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class FilteredDataset(Dataset):
def __init__(self, source, filterer=(lambda i, s: s[1]), target=[], verbosity=make_verbose()):
self.source = source
if (not isinstance(target, list)):
target = [target]
self.indices = [i for (i, s) in wrap_with_tqdm(enumerate(source), verbosity) if... |
class SimpleMajorityVote(Aggregate):
def aggregate(answers: Union[(str, List[str])], **kwargs: Any) -> Union[(str, List[str])]:
answer_count = {}
for answer in answers:
if isinstance(answer, str):
if (answer not in answer_count):
answer_count[answer] =... |
class NBD():
def __init__(self, **server_settings):
self.bd = server_settings.get('block_device', '')
self.netboot_directory = server_settings.get('netboot_directory', '.')
self.write = server_settings.get('write', False)
self.cow = server_settings.get('cow', True)
self.in_me... |
def quantize_sharded_embeddings(module: torch.nn.Module, dtype: torch.dtype) -> torch.nn.Module:
qconfig = quant.QConfigDynamic(activation=quant.PlaceholderObserver, weight=quant.PlaceholderObserver.with_args(dtype=dtype))
return quant.quantize_dynamic(module, qconfig_spec={BatchedFusedEmbeddingBag: qconfig, Ba... |
class TestOrderedLogistic(BaseTestDistributionRandom):
pymc_dist = _OrderedLogistic
pymc_dist_params = {'eta': 0, 'cutpoints': np.array([(- 2), 0, 2])}
expected_rv_op_params = {'p': np.array([0., 0., 0., 0.])}
checks_to_run = ['check_pymc_params_match_rv_op', 'check_rv_size']
.parametrize('eta, cutp... |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='Path of the detection model.', required=True)
parser.add_argument('--label', help='Path of the labels file.')
parser.add_argument('--output', help='File path of the output image.')
args = parser.parse_args()
if a... |
class F10_TestCase(F9_TestCase):
def runTest(self):
F9_TestCase.runTest(self)
self.assert_deprecated('xconfig', '--driver')
self.assert_deprecated('xconfig', '--depth')
self.assert_deprecated('xconfig', '--resolution')
self.assert_deprecated('xconfig', '--videoram') |
def _send_message_to_trio(trio_token: (TrioToken | None), message_to_trio: (Run[RetT] | RunSync[RetT])) -> RetT:
token_provided = (trio_token is not None)
if (not token_provided):
try:
trio_token = PARENT_TASK_DATA.token
except AttributeError:
raise RuntimeError("this thr... |
def get_head_node_ip(cluster_cfg: str) -> str:
logger.info(f"Getting Ray cluster head node IP for '{cluster_cfg}'")
proc = subprocess.run(f'ray get-head-ip {cluster_cfg}', shell=True, capture_output=True, text=True, check=True)
head_node_ip = proc.stdout.splitlines()[(- 1)]
logger.info(f"Ray cluster hea... |
_end_docstrings(PIPELINE_INIT_ARGS, '\n ignore_labels (`List[str]`, defaults to `["O"]`):\n A list of labels to ignore.\n grouped_entities (`bool`, *optional*, defaults to `False`):\n DEPRECATED, use `aggregation_strategy` instead. Whether or not to group the tokens corresponding to ... |
def test_value_radio(db, mocker):
mocker.patch('rdmo.options.models.Option.trans', mocked_trans)
value = Value.objects.get(id=4)
assert (value.value == 'Text: Lorem ipsum')
assert (value.value_and_unit == 'Text: Lorem ipsum')
assert (value.option_text == 'Text')
assert (value.option_additional_i... |
def _oom_observer(output_dir: str) -> Callable[([Union[(int, torch.device)], int, int, int], None)]:
def oom_logger(device: Union[(int, torch.device)], alloc: int, device_alloc: int, device_free: int) -> None:
logger.info(f'Saving memory snapshot device: {device}, alloc: {_bytes_to_mb_gb(alloc)}, device_all... |
def compute_dataset_normalization(dataloader, no_last_dim_norm=True):
states_list = []
action_list = []
print('computing normalization....')
for (i_batch, sample_batched) in enumerate(dataloader):
if ('actions' not in sample_batched):
raise NotImplementedError('todo!')
states... |
class VAE(base_ae.SingleLatentWithPriorAE):
def forward(self, x, beta):
return self.elbo(x, beta, return_extra_vals=False)
def elbo(self, x, beta=1.0, return_extra_vals=False):
self.encoder.update(x)
z_sample = self.encoder.sample_via_reparam(1)[0]
self._last_z_sample_on_obj = z_... |
_ordering
(eq=False, order=False, slots=True, frozen=True)
class VersionInfo():
year = attrib(type=int)
minor = attrib(type=int)
micro = attrib(type=int)
releaselevel = attrib(type=str)
def _from_version_string(cls, s):
v = s.split('.')
if (len(v) == 3):
v.append('final')... |
_canonicalize('fast_compile')
_specialize
_rewriter([sparse.DenseFromSparse])
def local_dense_from_sparse_sparse_from_dense(fgraph, node):
if isinstance(node.op, sparse.DenseFromSparse):
inp = node.inputs[0]
if (inp.owner and isinstance(inp.owner.op, sparse.SparseFromDense)):
return inp.... |
def recall(pr, gt, eps=1e-07, threshold=None, ignore_channels=None):
pr = _threshold(pr, threshold=threshold)
(pr, gt) = _take_channels(pr, gt, ignore_channels=ignore_channels)
tp = torch.sum((gt * pr))
fn = (torch.sum(gt) - tp)
score = ((tp + eps) / ((tp + fn) + eps))
return score |
class ProjectMemberAllManager(RetrieveMixin, RESTManager):
_path = '/projects/{project_id}/members/all'
_obj_cls = ProjectMemberAll
_from_parent_attrs = {'project_id': 'id'}
def get(self, id: Union[(str, int)], lazy: bool=False, **kwargs: Any) -> ProjectMemberAll:
return cast(ProjectMemberAll, s... |
def get_dataset(dataset_name):
if (dataset_name.lower() == 'cifar10'):
return cifar10(data_augmentation=True)
elif (dataset_name.lower() == 'cifar100'):
return cifar100(data_augmentation=True)
elif (dataset_name.lower() == 'cifarfs'):
return cifarfs(data_augmentation=True)
elif (... |
def test_taxon__listed_taxa():
taxon = Taxon.from_json(j_taxon_1)
listed_taxon = taxon.listed_taxa[0]
assert isinstance(listed_taxon, ListedTaxon)
assert (listed_taxon.taxon_id == taxon.id)
assert (listed_taxon.list.id == 299)
assert (listed_taxon.list.title == 'United States Check List')
as... |
class TestRedundantAssignmentChecker(pylint.testutils.CheckerTestCase):
CHECKER_CLASS = RedundantAssignmentChecker
def setUp(self):
self.setup_method()
def test_no_messages_simple(self):
src = '\n x = 10\n print(x)\n x = 10\n '
mod = astroid.parse(src)
... |
def cl_parse(command, args, setup=None, details=None):
usage = subcommand_usages[command]
descr = subcommand_descriptions[command]
if isinstance(usage, str):
usage = [usage]
susage = ('%s %s' % (program_name, usage[0]))
for s in usage[1:]:
susage += ('\n%s%s %s' % ((' ' * 7), program... |
def test_set_deployment_placement_options():
deployment_config = {'ray_actor_options': {'num_cpus': 2, 'resources': {'custom_resource': 1}}}
scaling_config = ScalingConfig(num_workers=2, resources_per_worker={'custom_resource_2': 1}, placement_group_strategy='PACK')
deployment_config = set_deployment_placem... |
class EventThread(Thread):
display = None
_stop = None
def __init__(self, display):
super(EventThread, self).__init__()
self.display = display
self.daemon = True
def run(self):
while True:
event = self.display.next_event()
print(('event: %r' % even... |
class TestOpenGLInfo():
(autouse=True)
def cache_clear(self):
version.opengl_info.cache_clear()
def test_func(self, qapp):
pytest.importorskip('qutebrowser.qt.opengl')
version.opengl_info()
def test_func_fake(self, qapp, monkeypatch):
monkeypatch.setenv('QUTE_FAKE_OPENGL'... |
def test_reg_field_configure():
field = uvm_reg_field()
parent = uvm_reg()
field.configure(parent, 8, 16, 'RW', True, 15)
assert (field.get_parent() == parent)
assert (field.get_n_bits() == 8)
assert (field.get_lsb_pos() == 16)
assert (field.get_access() == 'RW')
assert field.is_volatile... |
def _find_chromium_mac() -> Optional[str]:
default_dir = '/Applications/Chromium.app/Contents/MacOS/Chromium'
if os.path.exists(default_dir):
return default_dir
name = 'Chromium.app'
alternate_dirs = [x for x in sps.check_output(['mdfind', name]).decode().split('\n') if x.endswith(name)]
if ... |
class TestCollectCfDataset():
def test_collect_cf_dataset(self):
from satpy.cf.datasets import _collect_cf_dataset
geos = AreaDefinition(area_id='geos', description='geos', proj_id='geos', projection={'proj': 'geos', 'h': .0, 'a': 6378169.0, 'b': 6356583.8}, width=2, height=2, area_extent=[(- 1), (-... |
def validate(val_loader, model, criterion):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
for (i, (input, target)) in enumerate(val_loader):
input_var = torch.autograd.Variable(input, volatile=True).cuda()
target_var = torch.autograd.Variable(target, volatile=True).... |
def collect_results_gpu(result_part, size):
(rank, world_size) = get_dist_info()
part_tensor = torch.tensor(bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
shape_list = [shape_tensor.clone() for _ in range(world_size)]... |
def categorical_df_concat(df_list, inplace=False):
if (not inplace):
df_list = deepcopy(df_list)
df = df_list[0]
if (not all([df.dtypes.equals(df_i.dtypes) for df_i in df_list[1:]])):
raise ValueError('Input DataFrames must have the same columns/dtypes.')
categorical_columns = df.columns... |
def test_trigger_level():
with expected_protocol(Racal1992, [(' SLA 1.500000', None), (' SLB 1.500000', None), (' RLA', 'LA+.50E+00'), (' RLB', 'LB+.50E+00')]) as instr:
instr.trigger_level_a = 1.5
instr.trigger_level_b = 1.5
assert (instr.trigger_level_a == 1.5)
assert (instr.trigge... |
class TestTowerQPSMetric(TestMetric):
def __init__(self, world_size: int, rec_tasks: List[RecTaskInfo]) -> None:
super().__init__(world_size, rec_tasks)
def _get_states(labels: torch.Tensor, predictions: torch.Tensor, weights: torch.Tensor) -> Dict[(str, torch.Tensor)]:
return {}
def _reduce... |
def combine_dataset_vids(datasets, combined_dataset, do_filter_by_true_starts=False, do_filter_by_affine_breaks=False, do_filter_by_excluded_vids=False, load_n=None, _print=print):
all_train_vid_names = []
if (len(datasets) == 1):
ds = datasets[0]
ds._print = _print
_print('Loading vids ... |
class Generator(QIODevice):
def __init__(self, format, durationUs, sampleRate, parent):
super(Generator, self).__init__(parent)
self.m_pos = 0
self.m_buffer = QByteArray()
self.generateData(format, durationUs, sampleRate)
def start(self):
self.open(QIODevice.ReadOnly)
... |
def test_feature_path_ok_running_outside_rootdir(pytester):
base_dir = 'features'
prepare_testdir(pytester, base_dir)
old_dir = os.getcwd()
os.chdir('/')
try:
result = pytester.runpytest(pytester.path, '-k', 'test_ok_by_ini')
result.assert_outcomes(passed=2)
finally:
os.c... |
class SymbolTableNode():
__slots__ = ('kind', 'node', 'module_public', 'module_hidden', 'cross_ref', 'implicit', 'plugin_generated', 'no_serialize')
def __init__(self, kind: int, node: (SymbolNode | None), module_public: bool=True, implicit: bool=False, module_hidden: bool=False, *, plugin_generated: bool=False... |
def validate(config):
def is_bad_str(s):
return ((s is None) or (len(s) == 0))
if is_bad_str(config.database):
return 'database missing'
if is_bad_str(config.useragent):
return 'useragent missing'
if (config.ratelimit < 0):
warning("Rate limit can't be negative, defaultin... |
class FC6_Iscsi(KickstartCommand):
removedKeywords = KickstartCommand.removedKeywords
removedAttrs = KickstartCommand.removedAttrs
def __init__(self, writePriority=71, *args, **kwargs):
KickstartCommand.__init__(self, writePriority, *args, **kwargs)
self.op = self._getParser()
self.i... |
class DBlock(nn.Module):
def __init__(self, in_channels, out_channels, which_conv=SNConv2d, wide=True, preactivation=False, activation=None, downsample=None):
super(DBlock, self).__init__()
(self.in_channels, self.out_channels) = (in_channels, out_channels)
self.hidden_channels = (self.out_c... |
def _set_cuda_rng_state(new_state, device=(- 1)):
if (hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState)):
def cb():
with device_ctx_manager(device):
_C._cuda_setRNGState(new_state)
else:
if (device == (- 1)):
device = torch.device('cuda')... |
class Channel(CommonBase):
placeholder = 'ch'
def __init__(self, parent, id):
self.parent = parent
self.id = id
super().__init__()
def insert_id(self, command):
return command.format_map({self.placeholder: self.id})
def write(self, command, **kwargs):
self.parent.... |
class KnownValues(unittest.TestCase):
def test_tda_lda(self):
td = tdscf.TDA(mf_lda).run(nstates=nstates)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(td.xy[2])
self.assertAlmostEqual(g1[(0, 2)], (- 0.), 6)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 ... |
def deprecate_stdlib(tc, vers=None):
if ((vers is None) or (sys.version_info >= vers)):
return pytest.deprecated_call()
class _deprecate():
def __init__(self, tc):
pass
def __enter__(self):
return self
def __exit__(self, *tb):
pass
return _... |
class AttnUpDecoderBlock2D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, dropout: float=0.0, num_layers: int=1, resnet_eps: float=1e-06, resnet_time_scale_shift: str='default', resnet_act_fn: str='swish', resnet_groups: int=32, resnet_pre_norm: bool=True, attn_num_head_channels=1, output_scale... |
class Widget(QWidget):
def __init__(self, helper, parent):
super(Widget, self).__init__(parent)
self.helper = helper
self.elapsed = 0
self.setFixedSize(200, 200)
def animate(self):
self.elapsed = ((self.elapsed + self.sender().interval()) % 1000)
self.repaint()
... |
def _convert_dep_info_to_data_query(dep_info):
key_item = dep_info.copy()
key_item.pop('prerequisites', None)
key_item.pop('optional_prerequisites', None)
if ('modifiers' in key_item):
key_item['modifiers'] = tuple(key_item['modifiers'])
key = DataQuery.from_dict(key_item)
return key |
class DistilBertModel(nn.Module):
def __init__(self, embedding, projection, config=None) -> None:
super().__init__()
self.model = DistilBertForMaskedLM(config).to(device)
self.embedding = copy.deepcopy(embedding.requires_grad_(False))
self.projection = copy.deepcopy(projection.requir... |
def test_complete_headers_rpt(test_model_02):
headers = get_rpt_sections_details(test_model_02.rpt.path)
sections_in_rpt = ['Link Flow Summary', 'Link Flow Summary', 'Subcatchment Summary', 'Cross Section Summary', 'Link Summary']
assert all(((section in headers) for section in sections_in_rpt))
assert ... |
class ModelFormSingleTagFieldOptionalTest(TagTestManager, TestCase):
manage_models = [test_models.SingleTagFieldOptionalModel]
def setUpExtra(self):
self.form = test_forms.SingleTagFieldOptionalModelForm
self.model = test_models.SingleTagFieldOptionalModel
self.tag_model = self.model.tag... |
def _shufflenetv2(arch, pretrained, progress, *args, **kwargs):
model = ShuffleNetV2(*args, **kwargs)
if pretrained:
model_url = model_urls[arch]
if (model_url is None):
raise NotImplementedError('pretrained {} is not supported as of now'.format(arch))
else:
state... |
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'test_app.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError("Couldn't import Django. Are you sure it's installed and available on your PYTHONPATH environment va... |
def get_local_rank(world_size: Optional[int]=None, rank: Optional[int]=None) -> int:
my_local_rank = _env2int(['LOCAL_RANK', 'MPI_LOCALRANKID', 'OMPI_COMM_WORLD_LOCAL_RANK', 'MV2_COMM_WORLD_LOCAL_RANK'], (- 1))
local_size = get_local_size(world_size)
if ((my_local_rank == (- 1)) or (my_local_rank >= local_s... |
def wing_loss(output: torch.Tensor, target: torch.Tensor, width=5, curvature=0.5, reduction='mean'):
diff_abs = (target - output).abs()
loss = diff_abs.clone()
idx_smaller = (diff_abs < width)
idx_bigger = (diff_abs >= width)
loss[idx_smaller] = (width * torch.log((1 + (diff_abs[idx_smaller] / curva... |
class AH2500A(Instrument):
_reclv = re.compile('[FHZ0-9.=\\s]*C=\\s*(-?[0-9.]+)\\s*PF L=\\s*(-?[0-9.]+)\\s*NS V=\\s*(-?[0-9.]+)\\s*V')
_renumeric = re.compile('[-+]?(\\d*\\.?\\d+)')
def __init__(self, adapter, name=None, timeout=3000, write_termination='\n', read_termination='\n', **kwargs):
kwargs.... |
class FakeDBusMessage():
def __init__(self, signature: str, *arguments: Any, typ: QDBusMessage.MessageType=QDBusMessage.MessageType.ReplyMessage, error_name: Optional[str]=None) -> None:
self._signature = signature
self._arguments = arguments
self._type = typ
self._error_name = error... |
class HurdlePoisson():
def __new__(cls, name, psi, mu, **kwargs):
return _hurdle_mixture(name=name, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=mu), dtype='int', **kwargs)
def dist(cls, psi, mu, **kwargs):
return _hurdle_mixture(name=None, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=mu), dtype='... |
def feature_set_dates_output_dataframe(spark_context, spark_session):
data = [{'id': 1, 'timestamp': '2016-04-11 11:31:11', 'feature': 200}, {'id': 1, 'timestamp': '2016-04-12 11:44:12', 'feature': 300}]
df = spark_session.read.json(spark_context.parallelize(data, 1))
df = df.withColumn('timestamp', df.time... |
def write_tsp_file(fp, xs, ys, norm, name):
if (len(xs) != len(ys)):
raise ValueError('x and y coordinate vector must have the same length ({} != {})'.format(len(xs), len(ys)))
if (norm not in EDGE_WEIGHT_TYPES):
raise ValueError('Norm {!r} must be one of {}'.format(norm, ', '.join(EDGE_WEIGHT_T... |
def train(epochs, ctx):
net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
trainer = gluon.Trainer(net.collect_params(), opt.optimizer, {'learning_rate': opt.lr, 'momentum': opt.momentum})
metric = mx.metric.Accuracy()
loss = gluon.loss.SoftmaxCrossEntropyLoss()
for epoch in range(epochs):
... |
class _DSFID3(ID3):
_error(IOError, error)
def _pre_load_header(self, fileobj):
fileobj.seek(0)
id3_location = DSDChunk(fileobj).offset_metdata_chunk
if (id3_location == 0):
raise ID3NoHeaderError('File has no existing ID3 tag')
fileobj.seek(id3_location)
_error(I... |
class TestHuffmanDecoder():
(data=binary())
(b'\xff')
(b'_\xff\xff\xff\xff')
(b'\x00?\xff\xff\xff')
def test_huffman_decoder_properly_handles_all_bytestrings(self, data):
try:
result = decode_huffman(data)
except HPACKDecodingError:
result = b''
assert... |
class CommandLoader(Loadable, SignalDispatcher, FileManagerAware):
finished = False
process = None
def __init__(self, args, descr, silent=False, read=False, input=None, kill_on_pause=False, popenArgs=None):
SignalDispatcher.__init__(self)
Loadable.__init__(self, self.generate(), descr)
... |
def make_patches_from_region(slide_path):
with openslide.open_slide(slide_path) as slide:
thumbnail = slide.read_region((x, y), zoom_level, (1000, 1000))
patches_dir = ((str(BASE_TRUTH_DIR) + str(exp_folder_name)) + '/')
print('patches_dir', patches_dir)
assure_path_exists(patches_dir)
plt.i... |
def main(local_rank, args):
set_random_seed(args.seed)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
cfg = Config.fromfile(args.py_config)
cfg.work_dir = args.work_dir
if (args.gpus > 1):
distributed = True
ip = os.environ.get('MASTER_ADDR', '12... |
def generate_loc_dict(G_edges_data):
loc_dict = {}
index_dict = {}
count = 0
for triple in G_edges_data:
print(triple)
(h, t, attribute) = triple
l = attribute['loc']
if (l is not None):
if (l not in loc_dict):
loc_dict[l] = {}
... |
class BaseTestWindow(window.Window):
def __init__(self, multiline, wrap_lines, msg, *args, **kwargs):
super(BaseTestWindow, self).__init__(*args, width=640, height=480, **kwargs)
self.batch = graphics.Batch()
self.document = text.decode_attributed(msg)
self.margin = 2
self.la... |
def to_ani(frames: List[CursorFrame]) -> bytes:
ani_header = ANIParser.ANIH_HEADER.pack(ANIParser.ANIH_HEADER.size, len(frames), len(frames), 0, 0, 32, 1, 1, ANIParser.ICON_FLAG)
cur_list = get_ani_cur_list(frames)
chunks = [ANIParser.CHUNK_HEADER.pack(ANIParser.HEADER_CHUNK, len(ani_header)), ani_header, A... |
def test_single_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 = ... |
def test_search_for_file_sdist_with_extras(provider: Provider, fixture_dir: FixtureDirGetter) -> None:
dependency = FileDependency('demo', (fixture_dir('distributions') / 'demo-0.1.0.tar.gz'), extras=['foo'])
package = provider.search_for_direct_origin_dependency(dependency)
assert (package.name == 'demo')
... |
class CrumblingWall(TutorialObject, DefaultExit):
def at_init(self):
self.reset()
def at_object_creation(self):
super().at_object_creation()
self.aliases.add(['secret passage', 'passage', 'crack', 'opening', 'secret door'])
self.db.root_pos = {'yellow': 0, 'green': 0, 'red': 0, '... |
def main(args):
dataset = Dataset(args)
os.makedirs(args.save_dir, exist_ok=True)
with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
pickle.dump(dataset.dataset_info, wf)
if (args.task == 'rhyme'):
with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
... |
def select_cross_entropy_loss(pred, label):
pred = pred.view((- 1), 2)
label = label.view((- 1))
pos = label.data.eq(1).nonzero().squeeze().cuda()
neg = label.data.eq(0).nonzero().squeeze().cuda()
loss_pos = get_cls_loss(pred, label, pos)
loss_neg = get_cls_loss(pred, label, neg)
return ((lo... |
def _valid_command_options(cmdclass: Mapping=EMPTY) -> Dict[(str, Set[str])]:
from .._importlib import metadata
from setuptools.dist import Distribution
valid_options = {'global': _normalise_cmd_options(Distribution.global_options)}
unloaded_entry_points = metadata.entry_points(group='distutils.commands... |
def getProposedModelC(size=224, seq_len=32, cnn_weight='imagenet', cnn_trainable=True, lstm_type='sepconv', weight_decay=2e-05, frame_diff_interval=1, mode='both', cnn_dropout=0.25, lstm_dropout=0.25, dense_dropout=0.3, seed=42):
print('cnn_trainable:', cnn_trainable)
print('cnn dropout : ', cnn_dropout)
pr... |
def setUp():
for phase_class in uvm_common_phases:
phase_func = phase_class.__name__[4:]
phase_list[phase_func] = []
top = my_comp('top', None)
aa = my_comp('aa', top)
bb = my_comp('bb', top)
my_comp('cc', aa)
my_comp('dd', aa)
my_comp('ee', bb)
my_comp('ff', bb)
retu... |
class Session():
def __init__(self, model_dir=None, model_name=None):
logger.debug('Initializing %s: (model_dir: %s, model_name: %s)', self.__class__.__name__, model_dir, model_name)
self.serializer = JSONSerializer
self.state = None
self.modeldir = model_dir
self.modelname =... |
def _check_closed_doors(state: EnvironmentState, room1: GraphNode, room2: GraphNode):
return []
graph_adj_lists = _create_walkable_graph(state)
bfs_prev = BFS_check_closed(state, graph_adj_lists, room1.id)
if (room2.id in bfs_prev):
return []
bfs_prev = BFS(graph_adj_lists, room1.id)
if ... |
class BindTransmitterResp(Command):
params = {'system_id': Param(type=str, max=16), 'sc_interface_version': Param(type=int, size=1)}
params_order = ('system_id', 'sc_interface_version')
def __init__(self, command, **kwargs):
super(BindTransmitterResp, self).__init__(command, need_sequence=False, **k... |
class ClientStatusDB(SocketDB):
def __init__(self, sock_port):
super().__init__()
self.sock.connect(('localhost', sock_port))
def _set(self, i: str, k: str, v: int):
self._sock_send(self.sock, '|'.join(('set', i, k, str(v))))
def _get(self, i: str, k: str) -> int:
self._sock_... |
def add_metadata(runner):
runner.metadata['description'] = 'Async tree workloads.'
runner.metadata['async_tree_recurse_levels'] = NUM_RECURSE_LEVELS
runner.metadata['async_tree_recurse_branches'] = NUM_RECURSE_BRANCHES
runner.metadata['async_tree_random_seed'] = RANDOM_SEED
runner.metadata['async_tr... |
def get_directory_list(path):
directory_list = []
if os.path.isfile(path):
return []
if (len([f for f in os.listdir(path) if (f == 'config.json')]) > 0):
directory_list.append(path)
for d in os.listdir(path):
new_path = os.path.join(path, d)
if os.path.isdir(new_path):
... |
class GraspNetStage1(nn.Module):
def __init__(self, input_feature_dim=0, num_view=300):
super().__init__()
self.backbone = Pointnet2Backbone(input_feature_dim)
self.vpmodule = ApproachNet(num_view, 256)
def forward(self, end_points):
pointcloud = end_points['point_clouds']
... |
class BamResNet(nn.Module):
def __init__(self, channels, init_block_channels, bottleneck, in_channels=3, in_size=(224, 224), num_classes=1000):
super(BamResNet, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.features = nn.Sequential()
self.feature... |
class TestClose():
def adapterC(self):
return VISAAdapter(SIM_RESOURCE, visa_library='')
def test_connection_session_closed(self, adapterC):
assert (adapterC.connection.session is not None)
adapterC.close()
with pytest.raises(pyvisa.errors.InvalidSession, match='Invalid session')... |
class TestLRUDict(unittest.TestCase):
def test_lrudict_defaultbehaviour(self):
d = LRUDict()
dd = dict()
for count in range(1, 100):
d[count] = f'v{count}'
dd[count] = f'v{count}'
if ((count % 5) == 0):
d.get((count - 2))
dd... |
class CustomApi():
def __init__(self, port: int=24859):
self._handler: Optional[Callable] = None
self._app: web.Application = web.Application()
self._runner: Optional[web.AppRunner] = None
self._port = port
def on_update_custom_api(self) -> Callable:
if (self._handler is ... |
def test_custom_validator_class_can_detect_custom_conditions(run_line, tmp_path):
doc = (tmp_path / 'invalid.json')
doc.write_text(json.dumps(INVALID_DOC))
schema = (tmp_path / 'schema.json')
schema.write_text(json.dumps(SCHEMA))
result = run_line(['check-jsonschema', '--schemafile', str(schema), st... |
def rtn_errno_location(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('__errno_location hooked')
segs = pstate.memory.find_map(pstate.EXTERN_SEG)
if segs:
map = segs[0]
ERRNO = ((map.start + map.size) - 4)
else:
assert False
pstate.memory.write_dword(ERRNO, 0)
... |
class LongNegativeCategoryEntryTestCase(unittest.TestCase):
def setUpClass(cls):
cls.entry = CategoryEntry(name='This is quite a LOOONG Category', entries=[BaseEntry('entry', (- 100), '2000-08-13')])
def test_name(self):
self.assertEqual(self.entry.name, 'this is quite a looong category')
de... |
def list_pods(cli, namespace, label_selector=None):
pods = []
try:
if label_selector:
ret = cli.list_namespaced_pod(namespace, pretty=True, label_selector=label_selector)
else:
ret = cli.list_namespaced_pod(namespace, pretty=True)
except ApiException as e:
log... |
def getSplittedDataset(trainpart, testpart, predictpart, expset):
assert (((parameters['trainpart'] + parameters['testpart']) + parameters['predictpart']) == 1), 'Train + Test + Prediction should be 1'
(x, y) = expset[0]
logging.critical('\n[FUNCTION]: Splitting dataset by getSplittedDataset()......')
l... |
class PostNorm_Classifier(nn.Module):
def __init__(self, num_classes=10, in_dim=640, norm=False, feature_norm=False, lws=False, tau=0, bias=False, avg_T=1):
super(PostNorm_Classifier, self).__init__()
self.fc = nn.Linear(in_dim, num_classes)
self.weight_norm = norm
self.feature_norm ... |
def quantity_delta(base, changed):
old = base.mean()
new = changed.mean()
is_time = (base.get_unit() == 'second')
if ((old == 0) or (new == 0)):
return 'incomparable (one result was zero)'
if (new > old):
if is_time:
return ('%.2fx slower' % (new / old))
else:
... |
def init(disp, info):
disp.extension_add_method('display', 'xrandr_query_version', query_version)
disp.extension_add_method('window', 'xrandr_select_input', select_input)
disp.extension_add_method('window', 'xrandr_get_screen_info', get_screen_info)
disp.extension_add_method('drawable', 'xrandr_1_0set_s... |
def test_update_once():
class A(Component):
_port
def recv(s, v):
s.v = v
def construct(s):
s.send = CallerPort()
s.v = None
_once
def up():
if (s.v is not None):
s.send(s.v)
s.add_con... |
def num_to_str(num, unit=None, precision=2, number_only=False, auto_select_unit=False):
unit_list = ['K', 'M', 'G', 'T', 'P']
if (auto_select_unit and (unit is None)):
for (i, tmp) in enumerate(unit_list):
unit_num = (1024 ** (i + 1))
if (num < unit_num):
break
... |
def calculate_metrics(task_type: str, y: np.ndarray, prediction: np.ndarray, classification_mode: str, y_info: ty.Optional[ty.Dict[(str, ty.Any)]]) -> ty.Dict[(str, float)]:
if (task_type == util.REGRESSION):
del classification_mode
rmse = (skm.mean_squared_error(y, prediction) ** 0.5)
if y_... |
class Process():
def par(func, iterables, num_processes, desc=''):
pool = multiprocessing.Pool(processes=num_processes)
pool_func = pool.imap(func=func, iterable=iterables)
pool_func = tqdm(pool_func, total=len(iterables), ncols=100, desc=desc)
results = [r for r in pool_func]
... |
class HistogramTests(unittest.TestCase):
def setUp(self):
self.transport = mock.Mock(spec=metrics.NullTransport)
def test_log(self):
histogram = metrics.Histogram(self.transport, b'example_hist')
histogram.add_sample(33)
self.assertEqual(self.transport.send.call_count, 1)
... |
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