code stringlengths 281 23.7M |
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class TestPassportElementErrorFrontSideWithoutRequest(TestPassportElementErrorFrontSideBase):
def test_slot_behaviour(self, passport_element_error_front_side):
inst = passport_element_error_front_side
for attr in inst.__slots__:
assert (getattr(inst, attr, 'err') != 'err'), f"got extra s... |
def test_uniform_types_uniform_type():
for cls in material_classes:
ob = cls()
assert isinstance(ob.uniform_type, dict)
for super_cls in cls.mro():
if (super_cls is cls):
continue
elif (not hasattr(super_cls, 'uniform_type')):
break
... |
def calculate_d_to_volume_for_labels(dose_grid, labels, volume, volume_in_cc=False):
if (not isinstance(volume, list)):
volume = [volume]
metrics = []
for label in labels:
m = {'label': label}
for v in volume:
col_name = f'D{v}'
if volume_in_cc:
... |
def total_token_network_channels(chain_state: ChainState, token_network_registry_address: TokenNetworkRegistryAddress, token_address: TokenAddress) -> int:
token_network = get_token_network_by_token_address(chain_state, token_network_registry_address, token_address)
result = 0
if token_network:
resu... |
_module()
class MaskedCrossEntropyLoss(nn.Module):
def __init__(self, num_labels=None, ignore_index=0):
super().__init__()
self.num_labels = num_labels
self.criterion = CrossEntropyLoss(ignore_index=ignore_index)
def forward(self, logits, img_metas):
labels = img_metas['labels']
... |
def get_coord_add(dataset_name: str):
import numpy as np
options = {'mnist': ([[[8.0, 8.0], [12.0, 8.0], [16.0, 8.0]], [[8.0, 12.0], [12.0, 12.0], [16.0, 12.0]], [[8.0, 16.0], [12.0, 16.0], [16.0, 16.0]]], 28.0), 'smallNORB': ([[[8.0, 8.0], [12.0, 8.0], [16.0, 8.0], [24.0, 8.0]], [[8.0, 12.0], [12.0, 12.0], [16... |
def main():
args = parse_args()
if (os.path.splitext(args.source_model)[(- 1)] != '.pth'):
raise ValueError('You should save weights as pth file')
source_weights = torch.load(args.source_model, map_location=torch.device('cpu'))['model']
converted_weights = {}
keys = list(source_weights.keys(... |
class KnownValues(unittest.TestCase):
def test_KGKS(self):
mf = dft.KGKS(cell, kpts)
mf.xc = 'lda'
mf.conv_tol = 1e-10
e_kgks = mf.kernel()
self.assertAlmostEqual(e_kgks, (- 10.), 8)
def test_veff(self):
mf = dft.KGKS(cell, kpts)
n2c = (cell.nao * 2)
... |
class PSPFinalBlock(nn.Module):
def __init__(self, in_channels, out_channels, bottleneck_factor=4):
super(PSPFinalBlock, self).__init__()
assert ((in_channels % bottleneck_factor) == 0)
mid_channels = (in_channels // bottleneck_factor)
self.conv1 = conv3x3_block(in_channels=in_channe... |
def analogy_seq_encoding_model(inputs, params, is_training, reuse):
enc_cell_fn = NAME_TO_RNNCELL[params.enc_model]
recurrent_dropout_prob = 1.0
if is_training:
recurrent_dropout_prob = params.recurrent_dropout_prob
rnn_cell = get_rnn_cell(enc_cell_fn, params.enc_rnn_size, use_dropout=(is_traini... |
.django_project(extra_settings='\n ROOT_URLCONF = "empty"\n ')
def test_urls_cache_is_cleared(django_pytester: DjangoPytester) -> None:
django_pytester.makepyfile(empty='\n urlpatterns = []\n ', myurls="\n from django.urls import path\n\n def fake_view(request):\n pass\n... |
def save_state(filename, args, model_state_dict, criterion, optimizer, lr_scheduler, num_updates, optim_history=None, extra_state=None):
from fairseq import utils
if (optim_history is None):
optim_history = []
if (extra_state is None):
extra_state = {}
state_dict = {'args': args, 'model'... |
.parametrize('reporttype', reporttypes, ids=[x.__name__ for x in reporttypes])
def test_report_extra_parameters(reporttype: Type[reports.BaseReport]) -> None:
args = list(inspect.signature(reporttype.__init__).parameters.keys())[1:]
basekw: Dict[(str, List[object])] = dict.fromkeys(args, [])
report = report... |
class Testfocalloss(object):
def _test_softmax(self, dtype=torch.float):
if (not torch.cuda.is_available()):
return
from mmcv.ops import softmax_focal_loss
alpha = 0.25
gamma = 2.0
for (case, output) in zip(inputs, softmax_outputs):
np_x = np.array(cas... |
def test_no_cli_opts(default_file):
cli_opts = parse()
assert ('save_files' not in cli_opts)
opts = api.bootstrap_options(cli_opts)
assert (opts['config_files'] == [])
default_file.write_text('[pyscaffold]\n')
opts = api.bootstrap_options(cli_opts)
assert (opts['config_files'] == [default_fi... |
def codegen_kernel(kernel, device='cpu'):
adj = kernel.adj
forward_args = ['launch_bounds_t dim']
reverse_args = ['launch_bounds_t dim']
for arg in adj.args:
forward_args.append(((arg.ctype() + ' var_') + arg.label))
reverse_args.append(((arg.ctype() + ' var_') + arg.label))
for arg ... |
def run_experiment(num_nodes, failure_prob, max_iterations, averaging_algo, num_restarts, target_precision=None):
experiment_result = partial(run_iterative_averaging, num_nodes=num_nodes, failure_prob=failure_prob, max_iterations=max_iterations, averaging_algo=averaging_algo, target_precision=target_precision)
... |
class Tdecode_value(TestCase):
def test_main(self):
self.assertEqual(decode_value('~#foo', 0.25), '0.25')
self.assertEqual(decode_value('~#foo', 4), '4')
self.assertEqual(decode_value('~#foo', 'bar'), 'bar')
self.assertTrue(isinstance(decode_value('~#foo', 'bar'), str))
path ... |
def calculate_sentence_transformer_embedding(examples, embedding_model, mean_normal=False):
if args.add_prompt:
text_to_encode = [['Represent the Wikipedia sentence; Input: ', f'''{raw_item['sentence']}
{raw_item['question']}''', 0] for raw_item in examples]
else:
text_to_encode = [f'''{raw_item... |
class ConfirmationQuestion(Question):
def __init__(self, question: str, default: bool=True, true_answer_regex: str='(?i)^y') -> None:
super().__init__(question, default)
self._true_answer_regex = true_answer_regex
self._normalizer = self._default_normalizer
def _write_prompt(self, io: IO... |
def walk(x, path=()):
(yield (path, x))
if isinstance(x, Object):
for (name, val) in x.inamevals():
if isinstance(val, (list, tuple)):
for (iele, ele) in enumerate(val):
for y in walk(ele, path=(path + ((name, iele),))):
(yield y)
... |
class ActivityTest(unittest.TestCase):
def setUp(self):
self.ddbb = DDBB()
main = Mock()
main.ddbb = self.ddbb
main.profile = Profile()
main.ddbb.connect()
main.ddbb.create_tables(add_default=True)
self.uc = UC()
self.uc.set_us(False)
self.serv... |
class RERB(nn.Module):
def __init__(self, in_channels, num_channels, kernel_size, reduction, n_blocks, block):
super(RERB, self).__init__()
blocks = []
blocks.append(self._make_blocks(in_channels, num_channels, kernel_size, reduction, n_blocks, block))
blocks.append(ops.EoctConv(num_... |
def captured_output() -> Generator[(Tuple[(TextIO, TextIO)], None, None)]:
(new_out, new_err) = (StringIO(), StringIO())
(old_out, old_err) = (sys.stdout, sys.stderr)
try:
(sys.stdout, sys.stderr) = (new_out, new_err)
(yield (sys.stdout, sys.stderr))
finally:
(sys.stdout, sys.std... |
def apply_pose(pose, x):
if (x is None):
return x
if isinstance(pose, np.ndarray):
pose = Pose.from_transformation_matrix(pose)
assert isinstance(pose, Pose)
if isinstance(x, Pose):
return (pose * x)
elif isinstance(x, np.ndarray):
return to_nc((to_gc(x, dim=3) pose.... |
def test_invalid_default():
root = _create_test_config()
def validate(val):
if (val == 'invalid'):
raise ValueError('Test-triggered')
with pytest.raises(ValueError, match='Test-triggered'):
root.add('test__test_invalid_default_a', doc='unittest', configparam=ConfigParam('invalid'... |
def main():
best_acc = 0
for epoch in range(args.epochs):
adjust_learning_rate(optimizer_model, epoch)
train(train_loader, train_meta_loader, model, vnet, optimizer_model, optimizer_vnet, epoch)
test_acc = test(model=model, test_loader=test_loader)
if (test_acc >= best_acc):
... |
class ClassInfoClass(object):
structcode = None
def parse_binary(self, data, display):
(class_type, length) = struct.unpack('=HH', data[:4])
class_struct = INFO_CLASSES.get(class_type, AnyInfo)
(class_data, _) = class_struct.parse_binary(data, display)
data = data[(length * 4):]
... |
_REGISTRY.register()
class StyleGAN2Model(BaseModel):
def __init__(self, opt):
super(StyleGAN2Model, self).__init__(opt)
self.net_g = build_network(opt['network_g'])
self.net_g = self.model_to_device(self.net_g)
self.print_network(self.net_g)
load_path = self.opt['path'].get(... |
def get_vlnbert_models(args, config=None):
from transformers import PretrainedConfig
from models.vilmodel import GlocalTextPathNavCMT
model_name_or_path = args.bert_ckpt_file
new_ckpt_weights = {}
if (model_name_or_path is not None):
ckpt_weights = torch.load(model_name_or_path, mal_location... |
def log_likelihood(probability, indices, gold_indices, gold_labels):
gold_indice_labels = []
for (batch_idx, label) in enumerate(gold_indices):
for (i, l) in enumerate(label):
if (l in indices[batch_idx]):
idx = indices[batch_idx].index(l)
gold_indice_labels.a... |
def do_an_insert(wait_for_api):
(request_session, api_url) = wait_for_api
item_url = 'items/1'
data_string = 'some_data'
request_session.put(('%s%s?data_string=%s' % (api_url, item_url, data_string)))
(yield (item_url, data_string))
request_session.delete(urljoin(api_url, item_url)).json() |
class VectorBC(nn.Module):
def __init__(self, num_vector_tokens=64, num_action_queries=6, num_blocks=18):
super().__init__()
encoder_config = VectorEncoderConfig()
self.num_vector_tokens = num_vector_tokens
self.vector_encoder = VectorEncoder(encoder_config, VectorObservationConfig()... |
def test_add_with_path_dependency_no_loopiness(poetry_with_path_dependency: Poetry, repo: TestRepository, command_tester_factory: CommandTesterFactory) -> None:
'
tester = command_tester_factory('add', poetry=poetry_with_path_dependency)
requests_old = get_package('requests', '2.25.1')
requests_new = ge... |
def send_invoice_email(email, contents):
msg = Message('Quay payment received - Thank you!', recipients=[email])
msg.html = contents
if features.FIPS:
assert app.config['MAIL_USE_TLS'], 'MAIL_USE_TLS must be enabled to use SMTP in FIPS mode.'
with mock.patch('smtplib.SMTP.login', login_fips_... |
class ModbusDeviceIdentification():
__data = {0: '', 1: '', 2: '', 3: '', 4: '', 5: '', 6: '', 7: '', 8: ''}
__names = ['VendorName', 'ProductCode', 'MajorMinorRevision', 'VendorUrl', 'ProductName', 'ModelName', 'UserApplicationName']
def __init__(self, info=None, info_name=None):
if isinstance(info... |
def test_rapt_corner_case():
def __test(x, fs, hopsize, min, max, otype):
f0 = pysptk.rapt(x, fs, hopsize, min=min, max=max, otype=otype)
assert np.all(np.isfinite(f0))
if (otype == 1):
assert np.all((f0 >= 0))
np.random.seed(98765)
fs = 16000
x = np.random.rand(16000... |
def search_pypath(module_name: str) -> str:
try:
spec = importlib.util.find_spec(module_name)
except (AttributeError, ImportError, ValueError):
return module_name
if ((spec is None) or (spec.origin is None) or (spec.origin == 'namespace')):
return module_name
elif spec.submodule_... |
def source_radian_profile(path):
if path:
path = os.path.expanduser(path)
if os.path.exists(path):
source_file(path)
else:
if ('XDG_CONFIG_HOME' in os.environ):
xdg_profile = make_path(os.environ['XDG_CONFIG_HOME'], 'radian', 'profile')
elif (not sys.platf... |
.parametrize('data, expdata', [([100, 0, 0, 0], [100, 0, 0]), ([100, 10, 10, 0], [110, 10, 0]), ([100, 10, 10, (np.pi / 2)], [10, 110, (np.pi / 2)])])
def test_manual_geometry(data, expdata):
planview = pyodrx.PlanView()
planview.add_fixed_geometry(pyodrx.Line(data[0]), data[1], data[2], data[3])
(x, y, h) ... |
class PlayPluginMessageClientBound(Packet):
id = 23
to = 1
def __init__(self, channel: str, data: bytes) -> None:
super().__init__()
self.channel = channel
self.data = data
def encode(self) -> bytes:
return (Buffer.pack_string(self.channel) + self.data) |
class WriteDir(Dir, locations.WriteLocation):
def setup(self, src_repo, owners_map=None):
ret_code = super().setup()
if (ret_code & Globals.RET_CODE_ERR):
return ret_code
if (self.base_dir.conn is Globals.local_connection):
from rdiffbackup.locations import _dir_shado... |
class CLAVRXNetCDFFileHandler(_CLAVRxHelper, BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info):
super(CLAVRXNetCDFFileHandler, self).__init__(filename, filename_info, filetype_info)
self.nc = xr.open_dataset(filename, decode_cf=True, mask_and_scale=False, decode_coords=Tru... |
def test_features_only(hatch, helpers, temp_dir, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), result.output
project_path ... |
class Inference(torch.nn.Module, metaclass=abc.ABCMeta):
subclasses = {}
def register_subclass(cls, inference_type):
def decorator(subclass):
cls.subclasses[inference_type] = subclass
return subclass
return decorator
def create(cls, inference_type, **kwargs):
... |
class LayerNormLSTMCellBackend(nn.LSTMCell):
def __init__(self, input_dim, hidden_dim, bias=True, epsilon=1e-05):
super(LayerNormLSTMCellBackend, self).__init__(input_dim, hidden_dim, bias)
self.epsilon = epsilon
def _layerNormalization(self, x):
mean = x.mean(1, keepdim=True).expand_as(... |
def read_conll(file_in, tokenizer, max_seq_length=512):
(words, labels) = ([], [])
examples = []
is_title = False
with open(file_in, 'r') as fh:
for line in fh:
line = line.strip()
if line.startswith('-DOCSTART-'):
is_title = True
continue
... |
class Node():
path: str
method: str = GET
params: dict = field(default_factory=dict)
source: Optional[str] = None
requested: bool = False
status_code: Optional[int] = None
ignore_form_fields: set = field(default_factory=set)
def __post_init__(self):
self.method = self.method.uppe... |
def test_imatmul_ilshift():
class A():
x: Bits16
B = mk_bitstruct('B', {'x': Bits100, 'y': ([A] * 3), 'z': A})
b = B(, [A(2), A(3), A(4)], A(5))
b = Bits164()
assert (b.to_bits() == Bits164())
c = B(, [A(2), A(3), A(4)], A(5))
c <<= Bits164()
assert (c == B(, [A(2), A(3), A(4)], ... |
class HeuTopoUnrollSim(BasePass):
def __init__(s, *, waveform=None, print_line_trace=True, reset_active_high=True):
s.waveform = waveform
s.print_line_trace = print_line_trace
s.reset_active_high = reset_active_high
def __call__(s, top):
top.elaborate()
GenDAGPass()(top)
... |
class CaseVLibsTranslation():
class DUT(VerilogPlaceholder, Component):
def construct(s):
s.d = InPort(Bits32)
s.q = OutPort(Bits32)
s.set_metadata(VerilogPlaceholderPass.src_file, (dirname(__file__) + '/VRegPassThrough.v'))
s.set_metadata(VerilogPlaceholderPa... |
def test_env_info_displays_complete_info(tester: CommandTester) -> None:
tester.execute()
expected = f'''
Virtualenv
Python: 3.7.0
Implementation: CPython
Path: {Path('/prefix')}
Executable: {sys.executable}
Valid: True
Base
Platform: darwin
OS: posix
Python: {'.'.jo... |
def test_step_unit():
step_unit = StepUnit()
step_unit.elaborate()
step_unit.apply(DefaultPassGroup())
step_unit.word_in = 1
step_unit.sum1_in = 1
step_unit.sum2_in = 1
step_unit.sim_eval_combinational()
assert (step_unit.sum1_out == 2)
assert (step_unit.sum2_out == 3)
step_unit.... |
def handle_code(code, title):
run_js(CLIPBOARD_SETUP)
session_local.globals = dict(globals())
if title:
put_markdown(('## %s' % title))
for p in gen_snippets(code):
with use_scope() as scope:
put_code(p, 'python')
put_buttons([t('Run', ''), t('Edit', ''), t('Copy ... |
def ffmpeg_microphone_live(sampling_rate: int, chunk_length_s: float, stream_chunk_s: Optional[int]=None, stride_length_s: Optional[Union[(Tuple[(float, float)], float)]]=None, format_for_conversion: str='f32le'):
if (stream_chunk_s is not None):
chunk_s = stream_chunk_s
else:
chunk_s = chunk_le... |
def test_dynlib_close():
class Comb():
class A(Component):
def construct(s):
s.in_ = InPort(Bits32)
s.out = OutPort(Bits32)
def upblk():
s.out = s.in_
class Seq():
class A(Component):
def construct(s):
... |
class Encoder(nn.Module):
def __init__(self, make_mlp, latent_size):
super().__init__()
self._make_mlp = make_mlp
self._latent_size = latent_size
self.node_model = self._make_mlp(latent_size)
self.mesh_edge_model = self._make_mlp(latent_size)
self.world_edge_model = s... |
class DeviceNameHypothesis(Hypothesis):
def find_subsystems(cls, context):
sys_path = context.sys_path
dirnames = ('bus', 'class', 'subsystem')
absnames = (os.path.join(sys_path, name) for name in dirnames)
realnames = (d for d in absnames if os.path.isdir(d))
return frozense... |
(bind=True, base=MlTask)
def transform_ptt_post_to_spacy_post(self, post_id: str) -> Dict:
post = self.sess.query(PttPost).filter((PttPost.id == post_id)).first()
if (not post):
raise PostNotExistsError(f'Post: {post_id} is not exist')
logger.info('Transforming %s', post_id)
spacy_post = transfo... |
def download_file(url: str, dest: Path, session: ((Authenticator | Session) | None)=None, chunk_size: int=1024) -> None:
from poetry.puzzle.provider import Indicator
downloader = Downloader(url, dest, session)
set_indicator = False
with Indicator.context() as update_context:
update_context(f'Dow... |
class MultiScaleCornerCrop(object):
def __init__(self, scales, size, interpolation=Image.BILINEAR, crop_positions=['c', 'tl', 'tr', 'bl', 'br']):
self.scales = scales
self.size = size
self.interpolation = interpolation
self.crop_positions = crop_positions
def __call__(self, img):... |
def test_catalogreference():
catref = OSC.CatalogReference('VehicleCatalog', 'S60')
prettyprint(catref.get_element())
catref.add_parameter_assignment('stuffs', 1)
prettyprint(catref.get_element())
catref2 = OSC.CatalogReference('VehicleCatalog', 'S60')
catref2.add_parameter_assignment('stuffs', ... |
def run_leiden_windows(graph, gamma, nruns, weight=None, node_subset=None, attribute=None, output_dictionary=False, niterations=5, calc_sim_mat=True):
np.random.seed()
g = graph
if (node_subset != None):
if (attribute == None):
gdel = node_subset
else:
gdel = [i for (... |
class BlockDecoder(object):
def _decode_block_string(block_string):
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split('(\\d.*)', op)
if (len(splits) >= 2):
(key, value) = splits[:2]... |
def parse_parametrs(p):
ret = {}
while ((len(p) > 1) and (p.count('|') > 0)):
s = p.split('|')
l = int(s[0])
if (l > 0):
p = p[(len(s[0]) + 1):]
field_name = p.split('|')[0].split('=')[0]
field_value = p[(len(field_name) + 1):l]
p = p[(l + ... |
def create_logger(save_path='', file_type='', level='debug'):
if (level == 'debug'):
_level = logging.DEBUG
elif (level == 'info'):
_level = logging.INFO
logger = logging.getLogger()
logger.setLevel(_level)
cs = logging.StreamHandler()
cs.setLevel(_level)
logger.addHandler(cs... |
def write_topic_model_log(opt, results):
log_path = os.path.join(get_topic_root_folder(opt), 'eval_log.txt')
if (not os.path.exists(log_path)):
with open(log_path, 'a') as outfile:
outfile.writelines('{}\t{}\t{}\t{}\n'.format('num_topics', 'topic_alpha', 'coherence', 'perplexity'))
with ... |
def test_user_avatar_history_multiple_requests(api, mock_req):
mock_req({'getUserProfilePhotos': {'ok': True, 'result': {'total_count': 4, 'photos': [[{'file_id': 'aaaaaa', 'width': 50, 'height': 50, 'file_size': 128}], [{'file_id': 'bbbbbb', 'width': 50, 'height': 50, 'file_size': 128}]]}}})
user = botogram.ob... |
def naive_grouped_rowwise_apply(data, group_labels, func, func_args=(), out=None):
if (out is None):
out = np.empty_like(data)
for (row, label_row, out_row) in zip(data, group_labels, out):
for label in np.unique(label_row):
locs = (label_row == label)
out_row[locs] = fun... |
def instance_retrieval_test(args, cfg):
assert torch.cuda.is_available(), 'CUDA not available, Exit!'
train_dataset_name = cfg.IMG_RETRIEVAL.TRAIN_DATASET_NAME
eval_dataset_name = cfg.IMG_RETRIEVAL.EVAL_DATASET_NAME
spatial_levels = cfg.IMG_RETRIEVAL.SPATIAL_LEVELS
resize_img = cfg.IMG_RETRIEVAL.RES... |
class OsPathInjectionRegressionTest(TestCase):
def setUp(self):
self.filesystem = fake_filesystem.FakeFilesystem(path_separator='/')
self.os_path = os.path
self.os = fake_os.FakeOsModule(self.filesystem)
def tearDown(self):
os.path = self.os_path
def test_create_top_level_dir... |
((MANIFEST_DIGEST_ROUTE + '/labels/<labelid>'))
_param('repository', 'The full path of the repository. e.g. namespace/name')
_param('manifestref', 'The digest of the manifest')
_param('labelid', 'The ID of the label')
class ManageRepositoryManifestLabel(RepositoryParamResource):
_repo_read(allow_for_superuser=True)... |
def set_deserializer(func: callable, cls: Union[(type, Sequence[type])], high_prio: bool=True, fork_inst: type=StateHolder) -> None:
if isinstance(cls, Sequence):
for cls_ in cls:
set_deserializer(func, cls_, high_prio, fork_inst)
elif cls:
index = (0 if high_prio else len(fork_inst.... |
class FrozenBatchNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05):
super().__init__()
self.eps = eps
self.register_buffer('weight', torch.ones(num_features))
self.register_buffer('bias', torch.zeros(num_features))
self.register_buffer('running_mean', torch.zeros... |
class MyApp(App):
def __init__(self, *args):
super(MyApp, self).__init__(*args)
def main(self):
wid = gui.VBox(width=300, height=200, margin='0px auto')
self.lbl = gui.Label('Press the button', width='80%', height='50%')
self.lbl.style['margin'] = 'auto'
self.bt = gui.But... |
def init(win_id: int, parent: QObject) -> 'ModeManager':
commandrunner = runners.CommandRunner(win_id)
modeman = ModeManager(win_id, parent)
objreg.register('mode-manager', modeman, scope='window', window=win_id)
hintmanager = hints.HintManager(win_id, parent=parent)
objreg.register('hintmanager', h... |
class Optimizer(object):
_ARG_MAX_GRAD_NORM = 'max_grad_norm'
def __init__(self, optim, max_grad_norm=0):
self.optimizer = optim
self.scheduler = None
self.max_grad_norm = max_grad_norm
def set_scheduler(self, scheduler):
self.scheduler = scheduler
def step(self):
... |
class AddressBox(Form):
def __init__(self, view, address):
form_name = ('address_%s' % address.id)
super().__init__(view, form_name)
par = self.add_child(P(view, text=('%s: %s ' % (address.name, address.email_address))))
par.add_child(Button(self, address.events.edit.with_arguments(a... |
class Network(nn.Module):
def __init__(self, C, num_classes, layers, criterion, steps=4, multiplier=4, stem_multiplier=3):
super(Network, self).__init__()
self._C = C
self._num_classes = num_classes
self._layers = layers
self._criterion = criterion
self._steps = steps... |
class YAKE(LoadFile):
def __init__(self):
super(YAKE, self).__init__()
self.words = defaultdict(set)
self.contexts = defaultdict((lambda : ([], [])))
self.features = defaultdict(dict)
self.surface_to_lexical = {}
def candidate_selection(self, n=3, stoplist=None, **kwargs)... |
def get_shortest_unique_filename(filename, filenames):
filename1 = filename.replace('\\', '/')
filenames = [fn.replace('\\', '/') for fn in filenames]
filenames = [fn for fn in filenames if (fn != filename1)]
nameparts1 = filename1.split('/')
uniqueness = [len(filenames) for i in nameparts1]
for... |
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=16):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.sharedMLP = nn.Sequential(nn.Conv2d(in_planes, (in_planes // ratio), 1, bias=False), nn.ReLU(), nn.Conv2... |
class EncapsulateFieldTest(unittest.TestCase):
def setUp(self):
super().setUp()
self.project = testutils.sample_project()
self.pycore = self.project.pycore
self.mod = testutils.create_module(self.project, 'mod')
self.mod1 = testutils.create_module(self.project, 'mod1')
... |
def export_pinnacle(pinnacle_subparsers):
parser = pinnacle_subparsers.add_parser('export', help='Export a raw file to DICOM')
parser.add_argument('input_path', type=str, help="Root Patient directory of raw Pinnacle data (directory containing the 'Patient' file). Alternatively a TAR archive can be supplied.")
... |
(scope='session')
def lombscargle_gen(rand_data_gen):
def _generate(num_in_samps, num_out_samps):
A = 2.0
w = 1.0
phi = (0.5 * np.pi)
frac_points = 0.9
(r, _) = rand_data_gen(num_in_samps, 1)
cpu_x = np.linspace(0.01, (10 * np.pi), num_in_samps)
cpu_x = cpu_x[... |
class OpQuery():
def __init__(self, graph, op_map=None, ops_to_ignore=None, strict=True):
self._log = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)
self._graph = graph
self._strict = strict
if op_map:
self._op_map = op_map
else:
self._op_map ... |
def job_met_opt10(sample_source, tr, te, r, J):
met_opt_options = {'n_test_locs': J, 'max_iter': 50, 'locs_step_size': 10.0, 'gwidth_step_size': 0.2, 'seed': (r + 92856), 'tol_fun': 0.001}
(test_locs, gwidth, info) = tst.MeanEmbeddingTest.optimize_locs_width(tr, alpha, **met_opt_options)
met_opt = tst.MeanE... |
_constructor.register(scipy.sparse.spmatrix)
def sparse_constructor(value, name=None, strict=False, allow_downcast=None, borrow=False, format=None):
if (format is None):
format = value.format
type = SparseTensorType(format=format, dtype=value.dtype)
if (not borrow):
value = copy.deepcopy(val... |
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, number_net=4, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
super(ResNet, self).__init__()
if (norm_layer is None):
norm_layer = nn.BatchNorm2d
... |
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
super(GAT, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)]
for (i, attention) in enume... |
def test_stochasticoptimization():
last_time_replaced = [False]
_rewriter([add])
def insert_broken_add_sometimes(fgraph, node):
if (node.op == add):
last_time_replaced[0] = (not last_time_replaced[0])
if last_time_replaced[0]:
return [off_by_half(*node.inputs)... |
class CreationTests(AuthenticatedAPITestCase):
def setUpTestData(cls):
cls.user = User.objects.create(id=1234, name='joe dart', discriminator=1111)
cls.user2 = User.objects.create(id=9876, name='Who?', discriminator=1234)
def test_accepts_valid_data(self):
url = reverse('api:bot:nominati... |
def bitstring_to_alphanumeric(s):
text = ''
while (len(s) >= 11):
part = s[:11]
s = s[11:]
num = int(part, 2)
c1 = min(44, (num // 45))
c2 = (num % 45)
text += (find_table_char(c1) + find_table_char(c2))
if (len(s) >= 6):
num = min(44, int(s[:6], 2))
... |
class Fastformer(Layer):
def __init__(self, nb_head, size_per_head, **kwargs):
self.nb_head = nb_head
self.size_per_head = size_per_head
self.output_dim = (nb_head * size_per_head)
self.now_input_shape = None
super(Fastformer, self).__init__(**kwargs)
def build(self, inpu... |
def test_accuracy(logits, labels):
logits_idx = tf.to_int32(tf.argmax(logits, axis=1))
logits_idx = tf.reshape(logits_idx, shape=(cfg.batch_size,))
correct_preds = tf.equal(tf.to_int32(labels), logits_idx)
accuracy = (tf.reduce_sum(tf.cast(correct_preds, tf.float32)) / cfg.batch_size)
return accurac... |
class TestsSemiBayes():
def test_compare_to_modernepi3(self):
posterior_rr = math.exp(((math.log(3.51) / 0.569) / ((1 / 0.5) + (1 / 0.569))))
posterior_ci = (math.exp((0.587 - (1.96 * (0.266 ** 0.5)))), math.exp((0.587 + (1.96 * (0.266 ** 0.5)))))
sb = semibayes(prior_mean=1, prior_lcl=0.25,... |
.functions
def test_not_case_sensitive_but_nonstring():
df = pd.DataFrame({'ok1': ['ABC', None, 'zzz'], 'ok2': pd.Categorical(['A', 'b', 'A'], ordered=False), 'notok1': [1, 2, 3], 'notok2': [b'ABC', None, b'zzz']})
for okcol in ['ok1', 'ok2']:
_ = df.count_cumulative_unique(okcol, dest_column_name='ok_c... |
def _get_command_line_arguments() -> Dict:
parser = argparse.ArgumentParser()
parser.add_argument(('--' + Args.INPUT_DIRS), help='One or more input directories containing features', nargs='+', required=True, type=str)
parser.add_argument(('--' + Args.INPUT_FEATURE_NAMES), help='One or more feature file name... |
def read_class_weights(class_weights_path: Path, label_map: LabelMap) -> np.ndarray:
if (not class_weights_path.exists()):
return np.ones(label_map.num_classes())
num_classes = label_map.num_classes()
class_weights = np.empty(num_classes)
class_weights[:] = np.nan
with class_weights_path.ope... |
class HyperYan(DynSys):
def _rhs(x, y, z, w, t, a=37, b=3, c=26, d=38):
xdot = ((a * y) - (a * x))
ydot = ((((c - a) * x) - (x * z)) + (c * y))
zdot = ((((((- b) * z) + (x * y)) - (y * z)) + (x * z)) - w)
wdot = ((((- d) * w) + (y * z)) - (x * z))
return (xdot, ydot, zdot, wd... |
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