用意されているResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_classes int:\n 分類するクラス数を指定する。\n\n Returns:\n keras.Model:\n ResNet50を返す。\n '
return keras.applications.resnet50.ResNet50(include_top=include_top, weights=weights, input_tensor=None, input_shape=((224, 224, 3) if (weights == 'imagenet') else input_shape), pooling=None, classes=(classes if (include_top and (not weights)) else 1000))<|docstring|>Keras Applicationsに用意されているResNet50を読み込む。
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385
Args:
input_shape tuple:
入力の形状を指定する。
num_classes int:
分類するクラス数を指定する。
Returns:
keras.Model:
ResNet50を返す。<|endoftext|> |
d558c7449849fec455c88a665440847b31e17a43f87d60b2e5c5bb7453752001 | def model_resnet50(input_shape: tuple, classes: int) -> keras.Model:
'\n ResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_classes int:\n 分類するクラス数を指定する。\n\n Returns:\n keras.Model:\n ResNet50を返す。\n '
inputs = keras.layers.Input(shape=input_shape)
x = inputs
s = max(input_shape[0], input_shape[1])
if (s >= 224):
x = keras.layers.Conv2D(64, 7, 2, name='conv1')(x)
if (s >= 112):
pass
x = keras.layers.Conv2D(32, 3, 2)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.ReLU()(x) | ResNet50を読み込む。
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385
Args:
input_shape tuple:
入力の形状を指定する。
num_classes int:
分類するクラス数を指定する。
Returns:
keras.Model:
ResNet50を返す。 | models/resnet50.py | model_resnet50 | sugaok/my-deep-learning-base | 1 | python | def model_resnet50(input_shape: tuple, classes: int) -> keras.Model:
'\n ResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_classes int:\n 分類するクラス数を指定する。\n\n Returns:\n keras.Model:\n ResNet50を返す。\n '
inputs = keras.layers.Input(shape=input_shape)
x = inputs
s = max(input_shape[0], input_shape[1])
if (s >= 224):
x = keras.layers.Conv2D(64, 7, 2, name='conv1')(x)
if (s >= 112):
pass
x = keras.layers.Conv2D(32, 3, 2)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.ReLU()(x) | def model_resnet50(input_shape: tuple, classes: int) -> keras.Model:
'\n ResNet50を読み込む。\n\n Deep Residual Learning for Image Recognition\n Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\n https://arxiv.org/abs/1512.03385\n\n Args:\n input_shape tuple:\n 入力の形状を指定する。\n num_classes int:\n 分類するクラス数を指定する。\n\n Returns:\n keras.Model:\n ResNet50を返す。\n '
inputs = keras.layers.Input(shape=input_shape)
x = inputs
s = max(input_shape[0], input_shape[1])
if (s >= 224):
x = keras.layers.Conv2D(64, 7, 2, name='conv1')(x)
if (s >= 112):
pass
x = keras.layers.Conv2D(32, 3, 2)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.ReLU()(x)<|docstring|>ResNet50を読み込む。
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385
Args:
input_shape tuple:
入力の形状を指定する。
num_classes int:
分類するクラス数を指定する。
Returns:
keras.Model:
ResNet50を返す。<|endoftext|> |
cc09e176516213f63e22aa96c51173f4d0c4d0f2f7ba026ec27ba3e5832cab66 | def get_single_run_config(out_dir):
'Load the config file from a specified experiment.\n\n Args:\n out_dir (str): The path to the experiment.\n\n Returns:\n The Namespace object containing argument names and values.\n '
print(('Loading the configuration of run: %s' % out_dir))
if (not os.path.exists(os.path.join(out_dir, 'config.pickle'))):
raise NotImplementedError((('The run "%s" does not contain a ' % out_dir) + '"config.pickle" file.'))
with open(os.path.join(out_dir, 'config.pickle'), 'rb') as f:
config = pickle.load(f)
return config | Load the config file from a specified experiment.
Args:
out_dir (str): The path to the experiment.
Returns:
The Namespace object containing argument names and values. | hypnettorch/hpsearch/gather_random_seeds.py | get_single_run_config | pennfranc/hypnettorch | 31 | python | def get_single_run_config(out_dir):
'Load the config file from a specified experiment.\n\n Args:\n out_dir (str): The path to the experiment.\n\n Returns:\n The Namespace object containing argument names and values.\n '
print(('Loading the configuration of run: %s' % out_dir))
if (not os.path.exists(os.path.join(out_dir, 'config.pickle'))):
raise NotImplementedError((('The run "%s" does not contain a ' % out_dir) + '"config.pickle" file.'))
with open(os.path.join(out_dir, 'config.pickle'), 'rb') as f:
config = pickle.load(f)
return config | def get_single_run_config(out_dir):
'Load the config file from a specified experiment.\n\n Args:\n out_dir (str): The path to the experiment.\n\n Returns:\n The Namespace object containing argument names and values.\n '
print(('Loading the configuration of run: %s' % out_dir))
if (not os.path.exists(os.path.join(out_dir, 'config.pickle'))):
raise NotImplementedError((('The run "%s" does not contain a ' % out_dir) + '"config.pickle" file.'))
with open(os.path.join(out_dir, 'config.pickle'), 'rb') as f:
config = pickle.load(f)
return config<|docstring|>Load the config file from a specified experiment.
Args:
out_dir (str): The path to the experiment.
Returns:
The Namespace object containing argument names and values.<|endoftext|> |
3647241f3893a7fc6ba9b5c6e9bf0e4bf70b17d7a65f8f9e2a9b4b9973f623ae | def get_best_hpsearch_config(out_dir):
'Load the config file from the best run of a hyperparameter search.\n\n This file loads the results of the hyperparameter search, and select the\n configuration that lead to the best performance score.\n\n Args:\n out_dir (str): The path to the hpsearch result folder.\n\n Returns:\n (tuple): Tuple containing:\n\n - **config**: The config of the best run.\n - **best_out_dir**: The path to the best run.\n '
run_dirs = os.listdir(out_dir)
if ('TO_BE_DELETED' in run_dirs):
run_dirs.remove('TO_BE_DELETED')
run_dirs.extend(os.listdir(os.path.join(out_dir, 'TO_BE_DELETED')))
curr_best_dir = None
curr_best_score = None
for (i, sim_dir) in enumerate(run_dirs):
sim_path = os.path.join(run_dirs, sim_dir)
if (not os.path.isdir(sim_path)):
continue
if (not os.path.exists(os.path.join(sim_path, hpsearch._SUMMARY_FILENAME))):
continue
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(sim_path, i)
except:
continue
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
continue
curr_score = float(performance_dict[hpsearch._PERFORMANCE_KEY][0])
if (curr_best_dir is None):
curr_best_dir = sim_path
curr_best_score = curr_score
elif hpsearch._PERFORMANCE_SORT_ASC:
if (curr_score < curr_best_score):
curr_best_dir = sim_path
curr_best_score = curr_score
elif (curr_score > curr_best_score):
curr_best_dir = sim_path
curr_best_score = curr_score
if (curr_best_dir is None):
raise RuntimeError('Did not find any finished run!')
return (get_single_run_config(curr_best_dir), curr_best_dir) | Load the config file from the best run of a hyperparameter search.
This file loads the results of the hyperparameter search, and select the
configuration that lead to the best performance score.
Args:
out_dir (str): The path to the hpsearch result folder.
Returns:
(tuple): Tuple containing:
- **config**: The config of the best run.
- **best_out_dir**: The path to the best run. | hypnettorch/hpsearch/gather_random_seeds.py | get_best_hpsearch_config | pennfranc/hypnettorch | 31 | python | def get_best_hpsearch_config(out_dir):
'Load the config file from the best run of a hyperparameter search.\n\n This file loads the results of the hyperparameter search, and select the\n configuration that lead to the best performance score.\n\n Args:\n out_dir (str): The path to the hpsearch result folder.\n\n Returns:\n (tuple): Tuple containing:\n\n - **config**: The config of the best run.\n - **best_out_dir**: The path to the best run.\n '
run_dirs = os.listdir(out_dir)
if ('TO_BE_DELETED' in run_dirs):
run_dirs.remove('TO_BE_DELETED')
run_dirs.extend(os.listdir(os.path.join(out_dir, 'TO_BE_DELETED')))
curr_best_dir = None
curr_best_score = None
for (i, sim_dir) in enumerate(run_dirs):
sim_path = os.path.join(run_dirs, sim_dir)
if (not os.path.isdir(sim_path)):
continue
if (not os.path.exists(os.path.join(sim_path, hpsearch._SUMMARY_FILENAME))):
continue
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(sim_path, i)
except:
continue
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
continue
curr_score = float(performance_dict[hpsearch._PERFORMANCE_KEY][0])
if (curr_best_dir is None):
curr_best_dir = sim_path
curr_best_score = curr_score
elif hpsearch._PERFORMANCE_SORT_ASC:
if (curr_score < curr_best_score):
curr_best_dir = sim_path
curr_best_score = curr_score
elif (curr_score > curr_best_score):
curr_best_dir = sim_path
curr_best_score = curr_score
if (curr_best_dir is None):
raise RuntimeError('Did not find any finished run!')
return (get_single_run_config(curr_best_dir), curr_best_dir) | def get_best_hpsearch_config(out_dir):
'Load the config file from the best run of a hyperparameter search.\n\n This file loads the results of the hyperparameter search, and select the\n configuration that lead to the best performance score.\n\n Args:\n out_dir (str): The path to the hpsearch result folder.\n\n Returns:\n (tuple): Tuple containing:\n\n - **config**: The config of the best run.\n - **best_out_dir**: The path to the best run.\n '
run_dirs = os.listdir(out_dir)
if ('TO_BE_DELETED' in run_dirs):
run_dirs.remove('TO_BE_DELETED')
run_dirs.extend(os.listdir(os.path.join(out_dir, 'TO_BE_DELETED')))
curr_best_dir = None
curr_best_score = None
for (i, sim_dir) in enumerate(run_dirs):
sim_path = os.path.join(run_dirs, sim_dir)
if (not os.path.isdir(sim_path)):
continue
if (not os.path.exists(os.path.join(sim_path, hpsearch._SUMMARY_FILENAME))):
continue
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(sim_path, i)
except:
continue
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
continue
curr_score = float(performance_dict[hpsearch._PERFORMANCE_KEY][0])
if (curr_best_dir is None):
curr_best_dir = sim_path
curr_best_score = curr_score
elif hpsearch._PERFORMANCE_SORT_ASC:
if (curr_score < curr_best_score):
curr_best_dir = sim_path
curr_best_score = curr_score
elif (curr_score > curr_best_score):
curr_best_dir = sim_path
curr_best_score = curr_score
if (curr_best_dir is None):
raise RuntimeError('Did not find any finished run!')
return (get_single_run_config(curr_best_dir), curr_best_dir)<|docstring|>Load the config file from the best run of a hyperparameter search.
This file loads the results of the hyperparameter search, and select the
configuration that lead to the best performance score.
Args:
out_dir (str): The path to the hpsearch result folder.
Returns:
(tuple): Tuple containing:
- **config**: The config of the best run.
- **best_out_dir**: The path to the best run.<|endoftext|> |
bed1c12c4f6db9806efcb8fd5f621a3693e11b23ce04e289b488c9b3234d5dbc | def build_grid_and_conditions(cmd_args, config, seeds_list):
'Build the hpconfig for the random seed gathering.\n\n Args:\n cmd_args: CLI arguments of this script.\n config: The config to be translated into a search grid.\n seeds_list (list): The random seeds to be gathered.\n\n (tuple): Tuple containing:\n\n - **grid** (dict): The search grid.\n - **conditions** (list): Constraints for the search grid.\n '
grid = {}
for (k, v) in vars(config).items():
if isinstance(v, str):
v = v.strip('"')
v = (('"' + v) + '"')
grid[k] = [v]
grid['random_seed'] = seeds_list
conditions = []
if cmd_args.vary_data_seed:
for s in seeds_list:
conditions.append(({'random_seed': [s]}, {'data_random_seed': [s]}))
return (grid, conditions) | Build the hpconfig for the random seed gathering.
Args:
cmd_args: CLI arguments of this script.
config: The config to be translated into a search grid.
seeds_list (list): The random seeds to be gathered.
(tuple): Tuple containing:
- **grid** (dict): The search grid.
- **conditions** (list): Constraints for the search grid. | hypnettorch/hpsearch/gather_random_seeds.py | build_grid_and_conditions | pennfranc/hypnettorch | 31 | python | def build_grid_and_conditions(cmd_args, config, seeds_list):
'Build the hpconfig for the random seed gathering.\n\n Args:\n cmd_args: CLI arguments of this script.\n config: The config to be translated into a search grid.\n seeds_list (list): The random seeds to be gathered.\n\n (tuple): Tuple containing:\n\n - **grid** (dict): The search grid.\n - **conditions** (list): Constraints for the search grid.\n '
grid = {}
for (k, v) in vars(config).items():
if isinstance(v, str):
v = v.strip('"')
v = (('"' + v) + '"')
grid[k] = [v]
grid['random_seed'] = seeds_list
conditions = []
if cmd_args.vary_data_seed:
for s in seeds_list:
conditions.append(({'random_seed': [s]}, {'data_random_seed': [s]}))
return (grid, conditions) | def build_grid_and_conditions(cmd_args, config, seeds_list):
'Build the hpconfig for the random seed gathering.\n\n Args:\n cmd_args: CLI arguments of this script.\n config: The config to be translated into a search grid.\n seeds_list (list): The random seeds to be gathered.\n\n (tuple): Tuple containing:\n\n - **grid** (dict): The search grid.\n - **conditions** (list): Constraints for the search grid.\n '
grid = {}
for (k, v) in vars(config).items():
if isinstance(v, str):
v = v.strip('"')
v = (('"' + v) + '"')
grid[k] = [v]
grid['random_seed'] = seeds_list
conditions = []
if cmd_args.vary_data_seed:
for s in seeds_list:
conditions.append(({'random_seed': [s]}, {'data_random_seed': [s]}))
return (grid, conditions)<|docstring|>Build the hpconfig for the random seed gathering.
Args:
cmd_args: CLI arguments of this script.
config: The config to be translated into a search grid.
seeds_list (list): The random seeds to be gathered.
(tuple): Tuple containing:
- **grid** (dict): The search grid.
- **conditions** (list): Constraints for the search grid.<|endoftext|> |
5d5bdd5e2d9be29522c2fb6601bf7436ce2134ca97750a5af7306eb479b388ee | def get_hpsearch_call(cmd_args, num_seeds, grid_config, hpsearch_dir=None):
'Generate the command line for the hpsearch.\n\n Args:\n cmd_args: The command line arguments.\n num_seeds (int): Number of searches.\n grid_config (str): Location of search grid.\n hpsearch_dir (str, optional): Where the hpsearch should write its\n results to.\n\n Returns:\n (str): The command line to be executed.\n\n '
cluster_cmd_prefix = ''
cluster_cmd_suffix = ''
non_cluster_cmd_suffix = ''
if (cmd_args.run_cluster and (cmd_args.scheduler == 'lsf')):
cluster_cmd_prefix = (('bsub -n 1 -W %s:00 ' % cmd_args.hps_num_hours) + ('-e random_seeds.err -o random_seeds.out -R "%s" ' % cmd_args.hps_resources.strip('"')))
cluster_cmd_suffix = (((((' --run_cluster ' + ('--scheduler=%s ' % cmd_args.scheduler)) + ('--num_jobs=%s ' % cmd_args.num_jobs)) + ('--num_hours=%s ' % cmd_args.num_hours)) + ('--resources="\\"%s\\"" ' % cmd_args.resources.strip('"'))) + ('--num_searches=%d ' % num_seeds))
elif cmd_args.run_cluster:
assert (cmd_args.scheduler == 'slurm')
cluster_cmd_suffix = (((((((((' --run_cluster ' + ('--scheduler=%s ' % cmd_args.scheduler)) + ('--num_jobs=%s ' % cmd_args.num_jobs)) + ('--num_hours=%s ' % cmd_args.num_hours)) + ('--slurm_mem=%s ' % cmd_args.slurm_mem)) + ('--slurm_gres=%s ' % cmd_args.slurm_gres)) + ('--slurm_partition=%s ' % cmd_args.slurm_partition)) + ('--slurm_qos=%s ' % cmd_args.slurm_qos)) + ('--slurm_constraint=%s ' % cmd_args.slurm_constraint)) + ('--num_searches=%d ' % num_seeds))
else:
non_cluster_cmd_suffix = ((((('--visible_gpus=%s ' % cmd_args.visible_gpus) + ('--allowed_load=%f ' % cmd_args.allowed_load)) + ('--allowed_memory=%f ' % cmd_args.allowed_memory)) + ('--sim_startup_time=%d ' % cmd_args.sim_startup_time)) + ('--max_num_jobs_per_gpu=%d ' % cmd_args.max_num_jobs_per_gpu))
cmd_str = (((cluster_cmd_prefix + ('python3 hpsearch.py --grid_module=%s ' % cmd_args.grid_module)) + ('--grid_config=%s ' % grid_config)) + ('--run_cwd=%s ' % cmd_args.run_cwd))
if cmd_args.deterministic_search:
cmd_str += '--deterministic_search '
if cmd_args.dont_generate_full_grid:
cmd_str += '--dont_generate_full_grid '
if (hpsearch_dir is not None):
cmd_str += (('--out_dir=%s --force_out_dir ' % hpsearch_dir) + '--dont_force_new_dir ')
cmd_str += (cluster_cmd_suffix + non_cluster_cmd_suffix)
return cmd_str | Generate the command line for the hpsearch.
Args:
cmd_args: The command line arguments.
num_seeds (int): Number of searches.
grid_config (str): Location of search grid.
hpsearch_dir (str, optional): Where the hpsearch should write its
results to.
Returns:
(str): The command line to be executed. | hypnettorch/hpsearch/gather_random_seeds.py | get_hpsearch_call | pennfranc/hypnettorch | 31 | python | def get_hpsearch_call(cmd_args, num_seeds, grid_config, hpsearch_dir=None):
'Generate the command line for the hpsearch.\n\n Args:\n cmd_args: The command line arguments.\n num_seeds (int): Number of searches.\n grid_config (str): Location of search grid.\n hpsearch_dir (str, optional): Where the hpsearch should write its\n results to.\n\n Returns:\n (str): The command line to be executed.\n\n '
cluster_cmd_prefix =
cluster_cmd_suffix =
non_cluster_cmd_suffix =
if (cmd_args.run_cluster and (cmd_args.scheduler == 'lsf')):
cluster_cmd_prefix = (('bsub -n 1 -W %s:00 ' % cmd_args.hps_num_hours) + ('-e random_seeds.err -o random_seeds.out -R "%s" ' % cmd_args.hps_resources.strip('"')))
cluster_cmd_suffix = (((((' --run_cluster ' + ('--scheduler=%s ' % cmd_args.scheduler)) + ('--num_jobs=%s ' % cmd_args.num_jobs)) + ('--num_hours=%s ' % cmd_args.num_hours)) + ('--resources="\\"%s\\ ' % cmd_args.resources.strip('"'))) + ('--num_searches=%d ' % num_seeds))
elif cmd_args.run_cluster:
assert (cmd_args.scheduler == 'slurm')
cluster_cmd_suffix = (((((((((' --run_cluster ' + ('--scheduler=%s ' % cmd_args.scheduler)) + ('--num_jobs=%s ' % cmd_args.num_jobs)) + ('--num_hours=%s ' % cmd_args.num_hours)) + ('--slurm_mem=%s ' % cmd_args.slurm_mem)) + ('--slurm_gres=%s ' % cmd_args.slurm_gres)) + ('--slurm_partition=%s ' % cmd_args.slurm_partition)) + ('--slurm_qos=%s ' % cmd_args.slurm_qos)) + ('--slurm_constraint=%s ' % cmd_args.slurm_constraint)) + ('--num_searches=%d ' % num_seeds))
else:
non_cluster_cmd_suffix = ((((('--visible_gpus=%s ' % cmd_args.visible_gpus) + ('--allowed_load=%f ' % cmd_args.allowed_load)) + ('--allowed_memory=%f ' % cmd_args.allowed_memory)) + ('--sim_startup_time=%d ' % cmd_args.sim_startup_time)) + ('--max_num_jobs_per_gpu=%d ' % cmd_args.max_num_jobs_per_gpu))
cmd_str = (((cluster_cmd_prefix + ('python3 hpsearch.py --grid_module=%s ' % cmd_args.grid_module)) + ('--grid_config=%s ' % grid_config)) + ('--run_cwd=%s ' % cmd_args.run_cwd))
if cmd_args.deterministic_search:
cmd_str += '--deterministic_search '
if cmd_args.dont_generate_full_grid:
cmd_str += '--dont_generate_full_grid '
if (hpsearch_dir is not None):
cmd_str += (('--out_dir=%s --force_out_dir ' % hpsearch_dir) + '--dont_force_new_dir ')
cmd_str += (cluster_cmd_suffix + non_cluster_cmd_suffix)
return cmd_str | def get_hpsearch_call(cmd_args, num_seeds, grid_config, hpsearch_dir=None):
'Generate the command line for the hpsearch.\n\n Args:\n cmd_args: The command line arguments.\n num_seeds (int): Number of searches.\n grid_config (str): Location of search grid.\n hpsearch_dir (str, optional): Where the hpsearch should write its\n results to.\n\n Returns:\n (str): The command line to be executed.\n\n '
cluster_cmd_prefix =
cluster_cmd_suffix =
non_cluster_cmd_suffix =
if (cmd_args.run_cluster and (cmd_args.scheduler == 'lsf')):
cluster_cmd_prefix = (('bsub -n 1 -W %s:00 ' % cmd_args.hps_num_hours) + ('-e random_seeds.err -o random_seeds.out -R "%s" ' % cmd_args.hps_resources.strip('"')))
cluster_cmd_suffix = (((((' --run_cluster ' + ('--scheduler=%s ' % cmd_args.scheduler)) + ('--num_jobs=%s ' % cmd_args.num_jobs)) + ('--num_hours=%s ' % cmd_args.num_hours)) + ('--resources="\\"%s\\ ' % cmd_args.resources.strip('"'))) + ('--num_searches=%d ' % num_seeds))
elif cmd_args.run_cluster:
assert (cmd_args.scheduler == 'slurm')
cluster_cmd_suffix = (((((((((' --run_cluster ' + ('--scheduler=%s ' % cmd_args.scheduler)) + ('--num_jobs=%s ' % cmd_args.num_jobs)) + ('--num_hours=%s ' % cmd_args.num_hours)) + ('--slurm_mem=%s ' % cmd_args.slurm_mem)) + ('--slurm_gres=%s ' % cmd_args.slurm_gres)) + ('--slurm_partition=%s ' % cmd_args.slurm_partition)) + ('--slurm_qos=%s ' % cmd_args.slurm_qos)) + ('--slurm_constraint=%s ' % cmd_args.slurm_constraint)) + ('--num_searches=%d ' % num_seeds))
else:
non_cluster_cmd_suffix = ((((('--visible_gpus=%s ' % cmd_args.visible_gpus) + ('--allowed_load=%f ' % cmd_args.allowed_load)) + ('--allowed_memory=%f ' % cmd_args.allowed_memory)) + ('--sim_startup_time=%d ' % cmd_args.sim_startup_time)) + ('--max_num_jobs_per_gpu=%d ' % cmd_args.max_num_jobs_per_gpu))
cmd_str = (((cluster_cmd_prefix + ('python3 hpsearch.py --grid_module=%s ' % cmd_args.grid_module)) + ('--grid_config=%s ' % grid_config)) + ('--run_cwd=%s ' % cmd_args.run_cwd))
if cmd_args.deterministic_search:
cmd_str += '--deterministic_search '
if cmd_args.dont_generate_full_grid:
cmd_str += '--dont_generate_full_grid '
if (hpsearch_dir is not None):
cmd_str += (('--out_dir=%s --force_out_dir ' % hpsearch_dir) + '--dont_force_new_dir ')
cmd_str += (cluster_cmd_suffix + non_cluster_cmd_suffix)
return cmd_str<|docstring|>Generate the command line for the hpsearch.
Args:
cmd_args: The command line arguments.
num_seeds (int): Number of searches.
grid_config (str): Location of search grid.
hpsearch_dir (str, optional): Where the hpsearch should write its
results to.
Returns:
(str): The command line to be executed.<|endoftext|> |
4ee53bf97b210b5ab754d4d724247207d0f3efd40582bd38a560eef54f306aa1 | def write_seeds_summary(results_dir, summary_keys, summary_sem, summary_precs, ret_seeds=False, summary_fn=None, seeds_summary_fn='seeds_summary_text.txt'):
'Write the MEAN and STD (resp. SEM) while aggregating all seeds to text\n file.\n\n Args:\n results_dir (str): The results directory.\n summary_keys (list): See argument ``summary_keys`` of function\n :func:`run`.\n summary_sem (bool): See argument ``summary_sem`` of function\n :func:`run`.\n summary_precs (list or int, optional): See argument ``summary_precs`` of\n function :func:`run`.\n summary_fn (str, optional): If given, this will determine\n the name of the summary file within individual runs.\n seeds_summmary_fn (str, optional): The name to give to the summary\n file across all seeds.\n ret_seeds (bool, optional): If activated, the random seeds of all\n considered runs are returned as a list.\n '
random_seeds = []
if (summary_precs is None):
summary_precs = 2
if isinstance(summary_precs, int):
summary_precs = ([summary_precs] * len(summary_keys))
else:
assert (len(summary_keys) == len(summary_precs))
score_dict = {}
n_scores = 0
for k in summary_keys:
score_dict[k] = []
seed_dirs = []
seed_dir_prefix = {}
for (i, sim_dir) in enumerate(os.listdir(results_dir)):
sim_path = os.path.join(results_dir, sim_dir)
if (not os.path.isdir(sim_path)):
continue
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(sim_path, i, summary_fn=summary_fn)
except:
warn(('Cannot read results from simulation "%s"!' % sim_dir))
continue
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
warn(('Simulation "%s" did not finish!' % sim_dir))
continue
n_scores += 1
for k in summary_keys:
score_dict[k].append(float(performance_dict[k][0]))
if ret_seeds:
sim_config = get_single_run_config(sim_path)
random_seeds.append(sim_config.random_seed)
seed_dirs.append(sim_path)
if (sim_dir.count('_') == 2):
prefix = sim_dir[:sim_dir.rfind('_')]
if (prefix not in seed_dir_prefix.keys()):
seed_dir_prefix[prefix] = [sim_path, 1]
else:
seed_dir_prefix[prefix][1] += 1
else:
seed_dir_prefix[sim_dir] = [sim_path, 1]
original_seed_path = None
nunique = 0
for (k, v) in seed_dir_prefix.items():
if (v[1] == 1):
original_seed_path = v[0]
nunique += 1
if (nunique > 1):
original_seed_path = None
if (n_scores == 0):
raise RuntimeError('No results found!')
score_means = {}
score_devs = {}
for k in summary_keys:
score_means[k] = np.mean(score_dict[k])
score_devs[k] = np.std(score_dict[k])
if summary_sem:
score_devs[k] /= np.sqrt(n_scores)
filename = os.path.join(results_dir, seeds_summary_fn)
with open(filename, 'w') as f:
for (i, k) in enumerate(summary_keys):
p = summary_precs[i]
f.write(((((('%s (mean +/- %s): %.' + str(p)) + 'f +- %.') + str(p)) + 'f\n') % (k, ('sem' if summary_sem else 'std'), score_means[k], score_devs[k])))
f.write(('Number of seeds: %i \n\n' % n_scores))
f.write('Publication tables style: \n')
f.write(('%s \n' % summary_keys))
tab_str = ''
for (i, k) in enumerate(summary_keys):
if (i > 0):
tab_str += ' & '
p = summary_precs[i]
tab_str += ((((('%.' + str(p)) + 'f $\\pm$ %.') + str(p)) + 'f ') % (score_means[k], score_devs[k]))
f.write(('%s \n\n' % tab_str))
return (random_seeds if ret_seeds else None) | Write the MEAN and STD (resp. SEM) while aggregating all seeds to text
file.
Args:
results_dir (str): The results directory.
summary_keys (list): See argument ``summary_keys`` of function
:func:`run`.
summary_sem (bool): See argument ``summary_sem`` of function
:func:`run`.
summary_precs (list or int, optional): See argument ``summary_precs`` of
function :func:`run`.
summary_fn (str, optional): If given, this will determine
the name of the summary file within individual runs.
seeds_summmary_fn (str, optional): The name to give to the summary
file across all seeds.
ret_seeds (bool, optional): If activated, the random seeds of all
considered runs are returned as a list. | hypnettorch/hpsearch/gather_random_seeds.py | write_seeds_summary | pennfranc/hypnettorch | 31 | python | def write_seeds_summary(results_dir, summary_keys, summary_sem, summary_precs, ret_seeds=False, summary_fn=None, seeds_summary_fn='seeds_summary_text.txt'):
'Write the MEAN and STD (resp. SEM) while aggregating all seeds to text\n file.\n\n Args:\n results_dir (str): The results directory.\n summary_keys (list): See argument ``summary_keys`` of function\n :func:`run`.\n summary_sem (bool): See argument ``summary_sem`` of function\n :func:`run`.\n summary_precs (list or int, optional): See argument ``summary_precs`` of\n function :func:`run`.\n summary_fn (str, optional): If given, this will determine\n the name of the summary file within individual runs.\n seeds_summmary_fn (str, optional): The name to give to the summary\n file across all seeds.\n ret_seeds (bool, optional): If activated, the random seeds of all\n considered runs are returned as a list.\n '
random_seeds = []
if (summary_precs is None):
summary_precs = 2
if isinstance(summary_precs, int):
summary_precs = ([summary_precs] * len(summary_keys))
else:
assert (len(summary_keys) == len(summary_precs))
score_dict = {}
n_scores = 0
for k in summary_keys:
score_dict[k] = []
seed_dirs = []
seed_dir_prefix = {}
for (i, sim_dir) in enumerate(os.listdir(results_dir)):
sim_path = os.path.join(results_dir, sim_dir)
if (not os.path.isdir(sim_path)):
continue
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(sim_path, i, summary_fn=summary_fn)
except:
warn(('Cannot read results from simulation "%s"!' % sim_dir))
continue
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
warn(('Simulation "%s" did not finish!' % sim_dir))
continue
n_scores += 1
for k in summary_keys:
score_dict[k].append(float(performance_dict[k][0]))
if ret_seeds:
sim_config = get_single_run_config(sim_path)
random_seeds.append(sim_config.random_seed)
seed_dirs.append(sim_path)
if (sim_dir.count('_') == 2):
prefix = sim_dir[:sim_dir.rfind('_')]
if (prefix not in seed_dir_prefix.keys()):
seed_dir_prefix[prefix] = [sim_path, 1]
else:
seed_dir_prefix[prefix][1] += 1
else:
seed_dir_prefix[sim_dir] = [sim_path, 1]
original_seed_path = None
nunique = 0
for (k, v) in seed_dir_prefix.items():
if (v[1] == 1):
original_seed_path = v[0]
nunique += 1
if (nunique > 1):
original_seed_path = None
if (n_scores == 0):
raise RuntimeError('No results found!')
score_means = {}
score_devs = {}
for k in summary_keys:
score_means[k] = np.mean(score_dict[k])
score_devs[k] = np.std(score_dict[k])
if summary_sem:
score_devs[k] /= np.sqrt(n_scores)
filename = os.path.join(results_dir, seeds_summary_fn)
with open(filename, 'w') as f:
for (i, k) in enumerate(summary_keys):
p = summary_precs[i]
f.write(((((('%s (mean +/- %s): %.' + str(p)) + 'f +- %.') + str(p)) + 'f\n') % (k, ('sem' if summary_sem else 'std'), score_means[k], score_devs[k])))
f.write(('Number of seeds: %i \n\n' % n_scores))
f.write('Publication tables style: \n')
f.write(('%s \n' % summary_keys))
tab_str =
for (i, k) in enumerate(summary_keys):
if (i > 0):
tab_str += ' & '
p = summary_precs[i]
tab_str += ((((('%.' + str(p)) + 'f $\\pm$ %.') + str(p)) + 'f ') % (score_means[k], score_devs[k]))
f.write(('%s \n\n' % tab_str))
return (random_seeds if ret_seeds else None) | def write_seeds_summary(results_dir, summary_keys, summary_sem, summary_precs, ret_seeds=False, summary_fn=None, seeds_summary_fn='seeds_summary_text.txt'):
'Write the MEAN and STD (resp. SEM) while aggregating all seeds to text\n file.\n\n Args:\n results_dir (str): The results directory.\n summary_keys (list): See argument ``summary_keys`` of function\n :func:`run`.\n summary_sem (bool): See argument ``summary_sem`` of function\n :func:`run`.\n summary_precs (list or int, optional): See argument ``summary_precs`` of\n function :func:`run`.\n summary_fn (str, optional): If given, this will determine\n the name of the summary file within individual runs.\n seeds_summmary_fn (str, optional): The name to give to the summary\n file across all seeds.\n ret_seeds (bool, optional): If activated, the random seeds of all\n considered runs are returned as a list.\n '
random_seeds = []
if (summary_precs is None):
summary_precs = 2
if isinstance(summary_precs, int):
summary_precs = ([summary_precs] * len(summary_keys))
else:
assert (len(summary_keys) == len(summary_precs))
score_dict = {}
n_scores = 0
for k in summary_keys:
score_dict[k] = []
seed_dirs = []
seed_dir_prefix = {}
for (i, sim_dir) in enumerate(os.listdir(results_dir)):
sim_path = os.path.join(results_dir, sim_dir)
if (not os.path.isdir(sim_path)):
continue
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(sim_path, i, summary_fn=summary_fn)
except:
warn(('Cannot read results from simulation "%s"!' % sim_dir))
continue
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
warn(('Simulation "%s" did not finish!' % sim_dir))
continue
n_scores += 1
for k in summary_keys:
score_dict[k].append(float(performance_dict[k][0]))
if ret_seeds:
sim_config = get_single_run_config(sim_path)
random_seeds.append(sim_config.random_seed)
seed_dirs.append(sim_path)
if (sim_dir.count('_') == 2):
prefix = sim_dir[:sim_dir.rfind('_')]
if (prefix not in seed_dir_prefix.keys()):
seed_dir_prefix[prefix] = [sim_path, 1]
else:
seed_dir_prefix[prefix][1] += 1
else:
seed_dir_prefix[sim_dir] = [sim_path, 1]
original_seed_path = None
nunique = 0
for (k, v) in seed_dir_prefix.items():
if (v[1] == 1):
original_seed_path = v[0]
nunique += 1
if (nunique > 1):
original_seed_path = None
if (n_scores == 0):
raise RuntimeError('No results found!')
score_means = {}
score_devs = {}
for k in summary_keys:
score_means[k] = np.mean(score_dict[k])
score_devs[k] = np.std(score_dict[k])
if summary_sem:
score_devs[k] /= np.sqrt(n_scores)
filename = os.path.join(results_dir, seeds_summary_fn)
with open(filename, 'w') as f:
for (i, k) in enumerate(summary_keys):
p = summary_precs[i]
f.write(((((('%s (mean +/- %s): %.' + str(p)) + 'f +- %.') + str(p)) + 'f\n') % (k, ('sem' if summary_sem else 'std'), score_means[k], score_devs[k])))
f.write(('Number of seeds: %i \n\n' % n_scores))
f.write('Publication tables style: \n')
f.write(('%s \n' % summary_keys))
tab_str =
for (i, k) in enumerate(summary_keys):
if (i > 0):
tab_str += ' & '
p = summary_precs[i]
tab_str += ((((('%.' + str(p)) + 'f $\\pm$ %.') + str(p)) + 'f ') % (score_means[k], score_devs[k]))
f.write(('%s \n\n' % tab_str))
return (random_seeds if ret_seeds else None)<|docstring|>Write the MEAN and STD (resp. SEM) while aggregating all seeds to text
file.
Args:
results_dir (str): The results directory.
summary_keys (list): See argument ``summary_keys`` of function
:func:`run`.
summary_sem (bool): See argument ``summary_sem`` of function
:func:`run`.
summary_precs (list or int, optional): See argument ``summary_precs`` of
function :func:`run`.
summary_fn (str, optional): If given, this will determine
the name of the summary file within individual runs.
seeds_summmary_fn (str, optional): The name to give to the summary
file across all seeds.
ret_seeds (bool, optional): If activated, the random seeds of all
considered runs are returned as a list.<|endoftext|> |
f37708db3eaa56455a9d22f49dd274716b720fda2db47b735fe10f9dd363ddde | def run(grid_module=None, results_dir='./out/random_seeds', config=None, ignore_kwds=None, forced_params=None, summary_keys=None, summary_sem=False, summary_precs=None, hpmod_path=None):
"Run the script.\n\n Args:\n grid_module (str, optional): Name of the reference module which contains\n the hyperparameter search config that can be modified to gather\n random seeds.\n results_dir (str, optional): The path where the hpsearch should store\n its results.\n config: The Namespace object containing argument names and values.\n If provided, all random seeds will be gathered from zero, with no\n reference run.\n ignore_kwds (list, optional): A list of keywords in the config file\n to exclude from the grid.\n forced_params (dict, optional): Dict of key-value pairs specifying\n hyperparameter values that should be fixed across runs.\n summary_keys (list, optional): If provided, those mean and std of those\n summary keys will be written by function\n :func:`write_seeds_summary`. Otherwise, the performance key defined\n in ``grid_module`` will be used.\n summary_sem (bool): Whether SEM or SD should be calculated in function\n :func:`write_seeds_summary`.\n summary_precs (list or int, optional): The precision with which the\n summary statistics according to ``summary_keys`` should be listed.\n hpmod_path (str, optional): If the hpsearch doesn't reside in the same\n directory as the calling script, then we need to know from where to\n start the hpsearch.\n "
if (ignore_kwds is None):
ignore_kwds = []
if (forced_params is None):
forced_params = {}
parser = argparse.ArgumentParser(description='Gathering random seeds for the specified experiment.')
parser.add_argument('--seeds_dir', type=str, default='', help=(((((('If provided, all other arguments (except ' + '"grid_module") are ignored! ') + 'This is supposed to be the output folder of a ') + 'random seed gathering experiment. If provided, ') + 'the results (for different seeds) within this ') + 'directory are gathered and written to a human-') + 'readible text file.'))
parser.add_argument('--run_dir', type=str, default='', help=(((((('The output directory of a simulation or a ' + 'hyperparameter search. For single runs, the configuration will be ') + 'loaded and run with different seeds.') + 'For multiple runs, i.e. results of ') + 'hyperparameter searches, the configuration ') + 'leading to the best performance will be ') + 'selected and run with different seeds.'))
parser.add_argument('--config_name', type=str, default='hpsearch_random_seeds', help=(((('A name for this call of gathering random ' + 'seeds. As multiple gatherings might be running ') + 'in parallel, it is important that this name is ') + 'unique name for each experiment. ') + 'Default: %(default)s.'))
parser.add_argument('--grid_module', type=str, default=grid_module, help=(('See CLI argument "grid_module" of ' + 'hyperparameter search script "hpsearch". ') + ('Default: %(default)s.' if (grid_module is not None) else '')))
parser.add_argument('--num_seeds', type=int, default=10, help='The number of different random seeds.')
parser.add_argument('--seeds_list', type=str, default='', help=('The list of seeds to use. If specified, ' + '"num_seeds" will be ignored.'))
parser.add_argument('--vary_data_seed', action='store_true', help=(('If activated, "data_random_seed"s are set ' + 'equal to "random_seed"s. Otherwise only ') + '"random_seed"s are varied.'))
parser.add_argument('--start_gathering', action='store_true', help=('If activated, the actual gathering of random ' + 'seeds is started via the "hpsearch.py" script.'))
hpgroup = parser.add_argument_group('Hpsearch call options')
hpgroup.add_argument('--hps_num_hours', type=int, metavar='N', default=24, help=((('If "run_cluster" is activated, then this ' + 'option determines the maximum number of hours ') + 'the entire search may run on the cluster. ') + 'Default: %(default)s.'))
hpgroup.add_argument('--hps_resources', type=str, default='"rusage[mem=8000]"', help=(((('If "run_cluster" is activated and "scheduler" ' + 'is "lsf", then this option determines the ') + 'resources assigned to the entire ') + 'hyperparameter search (option -R of bsub). ') + 'Default: %(default)s.'))
hpgroup.add_argument('--hps_slurm_mem', type=str, default='8G', help=('See option "slum_mem". This argument effects ' + 'hyperparameter search itself. Default: %(default)s.'))
rsgroup = parser.add_argument_group('Random seed hpsearch options')
hpsearch.hpsearch_cli_arguments(rsgroup, show_out_dir=False, show_grid_module=False)
cmd_args = parser.parse_args()
grid_module = cmd_args.grid_module
if (grid_module is None):
raise ValueError('"grid_module" needs to be specified.')
grid_module = importlib.import_module(grid_module)
hpsearch._read_config(grid_module, require_perf_eval_handle=True)
if (summary_keys is None):
summary_keys = [hpsearch._PERFORMANCE_KEY]
if len(cmd_args.seeds_dir):
print('Writing seed summary ...')
write_seeds_summary(cmd_args.seeds_dir, summary_keys, summary_sem, summary_precs)
exit(0)
if (len(cmd_args.seeds_list) > 0):
seeds_list = misc.str_to_ints(cmd_args.seeds_list)
cmd_args.num_seeds = len(seeds_list)
else:
seeds_list = list(range(cmd_args.num_seeds))
if ((config is not None) and (cmd_args.run_dir != '')):
raise ValueError(('"run_dir" may not be specified if configuration ' + 'is provided directly.'))
hpsearch_dir = None
if (config is None):
if (not os.path.exists(cmd_args.run_dir)):
raise_error = True
if (cmd_args.run_cwd != ''):
tmp_dir = os.path.join(cmd_args.run_cwd, cmd_args.run_dir)
if os.path.exists(tmp_dir):
cmd_args.run_dir = tmp_dir
raise_error = False
if raise_error:
raise ValueError(('Directory "%s" does not exist!' % cmd_args.run_dir))
single_run = False
if os.path.exists(os.path.join(cmd_args.run_dir, 'config.pickle')):
single_run = True
if single_run:
config = get_single_run_config(cmd_args.run_dir)
run_dir = cmd_args.run_dir
else:
(config, run_dir) = get_best_hpsearch_config(cmd_args.run_dir)
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(run_dir, (- 1))
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
raise Exception()
use_run = True
except:
use_run = False
if use_run:
run_dir = os.path.normpath(run_dir)
if (not os.path.isabs(results_dir)):
if os.path.isdir(cmd_args.run_cwd):
results_dir = os.path.join(cmd_args.run_cwd, results_dir)
results_dir = os.path.abspath(results_dir)
hpsearch_dir = os.path.join(results_dir, os.path.basename(run_dir))
if os.path.exists(hpsearch_dir):
warn(('Folder "%s" already exists.' % hpsearch_dir))
print('Attempting to aggregate random seed results ...')
gathered_seeds = write_seeds_summary(hpsearch_dir, summary_keys, summary_sem, summary_precs, ret_seeds=True)
if (len(gathered_seeds) >= len(seeds_list)):
print('Already enough seeds have been gathered!')
exit(0)
for gs in gathered_seeds:
if (gs in seeds_list):
seeds_list.remove(gs)
else:
ignored_seed = seeds_list.pop()
if (len(cmd_args.seeds_list) > 0):
print((('Seed %d is ignored as seed %d already ' % (ignored_seed, gs)) + 'exists.'))
else:
os.makedirs(hpsearch_dir)
shutil.copytree(run_dir, os.path.join(hpsearch_dir, os.path.basename(run_dir)))
if (config.random_seed in seeds_list):
seeds_list.remove(config.random_seed)
else:
ignored_seed = seeds_list.pop()
if (len(cmd_args.seeds_list) > 0):
print(('Seed %d is ignored as seed %d already exists.' % (ignored_seed, config.random_seed)))
print(('%d random seeds will be gathered!' % len(seeds_list)))
if (hpsearch._OUT_ARG not in ignore_kwds):
ignore_kwds.append(hpsearch._OUT_ARG)
for kwd in ignore_kwds:
delattr(config, kwd)
if (len(forced_params.keys()) > 0):
for (kwd, value) in forced_params.items():
setattr(config, kwd, value)
(config_dn, config_bn) = os.path.split(cmd_args.config_name)
if (len(config_dn) == 0):
config_dn = tempfile.gettempdir()
else:
config_dn = os.path.abspath(config_dn)
config_fn_prefix = os.path.splitext(config_bn)[0]
config_name = os.path.join(config_dn, (config_fn_prefix + '.pickle'))
if os.path.exists(config_name):
if (len(config_dn) > 0):
overwrite = input((('The config file "%s" ' % config_name) + 'already exists! Do you want to overwrite the file? [y/n] '))
if (not (overwrite in ['yes', 'y', 'Y'])):
exit(1)
else:
config_name_temp = tempfile.NamedTemporaryFile(prefix=config_fn_prefix, suffix='.pickle')
print(('Search grid "%s" already exists, using name "%s" instead!' % (config_name, config_name_temp.name)))
config_name = config_name_temp.name
config_name_temp.close()
(grid, conditions) = build_grid_and_conditions(cmd_args, config, seeds_list)
rseed_config = {'grid': grid, 'conditions': conditions}
with open(config_name, 'wb') as f:
pickle.dump(rseed_config, f)
if cmd_args.start_gathering:
cmd_str = get_hpsearch_call(cmd_args, len(seeds_list), config_name, hpsearch_dir=hpsearch_dir)
print(cmd_str)
if (hpmod_path is not None):
backup_curr_path = os.getcwd()
os.chdir(hpmod_path)
if (cmd_args.run_cluster and (cmd_args.scheduler == 'slurm')):
job_script_fn = hpsearch._write_slurm_script(Namespace(**{'num_hours': cmd_args.hps_num_hours, 'slurm_mem': cmd_args.hps_slurm_mem, 'slurm_gres': 'gpu:0', 'slurm_partition': cmd_args.slurm_partition, 'slurm_qos': cmd_args.slurm_qos, 'slurm_constraint': cmd_args.slurm_constraint}), cmd_str, 'random_seeds')
cmd_str = ('sbatch %s' % job_script_fn)
print(('We will execute command "%s".' % cmd_str))
print('Starting gathering random seeds...')
ret = call(cmd_str, shell=True, executable='/bin/bash')
print(('Call finished with return code %d.' % ret))
if (hpmod_path is not None):
os.chdir(backup_curr_path)
if ((not cmd_args.run_cluster) and (hpsearch_dir is not None)):
write_seeds_summary(hpsearch_dir, summary_keys, summary_sem, summary_precs)
print('Random seed gathering finished successfully!')
exit(0)
print((hpsearch_dir is None))
if (hpsearch_dir is not None):
print((('IMPORTANT: At least one random seed has already been ' + 'gathered! Please ensure that the hpsearch forces the correct ') + 'output path.'))
print('Below is a possible hpsearch call:')
call_appendix = ''
if (hpsearch_dir is not None):
call_appendix = ('--force_out_dir --dont_force_new_dir ' + ('--out_dir=%s' % hpsearch_dir))
print()
print(('python3 hpsearch.py --grid_module=%s --grid_config=%s %s' % (cmd_args.grid_module, config_name, call_appendix)))
print()
if (hpsearch_dir is None):
print(('Below is the "grid_module" name and the path to the ' + '"grid_config".'))
print(cmd_args.grid_module, config_name)
else:
print((('Below is the "grid_module" name, the path to the ' + '"grid_config" and the output path that should be used for the ') + 'hpsearch.'))
print(cmd_args.grid_module, config_name, hpsearch_dir) | Run the script.
Args:
grid_module (str, optional): Name of the reference module which contains
the hyperparameter search config that can be modified to gather
random seeds.
results_dir (str, optional): The path where the hpsearch should store
its results.
config: The Namespace object containing argument names and values.
If provided, all random seeds will be gathered from zero, with no
reference run.
ignore_kwds (list, optional): A list of keywords in the config file
to exclude from the grid.
forced_params (dict, optional): Dict of key-value pairs specifying
hyperparameter values that should be fixed across runs.
summary_keys (list, optional): If provided, those mean and std of those
summary keys will be written by function
:func:`write_seeds_summary`. Otherwise, the performance key defined
in ``grid_module`` will be used.
summary_sem (bool): Whether SEM or SD should be calculated in function
:func:`write_seeds_summary`.
summary_precs (list or int, optional): The precision with which the
summary statistics according to ``summary_keys`` should be listed.
hpmod_path (str, optional): If the hpsearch doesn't reside in the same
directory as the calling script, then we need to know from where to
start the hpsearch. | hypnettorch/hpsearch/gather_random_seeds.py | run | pennfranc/hypnettorch | 31 | python | def run(grid_module=None, results_dir='./out/random_seeds', config=None, ignore_kwds=None, forced_params=None, summary_keys=None, summary_sem=False, summary_precs=None, hpmod_path=None):
"Run the script.\n\n Args:\n grid_module (str, optional): Name of the reference module which contains\n the hyperparameter search config that can be modified to gather\n random seeds.\n results_dir (str, optional): The path where the hpsearch should store\n its results.\n config: The Namespace object containing argument names and values.\n If provided, all random seeds will be gathered from zero, with no\n reference run.\n ignore_kwds (list, optional): A list of keywords in the config file\n to exclude from the grid.\n forced_params (dict, optional): Dict of key-value pairs specifying\n hyperparameter values that should be fixed across runs.\n summary_keys (list, optional): If provided, those mean and std of those\n summary keys will be written by function\n :func:`write_seeds_summary`. Otherwise, the performance key defined\n in ``grid_module`` will be used.\n summary_sem (bool): Whether SEM or SD should be calculated in function\n :func:`write_seeds_summary`.\n summary_precs (list or int, optional): The precision with which the\n summary statistics according to ``summary_keys`` should be listed.\n hpmod_path (str, optional): If the hpsearch doesn't reside in the same\n directory as the calling script, then we need to know from where to\n start the hpsearch.\n "
if (ignore_kwds is None):
ignore_kwds = []
if (forced_params is None):
forced_params = {}
parser = argparse.ArgumentParser(description='Gathering random seeds for the specified experiment.')
parser.add_argument('--seeds_dir', type=str, default=, help=(((((('If provided, all other arguments (except ' + '"grid_module") are ignored! ') + 'This is supposed to be the output folder of a ') + 'random seed gathering experiment. If provided, ') + 'the results (for different seeds) within this ') + 'directory are gathered and written to a human-') + 'readible text file.'))
parser.add_argument('--run_dir', type=str, default=, help=(((((('The output directory of a simulation or a ' + 'hyperparameter search. For single runs, the configuration will be ') + 'loaded and run with different seeds.') + 'For multiple runs, i.e. results of ') + 'hyperparameter searches, the configuration ') + 'leading to the best performance will be ') + 'selected and run with different seeds.'))
parser.add_argument('--config_name', type=str, default='hpsearch_random_seeds', help=(((('A name for this call of gathering random ' + 'seeds. As multiple gatherings might be running ') + 'in parallel, it is important that this name is ') + 'unique name for each experiment. ') + 'Default: %(default)s.'))
parser.add_argument('--grid_module', type=str, default=grid_module, help=(('See CLI argument "grid_module" of ' + 'hyperparameter search script "hpsearch". ') + ('Default: %(default)s.' if (grid_module is not None) else )))
parser.add_argument('--num_seeds', type=int, default=10, help='The number of different random seeds.')
parser.add_argument('--seeds_list', type=str, default=, help=('The list of seeds to use. If specified, ' + '"num_seeds" will be ignored.'))
parser.add_argument('--vary_data_seed', action='store_true', help=(('If activated, "data_random_seed"s are set ' + 'equal to "random_seed"s. Otherwise only ') + '"random_seed"s are varied.'))
parser.add_argument('--start_gathering', action='store_true', help=('If activated, the actual gathering of random ' + 'seeds is started via the "hpsearch.py" script.'))
hpgroup = parser.add_argument_group('Hpsearch call options')
hpgroup.add_argument('--hps_num_hours', type=int, metavar='N', default=24, help=((('If "run_cluster" is activated, then this ' + 'option determines the maximum number of hours ') + 'the entire search may run on the cluster. ') + 'Default: %(default)s.'))
hpgroup.add_argument('--hps_resources', type=str, default='"rusage[mem=8000]"', help=(((('If "run_cluster" is activated and "scheduler" ' + 'is "lsf", then this option determines the ') + 'resources assigned to the entire ') + 'hyperparameter search (option -R of bsub). ') + 'Default: %(default)s.'))
hpgroup.add_argument('--hps_slurm_mem', type=str, default='8G', help=('See option "slum_mem". This argument effects ' + 'hyperparameter search itself. Default: %(default)s.'))
rsgroup = parser.add_argument_group('Random seed hpsearch options')
hpsearch.hpsearch_cli_arguments(rsgroup, show_out_dir=False, show_grid_module=False)
cmd_args = parser.parse_args()
grid_module = cmd_args.grid_module
if (grid_module is None):
raise ValueError('"grid_module" needs to be specified.')
grid_module = importlib.import_module(grid_module)
hpsearch._read_config(grid_module, require_perf_eval_handle=True)
if (summary_keys is None):
summary_keys = [hpsearch._PERFORMANCE_KEY]
if len(cmd_args.seeds_dir):
print('Writing seed summary ...')
write_seeds_summary(cmd_args.seeds_dir, summary_keys, summary_sem, summary_precs)
exit(0)
if (len(cmd_args.seeds_list) > 0):
seeds_list = misc.str_to_ints(cmd_args.seeds_list)
cmd_args.num_seeds = len(seeds_list)
else:
seeds_list = list(range(cmd_args.num_seeds))
if ((config is not None) and (cmd_args.run_dir != )):
raise ValueError(('"run_dir" may not be specified if configuration ' + 'is provided directly.'))
hpsearch_dir = None
if (config is None):
if (not os.path.exists(cmd_args.run_dir)):
raise_error = True
if (cmd_args.run_cwd != ):
tmp_dir = os.path.join(cmd_args.run_cwd, cmd_args.run_dir)
if os.path.exists(tmp_dir):
cmd_args.run_dir = tmp_dir
raise_error = False
if raise_error:
raise ValueError(('Directory "%s" does not exist!' % cmd_args.run_dir))
single_run = False
if os.path.exists(os.path.join(cmd_args.run_dir, 'config.pickle')):
single_run = True
if single_run:
config = get_single_run_config(cmd_args.run_dir)
run_dir = cmd_args.run_dir
else:
(config, run_dir) = get_best_hpsearch_config(cmd_args.run_dir)
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(run_dir, (- 1))
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
raise Exception()
use_run = True
except:
use_run = False
if use_run:
run_dir = os.path.normpath(run_dir)
if (not os.path.isabs(results_dir)):
if os.path.isdir(cmd_args.run_cwd):
results_dir = os.path.join(cmd_args.run_cwd, results_dir)
results_dir = os.path.abspath(results_dir)
hpsearch_dir = os.path.join(results_dir, os.path.basename(run_dir))
if os.path.exists(hpsearch_dir):
warn(('Folder "%s" already exists.' % hpsearch_dir))
print('Attempting to aggregate random seed results ...')
gathered_seeds = write_seeds_summary(hpsearch_dir, summary_keys, summary_sem, summary_precs, ret_seeds=True)
if (len(gathered_seeds) >= len(seeds_list)):
print('Already enough seeds have been gathered!')
exit(0)
for gs in gathered_seeds:
if (gs in seeds_list):
seeds_list.remove(gs)
else:
ignored_seed = seeds_list.pop()
if (len(cmd_args.seeds_list) > 0):
print((('Seed %d is ignored as seed %d already ' % (ignored_seed, gs)) + 'exists.'))
else:
os.makedirs(hpsearch_dir)
shutil.copytree(run_dir, os.path.join(hpsearch_dir, os.path.basename(run_dir)))
if (config.random_seed in seeds_list):
seeds_list.remove(config.random_seed)
else:
ignored_seed = seeds_list.pop()
if (len(cmd_args.seeds_list) > 0):
print(('Seed %d is ignored as seed %d already exists.' % (ignored_seed, config.random_seed)))
print(('%d random seeds will be gathered!' % len(seeds_list)))
if (hpsearch._OUT_ARG not in ignore_kwds):
ignore_kwds.append(hpsearch._OUT_ARG)
for kwd in ignore_kwds:
delattr(config, kwd)
if (len(forced_params.keys()) > 0):
for (kwd, value) in forced_params.items():
setattr(config, kwd, value)
(config_dn, config_bn) = os.path.split(cmd_args.config_name)
if (len(config_dn) == 0):
config_dn = tempfile.gettempdir()
else:
config_dn = os.path.abspath(config_dn)
config_fn_prefix = os.path.splitext(config_bn)[0]
config_name = os.path.join(config_dn, (config_fn_prefix + '.pickle'))
if os.path.exists(config_name):
if (len(config_dn) > 0):
overwrite = input((('The config file "%s" ' % config_name) + 'already exists! Do you want to overwrite the file? [y/n] '))
if (not (overwrite in ['yes', 'y', 'Y'])):
exit(1)
else:
config_name_temp = tempfile.NamedTemporaryFile(prefix=config_fn_prefix, suffix='.pickle')
print(('Search grid "%s" already exists, using name "%s" instead!' % (config_name, config_name_temp.name)))
config_name = config_name_temp.name
config_name_temp.close()
(grid, conditions) = build_grid_and_conditions(cmd_args, config, seeds_list)
rseed_config = {'grid': grid, 'conditions': conditions}
with open(config_name, 'wb') as f:
pickle.dump(rseed_config, f)
if cmd_args.start_gathering:
cmd_str = get_hpsearch_call(cmd_args, len(seeds_list), config_name, hpsearch_dir=hpsearch_dir)
print(cmd_str)
if (hpmod_path is not None):
backup_curr_path = os.getcwd()
os.chdir(hpmod_path)
if (cmd_args.run_cluster and (cmd_args.scheduler == 'slurm')):
job_script_fn = hpsearch._write_slurm_script(Namespace(**{'num_hours': cmd_args.hps_num_hours, 'slurm_mem': cmd_args.hps_slurm_mem, 'slurm_gres': 'gpu:0', 'slurm_partition': cmd_args.slurm_partition, 'slurm_qos': cmd_args.slurm_qos, 'slurm_constraint': cmd_args.slurm_constraint}), cmd_str, 'random_seeds')
cmd_str = ('sbatch %s' % job_script_fn)
print(('We will execute command "%s".' % cmd_str))
print('Starting gathering random seeds...')
ret = call(cmd_str, shell=True, executable='/bin/bash')
print(('Call finished with return code %d.' % ret))
if (hpmod_path is not None):
os.chdir(backup_curr_path)
if ((not cmd_args.run_cluster) and (hpsearch_dir is not None)):
write_seeds_summary(hpsearch_dir, summary_keys, summary_sem, summary_precs)
print('Random seed gathering finished successfully!')
exit(0)
print((hpsearch_dir is None))
if (hpsearch_dir is not None):
print((('IMPORTANT: At least one random seed has already been ' + 'gathered! Please ensure that the hpsearch forces the correct ') + 'output path.'))
print('Below is a possible hpsearch call:')
call_appendix =
if (hpsearch_dir is not None):
call_appendix = ('--force_out_dir --dont_force_new_dir ' + ('--out_dir=%s' % hpsearch_dir))
print()
print(('python3 hpsearch.py --grid_module=%s --grid_config=%s %s' % (cmd_args.grid_module, config_name, call_appendix)))
print()
if (hpsearch_dir is None):
print(('Below is the "grid_module" name and the path to the ' + '"grid_config".'))
print(cmd_args.grid_module, config_name)
else:
print((('Below is the "grid_module" name, the path to the ' + '"grid_config" and the output path that should be used for the ') + 'hpsearch.'))
print(cmd_args.grid_module, config_name, hpsearch_dir) | def run(grid_module=None, results_dir='./out/random_seeds', config=None, ignore_kwds=None, forced_params=None, summary_keys=None, summary_sem=False, summary_precs=None, hpmod_path=None):
"Run the script.\n\n Args:\n grid_module (str, optional): Name of the reference module which contains\n the hyperparameter search config that can be modified to gather\n random seeds.\n results_dir (str, optional): The path where the hpsearch should store\n its results.\n config: The Namespace object containing argument names and values.\n If provided, all random seeds will be gathered from zero, with no\n reference run.\n ignore_kwds (list, optional): A list of keywords in the config file\n to exclude from the grid.\n forced_params (dict, optional): Dict of key-value pairs specifying\n hyperparameter values that should be fixed across runs.\n summary_keys (list, optional): If provided, those mean and std of those\n summary keys will be written by function\n :func:`write_seeds_summary`. Otherwise, the performance key defined\n in ``grid_module`` will be used.\n summary_sem (bool): Whether SEM or SD should be calculated in function\n :func:`write_seeds_summary`.\n summary_precs (list or int, optional): The precision with which the\n summary statistics according to ``summary_keys`` should be listed.\n hpmod_path (str, optional): If the hpsearch doesn't reside in the same\n directory as the calling script, then we need to know from where to\n start the hpsearch.\n "
if (ignore_kwds is None):
ignore_kwds = []
if (forced_params is None):
forced_params = {}
parser = argparse.ArgumentParser(description='Gathering random seeds for the specified experiment.')
parser.add_argument('--seeds_dir', type=str, default=, help=(((((('If provided, all other arguments (except ' + '"grid_module") are ignored! ') + 'This is supposed to be the output folder of a ') + 'random seed gathering experiment. If provided, ') + 'the results (for different seeds) within this ') + 'directory are gathered and written to a human-') + 'readible text file.'))
parser.add_argument('--run_dir', type=str, default=, help=(((((('The output directory of a simulation or a ' + 'hyperparameter search. For single runs, the configuration will be ') + 'loaded and run with different seeds.') + 'For multiple runs, i.e. results of ') + 'hyperparameter searches, the configuration ') + 'leading to the best performance will be ') + 'selected and run with different seeds.'))
parser.add_argument('--config_name', type=str, default='hpsearch_random_seeds', help=(((('A name for this call of gathering random ' + 'seeds. As multiple gatherings might be running ') + 'in parallel, it is important that this name is ') + 'unique name for each experiment. ') + 'Default: %(default)s.'))
parser.add_argument('--grid_module', type=str, default=grid_module, help=(('See CLI argument "grid_module" of ' + 'hyperparameter search script "hpsearch". ') + ('Default: %(default)s.' if (grid_module is not None) else )))
parser.add_argument('--num_seeds', type=int, default=10, help='The number of different random seeds.')
parser.add_argument('--seeds_list', type=str, default=, help=('The list of seeds to use. If specified, ' + '"num_seeds" will be ignored.'))
parser.add_argument('--vary_data_seed', action='store_true', help=(('If activated, "data_random_seed"s are set ' + 'equal to "random_seed"s. Otherwise only ') + '"random_seed"s are varied.'))
parser.add_argument('--start_gathering', action='store_true', help=('If activated, the actual gathering of random ' + 'seeds is started via the "hpsearch.py" script.'))
hpgroup = parser.add_argument_group('Hpsearch call options')
hpgroup.add_argument('--hps_num_hours', type=int, metavar='N', default=24, help=((('If "run_cluster" is activated, then this ' + 'option determines the maximum number of hours ') + 'the entire search may run on the cluster. ') + 'Default: %(default)s.'))
hpgroup.add_argument('--hps_resources', type=str, default='"rusage[mem=8000]"', help=(((('If "run_cluster" is activated and "scheduler" ' + 'is "lsf", then this option determines the ') + 'resources assigned to the entire ') + 'hyperparameter search (option -R of bsub). ') + 'Default: %(default)s.'))
hpgroup.add_argument('--hps_slurm_mem', type=str, default='8G', help=('See option "slum_mem". This argument effects ' + 'hyperparameter search itself. Default: %(default)s.'))
rsgroup = parser.add_argument_group('Random seed hpsearch options')
hpsearch.hpsearch_cli_arguments(rsgroup, show_out_dir=False, show_grid_module=False)
cmd_args = parser.parse_args()
grid_module = cmd_args.grid_module
if (grid_module is None):
raise ValueError('"grid_module" needs to be specified.')
grid_module = importlib.import_module(grid_module)
hpsearch._read_config(grid_module, require_perf_eval_handle=True)
if (summary_keys is None):
summary_keys = [hpsearch._PERFORMANCE_KEY]
if len(cmd_args.seeds_dir):
print('Writing seed summary ...')
write_seeds_summary(cmd_args.seeds_dir, summary_keys, summary_sem, summary_precs)
exit(0)
if (len(cmd_args.seeds_list) > 0):
seeds_list = misc.str_to_ints(cmd_args.seeds_list)
cmd_args.num_seeds = len(seeds_list)
else:
seeds_list = list(range(cmd_args.num_seeds))
if ((config is not None) and (cmd_args.run_dir != )):
raise ValueError(('"run_dir" may not be specified if configuration ' + 'is provided directly.'))
hpsearch_dir = None
if (config is None):
if (not os.path.exists(cmd_args.run_dir)):
raise_error = True
if (cmd_args.run_cwd != ):
tmp_dir = os.path.join(cmd_args.run_cwd, cmd_args.run_dir)
if os.path.exists(tmp_dir):
cmd_args.run_dir = tmp_dir
raise_error = False
if raise_error:
raise ValueError(('Directory "%s" does not exist!' % cmd_args.run_dir))
single_run = False
if os.path.exists(os.path.join(cmd_args.run_dir, 'config.pickle')):
single_run = True
if single_run:
config = get_single_run_config(cmd_args.run_dir)
run_dir = cmd_args.run_dir
else:
(config, run_dir) = get_best_hpsearch_config(cmd_args.run_dir)
try:
performance_dict = hpsearch._SUMMARY_PARSER_HANDLE(run_dir, (- 1))
has_finished = int(performance_dict['finished'][0])
if (not has_finished):
raise Exception()
use_run = True
except:
use_run = False
if use_run:
run_dir = os.path.normpath(run_dir)
if (not os.path.isabs(results_dir)):
if os.path.isdir(cmd_args.run_cwd):
results_dir = os.path.join(cmd_args.run_cwd, results_dir)
results_dir = os.path.abspath(results_dir)
hpsearch_dir = os.path.join(results_dir, os.path.basename(run_dir))
if os.path.exists(hpsearch_dir):
warn(('Folder "%s" already exists.' % hpsearch_dir))
print('Attempting to aggregate random seed results ...')
gathered_seeds = write_seeds_summary(hpsearch_dir, summary_keys, summary_sem, summary_precs, ret_seeds=True)
if (len(gathered_seeds) >= len(seeds_list)):
print('Already enough seeds have been gathered!')
exit(0)
for gs in gathered_seeds:
if (gs in seeds_list):
seeds_list.remove(gs)
else:
ignored_seed = seeds_list.pop()
if (len(cmd_args.seeds_list) > 0):
print((('Seed %d is ignored as seed %d already ' % (ignored_seed, gs)) + 'exists.'))
else:
os.makedirs(hpsearch_dir)
shutil.copytree(run_dir, os.path.join(hpsearch_dir, os.path.basename(run_dir)))
if (config.random_seed in seeds_list):
seeds_list.remove(config.random_seed)
else:
ignored_seed = seeds_list.pop()
if (len(cmd_args.seeds_list) > 0):
print(('Seed %d is ignored as seed %d already exists.' % (ignored_seed, config.random_seed)))
print(('%d random seeds will be gathered!' % len(seeds_list)))
if (hpsearch._OUT_ARG not in ignore_kwds):
ignore_kwds.append(hpsearch._OUT_ARG)
for kwd in ignore_kwds:
delattr(config, kwd)
if (len(forced_params.keys()) > 0):
for (kwd, value) in forced_params.items():
setattr(config, kwd, value)
(config_dn, config_bn) = os.path.split(cmd_args.config_name)
if (len(config_dn) == 0):
config_dn = tempfile.gettempdir()
else:
config_dn = os.path.abspath(config_dn)
config_fn_prefix = os.path.splitext(config_bn)[0]
config_name = os.path.join(config_dn, (config_fn_prefix + '.pickle'))
if os.path.exists(config_name):
if (len(config_dn) > 0):
overwrite = input((('The config file "%s" ' % config_name) + 'already exists! Do you want to overwrite the file? [y/n] '))
if (not (overwrite in ['yes', 'y', 'Y'])):
exit(1)
else:
config_name_temp = tempfile.NamedTemporaryFile(prefix=config_fn_prefix, suffix='.pickle')
print(('Search grid "%s" already exists, using name "%s" instead!' % (config_name, config_name_temp.name)))
config_name = config_name_temp.name
config_name_temp.close()
(grid, conditions) = build_grid_and_conditions(cmd_args, config, seeds_list)
rseed_config = {'grid': grid, 'conditions': conditions}
with open(config_name, 'wb') as f:
pickle.dump(rseed_config, f)
if cmd_args.start_gathering:
cmd_str = get_hpsearch_call(cmd_args, len(seeds_list), config_name, hpsearch_dir=hpsearch_dir)
print(cmd_str)
if (hpmod_path is not None):
backup_curr_path = os.getcwd()
os.chdir(hpmod_path)
if (cmd_args.run_cluster and (cmd_args.scheduler == 'slurm')):
job_script_fn = hpsearch._write_slurm_script(Namespace(**{'num_hours': cmd_args.hps_num_hours, 'slurm_mem': cmd_args.hps_slurm_mem, 'slurm_gres': 'gpu:0', 'slurm_partition': cmd_args.slurm_partition, 'slurm_qos': cmd_args.slurm_qos, 'slurm_constraint': cmd_args.slurm_constraint}), cmd_str, 'random_seeds')
cmd_str = ('sbatch %s' % job_script_fn)
print(('We will execute command "%s".' % cmd_str))
print('Starting gathering random seeds...')
ret = call(cmd_str, shell=True, executable='/bin/bash')
print(('Call finished with return code %d.' % ret))
if (hpmod_path is not None):
os.chdir(backup_curr_path)
if ((not cmd_args.run_cluster) and (hpsearch_dir is not None)):
write_seeds_summary(hpsearch_dir, summary_keys, summary_sem, summary_precs)
print('Random seed gathering finished successfully!')
exit(0)
print((hpsearch_dir is None))
if (hpsearch_dir is not None):
print((('IMPORTANT: At least one random seed has already been ' + 'gathered! Please ensure that the hpsearch forces the correct ') + 'output path.'))
print('Below is a possible hpsearch call:')
call_appendix =
if (hpsearch_dir is not None):
call_appendix = ('--force_out_dir --dont_force_new_dir ' + ('--out_dir=%s' % hpsearch_dir))
print()
print(('python3 hpsearch.py --grid_module=%s --grid_config=%s %s' % (cmd_args.grid_module, config_name, call_appendix)))
print()
if (hpsearch_dir is None):
print(('Below is the "grid_module" name and the path to the ' + '"grid_config".'))
print(cmd_args.grid_module, config_name)
else:
print((('Below is the "grid_module" name, the path to the ' + '"grid_config" and the output path that should be used for the ') + 'hpsearch.'))
print(cmd_args.grid_module, config_name, hpsearch_dir)<|docstring|>Run the script.
Args:
grid_module (str, optional): Name of the reference module which contains
the hyperparameter search config that can be modified to gather
random seeds.
results_dir (str, optional): The path where the hpsearch should store
its results.
config: The Namespace object containing argument names and values.
If provided, all random seeds will be gathered from zero, with no
reference run.
ignore_kwds (list, optional): A list of keywords in the config file
to exclude from the grid.
forced_params (dict, optional): Dict of key-value pairs specifying
hyperparameter values that should be fixed across runs.
summary_keys (list, optional): If provided, those mean and std of those
summary keys will be written by function
:func:`write_seeds_summary`. Otherwise, the performance key defined
in ``grid_module`` will be used.
summary_sem (bool): Whether SEM or SD should be calculated in function
:func:`write_seeds_summary`.
summary_precs (list or int, optional): The precision with which the
summary statistics according to ``summary_keys`` should be listed.
hpmod_path (str, optional): If the hpsearch doesn't reside in the same
directory as the calling script, then we need to know from where to
start the hpsearch.<|endoftext|> |
f4246c08450ca6fe2daed8261fd4a9d9915e6b94503dd42663449788dbc5970d | def get_domains_to_update_es_filter():
"\n Returns ES filter to filter domains that are never updated or\n domains that haven't been updated since a week or domains that\n have been updated within last week but have new form submissions\n in the last day.\n "
last_week = (datetime.utcnow() - timedelta(days=7))
more_than_a_week_ago = filters.date_range('cp_last_updated', lt=last_week)
less_than_a_week_ago = filters.date_range('cp_last_updated', gte=last_week)
not_updated = filters.missing('cp_last_updated')
domains_submitted_today = FormES().submitted(gte=(datetime.utcnow() - timedelta(days=1))).terms_aggregation('domain', 'domain').size(0).run().aggregations.domain.keys
return filters.OR(not_updated, more_than_a_week_ago, filters.AND(less_than_a_week_ago, filters.term('name', domains_submitted_today))) | Returns ES filter to filter domains that are never updated or
domains that haven't been updated since a week or domains that
have been updated within last week but have new form submissions
in the last day. | corehq/apps/reports/tasks.py | get_domains_to_update_es_filter | kkrampa/commcare-hq | 1 | python | def get_domains_to_update_es_filter():
"\n Returns ES filter to filter domains that are never updated or\n domains that haven't been updated since a week or domains that\n have been updated within last week but have new form submissions\n in the last day.\n "
last_week = (datetime.utcnow() - timedelta(days=7))
more_than_a_week_ago = filters.date_range('cp_last_updated', lt=last_week)
less_than_a_week_ago = filters.date_range('cp_last_updated', gte=last_week)
not_updated = filters.missing('cp_last_updated')
domains_submitted_today = FormES().submitted(gte=(datetime.utcnow() - timedelta(days=1))).terms_aggregation('domain', 'domain').size(0).run().aggregations.domain.keys
return filters.OR(not_updated, more_than_a_week_ago, filters.AND(less_than_a_week_ago, filters.term('name', domains_submitted_today))) | def get_domains_to_update_es_filter():
"\n Returns ES filter to filter domains that are never updated or\n domains that haven't been updated since a week or domains that\n have been updated within last week but have new form submissions\n in the last day.\n "
last_week = (datetime.utcnow() - timedelta(days=7))
more_than_a_week_ago = filters.date_range('cp_last_updated', lt=last_week)
less_than_a_week_ago = filters.date_range('cp_last_updated', gte=last_week)
not_updated = filters.missing('cp_last_updated')
domains_submitted_today = FormES().submitted(gte=(datetime.utcnow() - timedelta(days=1))).terms_aggregation('domain', 'domain').size(0).run().aggregations.domain.keys
return filters.OR(not_updated, more_than_a_week_ago, filters.AND(less_than_a_week_ago, filters.term('name', domains_submitted_today)))<|docstring|>Returns ES filter to filter domains that are never updated or
domains that haven't been updated since a week or domains that
have been updated within last week but have new form submissions
in the last day.<|endoftext|> |
be391f733777f9ae0dd4e4955c5abde3f76bf1feb4babc675fc1c8082ca41bd3 | def _get_export_properties(export_id):
'\n Return a list of strings corresponding to form questions that are\n included in the export.\n '
properties = set()
if export_id:
from corehq.apps.export.models import FormExportInstance
export = FormExportInstance.get(export_id)
for table in export.tables:
for column in table.columns:
if (column.selected and column.item):
path_parts = [n.name for n in column.item.path]
path_parts = (path_parts[1:] if (path_parts[0] == 'form') else path_parts)
properties.add('-'.join(path_parts))
return properties | Return a list of strings corresponding to form questions that are
included in the export. | corehq/apps/reports/tasks.py | _get_export_properties | kkrampa/commcare-hq | 1 | python | def _get_export_properties(export_id):
'\n Return a list of strings corresponding to form questions that are\n included in the export.\n '
properties = set()
if export_id:
from corehq.apps.export.models import FormExportInstance
export = FormExportInstance.get(export_id)
for table in export.tables:
for column in table.columns:
if (column.selected and column.item):
path_parts = [n.name for n in column.item.path]
path_parts = (path_parts[1:] if (path_parts[0] == 'form') else path_parts)
properties.add('-'.join(path_parts))
return properties | def _get_export_properties(export_id):
'\n Return a list of strings corresponding to form questions that are\n included in the export.\n '
properties = set()
if export_id:
from corehq.apps.export.models import FormExportInstance
export = FormExportInstance.get(export_id)
for table in export.tables:
for column in table.columns:
if (column.selected and column.item):
path_parts = [n.name for n in column.item.path]
path_parts = (path_parts[1:] if (path_parts[0] == 'form') else path_parts)
properties.add('-'.join(path_parts))
return properties<|docstring|>Return a list of strings corresponding to form questions that are
included in the export.<|endoftext|> |
ec2fe7c6668e3ffa633f4686b0a9a71cd5b08ab4dda34a3e887416ff15f92d73 | def _extract_form_attachment_info(form, properties):
'\n This is a helper function for build_form_multimedia_zip.\n Return a dict containing information about the given form and its relevant\n attachments\n '
def find_question_id(form, value):
for (k, v) in six.iteritems(form):
if isinstance(v, dict):
ret = find_question_id(v, value)
if ret:
return ([k] + ret)
elif isinstance(v, list):
for repeat in v:
ret = find_question_id(repeat, value)
if ret:
return ([k] + ret)
elif (v == value):
return [k]
return None
unknown_number = 0
case_blocks = extract_case_blocks(form.form_data)
form_info = {'form': form, 'attachments': [], 'case_ids': {c['@case_id'] for c in case_blocks}, 'username': form.get_data('form/meta/username')}
for (attachment_name, attachment) in six.iteritems(form.attachments):
if hasattr(attachment, 'content_type'):
content_type = attachment.content_type
else:
content_type = attachment['content_type']
if (content_type == 'text/xml'):
continue
try:
question_id = six.text_type('-'.join(find_question_id(form.form_data, attachment_name)))
except TypeError:
question_id = ('unknown' + six.text_type(unknown_number))
unknown_number += 1
if ((not properties) or (question_id in properties)):
extension = six.text_type(os.path.splitext(attachment_name)[1])
if hasattr(attachment, 'content_length'):
size = attachment.content_length
elif ('content_length' in attachment):
size = attachment['content_length']
else:
size = attachment['length']
form_info['attachments'].append({'size': size, 'name': attachment_name, 'question_id': question_id, 'extension': extension, 'timestamp': form.received_on.timetuple()})
return form_info | This is a helper function for build_form_multimedia_zip.
Return a dict containing information about the given form and its relevant
attachments | corehq/apps/reports/tasks.py | _extract_form_attachment_info | kkrampa/commcare-hq | 1 | python | def _extract_form_attachment_info(form, properties):
'\n This is a helper function for build_form_multimedia_zip.\n Return a dict containing information about the given form and its relevant\n attachments\n '
def find_question_id(form, value):
for (k, v) in six.iteritems(form):
if isinstance(v, dict):
ret = find_question_id(v, value)
if ret:
return ([k] + ret)
elif isinstance(v, list):
for repeat in v:
ret = find_question_id(repeat, value)
if ret:
return ([k] + ret)
elif (v == value):
return [k]
return None
unknown_number = 0
case_blocks = extract_case_blocks(form.form_data)
form_info = {'form': form, 'attachments': [], 'case_ids': {c['@case_id'] for c in case_blocks}, 'username': form.get_data('form/meta/username')}
for (attachment_name, attachment) in six.iteritems(form.attachments):
if hasattr(attachment, 'content_type'):
content_type = attachment.content_type
else:
content_type = attachment['content_type']
if (content_type == 'text/xml'):
continue
try:
question_id = six.text_type('-'.join(find_question_id(form.form_data, attachment_name)))
except TypeError:
question_id = ('unknown' + six.text_type(unknown_number))
unknown_number += 1
if ((not properties) or (question_id in properties)):
extension = six.text_type(os.path.splitext(attachment_name)[1])
if hasattr(attachment, 'content_length'):
size = attachment.content_length
elif ('content_length' in attachment):
size = attachment['content_length']
else:
size = attachment['length']
form_info['attachments'].append({'size': size, 'name': attachment_name, 'question_id': question_id, 'extension': extension, 'timestamp': form.received_on.timetuple()})
return form_info | def _extract_form_attachment_info(form, properties):
'\n This is a helper function for build_form_multimedia_zip.\n Return a dict containing information about the given form and its relevant\n attachments\n '
def find_question_id(form, value):
for (k, v) in six.iteritems(form):
if isinstance(v, dict):
ret = find_question_id(v, value)
if ret:
return ([k] + ret)
elif isinstance(v, list):
for repeat in v:
ret = find_question_id(repeat, value)
if ret:
return ([k] + ret)
elif (v == value):
return [k]
return None
unknown_number = 0
case_blocks = extract_case_blocks(form.form_data)
form_info = {'form': form, 'attachments': [], 'case_ids': {c['@case_id'] for c in case_blocks}, 'username': form.get_data('form/meta/username')}
for (attachment_name, attachment) in six.iteritems(form.attachments):
if hasattr(attachment, 'content_type'):
content_type = attachment.content_type
else:
content_type = attachment['content_type']
if (content_type == 'text/xml'):
continue
try:
question_id = six.text_type('-'.join(find_question_id(form.form_data, attachment_name)))
except TypeError:
question_id = ('unknown' + six.text_type(unknown_number))
unknown_number += 1
if ((not properties) or (question_id in properties)):
extension = six.text_type(os.path.splitext(attachment_name)[1])
if hasattr(attachment, 'content_length'):
size = attachment.content_length
elif ('content_length' in attachment):
size = attachment['content_length']
else:
size = attachment['length']
form_info['attachments'].append({'size': size, 'name': attachment_name, 'question_id': question_id, 'extension': extension, 'timestamp': form.received_on.timetuple()})
return form_info<|docstring|>This is a helper function for build_form_multimedia_zip.
Return a dict containing information about the given form and its relevant
attachments<|endoftext|> |
781d7c27232059dae25f9d95c09eb7fc35787735be1f2f950635659a167d65ba | def read_csv(filename):
'\n\n Parameters\n ----------\n filename : str\n Path to the CSV file.\n\n Returns\n -------\n df_new : dataframe\n Normalised coordinates of 3D pose.\n\n '
dataframe = pd.read_csv(filename, index_col='Body Part')
xmax = (- 10000)
ymax = (- 10000)
zmax = (- 10000)
xmin = 10000
ymin = 10000
zmin = 10000
for key in dataframe.keys():
data = list(dataframe[key][1:])
data = list(map(float, data))
data_num = np.array(data)
data_num = data_num[np.where((~ np.isnan(data_num)))]
keys = key.split('.')
if (len(keys) == 1):
key_new = (keys[0], 'x')
xmax = max(xmax, np.max(data_num))
xmin = min(xmin, np.min(data_num))
elif ((len(keys) == 2) and (keys[1] == '1')):
key_new = (keys[0], 'y')
ymax = max(ymax, np.max(data_num))
ymin = min(ymin, np.min(data_num))
elif ((len(keys) == 2) and (keys[1] == '2')):
key_new = (keys[0], 'y')
zmax = max(zmax, np.max(data_num))
zmin = min(zmin, np.min(data_num))
if (key == 'MidHip'):
data_midhip = data_num
if (key == 'Neck'):
data_neck = data_num
xc = ((np.mean(data_neck) + np.mean(data_midhip)) / 2)
yc = ((ymax + ymin) / 2)
zc = ((zmax + zmin) / 2)
width = (2 * max((xc - xmin), (xmax - xc)))
height = (ymax - ymin)
depth = (zmax - zmin)
sq = max(width, height)
sq = max(sq, depth)
sq = (np.ceil((sq / 100)) * 100)
depth = width = height = sq
xmin = (xc - (sq / 2))
ymin = (yc - (sq / 2))
zmin = (zc - (sq / 2))
df = dict()
for key in dataframe.keys():
data = list(dataframe[key][1:])
data = list(map(float, data))
nan_idx = np.where(np.isnan(data))[0]
if (len(nan_idx) == len(data)):
data[:] = 0
elif (len(nan_idx) > 0):
for jj in nan_idx:
if (jj == 0):
data[jj] = np.where((~ np.isnan(data)))[0][0]
else:
data[jj] = data[(jj - 1)]
keys = key.split('.')
if (len(keys) == 1):
key_new = (keys[0], 'x')
data = np.round(list(((np.array(data) - xmin) / width)), 5)
elif ((len(keys) == 2) and (keys[1] == '1')):
key_new = (keys[0], 'y')
data = np.round(list(((np.array(data) - ymin) / height)), 5)
elif ((len(keys) == 2) and (keys[1] == '2')):
key_new = (keys[0], 'z')
data = np.round(list(((np.array(data) - zmin) / depth)), 5)
else:
key_new = (keys[0], 'c')
data = np.array(data)
df[key_new] = data
df_new = pd.DataFrame(df)
return df_new | Parameters
----------
filename : str
Path to the CSV file.
Returns
-------
df_new : dataframe
Normalised coordinates of 3D pose. | utils/create_blank_3d.py | read_csv | alisonrclarke/raga-pose-estimation-1 | 1 | python | def read_csv(filename):
'\n\n Parameters\n ----------\n filename : str\n Path to the CSV file.\n\n Returns\n -------\n df_new : dataframe\n Normalised coordinates of 3D pose.\n\n '
dataframe = pd.read_csv(filename, index_col='Body Part')
xmax = (- 10000)
ymax = (- 10000)
zmax = (- 10000)
xmin = 10000
ymin = 10000
zmin = 10000
for key in dataframe.keys():
data = list(dataframe[key][1:])
data = list(map(float, data))
data_num = np.array(data)
data_num = data_num[np.where((~ np.isnan(data_num)))]
keys = key.split('.')
if (len(keys) == 1):
key_new = (keys[0], 'x')
xmax = max(xmax, np.max(data_num))
xmin = min(xmin, np.min(data_num))
elif ((len(keys) == 2) and (keys[1] == '1')):
key_new = (keys[0], 'y')
ymax = max(ymax, np.max(data_num))
ymin = min(ymin, np.min(data_num))
elif ((len(keys) == 2) and (keys[1] == '2')):
key_new = (keys[0], 'y')
zmax = max(zmax, np.max(data_num))
zmin = min(zmin, np.min(data_num))
if (key == 'MidHip'):
data_midhip = data_num
if (key == 'Neck'):
data_neck = data_num
xc = ((np.mean(data_neck) + np.mean(data_midhip)) / 2)
yc = ((ymax + ymin) / 2)
zc = ((zmax + zmin) / 2)
width = (2 * max((xc - xmin), (xmax - xc)))
height = (ymax - ymin)
depth = (zmax - zmin)
sq = max(width, height)
sq = max(sq, depth)
sq = (np.ceil((sq / 100)) * 100)
depth = width = height = sq
xmin = (xc - (sq / 2))
ymin = (yc - (sq / 2))
zmin = (zc - (sq / 2))
df = dict()
for key in dataframe.keys():
data = list(dataframe[key][1:])
data = list(map(float, data))
nan_idx = np.where(np.isnan(data))[0]
if (len(nan_idx) == len(data)):
data[:] = 0
elif (len(nan_idx) > 0):
for jj in nan_idx:
if (jj == 0):
data[jj] = np.where((~ np.isnan(data)))[0][0]
else:
data[jj] = data[(jj - 1)]
keys = key.split('.')
if (len(keys) == 1):
key_new = (keys[0], 'x')
data = np.round(list(((np.array(data) - xmin) / width)), 5)
elif ((len(keys) == 2) and (keys[1] == '1')):
key_new = (keys[0], 'y')
data = np.round(list(((np.array(data) - ymin) / height)), 5)
elif ((len(keys) == 2) and (keys[1] == '2')):
key_new = (keys[0], 'z')
data = np.round(list(((np.array(data) - zmin) / depth)), 5)
else:
key_new = (keys[0], 'c')
data = np.array(data)
df[key_new] = data
df_new = pd.DataFrame(df)
return df_new | def read_csv(filename):
'\n\n Parameters\n ----------\n filename : str\n Path to the CSV file.\n\n Returns\n -------\n df_new : dataframe\n Normalised coordinates of 3D pose.\n\n '
dataframe = pd.read_csv(filename, index_col='Body Part')
xmax = (- 10000)
ymax = (- 10000)
zmax = (- 10000)
xmin = 10000
ymin = 10000
zmin = 10000
for key in dataframe.keys():
data = list(dataframe[key][1:])
data = list(map(float, data))
data_num = np.array(data)
data_num = data_num[np.where((~ np.isnan(data_num)))]
keys = key.split('.')
if (len(keys) == 1):
key_new = (keys[0], 'x')
xmax = max(xmax, np.max(data_num))
xmin = min(xmin, np.min(data_num))
elif ((len(keys) == 2) and (keys[1] == '1')):
key_new = (keys[0], 'y')
ymax = max(ymax, np.max(data_num))
ymin = min(ymin, np.min(data_num))
elif ((len(keys) == 2) and (keys[1] == '2')):
key_new = (keys[0], 'y')
zmax = max(zmax, np.max(data_num))
zmin = min(zmin, np.min(data_num))
if (key == 'MidHip'):
data_midhip = data_num
if (key == 'Neck'):
data_neck = data_num
xc = ((np.mean(data_neck) + np.mean(data_midhip)) / 2)
yc = ((ymax + ymin) / 2)
zc = ((zmax + zmin) / 2)
width = (2 * max((xc - xmin), (xmax - xc)))
height = (ymax - ymin)
depth = (zmax - zmin)
sq = max(width, height)
sq = max(sq, depth)
sq = (np.ceil((sq / 100)) * 100)
depth = width = height = sq
xmin = (xc - (sq / 2))
ymin = (yc - (sq / 2))
zmin = (zc - (sq / 2))
df = dict()
for key in dataframe.keys():
data = list(dataframe[key][1:])
data = list(map(float, data))
nan_idx = np.where(np.isnan(data))[0]
if (len(nan_idx) == len(data)):
data[:] = 0
elif (len(nan_idx) > 0):
for jj in nan_idx:
if (jj == 0):
data[jj] = np.where((~ np.isnan(data)))[0][0]
else:
data[jj] = data[(jj - 1)]
keys = key.split('.')
if (len(keys) == 1):
key_new = (keys[0], 'x')
data = np.round(list(((np.array(data) - xmin) / width)), 5)
elif ((len(keys) == 2) and (keys[1] == '1')):
key_new = (keys[0], 'y')
data = np.round(list(((np.array(data) - ymin) / height)), 5)
elif ((len(keys) == 2) and (keys[1] == '2')):
key_new = (keys[0], 'z')
data = np.round(list(((np.array(data) - zmin) / depth)), 5)
else:
key_new = (keys[0], 'c')
data = np.array(data)
df[key_new] = data
df_new = pd.DataFrame(df)
return df_new<|docstring|>Parameters
----------
filename : str
Path to the CSV file.
Returns
-------
df_new : dataframe
Normalised coordinates of 3D pose.<|endoftext|> |
cf21f847fd6c089dc03aa470f49e7bc154155d85750c428f31319c061692bb64 | def create_3d_video(output_path, df, parts=PARTS, skeleton=SKELETON_EDGES, output=True):
'\n\n Parameters\n ----------\n output_path : str\n Path for the created video.\n df : dataframe\n 3D pose\n parts : list, optional\n The name of body parts. The default is PARTS.\n skeleton : narray, optional\n Indicating which two keypoints are connected. The default is SKELETON_EDGES.\n output : bool, optional\n True for storing the video file, otherwise only show the real-time window. The default is True.\n\n Returns\n -------\n None.\n\n '
canvas_3d = np.zeros((360, 640, 3), dtype=np.uint8)
canvas_3d_window_name = 'Pose_3D'
cv2.namedWindow(canvas_3d_window_name)
cv2.setMouseCallback(canvas_3d_window_name, Plotter3d.mouse_callback)
plotter = Plotter3d(canvas_3d.shape[:2], parts=parts, skeleton_edges=skeleton)
depth = width = height = 200
if output:
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out = cv2.VideoWriter(output_path, fourcc, 25, (640, 360))
poses_3d_array = np.zeros((len(df), int((len(df.keys()) / 4)), 3))
for (ii, part) in enumerate(plotter.parts):
poses_3d_array[(:, ii, 0)] = ((df[(part, 'x')] * width) - 125)
poses_3d_array[(:, ii, 1)] = ((df[(part, 'y')] * height) - 125)
poses_3d_array[(:, ii, 2)] = (df[(part, 'z')] * depth)
for num in range(len(df)):
poses_3d = poses_3d_array[num]
edges = plotter.skeleton_edges
plotter.plot(canvas_3d, poses_3d, edges)
if (not output):
cv2.imshow(canvas_3d_window_name, canvas_3d)
delay = 1
esc_code = 27
p_code = 112
space_code = 32
key = cv2.waitKey(delay)
if (key == esc_code):
break
if (key == p_code):
if (delay == 1):
delay = 0
else:
delay = 1
if (delay == 0):
key = 0
while ((key != p_code) and (key != esc_code) and (key != space_code)):
plotter.plot(canvas_3d, poses_3d, edges)
cv2.imshow(canvas_3d_window_name, canvas_3d)
key = cv2.waitKey(33)
if (key == esc_code):
break
else:
delay = 1
else:
out.write(canvas_3d)
if output:
out.release() | Parameters
----------
output_path : str
Path for the created video.
df : dataframe
3D pose
parts : list, optional
The name of body parts. The default is PARTS.
skeleton : narray, optional
Indicating which two keypoints are connected. The default is SKELETON_EDGES.
output : bool, optional
True for storing the video file, otherwise only show the real-time window. The default is True.
Returns
-------
None. | utils/create_blank_3d.py | create_3d_video | alisonrclarke/raga-pose-estimation-1 | 1 | python | def create_3d_video(output_path, df, parts=PARTS, skeleton=SKELETON_EDGES, output=True):
'\n\n Parameters\n ----------\n output_path : str\n Path for the created video.\n df : dataframe\n 3D pose\n parts : list, optional\n The name of body parts. The default is PARTS.\n skeleton : narray, optional\n Indicating which two keypoints are connected. The default is SKELETON_EDGES.\n output : bool, optional\n True for storing the video file, otherwise only show the real-time window. The default is True.\n\n Returns\n -------\n None.\n\n '
canvas_3d = np.zeros((360, 640, 3), dtype=np.uint8)
canvas_3d_window_name = 'Pose_3D'
cv2.namedWindow(canvas_3d_window_name)
cv2.setMouseCallback(canvas_3d_window_name, Plotter3d.mouse_callback)
plotter = Plotter3d(canvas_3d.shape[:2], parts=parts, skeleton_edges=skeleton)
depth = width = height = 200
if output:
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out = cv2.VideoWriter(output_path, fourcc, 25, (640, 360))
poses_3d_array = np.zeros((len(df), int((len(df.keys()) / 4)), 3))
for (ii, part) in enumerate(plotter.parts):
poses_3d_array[(:, ii, 0)] = ((df[(part, 'x')] * width) - 125)
poses_3d_array[(:, ii, 1)] = ((df[(part, 'y')] * height) - 125)
poses_3d_array[(:, ii, 2)] = (df[(part, 'z')] * depth)
for num in range(len(df)):
poses_3d = poses_3d_array[num]
edges = plotter.skeleton_edges
plotter.plot(canvas_3d, poses_3d, edges)
if (not output):
cv2.imshow(canvas_3d_window_name, canvas_3d)
delay = 1
esc_code = 27
p_code = 112
space_code = 32
key = cv2.waitKey(delay)
if (key == esc_code):
break
if (key == p_code):
if (delay == 1):
delay = 0
else:
delay = 1
if (delay == 0):
key = 0
while ((key != p_code) and (key != esc_code) and (key != space_code)):
plotter.plot(canvas_3d, poses_3d, edges)
cv2.imshow(canvas_3d_window_name, canvas_3d)
key = cv2.waitKey(33)
if (key == esc_code):
break
else:
delay = 1
else:
out.write(canvas_3d)
if output:
out.release() | def create_3d_video(output_path, df, parts=PARTS, skeleton=SKELETON_EDGES, output=True):
'\n\n Parameters\n ----------\n output_path : str\n Path for the created video.\n df : dataframe\n 3D pose\n parts : list, optional\n The name of body parts. The default is PARTS.\n skeleton : narray, optional\n Indicating which two keypoints are connected. The default is SKELETON_EDGES.\n output : bool, optional\n True for storing the video file, otherwise only show the real-time window. The default is True.\n\n Returns\n -------\n None.\n\n '
canvas_3d = np.zeros((360, 640, 3), dtype=np.uint8)
canvas_3d_window_name = 'Pose_3D'
cv2.namedWindow(canvas_3d_window_name)
cv2.setMouseCallback(canvas_3d_window_name, Plotter3d.mouse_callback)
plotter = Plotter3d(canvas_3d.shape[:2], parts=parts, skeleton_edges=skeleton)
depth = width = height = 200
if output:
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out = cv2.VideoWriter(output_path, fourcc, 25, (640, 360))
poses_3d_array = np.zeros((len(df), int((len(df.keys()) / 4)), 3))
for (ii, part) in enumerate(plotter.parts):
poses_3d_array[(:, ii, 0)] = ((df[(part, 'x')] * width) - 125)
poses_3d_array[(:, ii, 1)] = ((df[(part, 'y')] * height) - 125)
poses_3d_array[(:, ii, 2)] = (df[(part, 'z')] * depth)
for num in range(len(df)):
poses_3d = poses_3d_array[num]
edges = plotter.skeleton_edges
plotter.plot(canvas_3d, poses_3d, edges)
if (not output):
cv2.imshow(canvas_3d_window_name, canvas_3d)
delay = 1
esc_code = 27
p_code = 112
space_code = 32
key = cv2.waitKey(delay)
if (key == esc_code):
break
if (key == p_code):
if (delay == 1):
delay = 0
else:
delay = 1
if (delay == 0):
key = 0
while ((key != p_code) and (key != esc_code) and (key != space_code)):
plotter.plot(canvas_3d, poses_3d, edges)
cv2.imshow(canvas_3d_window_name, canvas_3d)
key = cv2.waitKey(33)
if (key == esc_code):
break
else:
delay = 1
else:
out.write(canvas_3d)
if output:
out.release()<|docstring|>Parameters
----------
output_path : str
Path for the created video.
df : dataframe
3D pose
parts : list, optional
The name of body parts. The default is PARTS.
skeleton : narray, optional
Indicating which two keypoints are connected. The default is SKELETON_EDGES.
output : bool, optional
True for storing the video file, otherwise only show the real-time window. The default is True.
Returns
-------
None.<|endoftext|> |
be5344ab918a97f12d47f11c8a705e418a5dc1a6aca20cf2eace2bc331b41bb1 | def _read_in_raw_data(data_dir: str) -> Tuple[(DataFrame, DataFrame)]:
'Read in the raw water pump features and labels.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame]\n DataFrames of the features and labels respectively.\n '
features_file_name = os.path.join(data_dir, 'water_pump_features.csv')
features = pd.read_csv(features_file_name, parse_dates=['date_recorded'], dtype={'region_code': str, 'district_code': str})
labels_file_name = os.path.join(data_dir, 'water_pump_labels.csv')
labels = pd.read_csv(labels_file_name)
return (features, labels) | Read in the raw water pump features and labels.
Parameters
----------
data_dir : str
Path of the directory where `water_pump_features.csv` and
`water_pump_labels.csv` can be found.
Returns
-------
Tuple[DataFrame, DataFrame]
DataFrames of the features and labels respectively. | src/data/dataset.py | _read_in_raw_data | amritpurshotam/mlops-example | 0 | python | def _read_in_raw_data(data_dir: str) -> Tuple[(DataFrame, DataFrame)]:
'Read in the raw water pump features and labels.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame]\n DataFrames of the features and labels respectively.\n '
features_file_name = os.path.join(data_dir, 'water_pump_features.csv')
features = pd.read_csv(features_file_name, parse_dates=['date_recorded'], dtype={'region_code': str, 'district_code': str})
labels_file_name = os.path.join(data_dir, 'water_pump_labels.csv')
labels = pd.read_csv(labels_file_name)
return (features, labels) | def _read_in_raw_data(data_dir: str) -> Tuple[(DataFrame, DataFrame)]:
'Read in the raw water pump features and labels.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame]\n DataFrames of the features and labels respectively.\n '
features_file_name = os.path.join(data_dir, 'water_pump_features.csv')
features = pd.read_csv(features_file_name, parse_dates=['date_recorded'], dtype={'region_code': str, 'district_code': str})
labels_file_name = os.path.join(data_dir, 'water_pump_labels.csv')
labels = pd.read_csv(labels_file_name)
return (features, labels)<|docstring|>Read in the raw water pump features and labels.
Parameters
----------
data_dir : str
Path of the directory where `water_pump_features.csv` and
`water_pump_labels.csv` can be found.
Returns
-------
Tuple[DataFrame, DataFrame]
DataFrames of the features and labels respectively.<|endoftext|> |
817da33f6dc3a7652e253e2ec0501f838394d2bba1f64cca3509023b3b6dda83 | def _align_features_and_labels(features: DataFrame, labels: DataFrame) -> Tuple[(DataFrame, DataFrame)]:
"Align the `feature`s and `labels` DataFrames so they're both in the same order\n removing the need to check the `id` columns in each.\n\n Parameters\n ----------\n features : DataFrame\n DataFrame containing the `id` attribute.\n labels : DataFrame\n DataFrame containing the `id` attribute that corresponds to the `id` in\n `features`.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame]\n DataFrames of the features and labels respectively with the `id` column\n in `labels` dropped making it more amenable to pass into classifiers for\n training.\n "
aligned = features.merge(labels, on='id', validate='one_to_one')
labels = aligned['status_group']
features = aligned.drop(columns=['status_group'])
return (features, labels) | Align the `feature`s and `labels` DataFrames so they're both in the same order
removing the need to check the `id` columns in each.
Parameters
----------
features : DataFrame
DataFrame containing the `id` attribute.
labels : DataFrame
DataFrame containing the `id` attribute that corresponds to the `id` in
`features`.
Returns
-------
Tuple[DataFrame, DataFrame]
DataFrames of the features and labels respectively with the `id` column
in `labels` dropped making it more amenable to pass into classifiers for
training. | src/data/dataset.py | _align_features_and_labels | amritpurshotam/mlops-example | 0 | python | def _align_features_and_labels(features: DataFrame, labels: DataFrame) -> Tuple[(DataFrame, DataFrame)]:
"Align the `feature`s and `labels` DataFrames so they're both in the same order\n removing the need to check the `id` columns in each.\n\n Parameters\n ----------\n features : DataFrame\n DataFrame containing the `id` attribute.\n labels : DataFrame\n DataFrame containing the `id` attribute that corresponds to the `id` in\n `features`.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame]\n DataFrames of the features and labels respectively with the `id` column\n in `labels` dropped making it more amenable to pass into classifiers for\n training.\n "
aligned = features.merge(labels, on='id', validate='one_to_one')
labels = aligned['status_group']
features = aligned.drop(columns=['status_group'])
return (features, labels) | def _align_features_and_labels(features: DataFrame, labels: DataFrame) -> Tuple[(DataFrame, DataFrame)]:
"Align the `feature`s and `labels` DataFrames so they're both in the same order\n removing the need to check the `id` columns in each.\n\n Parameters\n ----------\n features : DataFrame\n DataFrame containing the `id` attribute.\n labels : DataFrame\n DataFrame containing the `id` attribute that corresponds to the `id` in\n `features`.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame]\n DataFrames of the features and labels respectively with the `id` column\n in `labels` dropped making it more amenable to pass into classifiers for\n training.\n "
aligned = features.merge(labels, on='id', validate='one_to_one')
labels = aligned['status_group']
features = aligned.drop(columns=['status_group'])
return (features, labels)<|docstring|>Align the `feature`s and `labels` DataFrames so they're both in the same order
removing the need to check the `id` columns in each.
Parameters
----------
features : DataFrame
DataFrame containing the `id` attribute.
labels : DataFrame
DataFrame containing the `id` attribute that corresponds to the `id` in
`features`.
Returns
-------
Tuple[DataFrame, DataFrame]
DataFrames of the features and labels respectively with the `id` column
in `labels` dropped making it more amenable to pass into classifiers for
training.<|endoftext|> |
e9e0a5b65a0eec28e67d45df31da0b9bedb6f2e0f075b87e61d35d301d806e82 | def _split(features: DataFrame, labels: DataFrame, random_state: int=42) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Deterministic random 80/10/10 train/val/test split of the dataset stratified by\n the labels.\n\n Parameters\n ----------\n features : DataFrame\n\n labels : DataFrame\n\n Returns\n -------\n Tuple[DataFrame, DataFrame, DataFrame, DataFrame]\n A tuple of four DataFrames corresponding to the training features,\n testing features, training labels, and testing labels respectively.\n '
(train_features, test_features, train_labels, test_labels) = train_test_split(features, labels, test_size=0.2, random_state=random_state, stratify=labels)
(val_features, test_features, val_labels, test_labels) = train_test_split(test_features, test_labels, test_size=0.5, random_state=random_state, stratify=test_labels)
return (train_features, val_features, test_features, train_labels, val_labels, test_labels) | Deterministic random 80/10/10 train/val/test split of the dataset stratified by
the labels.
Parameters
----------
features : DataFrame
labels : DataFrame
Returns
-------
Tuple[DataFrame, DataFrame, DataFrame, DataFrame]
A tuple of four DataFrames corresponding to the training features,
testing features, training labels, and testing labels respectively. | src/data/dataset.py | _split | amritpurshotam/mlops-example | 0 | python | def _split(features: DataFrame, labels: DataFrame, random_state: int=42) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Deterministic random 80/10/10 train/val/test split of the dataset stratified by\n the labels.\n\n Parameters\n ----------\n features : DataFrame\n\n labels : DataFrame\n\n Returns\n -------\n Tuple[DataFrame, DataFrame, DataFrame, DataFrame]\n A tuple of four DataFrames corresponding to the training features,\n testing features, training labels, and testing labels respectively.\n '
(train_features, test_features, train_labels, test_labels) = train_test_split(features, labels, test_size=0.2, random_state=random_state, stratify=labels)
(val_features, test_features, val_labels, test_labels) = train_test_split(test_features, test_labels, test_size=0.5, random_state=random_state, stratify=test_labels)
return (train_features, val_features, test_features, train_labels, val_labels, test_labels) | def _split(features: DataFrame, labels: DataFrame, random_state: int=42) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Deterministic random 80/10/10 train/val/test split of the dataset stratified by\n the labels.\n\n Parameters\n ----------\n features : DataFrame\n\n labels : DataFrame\n\n Returns\n -------\n Tuple[DataFrame, DataFrame, DataFrame, DataFrame]\n A tuple of four DataFrames corresponding to the training features,\n testing features, training labels, and testing labels respectively.\n '
(train_features, test_features, train_labels, test_labels) = train_test_split(features, labels, test_size=0.2, random_state=random_state, stratify=labels)
(val_features, test_features, val_labels, test_labels) = train_test_split(test_features, test_labels, test_size=0.5, random_state=random_state, stratify=test_labels)
return (train_features, val_features, test_features, train_labels, val_labels, test_labels)<|docstring|>Deterministic random 80/10/10 train/val/test split of the dataset stratified by
the labels.
Parameters
----------
features : DataFrame
labels : DataFrame
Returns
-------
Tuple[DataFrame, DataFrame, DataFrame, DataFrame]
A tuple of four DataFrames corresponding to the training features,
testing features, training labels, and testing labels respectively.<|endoftext|> |
546ee13fed348901d420beb9db19ca2e0ab84f411543c1c43de567a4606c493c | def load_dataset(data_dir: str) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Read in the water pump dataset and split into the training and test sets.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame, DataFrame, DataFrame]\n A tuple of four DataFrames corresponding to the training features,\n testing features, training labels, and testing labels respectively.\n '
(features, labels) = _read_in_raw_data(data_dir)
(features, labels) = _align_features_and_labels(features, labels)
return _split(features, labels) | Read in the water pump dataset and split into the training and test sets.
Parameters
----------
data_dir : str
Path of the directory where `water_pump_features.csv` and
`water_pump_labels.csv` can be found.
Returns
-------
Tuple[DataFrame, DataFrame, DataFrame, DataFrame]
A tuple of four DataFrames corresponding to the training features,
testing features, training labels, and testing labels respectively. | src/data/dataset.py | load_dataset | amritpurshotam/mlops-example | 0 | python | def load_dataset(data_dir: str) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Read in the water pump dataset and split into the training and test sets.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame, DataFrame, DataFrame]\n A tuple of four DataFrames corresponding to the training features,\n testing features, training labels, and testing labels respectively.\n '
(features, labels) = _read_in_raw_data(data_dir)
(features, labels) = _align_features_and_labels(features, labels)
return _split(features, labels) | def load_dataset(data_dir: str) -> Tuple[(DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame)]:
'Read in the water pump dataset and split into the training and test sets.\n\n Parameters\n ----------\n data_dir : str\n Path of the directory where `water_pump_features.csv` and\n `water_pump_labels.csv` can be found.\n\n Returns\n -------\n Tuple[DataFrame, DataFrame, DataFrame, DataFrame]\n A tuple of four DataFrames corresponding to the training features,\n testing features, training labels, and testing labels respectively.\n '
(features, labels) = _read_in_raw_data(data_dir)
(features, labels) = _align_features_and_labels(features, labels)
return _split(features, labels)<|docstring|>Read in the water pump dataset and split into the training and test sets.
Parameters
----------
data_dir : str
Path of the directory where `water_pump_features.csv` and
`water_pump_labels.csv` can be found.
Returns
-------
Tuple[DataFrame, DataFrame, DataFrame, DataFrame]
A tuple of four DataFrames corresponding to the training features,
testing features, training labels, and testing labels respectively.<|endoftext|> |
4da268fdbe9c63b87734d4510b4787fee8435875c315ea31520ad4b9cfd966de | def __init__(self, options):
'Constructor\n\n Args -\n options - The result of OptionParser which contains, as attributes, all the options for the running program.\n '
self.options = options | Constructor
Args -
options - The result of OptionParser which contains, as attributes, all the options for the running program. | testify/test_reporter.py | __init__ | osarood/Testify | 1 | python | def __init__(self, options):
'Constructor\n\n Args -\n options - The result of OptionParser which contains, as attributes, all the options for the running program.\n '
self.options = options | def __init__(self, options):
'Constructor\n\n Args -\n options - The result of OptionParser which contains, as attributes, all the options for the running program.\n '
self.options = options<|docstring|>Constructor
Args -
options - The result of OptionParser which contains, as attributes, all the options for the running program.<|endoftext|> |
27faa50a741dca4491aaa5fa883d9d9565c5a79135dfa8df1ee00976c6db1385 | def test_counts(self, test_case_count, test_method_count):
'Called after discovery finishes. May not be called by all test runners, e.g. TestRunnerClient.'
pass | Called after discovery finishes. May not be called by all test runners, e.g. TestRunnerClient. | testify/test_reporter.py | test_counts | osarood/Testify | 1 | python | def test_counts(self, test_case_count, test_method_count):
pass | def test_counts(self, test_case_count, test_method_count):
pass<|docstring|>Called after discovery finishes. May not be called by all test runners, e.g. TestRunnerClient.<|endoftext|> |
60e39a6b259a988906f68b1de0aff06ef8860a253286e7df625fb31ae9390159 | def test_start(self, result):
'Called when a test method is being run. Gets passed a TestResult dict which should not be complete.'
pass | Called when a test method is being run. Gets passed a TestResult dict which should not be complete. | testify/test_reporter.py | test_start | osarood/Testify | 1 | python | def test_start(self, result):
pass | def test_start(self, result):
pass<|docstring|>Called when a test method is being run. Gets passed a TestResult dict which should not be complete.<|endoftext|> |
1e86bd749a2113cb2f9f129271295bd92158abebc81a814f6124a04b1e6842fa | def test_complete(self, result):
'Called when a test method is complete. result is a TestResult dict which should be complete.'
pass | Called when a test method is complete. result is a TestResult dict which should be complete. | testify/test_reporter.py | test_complete | osarood/Testify | 1 | python | def test_complete(self, result):
pass | def test_complete(self, result):
pass<|docstring|>Called when a test method is complete. result is a TestResult dict which should be complete.<|endoftext|> |
598d94cede61f31b4983d1ac402a3dfee04530e3a72f6edeca10169433940ada | def test_discovery_failure(self, exc):
'Called when there was a failure during test discovery. exc is the exception object generated during the error.' | Called when there was a failure during test discovery. exc is the exception object generated during the error. | testify/test_reporter.py | test_discovery_failure | osarood/Testify | 1 | python | def test_discovery_failure(self, exc):
| def test_discovery_failure(self, exc):
<|docstring|>Called when there was a failure during test discovery. exc is the exception object generated during the error.<|endoftext|> |
ef580bcb794087e4ef1d68c1ead6f4730a47d083e2796d829155d4dbc075f375 | def class_setup_start(self, result):
'Called when a class_setup or the first half of a class_setup_teardown starts'
pass | Called when a class_setup or the first half of a class_setup_teardown starts | testify/test_reporter.py | class_setup_start | osarood/Testify | 1 | python | def class_setup_start(self, result):
pass | def class_setup_start(self, result):
pass<|docstring|>Called when a class_setup or the first half of a class_setup_teardown starts<|endoftext|> |
be9757e522dad5bc0d97ab659b097f294a945c307631e4cc60f7d7cec2dfd3a1 | def class_setup_complete(self, result):
'Called when a class_setup or the first half of a class_setup_teardown finishes'
pass | Called when a class_setup or the first half of a class_setup_teardown finishes | testify/test_reporter.py | class_setup_complete | osarood/Testify | 1 | python | def class_setup_complete(self, result):
pass | def class_setup_complete(self, result):
pass<|docstring|>Called when a class_setup or the first half of a class_setup_teardown finishes<|endoftext|> |
b13993cbc2dac87e4a77f8f46bb4904b05ad5c96affe7891df7b71276f1b23fb | def class_teardown_start(self, result):
'Called when a class_teardown or the second half of a class_setup_teardown starts'
pass | Called when a class_teardown or the second half of a class_setup_teardown starts | testify/test_reporter.py | class_teardown_start | osarood/Testify | 1 | python | def class_teardown_start(self, result):
pass | def class_teardown_start(self, result):
pass<|docstring|>Called when a class_teardown or the second half of a class_setup_teardown starts<|endoftext|> |
b8769243139eb21f367fcf56641ffa963348c6ee59739328c945126a93dbbcdd | def class_teardown_complete(self, result):
'Called when a class_teardown or the second half of a class_setup_teardown finishes'
pass | Called when a class_teardown or the second half of a class_setup_teardown finishes | testify/test_reporter.py | class_teardown_complete | osarood/Testify | 1 | python | def class_teardown_complete(self, result):
pass | def class_teardown_complete(self, result):
pass<|docstring|>Called when a class_teardown or the second half of a class_setup_teardown finishes<|endoftext|> |
405ef5616bc14f45c089fafcb2b260cd0476646f85dfdfb1c91da92a4180c8cb | def test_case_start(self, result):
'Called when a test case is being run. Gets passed the special "run" method as a TestResult.'
pass | Called when a test case is being run. Gets passed the special "run" method as a TestResult. | testify/test_reporter.py | test_case_start | osarood/Testify | 1 | python | def test_case_start(self, result):
pass | def test_case_start(self, result):
pass<|docstring|>Called when a test case is being run. Gets passed the special "run" method as a TestResult.<|endoftext|> |
f1f622bc51f7ca932fff4ae5cf67c5ca3c4c4421d489740345cea21ea2072c9d | def test_case_complete(self, result):
'Called when a test case and all of its fixtures have been run.'
pass | Called when a test case and all of its fixtures have been run. | testify/test_reporter.py | test_case_complete | osarood/Testify | 1 | python | def test_case_complete(self, result):
pass | def test_case_complete(self, result):
pass<|docstring|>Called when a test case and all of its fixtures have been run.<|endoftext|> |
45fe5e328eb28c269bd948f1123006c9409c3bcb722d0a6e188d28dff32a575a | def report(self):
'Called at the end of the test run to report results\n\n Should return a bool to indicate if the reporter thinks the test run was successful\n '
return True | Called at the end of the test run to report results
Should return a bool to indicate if the reporter thinks the test run was successful | testify/test_reporter.py | report | osarood/Testify | 1 | python | def report(self):
'Called at the end of the test run to report results\n\n Should return a bool to indicate if the reporter thinks the test run was successful\n '
return True | def report(self):
'Called at the end of the test run to report results\n\n Should return a bool to indicate if the reporter thinks the test run was successful\n '
return True<|docstring|>Called at the end of the test run to report results
Should return a bool to indicate if the reporter thinks the test run was successful<|endoftext|> |
6986547924c4b504463a555bf5f4f919765ba60da31b8ed95b5f52da9401448a | def html_fragment(source):
'\n Parse an HTML string representing a single element, and return that element\n '
return BeautifulSoup(source, 'html.parser').contents[0] | Parse an HTML string representing a single element, and return that element | chirun/filter.py | html_fragment | sthagen/chirun-ncl-chirun | 5 | python | def html_fragment(source):
'\n \n '
return BeautifulSoup(source, 'html.parser').contents[0] | def html_fragment(source):
'\n \n '
return BeautifulSoup(source, 'html.parser').contents[0]<|docstring|>Parse an HTML string representing a single element, and return that element<|endoftext|> |
32691050f1c4a0af72bfb7b250b57755c343758a2dae61d86eb039e362bb2fcf | def fix_local_links(soup, item):
"\n Rewrite URLs relative to the top level, i.e. those starting with a /,\n to use the course's root URL or into paths relative to the item.\n "
tags = {'a': ['href'], 'img': ['src'], 'source': ['src'], 'section': ['data-background', 'data-background-video']}
for (tag, attrs) in tags.items():
for el in soup.find_all(tag):
for attr in attrs:
url = el.get(attr)
if (url and (url[0] == '/')):
el[attr] = item.course.make_relative_url(item, url[1:]) | Rewrite URLs relative to the top level, i.e. those starting with a /,
to use the course's root URL or into paths relative to the item. | chirun/filter.py | fix_local_links | sthagen/chirun-ncl-chirun | 5 | python | def fix_local_links(soup, item):
"\n Rewrite URLs relative to the top level, i.e. those starting with a /,\n to use the course's root URL or into paths relative to the item.\n "
tags = {'a': ['href'], 'img': ['src'], 'source': ['src'], 'section': ['data-background', 'data-background-video']}
for (tag, attrs) in tags.items():
for el in soup.find_all(tag):
for attr in attrs:
url = el.get(attr)
if (url and (url[0] == '/')):
el[attr] = item.course.make_relative_url(item, url[1:]) | def fix_local_links(soup, item):
"\n Rewrite URLs relative to the top level, i.e. those starting with a /,\n to use the course's root URL or into paths relative to the item.\n "
tags = {'a': ['href'], 'img': ['src'], 'source': ['src'], 'section': ['data-background', 'data-background-video']}
for (tag, attrs) in tags.items():
for el in soup.find_all(tag):
for attr in attrs:
url = el.get(attr)
if (url and (url[0] == '/')):
el[attr] = item.course.make_relative_url(item, url[1:])<|docstring|>Rewrite URLs relative to the top level, i.e. those starting with a /,
to use the course's root URL or into paths relative to the item.<|endoftext|> |
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