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class UnetCond5DS(nn.Module):
def __init__(self, input_nc=3, output_nc=3, nf=64, cond_dim=256, up_mode='upconv', use_dropout=False, return_lowres=False):
super(UnetCond5DS, self).__init__()
assert (up_mode in ('upconv', 'upsample'))
self.return_lowres = return_lowres
self.conv1 = Con... |
_fixtures(SqlAlchemyFixture, WebFixture)
def test_setting_cookies_on_response(sql_alchemy_fixture, web_fixture):
fixture = web_fixture
(UserSession)
class UserSessionStub(UserSession):
__tablename__ = 'usersessionstub'
__mapper_args__ = {'polymorphic_identity': 'usersessionstub'}
id ... |
def create_manifest_label(manifest_id, key, value, source_type_name, media_type_name=None):
if (not key):
raise InvalidLabelKeyException('Missing key on label')
if ((source_type_name != 'manifest') and (not validate_label_key(key))):
raise InvalidLabelKeyException(('Key `%s` is invalid or reserv... |
class KombuConsumerWorker(ConsumerMixin, PumpWorker):
def __init__(self, connection: kombu.Connection, queues: Sequence[kombu.Queue], work_queue: WorkQueue, serializer: Optional[KombuSerializer]=None, **kwargs: Any):
self.connection = connection
self.queues = queues
self.work_queue = work_qu... |
class IterativeOperatorWInfo(LinearOperator):
def __init__(self, A: LinearOperator, alg: Algorithm):
super().__init__(A.dtype, A.shape)
self.A = A
self.alg = alg
self.info = {}
def _matmat(self, X):
(Y, self.info) = self.alg(self.A, X)
return Y
def __str__(sel... |
class ListSearcher(Searcher):
def __init__(self, param_grid):
self._configurations = list(ParameterGrid(param_grid))
Searcher.__init__(self)
def suggest(self, trial_id):
if self._configurations:
return self._configurations.pop(0)
def on_trial_complete(self, **kwargs):
... |
def dataloader_didemo_train(args, tokenizer):
didemo_dataset = DiDeMo_DataLoader(subset='train', data_path=args.data_path, features_path=args.features_path, max_words=args.max_words, feature_framerate=args.feature_framerate, tokenizer=tokenizer, max_frames=args.max_frames, frame_order=args.train_frame_order, slice_... |
class GD32VF1xxUsart(QlConnectivityPeripheral):
class Type(ctypes.Structure):
_fields_ = [('STAT', ctypes.c_uint32), ('DATA', ctypes.c_uint32), ('BAUD', ctypes.c_uint32), ('CTL0', ctypes.c_uint32), ('CTL1', ctypes.c_uint32), ('CTL2', ctypes.c_uint32), ('GP', ctypes.c_uint32)]
def __init__(self, ql, labe... |
(simple_typeddicts(total=False, not_required=True, typeddict_cls=(None if (not is_py38) else ExtensionsTypedDict)), booleans())
def test_required(cls_and_instance: Tuple[(type, Dict)], detailed_validation: bool) -> None:
c = mk_converter(detailed_validation=detailed_validation)
(cls, instance) = cls_and_instanc... |
def save_config():
if (not config.test):
if (not os.path.exists(config.save_path)):
os.makedirs(config.save_path)
with open((config.save_path + '/config.txt'), 'w') as the_file:
for (k, v) in config.args.__dict__.items():
if ('False' in str(v)):
... |
class SquadTextLengthPreprocessor(Preprocessor):
def __init__(self, num_tokens_th):
self.num_tokens_th = num_tokens_th
def preprocess(self, question: SquadQuestionWithDistractors):
for par in (question.distractors + [question.paragraph]):
par.par_text = par.par_text[:self.num_tokens_... |
def main() -> None:
parser = ArgumentParser(prog=SCRIPT_NAME)
parser.add_argument('-v', '--verbose', action='store_true', default=False)
parser.add_argument('-n', '--dry-run', action='store_true', default=False)
commands = parser.add_subparsers(title='Sub-commands', required=True, dest='command')
co... |
class SnekIOTests(TestCase):
def test_safe_path(self) -> None:
cases = [('', ''), ('foo', 'foo'), ('foo/bar', 'foo/bar'), ('foo/bar.ext', 'foo/bar.ext')]
for (path, expected) in cases:
self.assertEqual(snekio.safe_path(path), expected)
def test_safe_path_raise(self):
cases = ... |
def main():
args = parser.parse_args()
if (args.model == 'all'):
parsed_model = open_clip.list_models()
else:
parsed_model = args.model.split(',')
results = []
for m in parsed_model:
row = profile_model(m)
results.append(row)
df = pd.DataFrame(results, columns=res... |
def get_arg_value(name_or_pos: Argument, arguments: BoundArgs) -> t.Any:
if isinstance(name_or_pos, int):
arg_values = tuple(arguments.items())
arg_pos = name_or_pos
try:
(name, value) = arg_values[arg_pos]
return value
except IndexError:
raise Val... |
class MainFrame(wx.Frame):
def __init__(self):
wx.Frame.__init__(self, None, title='pypilot client', size=(1000, 600))
host = ''
if (len(sys.argv) > 1):
host = sys.argv[1]
self.client = pypilotClient(host)
self.connected = False
ssizer = wx.FlexGridSizer(0... |
def define_D(input_nc, size, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', gpu_ids=[]):
netD = None
use_gpu = (len(gpu_ids) > 0)
norm_layer = get_norm_layer(norm_type=norm)
if use_gpu:
assert torch.cuda.is_available()
if (which_model_netD == 'basic... |
def ircformat(color, text):
if (len(color) < 1):
return text
add = sub = ''
if ('_' in color):
add += '\x1d'
sub = ('\x1d' + sub)
color = color.strip('_')
if ('*' in color):
add += '\x02'
sub = ('\x02' + sub)
color = color.strip('*')
if (len(co... |
class ResUnetGenerator(nn.Module):
def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
super(ResUnetGenerator, self).__init__()
unet_block = ResUnetSkipConnectionBlock((ngf * 8), (ngf * 8), input_nc=None, submodule=None, norm_layer=norm_layer, in... |
class DescribeCT_R():
.parametrize(('initial_cxml', 'text', 'expected_cxml'), [('w:r', 'foobar', 'w:r/w:t"foobar"'), ('w:r', 'foobar ', 'w:r/w:t{xml:space=preserve}"foobar "'), ('w:r/(w:rPr/w:rStyle{w:val=emphasis}, w:cr)', 'foobar', 'w:r/(w:rPr/w:rStyle{w:val=emphasis}, w:cr, w:t"foobar")')])
def it_can_add_a_... |
def export_data(data, dtype, file):
img = data[dtype]
data_file = open(file, 'w')
csv_writer = csv.writer(data_file)
count = 0
for i in img:
if (count == 0):
header = i.keys()
csv_writer.writerow(header)
count += 1
csv_writer.writerow(i.values())
... |
class RowIndex(tk.Canvas):
def __init__(self, *args, **kwargs):
tk.Canvas.__init__(self, kwargs['parentframe'], background=kwargs['index_bg'], highlightthickness=0)
self.parentframe = kwargs['parentframe']
self.MT = None
self.CH = None
self.TL = None
self.popup_menu_l... |
def main():
response =
response.raise_for_status()
contents = response.text
distributions = defaultdict(list)
ordering_data = defaultdict(dict)
for (i, distribution_type) in enumerate(('DEFAULT_CPYTHON_DISTRIBUTIONS', 'DEFAULT_PYPY_DISTRIBUTIONS')):
for (identifier, data, source) in par... |
def set_obj_goal(self, obj_goal):
self._obj_goal = obj_goal
self._env.PLACE_POSE = pp.get_pose(self._obj_goal)
c = safepicking.geometry.Coordinate(*self._env.PLACE_POSE)
c.translate([0, 0, 0.2], wrt='world')
self._env.PRE_PLACE_POSE = c.pose
visual_file = pp.get_visual_data(self._obj_goal)[0].me... |
class _Project():
prefs: Prefs
def __init__(self, fscommands):
self.observers = []
self.fscommands = fscommands
self.prefs = Prefs()
self.data_files = _DataFiles(self)
self._custom_source_folders = []
def get_resource(self, resource_name):
path = self._get_res... |
class FC6_TestCase(FC3_TestCase):
def runTest(self):
FC3_TestCase.runTest(self)
cmd = FC6_Reboot()
self.assertFalse(cmd.eject)
cmd = self.assert_parse('reboot --eject')
self.assertEqual(cmd.action, KS_REBOOT)
self.assertEqual(cmd.eject, True)
self.assertEqual(... |
def test_marker_without_description(pytester: Pytester) -> None:
pytester.makefile('.cfg', setup='\n [tool:pytest]\n markers=slow\n ')
pytester.makeconftest("\n import pytest\n pytest.mark.xfail('FAIL')\n ")
ftdir = pytester.mkdir('ft1_dummy')
pytester.path.joinpath('co... |
class DigitalPoleZeroResponse(FrequencyResponse):
zeros = List.T(Complex.T())
poles = List.T(Complex.T())
constant = Complex.T(default=(1.0 + 0j))
deltat = Float.T()
def __init__(self, zeros=None, poles=None, constant=(1.0 + 0j), deltat=None, **kwargs):
if (zeros is None):
zeros ... |
class TestBasic(TestCase):
def test_basic(self):
ann = Annotations()
a = Symbol('a')
next_a = Symbol('next(a)')
init_a = Symbol('init(a)')
ann.add(a, 'next', next_a)
ann.add(a, 'init', init_a)
ann.add(a, 'related', next_a)
ann.add(a, 'related', init_a)... |
class TriGamma(UnaryScalarOp):
def st_impl(x):
return scipy.special.polygamma(1, x)
def impl(self, x):
return TriGamma.st_impl(x)
def L_op(self, inputs, outputs, outputs_gradients):
(x,) = inputs
(g_out,) = outputs_gradients
if (x in complex_types):
raise ... |
def _parse_baseplate_script_args() -> Tuple[(argparse.Namespace, List[str])]:
parser = argparse.ArgumentParser(description='Run a function with app configuration loaded.', formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--debug', action='store_true', default=False, help='enable extra-... |
def main(_):
data_dir = FLAGS.data_dir
label_map_dict = label_map_util.get_label_map_dict(FLAGS.label_map_path)
logging.info('Reading from Pet dataset.')
image_dir = os.path.join(data_dir, 'images')
annotations_dir = os.path.join(data_dir, 'annotations')
examples_path = os.path.join(annotations_... |
class CueInput(reahl.web.ui.WrappedInput):
def __init__(self, html_input, cue_widget):
super().__init__(html_input)
div = self.add_child(Div(self.view))
self.set_html_representation(div)
div.append_class('reahl-bootstrapcueinput')
cue_widget.append_class('reahl-bootstrapcue')... |
.parametrize('trust_enabled,tuf_root', [(True, QUAY_TUF_ROOT), (False, DISABLED_TUF_ROOT)])
def test_trust_disabled(trust_enabled, tuf_root):
(app, principal) = app_with_principal()
with app.test_request_context('/'):
principal.set_identity(read_identity('namespace', 'repo'))
actual = _get_tuf_r... |
class GroupEpicNoteAwardEmojiManager(NoUpdateMixin, RESTManager):
_path = '/groups/{group_id}/epics/{epic_iid}/notes/{note_id}/award_emoji'
_obj_cls = GroupEpicNoteAwardEmoji
_from_parent_attrs = {'group_id': 'group_id', 'epic_iid': 'epic_iid', 'note_id': 'id'}
_create_attrs = RequiredOptional(required=... |
def load_json(p):
with p.open('r', encoding='utf-8') as f:
json_data = json.load(f)
article = json_data['article']
abstract = json_data['abstract']
source = [[tk.lower() for tk in sen.strip().split()] for sen in article]
tgt = [[tk.lower() for tk in sen.strip().split()] for s... |
class CommonTime_Tests(unittest.TestCase):
def test(self):
a = gpstk.CommonTime()
a.addDays(1234)
b = gpstk.CommonTime(a)
b.addSeconds(123.4)
c = (b - a)
self.assertAlmostEqual(1234.0, a.getDays())
self.assertEqual('0001234 0. UNK', str(a))
self.asser... |
def main():
scene = SceneManager.AddScene('Scene')
canvas = GameObject('Canvas')
scene.mainCamera.canvas = canvas.AddComponent(Canvas)
scene.Add(canvas)
imgObject = GameObject('Image', canvas)
rectTransform = imgObject.AddComponent(RectTransform)
rectTransform.offset = RectOffset.Rectangle(1... |
class _EntityConditionFactory():
def parse_entity_condition(element):
if (element.find('EndOfRoadCondition') is not None):
return EndOfRoadCondition.parse(element)
elif (element.find('CollisionCondition') is not None):
return CollisionCondition.parse(element)
elif (el... |
class LayerDepwiseDecode(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size=3, stride=1):
super(LayerDepwiseDecode, self).__init__()
block = [nn.Conv2d(in_channels=in_channel, out_channels=in_channel, kernel_size=kernel_size, stride=stride, padding=1, groups=in_channel), nn.Conv2d(i... |
def format_time(time: (((datetime.time | datetime.datetime) | float) | None)=None, format: (_PredefinedTimeFormat | str)='medium', tzinfo: (datetime.tzinfo | None)=None, locale: ((Locale | str) | None)=LC_TIME) -> str:
ref_date = (time.date() if isinstance(time, datetime.datetime) else None)
time = _get_time(ti... |
def test_align_left_multiline():
text = 'foo\nshoes'
fill_char = '-'
width = 7
aligned = cu.align_left(text, fill_char=fill_char, width=width)
assert (aligned == 'foo----\nshoes--')
reset_all = str(ansi.TextStyle.RESET_ALL)
blue = str(ansi.Fg.BLUE)
red = str(ansi.Fg.RED)
green = str(... |
class MLP(nn.Module):
class Block(nn.Module):
def __init__(self, *, d_in: int, d_out: int, bias: bool, activation: str, dropout: float) -> None:
super().__init__()
self.linear = nn.Linear(d_in, d_out, bias)
self.activation = make_module(activation)
self.dropou... |
def logSetup(filename, log_size, daemon):
logger = logging.getLogger('TinyHTTPProxy')
logger.setLevel(logging.INFO)
if (not filename):
if (not daemon):
handler = logging.StreamHandler()
else:
handler = logging.handlers.RotatingFileHandler(DEFAULT_LOG_FILENAME, maxByte... |
class KeySequence():
_MAX_LEN = 4
def __init__(self, *keys: KeyInfo) -> None:
self._sequences: List[QKeySequence] = []
for sub in utils.chunk(keys, self._MAX_LEN):
try:
args = [info.to_qt() for info in sub]
except InvalidKeyError as e:
rais... |
def main():
large_parameters = dict()
large_parameters['hidden_dim'] = 256
large_parameters['dim_feedforward'] = 512
large_parameters['class_embed_dim'] = 256
large_parameters['class_embed_num'] = 3
large_parameters['box_embed_dim'] = 256
large_parameters['box_embed_num'] = 3
large_param... |
class QueryScheduler():
__slots__ = ('_zc', '_types', '_addr', '_port', '_multicast', '_first_random_delay_interval', '_min_time_between_queries_millis', '_loop', '_startup_queries_sent', '_next_scheduled_for_alias', '_query_heap', '_next_run', '_clock_resolution_millis', '_question_type')
def __init__(self, zc... |
def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
class_info_set = set()
for (_, module_value) in inspect.getmembers(gitlab.v4.objects):
if (not inspect.ismodule(module_value)):
continue
for (class_name, class_value) in inspect.getmembers(module_value):
if (no... |
_module
class FocalLoss(nn.Module):
def __init__(self, use_sigmoid=True, gamma=2.0, alpha=0.25, reduction='mean', loss_weight=1.0):
super(FocalLoss, self).__init__()
assert (use_sigmoid is True), 'Only sigmoid focal loss supported now.'
self.use_sigmoid = use_sigmoid
self.gamma = gam... |
class BaseModel():
def name(self):
return self.__class__.__name__.lower()
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = (torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor)
self.save_dir = os.pa... |
class TestHistoryProgress():
def progress(self):
return history.HistoryProgress()
def test_no_start(self, progress):
progress.tick()
assert (progress._value == 1)
progress.finish()
assert (progress._progress is None)
def test_gui(self, qtbot, progress):
progre... |
def test_imapwidget_keyring_error(fake_qtile, monkeypatch, fake_window, patched_imap):
patched_imap.keyring.valid = False
imap = patched_imap.ImapWidget(user='qtile')
fakebar = FakeBar([imap], window=fake_window)
imap._configure(fake_qtile, fakebar)
text = imap.poll()
assert (text == 'Gnome Keyr... |
class ExportDialog(QtWidgets.QWidget):
def __init__(self, scene):
QtWidgets.QWidget.__init__(self)
self.setVisible(False)
self.setWindowTitle('Export')
self.shown = False
self.currentExporter = None
self.scene = scene
self.selectBox = QtWidgets.QGraphicsRectIt... |
class nnUNetDataset(object):
def __init__(self, folder: str, case_identifiers: List[str]=None, num_images_properties_loading_threshold: int=0, folder_with_segs_from_previous_stage: str=None):
super().__init__()
if (case_identifiers is None):
case_identifiers = get_case_identifiers(folder... |
def get_shared_secrets_along_route(payment_path_pubkeys: Sequence[bytes], session_key: bytes) -> Sequence[bytes]:
num_hops = len(payment_path_pubkeys)
hop_shared_secrets = (num_hops * [b''])
ephemeral_key = session_key
for i in range(0, num_hops):
hop_shared_secrets[i] = get_ecdh(ephemeral_key, ... |
class TestModelStatsCalculator(unittest.TestCase):
def test_compute_compression_ratio(self):
logger.debug(self.id())
network_cost = cc.Cost(50, 100)
with unittest.mock.patch('aimet_common.cost_calculator.CostCalculator.compute_network_cost') as mock_func:
mock_func.return_value =... |
class Transaction():
def __init__(self, current_packages: list[Package], result_packages: list[tuple[(Package, int)]], installed_packages: (list[Package] | None)=None, root_package: (Package | None)=None) -> None:
self._current_packages = current_packages
self._result_packages = result_packages
... |
class RegNetYLayer(nn.Module):
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int=1):
super().__init__()
should_apply_shortcut = ((in_channels != out_channels) or (stride != 1))
groups = max(1, (out_channels // config.groups_width))
self.shortcu... |
class PlayVehicleMoveClientBound(Packet):
id = 43
to = 1
def __init__(self, x: float, y: float, z: float, yaw: float, pitch: float) -> None:
super().__init__()
(self.x, self.y, self.z) = (x, y, z)
self.yaw = yaw
self.pitch = pitch
def encode(self) -> bytes:
return... |
class ViewAdminForm(forms.ModelForm):
uri_path = forms.SlugField(required=True)
class Meta():
model = View
fields = ['uri', 'uri_prefix', 'uri_path', 'comment', 'locked', 'catalogs', 'sites', 'editors', 'groups', 'template', 'title_lang1', 'title_lang2', 'title_lang3', 'title_lang4', 'title_lang... |
('the reported width of the cell is {width}')
def then_the_reported_width_of_the_cell_is_width(context, width):
expected_width = {'None': None, '1 inch': Inches(1)}[width]
actual_width = context.cell.width
assert (actual_width == expected_width), ('expected %s, got %s' % (expected_width, actual_width)) |
.parametrize('M, a, p, size', [(np.array(10, dtype=np.int64), np.array(0.5, dtype=config.floatX), np.array(0.5, dtype=config.floatX), None), (np.array(10, dtype=np.int64), np.array(0.5, dtype=config.floatX), np.array(0.5, dtype=config.floatX), []), (np.array(10, dtype=np.int64), np.array(0.5, dtype=config.floatX), np.a... |
def downsample(img0, size, filter=None):
down = (img0.size((- 1)) // size)
if (down <= 1):
return img0
if (filter is not None):
from third_party.stylegan2_official_ops import upfirdn2d
for _ in range(int(math.log2(down))):
img0 = upfirdn2d.downsample2d(img0, filter, down=... |
class FullConvolutionFunction(Function):
def forward(ctx, input_features, weight, bias, input_metadata, output_metadata, input_spatial_size, output_spatial_size, dimension, filter_size, filter_stride):
output_features = input_features.new()
ctx.input_metadata = input_metadata
ctx.output_meta... |
class WarmUpLR(_LRScheduler):
def __init__(self, optimizer, total_iters, last_epoch=(- 1)):
self.total_iters = total_iters
super().__init__(optimizer, last_epoch)
def get_lr(self):
return [((base_lr * self.last_epoch) / (self.total_iters + 1e-08)) for base_lr in self.base_lrs] |
class NormalImport(ImportInfo):
def __init__(self, names_and_aliases):
self.names_and_aliases = names_and_aliases
def get_imported_primaries(self, context):
result = []
for (name, alias) in self.names_and_aliases:
if alias:
result.append(alias)
els... |
def one_round(ql: Qiling, key: bytes, key_address):
gkeys = generate_key(key)
ql.mem.write(key_address, gkeys)
ql.run(begin=verfication_start_ip, end=(verfication_start_ip + 6))
lba37 = ql.mem.read((ql.arch.regs.sp + 544), 512)
for ch in lba37:
if (ch != 55):
return False
ret... |
def _lex(term, others, operator, matrix):
if (len(others) == 0):
lists = [flatten(l) for l in term]
assert is_matrix(lists, Variable)
elif (not is_1d_list(term, Variable)):
(l1, l2) = (flatten(term), flatten(others))
assert (len(l1) == len(l2))
lists = [l1, l2]
elif (... |
class PIXELTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
if ((self.label_smoother is not None) and ('labels' in inputs)):
labels = inputs.pop('labels')
else:
labels = None
outputs = model(**inputs)
if (self.args.past_index >= 0... |
_torch
_sentencepiece
_tokenizers
class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
def setUp(self):
super().setUp()
args = TrainingArguments('..')
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
trainer = get_regression... |
class ColorBufferImage(BufferImage):
gl_format = GL_RGBA
format = 'RGBA'
def get_texture(self, rectangle=False):
texture = Texture.create(self.width, self.height, GL_TEXTURE_2D, GL_RGBA, blank_data=False)
self.blit_to_texture(texture.target, texture.level, self.anchor_x, self.anchor_y, 0)
... |
class MegaupNet(SimpleDownloader):
__name__ = 'MegaupNet'
__type__ = 'downloader'
__version__ = '0.03'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fallback to... |
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer: torch.optim.Optimizer, milestones: List[int], gamma: float=0.1, warmup_factor: float=0.001, warmup_iters: int=1000, warmup_method: str='linear', last_epoch: int=(- 1)):
if (not (list(milestones) == sorted(milestone... |
def test_paramdeclaration():
pardec = OSC.ParameterDeclarations()
pardec.add_parameter(OSC.Parameter('myparam1', OSC.ParameterType.boolean, 'true'))
pardec.add_parameter(OSC.Parameter('myparam1', OSC.ParameterType.double, '0.01'))
pardec2 = OSC.ParameterDeclarations()
pardec2.add_parameter(OSC.Param... |
def _flatten_obs(obs):
assert (isinstance(obs, list) or isinstance(obs, tuple))
assert (len(obs) > 0)
if isinstance(obs[0], dict):
import collections
assert isinstance(obs, collections.OrderedDict)
keys = obs[0].keys()
return {k: np.stack([o[k] for o in obs]) for k in keys}
... |
class Encoding(BaseType):
def to_py(self, value: _StrUnset) -> _StrUnsetNone:
self._basic_py_validation(value, str)
if isinstance(value, usertypes.Unset):
return value
elif (not value):
return None
try:
codecs.lookup(value)
except LookupErr... |
class Generator(nn.Module):
def __init__(self, in_channels):
super(Generator, self).__init__()
self.generator = nn.Sequential(nn.ReflectionPad1d(3), nn.utils.weight_norm(nn.Conv1d(in_channels, 512, kernel_size=7)), nn.LeakyReLU(0.2, True), UpsampleNet(512, 256, 8), ResStack(256), nn.LeakyReLU(0.2, T... |
def ssim(img1, img2, window_size=11, size_average=True, mask=None, sigma=0.5):
img1 = img1.mean(1).unsqueeze(1)
img2 = img2.mean(1).unsqueeze(1)
(_, channel, _, _) = img1.size()
window = create_window(window_size, channel, sigma)
if img1.is_cuda:
window = window.cuda(img1.get_device())
w... |
class TrainRegSet(torch.utils.data.Dataset):
def __init__(self, data_root, image_size):
super().__init__()
self.data_root = data_root
self.img_file = [l.split(',')[1].strip() for l in open(os.path.join(data_root, 'data_train.csv'))][1:]
with open(os.path.join(data_root, 'data_train.j... |
class Widgets(object):
def top(self, Form):
if (not Form.objectName()):
Form.setObjectName(u'Form')
self.container_top = QFrame(Form)
self.container_top.setObjectName(u'container_top')
self.container_top.setGeometry(QRect(0, 0, 500, 10))
self.container_top.setMini... |
_optimizer('lamb')
class FairseqLAMB(FairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
try:
from apex.optimizers import FusedLAMB
self._optimizer = FusedLAMB(params, **self.optimizer_config)
except ImportError:
raise ImportError('... |
def combine_trk_cat(split, dataset, method, suffix, num_hypo):
file_path = os.path.dirname(os.path.realpath(__file__))
root_dir = os.path.join(file_path, '../../results', dataset)
(_, det_id2str, _, seq_list, _) = get_subfolder_seq(dataset, split)
config_path = os.path.join(file_path, ('../../configs/%s... |
class TemporalBottleneck(nn.Module):
def __init__(self, net, n_segment=8, t_kernel_size=3, t_stride=1, t_padding=1):
super(TemporalBottleneck, self).__init__()
self.net = net
assert isinstance(net, torchvision.models.resnet.Bottleneck)
self.n_segment = n_segment
self.tam = TA... |
def parse_options():
try:
(opts, args) = getopt.getopt(sys.argv[1:], 'hs:v', ['help', 'solver=', 'verbose'])
except getopt.GetoptError as err:
sys.stderr.write(str(err).capitalize())
usage()
sys.exit(1)
solver = 'm22'
verbose = 0
for (opt, arg) in opts:
if (op... |
class MultiLatentRPN(RPN):
def __init__(self, anchor_num, in_channels, weighted=False):
super(MultiLatentRPN, self).__init__()
self.weighted = weighted
for i in range(len(in_channels)):
self.add_module(('rpn' + str((i + 2))), LatentDepthwiseRPN(anchor_num, in_channels[i], in_chan... |
class MV2Block(nn.Module):
def __init__(self, inp, out, stride=1, expansion=4):
super().__init__()
self.stride = stride
hidden_dim = (inp * expansion)
self.use_res_connection = ((stride == 1) and (inp == out))
if (expansion == 1):
self.conv = nn.Sequential(nn.Conv... |
def render_image(state, messages, wumpus, creature):
board = Image.open(io.BytesIO(images['board'])).convert('RGBA')
for img in (wumpus.images + creature.images):
i = Image.open(io.BytesIO(images[img])).convert('RGBA')
i = ImageEnhance.Color(i).enhance(0.0)
i = ImageEnhance.Brightness(i)... |
def test_excinfo_getstatement():
def g():
raise ValueError
def f():
g()
try:
f()
except ValueError:
excinfo = _pytest._code.ExceptionInfo.from_current()
linenumbers = [((f.__code__.co_firstlineno - 1) + 4), ((f.__code__.co_firstlineno - 1) + 1), ((g.__code__.co_firstl... |
class MakeAnyNonExplicit(TrivialSyntheticTypeTranslator):
def visit_any(self, t: AnyType) -> Type:
if (t.type_of_any == TypeOfAny.explicit):
return t.copy_modified(TypeOfAny.special_form)
return t
def visit_type_alias_type(self, t: TypeAliasType) -> Type:
return t.copy_modifi... |
class PhysPkgReader():
def __new__(cls, pkg_file):
if isinstance(pkg_file, str):
if os.path.isdir(pkg_file):
reader_cls = _DirPkgReader
elif is_zipfile(pkg_file):
reader_cls = _ZipPkgReader
else:
raise PackageNotFoundError((... |
class TestIncompleteExp(unittest.TestCase):
def IncompleteExp(self, name, fields):
expr = MockTemplate(name, fields)
self.assertIsInstance(expr, grammar.IncompleteExp)
return expr
def test_construction_sanity(self):
expr = MockTemplate('foo')
with self.assertRaisesRegex(V... |
def main(input_csv, output_dir, anno_file, num_jobs=24, is_bsn_case=False):
youtube_ids = parse_activitynet_annotations(input_csv, is_bsn_case)
if (not os.path.exists(output_dir)):
os.makedirs(output_dir)
if (num_jobs == 1):
status_list = []
for index in youtube_ids:
stat... |
class InterceptingSocket():
def __init__(self, socket):
self.socket = socket
self.delay_sendall = None
self.delay_shutdown = None
self.drop_sendall = False
self.drop_shutdown = False
def __getattr__(self, name):
return getattr(self.socket, name)
def sendall(se... |
class ObjectIdentityTestCase(TestCase):
def assertSameObject(self, *objs):
first = objs[0]
for obj in objs:
self.assertIs(first, obj)
def assertDifferentObjects(self, *objs):
id_counts = Counter(map(id, objs))
((most_common_id, count),) = id_counts.most_common(1)
... |
def load_backbone_pretrained(model, backbone):
if ((cfg.PHASE == 'train') and cfg.TRAIN.BACKBONE_PRETRAINED and (not cfg.TRAIN.PRETRAINED_MODEL_PATH)):
if os.path.isfile(cfg.TRAIN.BACKBONE_PRETRAINED_PATH):
logging.info('Load backbone pretrained model from {}'.format(cfg.TRAIN.BACKBONE_PRETRAINE... |
def split_tensors(n, x):
if torch.is_tensor(x):
assert ((x.shape[0] % n) == 0)
x = x.reshape((x.shape[0] // n), n, *x.shape[1:]).unbind(1)
elif ((type(x) is list) or (type(x) is tuple)):
x = [split_tensors(n, _) for _ in x]
elif (x is None):
x = ([None] * n)
return x |
def main():
phi = 1
weighted_bifpn = False
model_path = 'checkpoints/2019-12-03/pascal_05_0.6283_1.1975_0.8029.h5'
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
classes = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', ... |
class Tickers():
def __repr__(self):
return f"yfinance.Tickers object <{','.join(self.symbols)}>"
def __init__(self, tickers, session=None):
tickers = (tickers if isinstance(tickers, list) else tickers.replace(',', ' ').split())
self.symbols = [ticker.upper() for ticker in tickers]
... |
class NovoGrad(Optimizer):
def __init__(self, params, grad_averaging=False, lr=0.1, betas=(0.95, 0.98), eps=1e-08, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
super(NovoGrad, self).__init__(params, defaults)
self._lr = lr
self._beta1 = bet... |
class MetricLogger(object):
def __init__(self, delimiter='\t'):
self.meters = defaultdict(SmoothedValue)
self.delimiter = delimiter
def update(self, **kwargs):
for (k, v) in kwargs.items():
if isinstance(v, torch.Tensor):
v = v.item()
assert isinst... |
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