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def load_wav_to_torch(full_path):
(sampling_rate, data) = read(full_path)
return (torch.FloatTensor(data.astype(np.float32)), sampling_rate) |
def calculate_metrics(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
true_positives = np.sum(np.logical_and(predict_issame, actual_issame))
false_positives = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
true_negatives = np.sum(np.logical_and(np.logica... |
def query_on_voxel(query, feature, min_, max_, use_ste=False, boundary_check=False, ctx=None):
func = LanczosQueryOnVoxel(ctx, min_, max_, use_ste, boundary_check)
return func(query, feature) |
def _simplify_cells(d):
for key in d:
if isinstance(d[key], mat_struct):
d[key] = _matstruct_to_dict(d[key])
elif _has_struct(d[key]):
d[key] = _inspect_cell_array(d[key])
return d |
def decompress(data_blocks):
d = zlib.decompressobj()
for data in data_blocks:
(yield bytearray(d.decompress(data)))
(yield bytearray(d.flush())) |
class TwistedAffineIndices(UniqueRepresentation, Set_generic):
def __classcall_private__(cls, cartan_type):
cartan_type = CartanType(cartan_type)
if ((not cartan_type.is_affine()) or cartan_type.is_untwisted_affine()):
raise ValueError('the Cartan type must be a twisted affine type')
... |
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.relu = nn.ReLU()
self.co... |
def variation_of_information(image0=None, image1=None, *, table=None, ignore_labels=()):
(h0g1, h1g0) = _vi_tables(image0, image1, table=table, ignore_labels=ignore_labels)
return np.array([h1g0.sum(), h0g1.sum()]) |
(Output('female-corpus-stats', 'children'), [Input('topic-data', 'data'), Input('date-dropdown', 'value')])
def display_female_corpus_stats(data, date_val):
female_corpus_size = data['params']['femaleDominantArticleCount']
display_text = f'''
In {num2str_month(date_val)}, there were {female_corpus_size:... |
class ArchGenerate(BaseArchGenerate):
def __init__(self, super_network, config):
super(ArchGenerate, self).__init__(super_network, config)
def derive_archs(self, betas, head_alphas, stack_alphas, if_display=True):
self.update_arch_params(betas, head_alphas, stack_alphas)
derived_archs = ... |
class rel_pyramid_module(nn.Module):
def __init__(self, num_backbone_stages):
super().__init__()
fpn_dim = cfg.FPN.DIM
self.num_backbone_stages = num_backbone_stages
self.prd_conv_lateral = nn.ModuleList()
for i in range(self.num_backbone_stages):
if cfg.FPN.USE_G... |
class CountingIterator(object):
def __init__(self, iterable, start=None, total=None):
self.iterable = iterable
self.itr = iter(self)
if (start is None):
self.n = getattr(iterable, 'n', 0)
else:
self.n = start
if (total is None):
self.total ... |
class InterpretCompilerDirectives(CythonTransform):
unop_method_nodes = {'typeof': ExprNodes.TypeofNode, 'operator.address': ExprNodes.AmpersandNode, 'operator.dereference': ExprNodes.DereferenceNode, 'operator.preincrement': ExprNodes.inc_dec_constructor(True, '++'), 'operator.predecrement': ExprNodes.inc_dec_cons... |
class LinearGRP(T.nn.Linear):
def __init__(self, in_features: int, out_features: int, bias: bool=True, device=None, dtype=None, proj_dim_ratio: Optional[float]=None, proj_dim: Optional[int]=None, proj_dim_min: Optional[int]=None, proj_dim_max: Optional[int]=None, matmul: MatMulType='gaussian', generator: Optional[T... |
class InstallWithExtras(install):
def run(self) -> None:
super().run()
build_ext_obj = self.distribution.get_command_obj('build_ext')
build_dir = Path(self.distribution.get_command_obj('build_ext').build_temp)
self.copy_file(find_symengine_wrapper(build_dir, build_ext_obj.get_ext_fil... |
def test_clean_remove_stopwords(df_text: pd.DataFrame) -> None:
pipeline = [{'operator': 'remove_stopwords'}]
df_clean = clean_text(df_text, 'text', pipeline=pipeline)
df_check = df_text.copy()
df_check['text'] = ["'ZZZZZ!' IMDb would allow one-word reviews, that's mine would be.", 'cast played Shakespe... |
def get_setup_nets(key, steps_or_nets, target):
init_net = core.Net((key + '/init'))
exit_net = core.Net((key + '/exit'))
init_nets = []
exit_nets = []
objs = []
for step_or_net in steps_or_nets:
if hasattr(step_or_net, 'get_all_attributes'):
objs += step_or_net.get_all_attri... |
class WFRadiationMeshSliceMax(RadiationField):
glossary_name = 'params/Mesh/sliceMax'
def __init__(self, wf):
super(WFRadiationMeshSliceMax, self).__init__(wf)
self.attributes.update({'limits': '[1:LONG_MAX]', 'alias': 'mesh.eStart'})
def value(self):
return self._wf._srwl_wf.mesh.eF... |
class GiniIndex(BaseMetric):
def __init__(self, recommendations, config, params, eval_objects):
super().__init__(recommendations, config, params, eval_objects)
self._cutoff = self._evaluation_objects.cutoff
self._num_items = self._evaluation_objects.num_items
self._item_count = {}
... |
def ia_minus_iadag_sparse(dimension: int, prefactor: Union[(float, complex, None)]=None) -> csc_matrix:
prefactor = (prefactor if (prefactor is not None) else 1.0)
return (prefactor * ((1j * annihilation_sparse(dimension)) - (1j * creation_sparse(dimension)))) |
def convert_element_list(monitor_descriptions: List[MonitorDescription]) -> MonitorDescriptionList:
monitor_dict = {}
for m in monitor_descriptions:
if (m.joiner_id in monitor_dict):
m_joined = monitor_dict[m.joiner_id]
if (not (m.monitor_type == m_joined.monitor_type)):
... |
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, init_weights=True):
super(VGG, self).__init__()
self.features = features
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(nn.Linear(((512 * 7) * 7), 4096), nn.ReLU(True), nn.Dropout(), nn.... |
def mask_and_save_to_dicom(dcm_path, args, filename):
dicom = Dicom(dcm_path)
metadata = dicom.metadata()
included_path = os.path.join(args.savepath, 'included')
excluded_path = os.path.join(args.savepath, 'excluded')
filename = number_image(filename, metadata['patient_name'])
outpath = os.path.... |
class Algorithm(metaclass=ABCMeta):
def run_classification(x, y, epsilon, delta, lambda_param, learning_rate, num_iters):
return NotImplemented
def name(self):
return NotImplemented |
class ExcludeFeatures():
def __init__(self, feature_names):
self.feature_names = feature_names
def __call__(self, inputs):
outputs = dict(((k, inputs[k]) for k in inputs if (k not in self.feature_names)))
return outputs |
def register_Ns3DefaultDeleter__Ns3AttributeAccessor_methods(root_module, cls):
cls.add_constructor([])
cls.add_constructor([param('ns3::DefaultDeleter< ns3::AttributeAccessor > const &', 'arg0')])
cls.add_method('Delete', 'void', [param('ns3::AttributeAccessor *', 'object')], is_static=True)
return |
def cal_rouge(evaluated_ngrams, reference_ngrams):
reference_count = len(reference_ngrams)
evaluated_count = len(evaluated_ngrams)
overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)
overlapping_count = len(overlapping_ngrams)
if (evaluated_count == 0):
precision = 0.0
e... |
def down_sample(x, scale_factor_h, scale_factor_w):
(_, h, w, _) = x.get_shape().as_list()
new_size = [(h // scale_factor_h), (w // scale_factor_w)]
return tf.image.resize_nearest_neighbor(x, size=new_size) |
class TwitterProcessor(QueryNERProcessor):
def get_labels(self):
return ['PER', 'LOC', 'ORG', 'O'] |
class PositionWiseFeedForwardNet(nn.Module):
def __init__(self, d_model: int=512, d_ff: int=2048, dropout_p: float=0.3, ffnet_style: str='ff') -> None:
super(PositionWiseFeedForwardNet, self).__init__()
self.ffnet_style = ffnet_style.lower()
if (self.ffnet_style == 'ff'):
self.fe... |
class BDFuncType(Enum):
UNKNOWN = (- 1)
CONV_NEURON = 0
DEPTHWISE_OR_POOLING = 1
FC = 2
TENSOR_ARITHMETIC = 3
FC2 = 4
CONV_CORRELATION = 5
TABLE_LOOKUP = 6
MD_SUM = 7
MD_SCALAR = 8
MD_SFU = 9
MD_LINEAR = 10
LOCALMEM_ARANGE = 11
DECOMPRESS = 12
MD_CMP = 13
... |
def _parse_inputs(actual: Any, expected: Any, *, allow_subclasses: bool) -> Tuple[(Optional[_TestingErrorMeta], Optional[Union[(_TensorPair, List, Dict)]])]:
error_meta: Optional[_TestingErrorMeta]
if (isinstance(actual, collections.abc.Sequence) and (not isinstance(actual, str)) and isinstance(expected, collec... |
def add_code_sample_docstrings(*docstr, processor_class=None, checkpoint=None, output_type=None, config_class=None, mask='[MASK]', qa_target_start_index=14, qa_target_end_index=15, model_cls=None, modality=None, expected_output=None, expected_loss=None, real_checkpoint=None):
def docstring_decorator(fn):
mo... |
class TSTestDataset(Dataset):
def __init__(self, x):
self.x = x
def __len__(self):
return len(self.x)
def __getitem__(self, idx):
return self.x[idx] |
class ModularPolynomialDatabase():
def _dbpath(self, level):
return ('PolMod/%s/pol.%03d.dbz' % (self.model, level))
def __repr__(self):
if self.model.startswith('Cls'):
head = 'Classical'
elif self.model.startswith('Atk'):
head = 'Atkin'
elif self.model.s... |
def phi_square_calc(chi_square, POP):
try:
return (chi_square / POP)
except Exception:
return 'None' |
def _to_polynomials(lf, R):
n = max((max((part[0] for part in f.support() if part), default=0) for f in lf))
n = max(n, 1)
P = PolynomialRing(R, [('v%s' % a) for a in range(1, (n + 1))])
if (n == 1):
return [P({part.to_exp(n)[0]: c for (part, c) in f}) for f in lf]
return [P({tuple(part.to_e... |
class DefaultProcessingUnitConfig(dict, ProcessingUnitConfig):
def unit_name(self):
return self['unit_name']
def set_unit_name(self, value):
self['unit_name'] = value
def to_dict(self):
return self
def from_dict(cls, obj_dict):
return cls(obj_dict) |
.parametrize('param_distributions, expected_n_candidates', [({'a': [1, 2]}, 2), ({'a': randint(1, 3)}, 10)])
def test_random_search_discrete_distributions(param_distributions, expected_n_candidates):
n_samples = 1024
(X, y) = make_classification(n_samples=n_samples, random_state=0)
base_estimator = FastClas... |
def mock_latex_file():
with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.tex') as f:
latex_str = f'\documentclass{{article}}egin{{document}}{plain_text_str}\end{{document}}'
f.write(latex_str)
return f.name |
_model
def regnetx_040(pretrained=False, **kwargs):
return _regnet('regnetx_040', pretrained, **kwargs) |
class AlgebraMorphism(ModuleMorphismByLinearity):
def __init__(self, domain, on_generators, position=0, codomain=None, category=None, anti=False):
assert (position == 0)
assert (codomain is not None)
if (category is None):
if anti:
category = ModulesWithBasis(doma... |
class Seq2SeqModelCaffe2(object):
def _build_model(self, init_params):
model = Seq2SeqModelHelper(init_params=init_params)
self._build_shared(model)
self._build_embeddings(model)
forward_model = Seq2SeqModelHelper(init_params=init_params)
self._build_shared(forward_model)
... |
class FromTableauIsomorphism(Morphism):
def _repr_type(self):
return 'Crystal Isomorphism'
def __invert__(self):
return FromRCIsomorphism(Hom(self.codomain(), self.domain()))
def _call_(self, x):
conj = x.to_tableau().conjugate()
ct = self.domain().cartan_type()
act =... |
def parse_args():
parser = argparse.ArgumentParser(description='Script that retags a tree file')
parser.add_argument('--lang', default='vi', type=str, help='Language')
parser.add_argument('--input_file', default='data/constituency/vi_vlsp21_train.mrg', help='File to retag')
parser.add_argument('--output... |
def test_object():
image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
e... |
def register_Ns3RvBatteryModelHelper_methods(root_module, cls):
cls.add_constructor([param('ns3::RvBatteryModelHelper const &', 'arg0')])
cls.add_constructor([])
cls.add_method('Set', 'void', [param('std::string', 'name'), param('ns3::AttributeValue const &', 'v')], is_virtual=True)
cls.add_method('DoIn... |
def set_principled_node(principled_node: bpy.types.Node, base_color: Tuple[(float, float, float, float)]=(0.6, 0.6, 0.6, 1.0), subsurface: float=0.0, subsurface_color: Tuple[(float, float, float, float)]=(0.8, 0.8, 0.8, 1.0), subsurface_radius: Tuple[(float, float, float)]=(1.0, 0.2, 0.1), metallic: float=0.0, specular... |
class EchoingStdin(object):
def __init__(self, input, output):
self._input = input
self._output = output
def __getattr__(self, x):
return getattr(self._input, x)
def _echo(self, rv):
self._output.write(rv)
return rv
def read(self, n=(- 1)):
return self._ec... |
def from_path(path: PathLike, *, app: Any=None, base_url: (str | None)=None, data_generation_methods: DataGenerationMethodInput=DEFAULT_DATA_GENERATION_METHODS, code_sample_style: str=CodeSampleStyle.default().name, rate_limit: (str | None)=None, encoding: str='utf8', sanitize_output: bool=True) -> GraphQLSchema:
w... |
def setup(with_data=False):
if (not os.path.exists(TEST_PATH)):
os.mkdir(TEST_PATH)
if with_data:
with_data_path = (lambda f: os.path.join(DATA_DIR, f))
with_test_path = (lambda f: os.path.join(TEST_PATH, f))
copy_file = (lambda f: shutil.copy(with_data_path(f), with_test_path(f)... |
def report_errors(dataset, result_file):
df = pd.read_csv(((RESULT_ROOT / dataset) / result_file))
evaluate_errors(df['error']) |
class ConsumedResource(Resource):
def update_agent_infos(self, state_infos, agent_infos):
super().update_agent_infos(state_infos, agent_infos)
if (self._reward_this_step > 0):
return
agent_infos['inventory'][self._resource_name] = 0 |
def ocp_mixed(F, bcs_m, J_m, state_m, controls, adjoint_m, config_picard):
return cashocs.OptimalControlProblem(F, bcs_m, J_m, state_m, controls, adjoint_m, config=config_picard) |
class ContrastMemory(nn.Module):
def __init__(self, inputSize, outputSize, K, T=0.07, momentum=0.5):
super(ContrastMemory, self).__init__()
self.nLem = outputSize
self.unigrams = torch.ones(self.nLem)
self.multinomial = AliasMethod(self.unigrams)
self.multinomial.cuda()
... |
def _reduced_texutalization_method(expr, entity_label_map):
textual_form = textualize_s_expr(expr)
toks = textual_form.split(' ')
norm_toks = []
for t in toks:
if t.startswith('m.'):
if (t in entity_label_map):
t = entity_label_map[t]
else:
... |
class Indices2Dataset(Dataset):
def __init__(self, dataset):
self.dataset = dataset
self.indices = None
def load(self, indices: list):
self.indices = indices
def __getitem__(self, idx):
idx = self.indices[idx]
(image, label) = self.dataset[idx]
return (image, ... |
def get_prefix(sentence, prefix_len):
tokens = sentence.strip('\n').split()
if (prefix_len >= len(tokens)):
return sentence.strip('\n')
else:
return ' '.join(tokens[:prefix_len]) |
class FiniteField_prime_modn(FiniteField_generic, integer_mod_ring.IntegerModRing_generic):
def __init__(self, p, check=True, modulus=None):
p = Integer(p)
if (check and (not p.is_prime())):
raise ArithmeticError('p must be prime')
self.__char = p
integer_mod_ring.Integer... |
class AverageMeter():
def __init__(self, last_n=None):
self._records = []
self.last_n = last_n
def update(self, result):
if isinstance(result, (list, tuple)):
self._records += result
else:
self._records.append(result)
def reset(self):
self._rec... |
def test_execute_filter_endpoint(app, schema_url):
schema = oas_loaders.from_uri(schema_url, endpoint=['success'])
execute(schema)
assert_incoming_requests_num(app, 1)
assert_request(app, 0, 'GET', '/api/success')
assert_not_request(app, 'GET', '/api/failure') |
def test_unbounded_state(cl):
input = NamedVideoStream(cl, 'test1')
frame = cl.io.Input([input])
slice_frame = cl.streams.Slice(frame, partitions=[cl.partitioner.all(50)])
increment = cl.ops.TestIncrementUnbounded(ignore=slice_frame)
unsliced_increment = cl.streams.Unslice(increment)
output = Na... |
def b_cubed(clusters, mention_to_gold):
(num, dem) = (0, 0)
for c in clusters:
if (len(c) == 1):
continue
gold_counts = Counter()
correct = 0
for m in c:
if (m in mention_to_gold):
gold_counts[tuple(mention_to_gold[m])] += 1
for (c2... |
def load_sqls(in_json, normalize_variables):
def fill_in_variables(s, s_vars, variables):
var_list = {}
for (v_key, v_val) in s_vars.items():
if (len(v_val) == 0):
for var in variables:
if (var['name'] == v_key):
v_val = var['ex... |
def make_dense(targets, noclass):
with tf.device('/cpu:0'):
shape = tf.shape(targets)
batch_size = shape[0]
indices = (targets + (noclass * tf.range(0, batch_size)))
length = tf.expand_dims((batch_size * noclass), 0)
dense = tf.sparse_to_dense(indices, length, 1.0, 0.0)
r... |
def get_training_stats(stats):
if (('nll_loss' in stats) and ('ppl' not in stats)):
stats['ppl'] = utils.get_perplexity(stats['nll_loss'])
stats['wall'] = round(metrics.get_meter('default', 'wall').elapsed_time, 0)
return stats |
def printTensor(dataTable, message='', nPrintedRows=0, nPrintedCols=0, interval=10):
dims = dataTable.getDimensions()
nRows = int(dims[0])
if (nPrintedRows != 0):
nPrintedRows = min(nRows, nPrintedRows)
else:
nPrintedRows = nRows
block = SubtensorDescriptor()
dataTable.getSubtens... |
.parametrize('ctx', ctx_list)
.parametrize('seed', [313])
.parametrize('window_size, stride, fft_size', [(16, 2, 16), (16, 4, 16), (16, 8, 32)])
.parametrize('window_type', ['hanning', 'hamming', 'rectangular'])
.parametrize('center', [True, False])
.parametrize('pad_mode', ['reflect', 'constant'])
.parametrize('as_ist... |
def scatter(tensor, **kwargs):
assert (torch.distributed.deprecated._initialized == _INITIALIZED_PG), 'collective only supported in process-group mode'
my_rank = get_rank()
src = kwargs.pop('src', my_rank)
scatter_list = kwargs.pop('scatter_list', None)
_group = kwargs.pop('group', group.WORLD)
... |
def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[(torch.Tensor, torch.Tensor)]:
if (tokenizer.mask_token is None):
raise ValueError('This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this to... |
def read_mk(fobj, start_length, size):
start = start_length[0]
fobj.seek(start)
pixel_size = ((size[0] * size[2]), (size[1] * size[2]))
sizesq = (pixel_size[0] * pixel_size[1])
band = Image.frombuffer('L', pixel_size, fobj.read(sizesq), 'raw', 'L', 0, 1)
return {'A': band} |
def batch_data_test(cfg, data, device='cuda'):
batch = {}
roi_keys = ['im_H', 'im_W', 'roi_img', 'inst_id', 'roi_coord_2d', 'roi_cls', 'score', 'roi_extent', 'bbox', 'bbox_est', 'bbox_mode', 'roi_wh', 'scale', 'resize_ratio']
for key in roi_keys:
if (key in ['roi_cls']):
dtype = torch.lo... |
def convert_ndarray(x):
y = list(range(len(x)))
for (k, v) in x.items():
y[int(k)] = v
return np.array(y) |
def init_array(A, B, C, D, G):
ni = NI.get()
nj = NJ.get()
nk = NK.get()
nl = NL.get()
nm = NM.get()
for i in range(ni):
for j in range(nk):
A[(i, j)] = (datatype((((i * j) + 1) % ni)) / (5 * ni))
for i in range(nk):
for j in range(nj):
B[(i, j)] = (da... |
class TensorDataset(Dataset):
def __init__(self, *tensors):
assert all(((tensors[0].size(0) == tensor.size(0)) for tensor in tensors))
self.tensors = tensors
def __getitem__(self, index):
return tuple((tensor[index] for tensor in self.tensors))
def __len__(self):
return self.... |
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--input_json', type=str, default='data/coco.json', help='path to the json file containing additional info and vocab')
parser.add_argument('--input_fc_h5', type=str, default='data/coco_ai_challenger_talk_fc.h5', help='path to the direct... |
def log_2items_per_user(spark):
date = datetime(2019, 1, 1)
return spark.createDataFrame(data=[[0, 0, date, 1.0], [0, 1, date, 1.0], [1, 0, date, 1.0], [1, 1, date, 1.0], [2, 2, date, 1.0], [2, 3, date, 1.0]], schema=INTERACTIONS_SCHEMA) |
def double_linear_logits(args, size, bias, bias_start=0.0, scope=None, mask=None, wd=0.0, input_keep_prob=1.0, is_train=None):
with tf.variable_scope((scope or 'Double_Linear_Logits')):
first = tf.tanh(linear(args, size, bias, bias_start=bias_start, scope='first', wd=wd, input_keep_prob=input_keep_prob, is_... |
def synchronized(func):
(func)
def wrapper(*args, **kwargs):
with _module_lock:
return func(*args, **kwargs)
return wrapper |
def test_constructor_statement_clone(default_test_case, constructor_mock):
int_prim = st.IntPrimitiveStatement(default_test_case, 5)
method_stmt = st.ConstructorStatement(default_test_case, constructor_mock, {'y': int_prim.ret_val})
default_test_case.add_statement(int_prim)
default_test_case.add_stateme... |
class ReportBuilderBase():
def __init__(self, file=None):
database_file = (file if (file is not None) else ':memory:')
self._connection = sqlite3.connect(database_file)
self._create_report_tables()
def process_tracker(self, tracker):
tracker.populate_report(self)
return s... |
def main():
(args, cfg) = parse_config()
logger = common_utils.create_logger()
logger.info('Quick Demo')
(train_set, train_loader, train_sampler) = build_dataloader(dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, batch_size=1, dist=False, workers=0, logger=logger, training=True, merge_all_iter... |
def inception_block_2b(X):
X_3x3 = fr_utils.conv2d_bn(X, layer='inception_4e_3x3', cv1_out=160, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1))
X_5x5 = fr_utils.conv2d_bn(X, layer='inception_4e_5x5', cv1_out=64, cv1_filter=(1, 1), cv2_out=128, cv2_filter=(5, 5), cv2_stride... |
class Decoder_64(nn.Module):
def __init__(self, img_size=64, latent_dim=10, noise_dim=100):
super(Decoder_64, self).__init__()
in_channels = (latent_dim + noise_dim)
self.linear = snlinear(in_features=in_channels, out_features=512)
self.deconv1 = snconvTrans2d(in_channels=512, out_ch... |
class TestGeneralController(testing_utils.TestCase):
def setUp(self):
super(TestGeneralController, self).setUp()
self.session = tf.Session()
(self.model_space, _) = testing_utils.get_example_conv1d_space()
self.controller = architect.GeneralController(model_space=self.model_space, bu... |
def test_RecordArray():
array = ak.highlevel.Array([{'x': 0.0, 'y': []}, {'x': 8.0, 'y': [1]}, {'x': 2.2, 'y': [2, 2]}, {'x': 3.3, 'y': [3, 1, 3]}, {'x': 4.4, 'y': [4, 1, 1, 4]}, {'x': 5.5, 'y': [5, 4, 5]}, {'x': 1.1, 'y': [6, 1]}, {'x': 7.7, 'y': [7]}, {'x': 10.0, 'y': [99]}])
assert (ak._do.is_unique(array.la... |
def numpy_azimint_hist(data, radius, npt):
histu = np.histogram(radius, npt)[0]
histw = np.histogram(radius, npt, weights=data)[0]
return (histw / histu) |
class Config(BaseConfig):
numPeriods: int = 60
tstep: int = 5
elasmu: float = 1.45
prstp: float = 0.015
gama: float = 0.3
pop0: float = 6838
popadj: float = 0.134
popasym: float = 10500
dk: float = 0.1
q0: float = 63.69
k0: float = 135
a0: float = 3.8
ga0: float = 0.0... |
_SEG_HEADS_REGISTRY.register()
class BasePixelDecoder(nn.Module):
def __init__(self, input_shape: Dict[(str, ShapeSpec)], *, conv_dim: int, mask_dim: int, norm: Optional[Union[(str, Callable)]]=None):
super().__init__()
input_shape = sorted(input_shape.items(), key=(lambda x: x[1].stride))
s... |
def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path:
in_units_file = (fst_dir / f'kaldi_dict.{in_labels}.txt')
if (not in_units_file.exists()):
logger.info(f'Creating {in_units_file}')
with open(in_units_file, 'w') as f:
print('<eps> 0', file=f)
i =... |
def split(s, splitter, reg=False):
if (not reg):
return s.split(splitter)
import re
return re.split(splitter, s) |
class DlaRoot(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, residual):
super(DlaRoot, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=1, bias=False, padding=((kernel_size - 1) // 2))
self.bn = nn.BatchNorm2d(out_channels)
self.relu... |
def convert_file_if_needed(file, debug):
if file.endswith('.sgm'):
return sgm2raw(file, debug)
elif file.endswith('.tmx'):
return tmx2raw(file, debug)
elif file.endswith('wiki/fi-en/titles.fi-en'):
return cut_wikitles(file, debug)
elif file.endswith('.tsv'):
return cut_ts... |
class GaussianMLPTwoHeadedModuleEx(GaussianMLPTwoHeadedModule, ForwardWithTransformTrait, ForwardWithChunksTrait, ForwardModeTrait):
pass |
def i_take_set(s: set) -> str:
if (len(s) > 0):
return 'not empty!'
else:
return 'empty!' |
def _find_loop_nest_roots(loop_nest_tree: Dict[(SDFGState, Set[SDFGState])]) -> Set[SDFGState]:
all_nodes = set()
child_nodes = set()
for (parent, children) in loop_nest_tree.items():
all_nodes.add(parent)
all_nodes.update(children)
child_nodes.update(children)
roots = (all_nodes... |
class runtime_validation_disabled(object):
prev: bool
def __init__(self) -> None:
global _runtime_validation_enabled
self.prev = _runtime_validation_enabled
_runtime_validation_enabled = False
def __enter__(self) -> None:
pass
def __exit__(self, exc_type: Any, exc_value: ... |
def scalar_imp_test(listener=False):
data = [('blue', (240.0, 100.0, 100.0)), ('blue', (180.0, 100.0, 100.0)), ('teal', (240.0, 100.0, 100.0)), ('teal', (180.0, 100.0, 100.0))]
return pairs_to_insts(data, listener=listener) |
def create_f0_hparams(hparams_string=None, verbose=False):
hparams = tf.contrib.training.HParams(type=3, layers=3, blocks=2, dilation_channels=130, residual_channels=130, skip_channels=240, input_channel=60, condition_channel=1126, cgm_factor=4, initial_kernel=10, kernel_size=2, bias=True)
if hparams_string:
... |
class RegularPartitions_all(RegularPartitions):
def __init__(self, ell):
RegularPartitions.__init__(self, ell, bool((ell > 1)))
def _repr_(self):
return '{}-Regular Partitions'.format(self._ell)
def __iter__(self):
if (self._ell == 1):
(yield self.element_class(self, []))... |
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