code stringlengths 281 23.7M |
|---|
class BiFPN(Backbone):
def __init__(self, bottom_up, in_features, out_channels, num_top_levels, num_repeats, norm=''):
super(BiFPN, self).__init__()
assert isinstance(bottom_up, Backbone)
self.bottom_up = BackboneWithTopLevels(bottom_up, out_channels, num_top_levels, norm)
bottom_up_... |
class ANSIParser(object):
ansi_map = [('|n', ANSI_NORMAL), ('|/', ANSI_RETURN), ('|-', ANSI_TAB), ('|_', ANSI_SPACE), ('|*', ANSI_INVERSE), ('|^', ANSI_BLINK), ('|u', ANSI_UNDERLINE), ('|r', (ANSI_HILITE + ANSI_RED)), ('|g', (ANSI_HILITE + ANSI_GREEN)), ('|y', (ANSI_HILITE + ANSI_YELLOW)), ('|b', (ANSI_HILITE + ANS... |
class DrumViewMain(qw.QWidget, TalkieConnectionOwner):
def __init__(self, pile, *args):
qw.QWidget.__init__(self, *args)
self.setAttribute(qc.Qt.WA_AcceptTouchEvents, True)
st = self.state = State()
self.markers = MarkerStore()
self.markers.add_listener(self._markers_changed)... |
_module(force=True)
class PascalContextDataset(CustomDataset):
CLASSES = ('background', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle', 'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet', 'car', 'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow', 'cup', 'curtain', 'dog', 'door', 'fence',... |
def parse_args(args):
today = str(date.today())
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
subparsers = parser.add_subparsers(help='Arguments for specific dataset types.', dest='dataset_type')
subparsers.required = True
coco_parser = subp... |
class Version():
__slots__ = ('_major', '_minor', '_patch', '_prerelease', '_build')
NAMES: ClassVar[Tuple[(str, ...)]] = tuple([item[1:] for item in __slots__])
_LAST_NUMBER: ClassVar[Pattern[str]] = re.compile('(?:[^\\d]*(\\d+)[^\\d]*)+')
_REGEX_TEMPLATE: ClassVar[str] = '\n ^\n ... |
class SearchScope(GitlabEnum):
PROJECTS: str = 'projects'
ISSUES: str = 'issues'
MERGE_REQUESTS: str = 'merge_requests'
MILESTONES: str = 'milestones'
WIKI_BLOBS: str = 'wiki_blobs'
COMMITS: str = 'commits'
BLOBS: str = 'blobs'
USERS: str = 'users'
GLOBAL_SNIPPET_TITLES: str = 'snipp... |
def create_model(ema=False):
model = VoteNet(num_class=DATASET_CONFIG.num_class, num_heading_bin=DATASET_CONFIG.num_heading_bin, num_size_cluster=DATASET_CONFIG.num_size_cluster, mean_size_arr=DATASET_CONFIG.mean_size_arr, dataset_config=DATASET_CONFIG, num_proposal=FLAGS.num_target, input_feature_dim=num_input_cha... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channels, channels, stride=1, groups=1, width_per_group=64, sd=0.0, **block_kwargs):
super(Bottleneck, self).__init__()
width = (int((channels * (width_per_group / 64.0))) * groups)
self.shortcut = []
if ((stride !=... |
def LJ_force(pos, dim=3):
N_atom = int((len(pos) / dim))
pos = np.reshape(pos, [N_atom, dim])
force = np.zeros([N_atom, dim])
for (i, pos0) in enumerate(pos):
pos1 = pos.copy()
pos1 = np.delete(pos1, i, 0)
distance = cdist([pos0], pos1)
r = (pos1 - pos0)
r2 = np.p... |
def tiny_conv_net():
inputs = tf.keras.Input(shape=(32, 32, 3))
x = tf.keras.layers.Conv2D(32, kernel_size=2, strides=2, padding='same', use_bias=False)(inputs)
x = tf.keras.layers.BatchNormalization(beta_initializer='glorot_uniform', gamma_initializer='glorot_uniform')(x)
x = tf.keras.layers.ReLU()(x)
... |
def test_package_is_uploaded_200s_with_no_releases(default_repo):
default_repo.session = pretend.stub(get=(lambda url, headers: response_with(status_code=200, _content=b'{"releases": {}}', _content_consumed=True)))
package = pretend.stub(safe_name='fake', metadata=pretend.stub(version='2.12.0'))
assert (def... |
def print_model_with_flops(model, units='GMac', precision=3, ost=sys.stdout):
total_flops = model.compute_average_flops_cost()
def accumulate_flops(self):
if is_supported_instance(self):
return (self.__flops__ / model.__batch_counter__)
else:
sum = 0
for m in ... |
class ProjectNotificationSettingsManager(NotificationSettingsManager):
_path = '/projects/{project_id}/notification_settings'
_obj_cls = ProjectNotificationSettings
_from_parent_attrs = {'project_id': 'id'}
def get(self, **kwargs: Any) -> ProjectNotificationSettings:
return cast(ProjectNotificat... |
class _ConditionOperand(_Operand):
def __eq__(self, other: Any) -> Comparison:
return Comparison('=', self, self._to_operand(other))
def __ne__(self, other: Any) -> Comparison:
return Comparison('<>', self, self._to_operand(other))
def __lt__(self, other: Any) -> Comparison:
return C... |
class Bernoulli(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Bernoulli, self).__init__()
init_ = (lambda m: init(m, nn.init.orthogonal_, (lambda x: nn.init.constant_(x, 0))))
self.linear = init_(nn.Linear(num_inputs, num_outputs))
def forward(self, x):
x = self.... |
.parametrize('arg', [1, 2.0, 'hello world', ((sympy.Symbol('a') * sympy.Symbol('b')) + (sympy.Symbol('c') / 10)), np.array([*range(100)], dtype=np.complex128).reshape((10, 10))])
def test_arg_to_proto_round_trip(arg):
proto = args.arg_to_proto(name='custom_name', val=arg)
arg_dict = args.arg_from_proto(proto)
... |
def find_bounding_sphere(mrc, L):
v = AIF.read_mrc(mrc)
points = []
density_max = v.max()
contour_level = (L * density_max)
for ijk in np.ndindex(v.shape):
if (v[ijk] >= contour_level):
points.append([float(ijk[0]), float(ijk[1]), float(ijk[2])])
points = np.asarray(points)
... |
class Observer():
def __init__(self, batch=True):
self.id = (rpc.get_worker_info().id - 1)
self.env = gym.make('CartPole-v1')
self.env.seed(args.seed)
self.select_action = (Agent.select_action_batch if batch else Agent.select_action)
def run_episode(self, agent_rref, n_steps):
... |
def main():
Format()
basic_multivector_operations_3D()
basic_multivector_operations_2D()
basic_multivector_operations_2D_orthogonal()
check_generalized_BAC_CAB_formulas()
rounding_numerical_components()
derivatives_in_rectangular_coordinates()
derivatives_in_spherical_coordinates()
c... |
def parse_genia() -> None:
output_dir_path = 'data/genia/'
os.makedirs(output_dir_path, mode=493, exist_ok=True)
output_file_list = ['genia.train', 'genia.dev', 'genia.test']
dataset_size_list = [15022, 1669, 1855]
with open(CORPUS_FILE_PATH, 'r') as f:
for (output_file, dataset_size) in zip... |
class CustomDecayLR(object):
def __init__(self, optimizer, lr):
self.optimizer = optimizer
self.lr = lr
def epoch_step(self, epoch):
lr = self.lr
if (epoch > 12):
lr = (lr / 1000)
elif (epoch > 8):
lr = (lr / 100)
elif (epoch > 4):
... |
class BaseFairseqModel(nn.Module):
def __init__(self):
super().__init__()
self._is_generation_fast = False
def add_args(cls, parser):
dc = getattr(cls, '__dataclass', None)
if (dc is not None):
gen_parser_from_dataclass(parser, dc())
def build_model(cls, args, tas... |
class FasterRcnnInceptionResnetV2FeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
return frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor(is_training=False, first_stage_features_stride=first_stage_features_stride, reuse_weights=None, weight_de... |
class TextDetector():
def __init__(self):
self.mode = cfg.TEST.DETECT_MODE
if (self.mode == 'H'):
self.text_proposal_connector = TextProposalConnector()
elif (self.mode == 'O'):
self.text_proposal_connector = TextProposalConnectorOriented()
def detect(self, text_p... |
class pq_message_stream(object):
_block = 512
_limit = (_block * 4)
def __init__(self):
self._strio = BytesIO()
self._start = 0
def truncate(self):
self._strio.truncate(0)
self._start = 0
def _rtruncate(self, amt=None):
strio = self._strio
if (amt is N... |
def get_node_attr(node):
attrs = inspect.getmembers(node, (lambda a: (not inspect.isroutine(a))))
attribute_data = []
for att in attrs:
(attr_name, attr_val) = att
if attr_name.startswith('_'):
continue
attr_type = type(attr_val).__name__
attrs_to_skip = ['compone... |
class DropConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False, dropout_prob=0.0):
super(DropConvBlock, self).__init__()
self.use_dropout = (dropout_prob != 0.0)
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, ... |
def hf_bucket_url(model_id: str, filename: str, use_cdn=True) -> str:
endpoint = (CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX)
legacy_format = ('/' not in model_id)
if legacy_format:
return f'{endpoint}/{model_id}-{filename}'
else:
return f'{endpoint}/{model_id}/{filename}... |
def get_from_ini(key: str, default: str) -> str:
config = CONFIG_STACK[(- 1)]
value = config.getini(key)
if (not isinstance(value, str)):
raise TypeError(f'Expected a string for configuration option {value!r}, got a {type(value)} instead')
return (value if (value != '') else default) |
def my_route(app):
('/trigger', methods=['GET', 'POST'])
def trigger_handler():
if (request.method == 'POST'):
data = request.get_json()
status = 'yes'
detail = {}
message = data.get('message')
if (message == 'fetch_user_id'):
d... |
class MLP(nn.Module):
def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'):
super(MLP, self).__init__()
self.model = []
self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)]
for i in range((n_blk - 2)):
self.model += [LinearB... |
def test_custom_hud_colors(skip_qtbot):
cosmetic_patches = AM2RCosmeticPatches(health_hud_rotation=0, etank_hud_rotation=0, dna_hud_rotation=0)
dialog = AM2RCosmeticPatchesDialog(None, cosmetic_patches)
skip_qtbot.addWidget(dialog)
assert (dialog.custom_health_rotation_square.styleSheet() == 'background... |
('pypyr.venv.subprocess.check_call')
def test_env_builder_upgrade_deps_quiet(mock_subproc_run):
eb = EnvBuilderWithExtraDeps(is_quiet=True)
context = get_simple_context()
eb.post_setup(context)
eb.upgrade_dependencies(context)
mock_subproc_run.assert_called_once_with(['/python', '-m', 'pip', 'instal... |
class AttrVI_ATTR_ASRL_BAUD(RangeAttribute):
resources = [(constants.InterfaceType.asrl, 'INSTR')]
py_name = 'baud_rate'
visa_name = 'VI_ATTR_ASRL_BAUD'
visa_type = 'ViUInt32'
default = 9600
(read, write, local) = (True, True, False)
(min_value, max_value, values) = (0, , None) |
class Transaction(object):
def __init__(self, conn, label='', mode='immediate', retry_interval=0.1, callback=None):
self.cursor = conn.cursor()
assert (mode in ('deferred', 'immediate', 'exclusive'))
self.mode = mode
self.depth = 0
self.rollback_wanted = False
self.re... |
def test_parse_args_ls(capsys):
args = client_parameters.parse_args(('ls',))
assert (args.func == client_parameters.ls_handler)
assert (args.hostname is None)
assert (args.parameter is None)
args = client_parameters.parse_args(('ls', '--hostname', 'froodle'))
assert (args.func == client_paramete... |
class DPR():
def __init__(self, model_path: Union[(str, Tuple)]=None, **kwargs):
self.q_tokenizer = DPRQuestionEncoderTokenizerFast.from_pretrained(model_path[0])
self.q_model = DPRQuestionEncoder.from_pretrained(model_path[0])
self.q_model.cuda()
self.q_model.eval()
self.ctx... |
def test_version_hash_varies_on_user_preferences(project):
actual_version_hash = versioning.calculate_version_hash(project)
assert (project.prefs.get('automatic_soa') is False)
project.prefs.set('automatic_soa', True)
patched_version_hash = versioning.calculate_version_hash(project)
assert (actual_v... |
class MainTrainer(_baseTrainer):
def __init__(self, config: Config, tmpFile: Optional[StrPath], modelFn: Callable[([], Tuple[(BaseCompressor, Distortion)])], optimizer: Type[torch.optim.Optimizer], scheduler: Type[torch.optim.lr_scheduler._LRScheduler], saver: Saver):
if (dist.get_rank() != 0):
... |
def ruleR2(node_a: Node, node_b: Node, node_c: Node, graph: Graph, bk: (BackgroundKnowledge | None), changeFlag: bool, verbose=False) -> bool:
if (graph.is_adjacent_to(node_a, node_c) and (graph.get_endpoint(node_a, node_c) == Endpoint.CIRCLE)):
if ((graph.get_endpoint(node_a, node_b) == Endpoint.ARROW) and... |
def _install_restore_mode_child():
global _mode_write_pipe
global _restore_mode_child_installed
if _restore_mode_child_installed:
return
(mode_read_pipe, _mode_write_pipe) = os.pipe()
if (os.fork() == 0):
os.close(_mode_write_pipe)
PR_SET_PDEATHSIG = 1
libc = ctypes.c... |
class TempfileTestMixin():
def setUp(self):
self._tempfiles = []
def tearDown(self):
for fn in self._tempfiles:
try:
os.remove(fn)
except IOError as exc:
if (exc.errno != errno.ENOENT):
raise
def mktemp(self):
... |
def test_wrap_block():
candidates = CompletedKeys(10)
assert (candidates.num_remaining == 10)
candidates.mark_completed(2, 5)
assert (len(candidates._slabs) == 1)
assert (candidates.num_remaining == 7)
candidates.mark_completed(1, 8)
assert (len(candidates._slabs) == 1)
assert (candidate... |
def detect_pyrocko_events(first512):
try:
first512 = first512.decode('utf-8')
except UnicodeDecodeError:
return False
lines = first512.splitlines()[:(- 1)]
ok = 0
for line in lines:
line = line.strip()
if ((not line) or line.startswith('#')):
continue
... |
class ProjectConfig(models.Model):
project_models = (('svn', 'svn'), ('git', 'git'))
repo_models = (('branch', 'branch'), ('tag', 'tag'), ('trunk', 'trunk'))
project = models.OneToOneField('Project', on_delete=models.CASCADE)
repo = models.CharField(choices=project_models, max_length=3, verbose_name='')... |
def reporthook(blocknum, blocksize, totalsize):
readsofar = (blocknum * blocksize)
if (totalsize > 0):
percent = ((readsofar * 100.0) / totalsize)
s = ('\r%5.1f%% %*d / %d' % (percent, len(str(totalsize)), readsofar, totalsize))
sys.stderr.write(s)
if (readsofar >= totalsize):
... |
def main(args):
time_str = time.strftime('%Y-%m-%d_%H_%M')
logger_name = f'test_logger{time_str}.log'
print_logger = get_logger(os.path.join(args.output_dir, logger_name))
print_logger.info(pprint.pformat(args))
print_logger.info('==> loading HRNet...')
devices = try_all_gpus()
net = get_net... |
def getmsg(f, extra_ns: Optional[Mapping[(str, object)]]=None, *, must_pass: bool=False) -> Optional[str]:
src = '\n'.join(_pytest._code.Code.from_function(f).source().lines)
mod = rewrite(src)
code = compile(mod, '<test>', 'exec')
ns: Dict[(str, object)] = {}
if (extra_ns is not None):
ns.u... |
class TensoredOp(ListOp):
def __init__(self, oplist: List[OperatorBase], coeff: Union[(int, float, complex, ParameterExpression)]=1.0, abelian: bool=False) -> None:
super().__init__(oplist, combo_fn=partial(reduce, np.kron), coeff=coeff, abelian=abelian)
def num_qubits(self) -> int:
return sum([... |
def get_devices() -> List[str]:
output = subprocess.check_output('ip route show default'.split(), universal_newlines=True)
words = output.split()
devices = []
for (cur, nex) in zip(words, words[1:]):
if (cur == 'dev'):
devices.append(nex)
if (not devices):
raise ValueErro... |
class Color(object):
def __init__(self, rgb_val=0):
rgb_val_int = int(rgb_val)
if (rgb_val_int < 0):
raise ValueError('RGB value must not be negative.')
if (rgb_val_int > ):
raise ValueError('RGB value must not be greater than 0xffffff.')
self._rgb_val = rgb_v... |
class GetCurrentAppNameCommand(GetCurrentAppConfigCommand):
def __init__(self, device_type: str, apps_list: List[Dict[(str, Union[(str, List[Union[(str, Dict[(str, Any)])]])])]]) -> None:
super(GetCurrentAppNameCommand, self).__init__(device_type)
self.apps_list = apps_list
def process_response(... |
class World(object):
def __init__(self):
self.agents = []
self.landmarks = []
self.dim_c = 2
self.dim_p = 2
self.dim_color = 3
self.length = 960
self.width = 355
self.height = 600
self.net_height = (155 / 2)
self.gravitational_acceratio... |
class FakeAdapter(Adapter):
_buffer = ''
def _read(self):
result = copy(self._buffer)
self._buffer = ''
return result
def _read_bytes(self, count, break_on_termchar):
result = copy(self._buffer)
self._buffer = ''
return result[:count].encode()
def _write(s... |
class FakeIoModule():
def dir() -> List[str]:
_dir = ['open']
if (sys.version_info >= (3, 8)):
_dir.append('open_code')
return _dir
def __init__(self, filesystem: 'FakeFilesystem'):
self.filesystem = filesystem
self.skip_names: List[str] = []
self._io_... |
def score_jnd_dataset(data_loader, func, name=''):
ds = []
gts = []
for data in tqdm(data_loader.load_data(), desc=name):
ds += func(data['p0'], data['p1']).data.cpu().numpy().tolist()
gts += data['same'].cpu().numpy().flatten().tolist()
sames = np.array(gts)
ds = np.array(ds)
so... |
class SmoothCrossEntropyLoss(torch.nn.Module):
def __init__(self, smoothing=0.0, reduction='mean'):
super(SmoothCrossEntropyLoss, self).__init__()
self.smoothing = smoothing
self.confidence = (1.0 - smoothing)
self.reduction = reduction
def forward(self, x, target):
logpr... |
class KeypointDetector(nn.Module):
def __init__(self, cfg):
super(KeypointDetector, self).__init__()
self.backbone = build_backbone(cfg)
self.heads = build_heads(cfg, self.backbone.out_channels)
def forward(self, images, targets=None):
if (self.training and (targets is None)):
... |
class TestRFC822Name():
def test_repr(self):
gn = x509.RFC822Name('string')
assert (repr(gn) == "<RFC822Name(value='string')>")
def test_equality(self):
gn = x509.RFC822Name('string')
gn2 = x509.RFC822Name('string2')
gn3 = x509.RFC822Name('string')
assert (gn != g... |
def visualize_regression(image, gt):
image = np.rollaxis(image, axis=2, start=0)
image = (np.rollaxis(image, axis=2, start=0) * 255.0)
image = image.astype(np.uint8).copy()
for i in gt:
for j in range(p.regression_size):
y_value = (p.y_size - ((p.regression_size - j) * (220 / p.regre... |
def single_rank_execution(rank: int, world_size: int, constraints: Dict[(str, ParameterConstraints)], module: torch.nn.Module, backend: str) -> None:
import os
import torch
import torch.distributed as dist
from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
from torchrec.dist... |
def test_run_with_dependencies(installer: Installer, locker: Locker, repo: Repository, package: ProjectPackage) -> None:
package_a = get_package('A', '1.0')
package_b = get_package('B', '1.1')
repo.add_package(package_a)
repo.add_package(package_b)
package.add_dependency(Factory.create_dependency('A... |
class TelegramAPI():
def __init__(self, api_key, endpoint=None):
if (endpoint is None):
endpoint = '
self._api_key = api_key
self._endpoint = endpoint
self._session_cache = None
self._session_pid = (- 1)
def _session(self):
if ((self._session_pid != os... |
def filter_14(dataset):
for example in dataset:
example = copy(example)
correct_aspect_sentiment = dict()
for (k, v) in example['aspect_sentiment'].items():
if (v in ['positive', 'negative']):
correct_aspect_sentiment[k] = v
example['aspect_sentiment'] = c... |
class BasePlayer(GObject.GObject, Equalizer):
name = ''
version_info = ''
song = None
info = None
error = False
replaygain_profiles = [None, None, None, ['none']]
_paused = True
_source = None
__gsignals__ = {'song-started': (GObject.SignalFlags.RUN_LAST, None, (object,)), 'song-ende... |
def test_main(fancy_wheel, tmp_path):
destdir = (tmp_path / 'dest')
main([str(fancy_wheel), '-d', str(destdir)], 'python -m installer')
installed_py_files = destdir.rglob('*.py')
assert ({f.stem for f in installed_py_files} == {'__init__', '__main__', 'data'})
installed_pyc_files = destdir.rglob('*.... |
def locate_file(root: str, file_name: str) -> (str | None):
while True:
file_path = os.path.join(root, file_name)
if os.path.isfile(file_path):
return file_path
new_root = os.path.dirname(root)
if (new_root == root):
return None
root = new_root |
def get_list_of_products(update, context):
category_name = update.message.text
name_of_all_categories = get_name_of_all_categories()
if (category_name in name_of_all_categories):
save_products_in_user_data(context.user_data, category_name)
if (not context.user_data[products_data_key]['produc... |
.parametrize('style, expected_urls', [pytest.param(" 'default.css'", ['default.css'], id='import with apostrophe'), pytest.param(' "default.css"', ['default.css'], id='import with quote'), pytest.param(" \t 'tabbed.css'", ['tabbed.css'], id='import with tab'), pytest.param(" url('default.css')", ['default.css'], id='im... |
def convert_annotations(root_path, split, format):
assert isinstance(root_path, str)
assert isinstance(split, str)
lines = []
with open(osp.join(root_path, 'annotations', f'Challenge2_{split}_Task3_GT.txt'), 'r', encoding='"utf-8-sig') as f:
annos = f.readlines()
dst_image_root = osp.join(ro... |
class UserDeposit():
def __init__(self, jsonrpc_client: JSONRPCClient, user_deposit_address: UserDepositAddress, contract_manager: ContractManager, proxy_manager: 'ProxyManager', block_identifier: BlockIdentifier) -> None:
if (not is_binary_address(user_deposit_address)):
raise ValueError('Expec... |
def run_qdb_script(qdb, filename: str) -> None:
with open(filename) as fd:
for line in iter(fd.readline, ''):
if (line.startswith('#') or (line == '\n')):
continue
(cmd, arg, _) = qdb.parseline(line)
func = getattr(qdb, f'do_{cmd}')
if arg:
... |
class EfficientNetImageProcessor(BaseImageProcessor):
model_input_names = ['pixel_values']
def __init__(self, do_resize: bool=True, size: Dict[(str, int)]=None, resample: PILImageResampling=PIL.Image.NEAREST, do_center_crop: bool=False, crop_size: Dict[(str, int)]=None, rescale_factor: Union[(int, float)]=(1 / ... |
class PipeConnection(LowLevelPipeConnection):
def __init__(self, inpipe, outpipe, conn_number=0, process=None):
super().__init__(inpipe, outpipe)
self.conn_number = conn_number
self.unused_request_numbers = set(range(256))
self.process = process
def __str__(self):
return ... |
def pad(batch):
f = (lambda x: [sample[x] for sample in batch])
words = f(0)
is_heads = f(2)
tags = f(3)
seqlens = f((- 1))
maxlen = np.array(seqlens).max()
f = (lambda x, seqlen: [(sample[x] + ([0] * (seqlen - len(sample[x])))) for sample in batch])
x = f(1, maxlen)
y = f((- 2), max... |
def _get_tuf_root(repository_ref, namespace, reponame):
if ((not features.SIGNING) or (repository_ref is None) or (not repository_ref.trust_enabled)):
return DISABLED_TUF_ROOT
if ModifyRepositoryPermission(namespace, reponame).can():
return SIGNER_TUF_ROOT
return QUAY_TUF_ROOT |
def cal_rouge_path(pred_name, ref_name):
with open(pred_name, 'r') as f:
refs = get_sents_str(f)
with open(ref_name, 'r') as f:
preds = get_sents_str(f)
(ref_ids, pred_ids) = ([], [])
for (ref, pred) in zip(refs, preds):
(ref_id, pred_id) = change_word2id(ref, pred)
ref_i... |
_config
def test_wide_shuffle(manager):
manager.test_window('one')
manager.test_window('two')
manager.test_window('three')
manager.test_window('four')
assert (manager.c.layout.info()['main'] == 'one')
assert (manager.c.layout.info()['secondary'] == ['two', 'three', 'four'])
manager.c.layout.... |
def hide_cmd2_modules(self):
self.hidden_commands.append('py')
self.hidden_commands.append('alias')
self.hidden_commands.append('macro')
self.hidden_commands.append('script')
self.hidden_commands.append('shortcuts')
self.hidden_commands.append('pyscript')
self.hidden_commands.append('run_pys... |
def test():
model.eval()
output = model(features, adj)
loss_test = F.nll_loss(output[idx_test], labels[idx_test])
acc_test = accuracy(output[idx_test], labels[idx_test])
print('Test set results:', 'loss= {:.4f}'.format(loss_test.item()), 'accuracy= {:.4f}'.format(acc_test.item())) |
class ScikitUniform2DSubMesh(ScikitSubMesh2D):
def __init__(self, lims, npts):
(spatial_vars, tabs) = self.read_lims(lims)
coord_sys = spatial_vars[0].coord_sys
edges = {}
for var in spatial_vars:
if (var.name not in ['y', 'z']):
raise pybamm.DomainError(f... |
def insert_db(event):
if (event.ev_type == EVTYPE_GENERIC):
con.execute('insert into gen_events values(?, ?, ?, ?)', (event.name, event.symbol, event.comm, event.dso))
elif (event.ev_type == EVTYPE_PEBS_LL):
event.ip &=
event.dla &=
con.execute('insert into pebs_ll values (?, ?... |
class TestKeySequence():
def test_init(self):
seq = keyutils.KeySequence(keyutils.KeyInfo(Qt.Key.Key_A), keyutils.KeyInfo(Qt.Key.Key_B), keyutils.KeyInfo(Qt.Key.Key_C), keyutils.KeyInfo(Qt.Key.Key_D), keyutils.KeyInfo(Qt.Key.Key_E))
assert (len(seq._sequences) == 2)
assert (len(seq._sequence... |
def force_fp32(apply_to=None, out_fp16=False):
warnings.warn('force_fp32 in mmpose will be deprecated in the next release.Please use mmcv.runner.force_fp32 instead (mmcv>=1.3.1).', DeprecationWarning)
def force_fp32_wrapper(old_func):
(old_func)
def new_func(*args, **kwargs):
if (not... |
class OptimizerAE(object):
def __init__(self, preds, labels, pos_weight, norm):
preds_sub = preds
labels_sub = labels
self.cost = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight))
self.log_lik = self.cost
sel... |
class TextureGroup(Group):
def __init__(self, texture, order=0, parent=None):
super().__init__(order, parent)
self.texture = texture
def set_state(self):
glActiveTexture(GL_TEXTURE0)
glBindTexture(self.texture.target, self.texture.id)
def __hash__(self):
return hash((... |
def _delete_bn_from_model(sess: tf.compat.v1.Session, bn_op: OpWithMetaInfoType, is_bias_valid: bool):
bn_tf_op = sess.graph.get_operation_by_name(bn_op.op.name)
bn_in_tensor = sess.graph.get_tensor_by_name(bn_op.in_tensor.name)
bn_out_tensor = sess.graph.get_tensor_by_name(bn_op.out_tensor.name)
if (no... |
class IdentityFirstStage(torch.nn.Module):
def __init__(self, *args, vq_interface=False, **kwargs):
self.vq_interface = vq_interface
super().__init__()
def encode(self, x, *args, **kwargs):
return x
def decode(self, x, *args, **kwargs):
return x
def quantize(self, x, *arg... |
class OrientedPushNormalizedOracle(py_policy.PyPolicy):
def __init__(self, env):
super(OrientedPushNormalizedOracle, self).__init__(env.time_step_spec(), env.action_spec())
self._oracle = OrientedPushOracle(env)
self._env = env
def reset(self):
self._oracle.reset()
def _actio... |
.parametrize('compressor_size', zip(managers(), [{'max_compressed_buffer_size': 89373, 'num_chunks': 1, 'uncompressed_buffer_size': 10000}, {'max_compressed_buffer_size': 16432, 'num_chunks': 1, 'uncompressed_buffer_size': 10000}, {'max_compressed_buffer_size': 12460, 'num_chunks': 3, 'uncompressed_buffer_size': 10000}... |
()
def fixed_windows_output_feature_set_dataframe(spark_context, spark_session):
data = [{'id': 1, 'timestamp': '2016-04-11 11:31:11', 'feature1__avg_over_2_minutes_fixed_windows': 200, 'feature1__avg_over_15_minutes_fixed_windows': 200, 'feature1__stddev_pop_over_2_minutes_fixed_windows': 0, 'feature1__stddev_pop_... |
class EndTradingEventNotifier(EventNotifier[(EndTradingEvent, EndTradingEventListener)]):
def __init__(self, event_notifier: AllEventNotifier) -> None:
super().__init__()
self.event_notifier = event_notifier
def notify_all(self, event: EndTradingEvent):
self.event_notifier.notify_all(eve... |
class TagTestManager(object):
maxDiff = (1024 * 20)
manage_models = None
longMessage = True
def setUp(self):
if (self.manage_models is not None):
for model in self.manage_models:
tag_models.initial.model_initialise_tags(model)
tag_models.initial.model_... |
class PublisherGeoReportView(PublisherAccessMixin, BaseReportView):
template_name = 'adserver/reports/publisher-geo.html'
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
publisher_slug = kwargs.get('publisher_slug', '')
publisher = get_object_or_404(Pub... |
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env, skip=4):
gym.Wrapper.__init__(self, env)
self.obs_buffer = np.zeros(((2,) + env.observation_space.shape), dtype=np.uint8)
self.skip = skip
def step(self, action):
total_reward = 0.0
done = None
for i in ran... |
class DataEditorCanvas(QtWidgets.QWidget):
game: (RandovaniaGame | None) = None
region: (Region | None) = None
area: (Area | None) = None
highlighted_node: (Node | None) = None
connected_node: (Node | None) = None
_background_image: (QtGui.QImage | None) = None
region_bounds: BoundsFloat
... |
(everythings(min_int=(- ), max_int=, allow_null_bytes_in_keys=False, allow_datetime_microseconds=False), booleans())
def test_bson(everything: Everything, detailed_validation: bool):
from bson import decode as bson_loads
from bson import encode as bson_dumps
converter = bson_make_converter(detailed_validati... |
.parametrize('username,password', users)
def test_create(db, client, username, password):
client.login(username=username, password=password)
xml_file = (((Path(settings.BASE_DIR) / 'xml') / 'elements') / 'catalogs.xml')
url = reverse(urlnames['list'])
with open(xml_file, encoding='utf8') as f:
r... |
def copy_BraTS_segmentation_and_convert_labels_to_nnUNet(in_file: str, out_file: str) -> None:
img = sitk.ReadImage(in_file)
img_npy = sitk.GetArrayFromImage(img)
uniques = np.unique(img_npy)
for u in uniques:
if (u not in [0, 1, 2, 4]):
raise RuntimeError('unexpected label')
seg... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.