code stringlengths 281 23.7M |
|---|
class SwinTransformer(nn.Module):
def __init__(self, img_size=224, patch_size=4, embed_dim=96, depths=[2, 2, 6], num_heads=[3, 6, 12], window_size=7, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, **kwargs):
... |
def str_to_bool(value):
if isinstance(value, basestring):
value = value.strip().lower()
if (value in ['true', 't', 'yes', 'y']):
return True
elif (value in ['false', 'f', 'no', 'n', '']):
return False
else:
raise NotImplementedError(('Unknown bool ... |
def callgraphHTML(sv, hf, num, cg, title, color, devid):
html_func_top = '<article id="{0}" class="atop" style="background:{1}">\n<input type="checkbox" class="pf" id="f{2}" checked/><label for="f{2}">{3} {4}</label>\n'
html_func_start = '<article>\n<input type="checkbox" class="pf" id="f{0}" checked/><label fo... |
class ParseError(RuntimeError):
def __init__(self, expected, stream, index):
self.expected = expected
self.stream = stream
self.index = index
def line_info(self):
try:
return '{}:{}'.format(*line_info_at(self.stream, self.index))
except (TypeError, AttributeEr... |
def test_channelstate_repeated_contract_balance():
deposit_block_number = 10
block_number = ((deposit_block_number + DEFAULT_NUMBER_OF_BLOCK_CONFIRMATIONS) + 1)
deposit_block_hash = make_block_hash()
(our_model1, _) = create_model(70)
(partner_model1, partner_pkey1) = create_model(100)
channel_s... |
class InitDataset(Dataset):
def __init__(self, data_root, img_transform, mask_transform, data='train'):
super(InitDataset, self).__init__()
self.img_transform = img_transform
self.mask_transform = mask_transform
if (data == 'train'):
self.paths = glob('{}/train/**/*.jpg'.... |
def jsonl_iterator(input_fname, positive_only=False, ngram_order=3, eval=False):
detok = get_detokenizer()
nlp = get_spacy_nlp()
with open(input_fname) as fin:
for line in fin:
sample = json.loads(line.strip())
if (positive_only and ('label' in sample) and (not sample['label'... |
def testQueryURL(run_cli, backends):
bz = _open_bz(REDHAT_URL, **backends)
qurl = '/buglist.cgi?f1=creation_ts&list_id=973582&o1=greaterthaneq&classification=Fedora&o2=lessthaneq&query_format=advanced&f2=creation_ts&v1=2010-01-01&component=python-bugzilla&v2=2010-06-01&product=Fedora'
url = REDHAT_URL
i... |
class DockerSchema2ManifestBuilder(object):
def __init__(self):
self.config = None
self.filesystem_layers = []
def clone(self):
cloned = DockerSchema2ManifestBuilder()
cloned.config = self.config
cloned.filesystem_layers = list(self.filesystem_layers)
return clone... |
def test_unique_uri_validator_serializer_create_error(db):
validator = OptionSetUniqueURIValidator()
serializer = OptionSetSerializer()
with pytest.raises(RestFameworkValidationError):
validator({'uri_prefix': settings.DEFAULT_URI_PREFIX, 'uri_path': OptionSet.objects.first().uri_path}, serializer) |
def fill_result_with_error(result, error, trace, models_to_create):
error = (error, trace)
result['error'] = error
for framework in FRAMEWORKS:
if (framework in models_to_create):
result[framework] = {}
for model_arch in models_to_create[framework]:
result[fra... |
def get_patch_size(final_patch_size, rot_x, rot_y, rot_z, scale_range):
if isinstance(rot_x, (tuple, list)):
rot_x = max(np.abs(rot_x))
if isinstance(rot_y, (tuple, list)):
rot_y = max(np.abs(rot_y))
if isinstance(rot_z, (tuple, list)):
rot_z = max(np.abs(rot_z))
rot_x = min((((9... |
def check_keys(model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = (model_keys & ckpt_keys)
unused_pretrained_keys = (ckpt_keys - model_keys)
missing_keys = (model_keys - ckpt_keys)
missing_keys = [x for ... |
def train(cfg, args, model, device, distributed):
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
arguments = {}
arguments['iteration'] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = (comm.get_rank() == 0)
checkpointer = DetectronCheckpointer(cfg, model, ... |
def _verify_static_class_methods(stub: nodes.FuncBase, runtime: Any, static_runtime: MaybeMissing[Any], object_path: list[str]) -> Iterator[str]:
if (stub.name in ('__new__', '__init_subclass__', '__class_getitem__')):
return
if inspect.isbuiltin(runtime):
probably_class_method = isinstance(geta... |
_cache(maxsize=16)
def _get_enum_field_values(enum_field):
values = []
for row in enum_field.rel_model.select():
key = getattr(row, enum_field.enum_key_field)
value = getattr(row, 'id')
values.append((key, value))
return Enum(enum_field.rel_model.__name__, values) |
_module
class SingleStageDetector(BaseDetector):
def __init__(self, backbone, neck=None, bbox_head=None, train_cfg=None, test_cfg=None, pretrained=None):
super(SingleStageDetector, self).__init__()
self.backbone = builder.build_backbone(backbone)
if (neck is not None):
self.neck ... |
def rotation_matrix_axis(dim, theta):
if (dim == 0):
rm = np.array([[1.0, 0.0, 0.0], [0.0, math.cos(theta), (- math.sin(theta))], [0.0, math.sin(theta), math.cos(theta)]])
elif (dim == 1):
rm = np.array([[math.cos(theta), 0.0, math.sin(theta)], [0.0, 1.0, 0.0], [(- math.sin(theta)), 0.0, math.co... |
def _process_css(default_css, extra_css):
with open(default_css, encoding='utf-8') as f:
css = f.read()
for path in extra_css:
css += '\n/'
css += '\n * CUSTOM CSS'
css += f'''
* {path}'''
css += '\n /\n\n'
with open(path, encoding='utf-8') as f:
css ... |
class TFAutoData():
def __init__(self):
self.features_list = []
self._train_data_path = ''
self.schema = ''
self.stats_train = ''
self.stats_eval = ''
self.anom_train = ''
self.anom_eval = ''
self.file_headers = []
self._len_train = 0
s... |
def _setup_single_view_dispatcher_route(api_blueprint: Blueprint, options: Options, constructor: RootComponentConstructor) -> None:
sock = Sock(api_blueprint)
def model_stream(ws: WebSocket, path: str='') -> None:
def send(value: Any) -> None:
ws.send(json.dumps(value))
def recv() ->... |
def determine_ctype_from_vconv(ctype, unit, velocity_convention=None):
unit = u.Unit(unit)
if (len(ctype) > 4):
in_physchar = ctype[5]
else:
lin_cunit = LINEAR_CUNIT_DICT[ctype]
in_physchar = PHYSICAL_TYPE_TO_CHAR[parse_phys_type(lin_cunit)]
if (parse_phys_type(unit) == 'speed'):... |
def routedict_to_routelist_single(name, D, indent=1):
Locals = dict()
indents = dict(I0=('\t' * indent), I1=('\t' * (indent + 1)), I2=('\t' * (indent + 2)), I3=('\t' * (indent + 3)), I4=('\t' * (indent + 4)))
if False:
D0 = D
keyname = 'src'
valname = 'dest'
else:
keyname... |
def _get_ordered_conv_linears(node_layer_map: Dict, layer_out_node_map: Dict) -> List[Union[(ConvType, LinearType)]]:
list_of_ordered_layers = _get_ordered_layers(node_layer_map, layer_out_node_map)
ordered_conv_linears = []
for layer in list_of_ordered_layers:
if isinstance(layer, _supported_layers... |
_tf
class TFViTBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = TFVisionTextDualEncoderModel.from_vision_text_pretrained('hf-internal-testing/tiny-random-vit', 'hf-internal-testing/tiny-random-bert')
batch_size = 13
pixel_valu... |
def test_init_pq_lower_limit_option(tmpdir, mocker):
mocker.patch.object(SentinelOne, '_authenticate')
cred_file_path = tmpdir.mkdir('test_dir').join('test_creds.ini')
cred_file_path.write('asdfasdfasdf')
s1_product = SentinelOne(profile='default', creds_file=cred_file_path, account_id=None, site_id=Non... |
def _print_collected_tasks(dictionary: dict[(Path, list[PTaskWithPath])], show_nodes: bool, editor_url_scheme: str, common_ancestor: Path) -> None:
console.print()
tree = Tree('Collected tasks:', highlight=True)
for (module, tasks) in dictionary.items():
reduced_module = relative_to(module, common_a... |
class TestSimpleUpdateProcessor():
def test_slot_behaviour(self):
inst = SimpleUpdateProcessor(1)
for attr in inst.__slots__:
assert (getattr(inst, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(inst)) == len(set(mro_slots(inst)))), 'duplicate slot'
... |
def generate_random_object_cluster(n_objects, object_generator, max_cluster_trans=1.0, max_cluster_rot=(np.pi / 8)):
ref_obj = object_generator()
cluster_objects = []
for i in range(n_objects):
r = random_rotation_translation_rotor(maximum_translation=max_cluster_trans, maximum_angle=max_cluster_rot... |
()
def daily_update_geos(day=None, geo=True, region=True):
(start_date, end_date) = get_day(day)
if ((not geo) and (not region)):
log.error('geo or region required, please pass one as True')
return
if geo:
log.info('Updating GeoImpressions for %s-%s', start_date, end_date)
if reg... |
class RegStage(nn.Module):
def __init__(self, depth, in_chs, out_chs, stride, dilation, drop_path_rates=None, block_fn=Bottleneck, **block_kwargs):
super(RegStage, self).__init__()
self.grad_checkpointing = False
first_dilation = (1 if (dilation in (1, 2)) else 2)
for i in range(dept... |
def stop(name, location='\\'):
if (name not in list_tasks(location)):
return '{0} not found in {1}'.format(name, location)
pythoncom.CoInitialize()
task_service = win32com.client.Dispatch('Schedule.Service')
task_service.Connect()
task_folder = task_service.GetFolder(location)
task = tas... |
def is_valid_signature(data: bytes, signature: Signature, sender_address: Address) -> SuccessOrError:
try:
signer_address = recover(data=data, signature=signature)
except Exception:
return SuccessOrError('Signature invalid, could not be recovered.')
is_correct_sender = (sender_address == sig... |
class JaggedSparse(Callable):
def __init__(self, *, weighted: bool, combine_option: CombineOption=CombineOption.JAGGED):
self._weighted = weighted
self._combine_option = combine_option
def __call__(self, df):
assert (df.device == 'cpu')
raise NotImplementedError
def weighted(... |
class TestEvolution(QiskitAquaTestCase):
def test_exp_i(self):
op = Z.exp_i()
gate = op.to_circuit().data[0][0]
self.assertIsInstance(gate, qiskit.circuit.library.RZGate)
self.assertEqual(gate.params[0], 2)
def test_trotter_with_identity(self):
op = (((2.0 * I) ^ I) + (Z ... |
class TruncatedNormal(BoundedContinuous):
rv_op = truncated_normal
bound_args_indices = (5, 6)
def dist(cls, mu: Optional[DIST_PARAMETER_TYPES]=0, sigma: Optional[DIST_PARAMETER_TYPES]=None, *, tau: Optional[DIST_PARAMETER_TYPES]=None, lower: Optional[DIST_PARAMETER_TYPES]=None, upper: Optional[DIST_PARAMET... |
class _RepoIncrementITRB(_RepoPatchITRB):
def __init__(self, basis_root_rp, inc_root_rp, rorp_cache, previous_time):
self.inc_root_rp = inc_root_rp
self.previous_time = previous_time
_RepoPatchITRB.__init__(self, basis_root_rp, rorp_cache)
def fast_process_file(self, index, diff_rorp):
... |
def get_contrastive_aug(dataset, aug_type='simclr'):
if ((dataset == 'miniImageNet') or (dataset == 'tieredImageNet') or (dataset == 'cross')):
(mean, std) = MEAN_STD['imagenet']
crop_size = 84
elif ((dataset == 'CIFAR-FS') or (dataset == 'FC100')):
(mean, std) = MEAN_STD['cifar']
... |
def undo_rule(workflow, ruleid):
(r2s, s2r, r2subscopes) = utils.rule_steps_indices(workflow)
if (not (ruleid in [r.identifier for r in workflow.applied_rules])):
ruleobj = workflow.view().getRule(identifier=ruleid)
log.debug('rule %s/%s not in list of applied rules. possibly already undone duri... |
def check_molecule_data_structure(fname, verbose=True):
with open(fname) as f:
try:
db = json.load(f)
except json.JSONDecodeError as err:
raise json.JSONDecodeError("Error reading '{0}' (line {2} col {3}): \n{1}".format(fname, err.msg, err.lineno, err.colno), err.doc, err.pos... |
class RolloutBaseline(Baseline):
def __init__(self, model, problem, opts, epoch=0):
super(Baseline, self).__init__()
self.problem = problem
self.opts = opts
self._update_model(model, epoch)
def _update_model(self, model, epoch, dataset=None):
self.model = copy.deepcopy(mo... |
class PriceViewMinimal(StatsView):
name = 'priceViewMinimal'
def __init__(self, parent):
StatsView.__init__(self)
self.parent = parent
self.settings = MarketPriceSettings.getInstance()
def getHeaderText(self, fit):
return 'Price'
def populatePanel(self, contentPanel, head... |
class BaseClient():
def __init__(self, client_id: str, **kwargs):
loop = kwargs.get('loop', None)
handler = kwargs.get('handler', None)
self.pipe = kwargs.get('pipe', None)
self.isasync = kwargs.get('isasync', False)
self.connection_timeout = kwargs.get('connection_timeout', ... |
def _compute_adjusted_exponent_length(exponent_length: int, first_32_exponent_bytes: bytes) -> int:
exponent = big_endian_to_int(first_32_exponent_bytes)
if ((exponent_length <= 32) and (exponent == 0)):
return 0
elif (exponent_length <= 32):
return get_highest_bit_index(exponent)
else:
... |
def generate_kaldi_data_files(utterances, outdir):
logger.info('Exporting to {}...'.format(outdir))
speakers = {}
with open(os.path.join(outdir, 'text'), 'w', encoding='latin-1') as f:
for utt in utterances:
f.write((utt.to_kaldi_utt_str() + '\n'))
with open(os.path.join(outdir, 'wav... |
def test_extra_saturate(debug_ctx, debug_trail):
loader_getter = make_loader_getter(shape=shape(TestField('a', ParamKind.POS_ONLY, is_required=True)), name_layout=InputNameLayout(crown=InpDictCrown({'a': InpFieldCrown('a')}, extra_policy=ExtraCollect()), extra_move=ExtraSaturate(Gauge.saturate)), debug_trail=debug_... |
class TestDOTAR2CNNKF(TestDOTA):
def eval(self):
txt_name = '{}.txt'.format(self.cfgs.VERSION)
real_test_img_list = self.get_test_image()
r2cnn_kf = build_whole_network.DetectionNetworkR2CNNKF(cfgs=self.cfgs, is_training=False)
self.test_dota(det_net=r2cnn_kf, real_test_img_list=real... |
def export_foam_mesh(obj, meshfileString, foamCaseFolder=None):
gmsh = CaeMesherGmsh.CaeMesherGmsh(obj, CfdTools.getParentAnalysisObject(obj))
meshfile = gmsh.export_mesh(u'Gmsh MSH', meshfileString)
if meshfile:
msg = 'Info: Mesh is not written to `{}` by Gmsh\n'.format(meshfile)
FreeCAD.Co... |
class TestTaskRc(TestCase):
def setUp(self):
self.path_to_taskrc = os.path.join(os.path.dirname(__file__), 'data/default.taskrc')
self.taskrc = TaskRc(self.path_to_taskrc)
def test_taskrc_parsing(self):
expected_config = {'data': {'location': '~/.task'}, 'alpha': {'one': 'yes', 'two': '2... |
def _search_cross_references(call_graph_analysis_list, search_depth):
reference_dict = defaultdict(set)
if (not call_graph_analysis_list):
return reference_dict
apkinfo = call_graph_analysis_list[0]['apkinfo']
parent_set = {item['parent'] for item in call_graph_analysis_list}
for parent in p... |
def test_info_setup_complex_pep517_error(mocker: MockerFixture, demo_setup_complex: Path) -> None:
mocker.patch('poetry.utils.env.VirtualEnv.run', autospec=True, side_effect=EnvCommandError(CalledProcessError(1, 'mock', output='mock')))
with pytest.raises(PackageInfoError):
PackageInfo.from_directory(de... |
class Generator(nn.Module):
def __init__(self, dim=64):
super(Generator, self).__init__()
self.dim = dim
self.linear1 = nn.Linear(128, (((4 * 4) * 4) * dim))
self.bn1 = nn.BatchNorm1d((((4 * 4) * 4) * dim))
self.relu1 = nn.ReLU(True)
self.block1 = nn.Sequential(nn.Con... |
_torch
class TestActivations(unittest.TestCase):
def test_gelu_versions(self):
x = torch.tensor([(- 100), (- 1), (- 0.1), 0, 0.1, 1.0, 100])
torch_builtin = get_activation('gelu')
self.assertTrue(torch.allclose(gelu_python(x), torch_builtin(x)))
self.assertFalse(torch.allclose(gelu_p... |
class InceptionCUnit(nn.Module):
def __init__(self, in_channels, out_channels):
super(InceptionCUnit, self).__init__()
assert (out_channels == 2048)
self.branches = Concurrent()
self.branches.add_module('branch1', Conv1x1Branch(in_channels=in_channels, out_channels=320))
self... |
class FrequencyValue(SensorValue):
def __init__(self, name):
super(FrequencyValue, self).__init__(name)
self.loopc = 0
self.t0 = time.monotonic()
def strobe(self):
self.loopc += 1
if (self.loopc == 4):
t1 = time.monotonic()
self.set((self.loopc / (... |
def test_properties():
prop = OSC.Properties()
prop.add_property('mything', '2')
prop.add_property('theotherthing', 'true')
prop.add_file('propfile.xml')
prettyprint(prop)
prop2 = OSC.Properties()
prop2.add_property('mything', '2')
prop2.add_property('theotherthing', 'true')
prop2.ad... |
def _sharded_tensor_to_gpu(tensor: sharded_tensor.ShardedTensor) -> sharded_tensor.ShardedTensor:
device = torch.device(f'cuda:{torch.cuda.current_device()}')
shards: List[sharded_tensor.Shard] = []
for shard in tensor.local_shards():
new_tensor = shard.tensor.to(device=device)
metadata = co... |
class MatrixOperator(LegacyBaseOperator):
def __init__(self, matrix, basis=None, z2_symmetries=None, atol=1e-12, name=None):
super().__init__(basis, z2_symmetries, name)
if (matrix is not None):
matrix = (matrix if scisparse.issparse(matrix) else scisparse.csr_matrix(matrix))
... |
class Effect1020(BaseEffect):
type = 'passive'
def handler(fit, skill, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Large Artillery Specialization')), 'damageMultiplier', (skill.getModifiedItemAttr('damageMultiplierBonus') * skill.level), **kwar... |
class TestLoadCFAreaPublic():
def test_load_cf_no_exist(self):
cf_file = os.path.join(TEST_FILES_PATH, 'does_not_exist.nc')
with pytest.raises(FileNotFoundError):
load_cf_area(cf_file)
def test_load_cf_from_not_nc(self):
cf_file = os.path.join(TEST_FILES_PATH, 'areas.yaml')
... |
class CompressedData():
__slots__ = ['data', 'dtype']
def __init__(self):
self.data = None
self.dtype = None
def compression(self, a):
self.data = compress(a.tobytes())
self.dtype = a.dtype
def decompression(self):
return fromstring(decompress(self.data), dtype=se... |
class Effect5620(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Missile Launcher Rapid Heavy')), 'speed', ship.getModifiedItemAttr('shipBonusMB'), skill='Minmatar Battleship', **kwargs) |
class ProjectDeployTokenManager(RetrieveMixin, CreateMixin, DeleteMixin, RESTManager):
_path = '/projects/{project_id}/deploy_tokens'
_from_parent_attrs = {'project_id': 'id'}
_obj_cls = ProjectDeployToken
_create_attrs = RequiredOptional(required=('name', 'scopes'), optional=('expires_at', 'username'))... |
class FragDBInfo():
filename: str
num_compounds: int
num_error_compounds: int
num_fragmentations: int
num_constants: int
num_variables: int
max_num_pairs: int
options: object
def get_cols(self):
return [self.filename, str(self.num_compounds), str(self.num_error_compounds), st... |
def ttrl(x, ranks, n_outputs):
weight_initializer = tf.contrib.layers.xavier_initializer()
suffix = n_outputs
input_shape = x.get_shape().as_list()[1:]
bias = tf.get_variable('bias_{}'.format(np.prod(n_outputs)), shape=(1, np.prod(n_outputs)))
cores = []
for i in range(1, (len(ranks) - 1)):
... |
def test_run_with_verbosity(tester: ApplicationTester) -> None:
tester.execute('list --verbose')
assert tester.io.is_verbose()
tester.execute('list -v')
assert tester.io.is_verbose()
tester.execute('list -vv')
assert tester.io.is_very_verbose()
tester.execute('list -vvv')
assert tester.i... |
class RanksComparatorPlotter(AccessorABC):
_default_kind = 'box'
def __init__(self, ranks_cmp):
self._ranks_cmp = ranks_cmp
def flow(self, *, untied=False, grid_kws=None, **kwargs):
df = self._ranks_cmp.to_dataframe(untied=untied)
ax = sns.lineplot(data=df.T, estimator=None, sort=Fal... |
def _load_model_file(load_path, model):
load_optimizer_state_dict = None
print(' [*] Loading model from {}'.format(load_path))
load_data = torch.load(os.path.join(os.getcwd(), load_path), map_location=(lambda storage, loc: storage))
if isinstance(load_data, dict):
load_optimizer_state_dict = lo... |
class PassThroughOpLastLayerModel(nn.Module):
def __init__(self):
super(PassThroughOpLastLayerModel, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=2, stride=2, padding=2, bias=False)
self.passthrough = torch.nn.Identity()
def forward(self, *inputs):
x = self.conv1(in... |
def makeUpdateMatrixSv(qnnArch, unitaries, trainingData, storedStates, lda, ep, l, j):
numInputQubits = qnnArch[(l - 1)]
summ = 0
for x in range(len(trainingData)):
firstPart = updateMatrixFirstPart(qnnArch, unitaries, storedStates, l, j, x)
secondPart = updateMatrixSecondPart(qnnArch, unita... |
def _infer_single_node_init(cfg: DistributedTrainingConfig):
assert (cfg.distributed_world_size <= torch.cuda.device_count()), f'world size is {cfg.distributed_world_size} but have {torch.cuda.device_count()} available devices'
port = random.randint(10000, 20000)
cfg.distributed_init_method = 'tcp://localho... |
class FinalBlock(nn.Module):
def __init__(self, input_dim, hidden_units=[], hidden_activations=None, dropout_rates=[], batch_norm=True, residual_type='sum'):
super(FinalBlock, self).__init__()
if (type(dropout_rates) != list):
dropout_rates = ([dropout_rates] * len(hidden_units))
... |
(nopython=True)
def create_indices(episode_ends: np.ndarray, sequence_length: int, episode_mask: np.ndarray, pad_before: int=0, pad_after: int=0, debug: bool=True) -> np.ndarray:
(episode_mask.shape == episode_ends.shape)
pad_before = min(max(pad_before, 0), (sequence_length - 1))
pad_after = min(max(pad_af... |
def specify_shape(x: Union[(np.ndarray, Number, Variable)], shape: Union[(ShapeValueType, list[ShapeValueType], tuple[(ShapeValueType, ...)])]):
if (not isinstance(shape, (tuple, list))):
shape = (shape,)
if ((len(shape) == 1) and (shape[0] is not None)):
shape_vector = ptb.as_tensor_variable(sh... |
.fast
def test_slit_energy_conservation(verbose=True, plot=True, close_plots=True, *args, **kwargs):
from radis.test.utils import getTestFile
_clean(plot, close_plots)
if verbose:
print('\n>>> _test_slit_energy_conservation\n')
s = calculated_spectrum(*np.loadtxt(getTestFile('calc_N2C_spectrum_T... |
def get_sde_loss_fn(sde, model, train, reduce_mean=True, continuous=True, likelihood_weighting=True, eps=1e-05):
reduce_op = (jnp.mean if reduce_mean else (lambda *args, **kwargs: (0.5 * jnp.sum(*args, **kwargs))))
def loss_fn(rng, params, states, batch):
score_fn = mutils.get_score_fn(sde, model, param... |
class SystemIrreducibilityAnalysis(cmp.OrderableByPhi):
def __init__(self, phi=None, ces=None, partitioned_ces=None, subsystem=None, cut_subsystem=None):
self.phi = phi
self.ces = ces
self.partitioned_ces = partitioned_ces
self.subsystem = subsystem
self.cut_subsystem = cut_s... |
class _BertWordPieceTokenizer(AbstractTokenizer):
def __init__(self, vocab_file, lower_case=True):
if lower_case:
name = 'BERT Lower Case'
else:
name = 'BERT Upper Case'
super().__init__(name)
self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_... |
class TestHook():
def test_unknown(self, isolation):
metadata = ProjectMetadata(str(isolation), PluginManager(), {'project': {'name': 'foo'}, 'tool': {'hatch': {'metadata': {'hooks': {'foo': {}}}}}})
with pytest.raises(ValueError, match='Unknown metadata hook: foo'):
_ = metadata.core
... |
_model
def test_ic_expression_with_one_parameter():
Monomer('A')
Parameter('k1', 1)
Expression('e1', k1)
Rule('A_deg', (A() >> None), k1)
Initial(A(), e1)
generate_equations(model)
t = np.linspace(0, 1000, 100)
sol = Solver(model, t, use_analytic_jacobian=True)
sol.run() |
def get_extensions():
ext_dirs = ((cwd / package_name) / 'cpp_exts')
ext_modules = []
rans_lib_dir = (cwd / 'third_party/ryg_rans')
rans_ext_dir = (ext_dirs / 'rans')
extra_compile_args = ['-std=c++17']
if os.getenv('DEBUG_BUILD', None):
extra_compile_args += ['-O0', '-g', '-UNDEBUG']
... |
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init__()
def init_weights(self, init_type='normal', gain=0.02, bias_value=0.0, target_op=None):
def init_func(m):
classname = m.__class__.__name__
if (target_op is not None):
if (... |
def onresource(unit, *args):
def split(lst, limit):
root_lenght = 200
filepath = None
lenght = 0
bucket = []
for item in lst:
if filepath:
lenght += ((root_lenght + len(filepath)) + len(item))
if ((lenght > limit) and bucket):
... |
class DistanceCondition(_EntityTriggerType):
def __init__(self, value, rule, position, alongroute=True, freespace=True, distance_type=RelativeDistanceType.longitudinal, coordinate_system=CoordinateSystem.road, routing_algorithm=None):
self.value = value
self.alongroute = convert_bool(alongroute)
... |
def vk_request_one_param_pool(vk_session, method, key, values, default_values=None):
result = {}
errors = {}
if (default_values is None):
default_values = {}
for i in range(0, len(values), 25):
current_values = values[i:(i + 25)]
response_raw = vk_one_param(vk_session, method, cu... |
class Distribution(metaclass=abc.ABCMeta):
def read_text(self, filename):
def locate_file(self, path):
def from_name(cls, name: str):
if (not name):
raise ValueError('A distribution name is required.')
try:
return next(cls.discover(name=name))
except StopItera... |
def _derive_metrics(df: pd.DataFrame) -> pd.DataFrame:
logger.info('Deriving metrics...')
df['workflow_number'] = df.apply((lambda row: _get_workflow_number_from_name(row['name'])), axis=1)
def _calculate_difference(row: pd.Series, start_column: str, end_column: str) -> Optional[int]:
start_date = r... |
class LocalScoreClass(object):
def __init__(self, data: Any, local_score_fun: Callable[([Any, int, List[int], Any], float)], parameters=None):
self.data = data
self.local_score_fun = local_score_fun
self.parameters = parameters
self.score_cache = {}
if (self.local_score_fun =... |
def pad(x: Tensor, p: int=(2 ** (4 + 3))) -> Tuple[(Tensor, Tuple[(int, ...)])]:
(h, w) = (x.size(2), x.size(3))
new_h = ((((h + p) - 1) // p) * p)
new_w = ((((w + p) - 1) // p) * p)
padding_left = ((new_w - w) // 2)
padding_right = ((new_w - w) - padding_left)
padding_top = ((new_h - h) // 2)
... |
class DAM_Module(nn.Module):
def __init__(self, in_dim):
super(DAM_Module, self).__init__()
self.chanel_in = in_dim
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=(- 1))
def forward(self, x):
(m_batchsize, N, C, height, width) = x.size()
p... |
.end_to_end()
.parametrize(('depends_on', 'produces'), [("'in.txt'", "'out.txt'"), ("Path('in.txt')", "Path('out.txt')")])
def test_collect_file_with_relative_path(tmp_path, depends_on, produces):
source = f'''
import pytask
from pathlib import Path
.depends_on({depends_on})
.produces({produces})
... |
class ServerWatch(pypilotValue):
def __init__(self, values):
super(ServerWatch, self).__init__(values, 'watch')
def set(self, msg, connection):
(name, data) = msg.rstrip().split('=', 1)
watches = pyjson.loads(data)
values = self.server_values.values
for name in watches:
... |
class F17_LogVolData(F15_LogVolData):
removedKeywords = F15_LogVolData.removedKeywords
removedAttrs = F15_LogVolData.removedAttrs
def __init__(self, *args, **kwargs):
F15_LogVolData.__init__(self, *args, **kwargs)
self.resize = kwargs.get('resize', False)
def _getArgsAsStr(self):
... |
class AsciiContainer(Container):
widget_layout_map = None
def __init__(self, *args, **kwargs):
Container.__init__(self, *args, **kwargs)
self.css_position = 'relative'
def set_from_asciiart(self, asciipattern, gap_horizontal=0, gap_vertical=0):
pattern_rows = asciipattern.split('\n')... |
def genee_loop_chunk(args, chunk_window_starts, chunk_window_stops, abs_chunk_start, chunk_max_window_start, epsilon_effect):
(betas, ld) = args
rows = list()
for ti in range(len(chunk_window_starts)):
window_start = chunk_window_starts[ti]
window_stop = chunk_window_stops[ti]
rows.a... |
class AdaptiveRelativeAttn(nn.Module):
def __init__(self, model_size, num_heads, factor_size, dropout=0.0, adaptive_type='shared'):
super().__init__()
self.model_size = model_size
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = (model_size // num_heads)
... |
_module()
class ResNet(BaseModule):
arch_settings = {18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3))}
def __init__(self, depth, in_channels=3, stem_channels=64, base_channels=64, num_stages=4, stri... |
def parse_mmit_splits():
def line_to_map(x):
video = osp.splitext(x[0])[0]
labels = [int(digit) for digit in x[1:]]
return (video, labels)
csv_reader = csv.reader(open('data/mmit/annotations/trainingSet.csv'))
train_list = [line_to_map(x) for x in csv_reader]
csv_reader = csv.rea... |
class Solution(object):
def sortedSquares(self, A):
pos = 0
while ((pos < len(A)) and (A[pos] < 0)):
pos += 1
npos = (pos - 1)
res = []
while ((pos < len(A)) and (npos >= 0)):
if ((A[npos] ** 2) < (A[pos] ** 2)):
res.append((A[npos] ** ... |
def test_r2plus1d():
config = get_recognizer_cfg('r2plus1d/r2plus1d_r34_8x8x1_180e_kinetics400_rgb.py')
config.model['backbone']['pretrained2d'] = False
config.model['backbone']['pretrained'] = None
config.model['backbone']['norm_cfg'] = dict(type='BN3d')
recognizer = build_recognizer(config.model)
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.