code stringlengths 101 5.91M |
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def main():
parser = argparse.ArgumentParser(description='Prepare SCROLLS predictions')
parser.add_argument('--output_dir', type=str, help='Path to output the predictions file', required=True)
parser.add_argument('--qmsum_file', type=str, help='The path to the qmsum dataset json file containing predictions'... |
class SSHClient():
def __init__(self, ip_address, ssh_credentials):
self.ip_address = ip_address
self.ssh_credentials = ssh_credentials
self.ssh_client = None
if ('key_filename' in self.ssh_credentials):
fpath = os.path.expanduser(self.ssh_credentials['key_filename'])
... |
def _standardize_domains_of_(systems):
identical_domains = True
for ds in systems:
if (ds.domain() != systems[0].domain()):
identical_domains = False
break
over_number_fields = True
all_over_QQ = True
for ds in systems:
if (ds.base_ring() not in NumberFields()... |
_builder('webvid2m_caption_instruct')
class WebVid2MCapInstructBuilder(BaseDatasetBuilder):
train_dataset_cls = WebVideoCaptionInstructDataset
DATASET_CONFIG_DICT = {'default': 'configs/datasets/webvid/defaults_cap_instruct.yaml'} |
def unflatten_linear_layers(prefix, statedict: StateDict, layer: hnn.Linear, out_dims_first_in_dict: Optional[bool]) -> StateDict:
ret_dict: StateDict = {}
def _unflatten_linear(layer, prefix):
nonlocal out_dims_first_in_dict
if (not isinstance(layer, hnn.Linear)):
return layer
... |
def clone_model(model, input_tensors=None):
if isinstance(model, Sequential):
return _clone_sequential_model(model, input_tensors=input_tensors)
else:
return _clone_functional_model(model, input_tensors=input_tensors) |
def test_incompatible_shapes_raise_valueerror():
data = [[(3,), (4,)], [(2, 3), (2,)], [(3,), (3,), (4,)], [(1, 3, 4), (2, 3, 3)]]
for input_shapes in data:
assert_incompatible_shapes_raise(input_shapes)
assert_incompatible_shapes_raise(input_shapes[::(- 1)]) |
def test_divmod():
value = 7
proxy = tt.ObjectProxy(value)
assert (divmod(value, 3) == divmod(proxy, 3))
assert (int in tt.UsageTraceNode.from_proxy(proxy).children['__divmod__'].arg_types[0]) |
def curves_with_j_0_char3(K):
if ((not K.is_finite()) or (K.characteristic() != 3)):
raise ValueError('field must be finite of characteristic 3')
b = None
while ((not b) or (not b.trace())):
b = K.random_element()
if (K.degree() % 2):
return [EllipticCurve(K, a4a6) for a4a6 in [[... |
class MutualInformation(ConfusionMatrixMetric):
def __init__(self, metric: str='MUTINF'):
super().__init__(metric)
def calculate(self):
tp = self.confusion_matrix.tp
tn = self.confusion_matrix.tn
fp = self.confusion_matrix.fp
fn = self.confusion_matrix.fn
n = self... |
_function
def get_cython_cache_dir():
if ('CYTHON_CACHE_DIR' in os.environ):
return os.environ['CYTHON_CACHE_DIR']
parent = None
if (os.name == 'posix'):
if (sys.platform == 'darwin'):
parent = os.path.expanduser('~/Library/Caches')
else:
parent = os.environ.g... |
('/list_combiners_data', methods=['POST'])
def list_combiners_data():
json_data = request.get_json()
combiners = json_data.get('combiners', None)
try:
response = api.list_combiners_data(combiners)
except TypeError as e:
return (jsonify({'success': False, 'message': str(e)}), 400)
ret... |
def create_split_tone_node(node_tree: bpy.types.NodeTree) -> bpy.types.Node:
split_tone_node_group = add_split_tone_node_group()
node = node_tree.nodes.new(type='CompositorNodeGroup')
node.name = 'SplitTone'
node.node_tree = split_tone_node_group
return node |
def posat(context, builder, pos, offset):
return builder.add(pos, context.get_constant(numba.intp, offset)) |
def subst(pattern: List[str], rule_symbol: str, substitute_str: str) -> List[str]:
assert (rule_symbol in pattern)
indices = [i for (i, x) in enumerate(pattern) if (x == rule_symbol)]
new_string = (pattern[:indices[0]] + [substitute_str])
for (i, j) in zip(indices[:(- 1)], indices[1:]):
new_stri... |
class WFRadiationMeshXMin(RadiationField):
glossary_name = 'params/Mesh/xMin'
def __init__(self, wf):
super(WFRadiationMeshXMin, self).__init__(wf)
def value(self):
if (self._wf.params.wSpace == 'R-space'):
return self._wf._srwl_wf.mesh.xStart
else:
warnings.w... |
class CustomTextDatasetForGenLatentSpace(Dataset):
def __init__(self, df, tokenizer, split: str, in_memory: bool=False, train_ratio: float=1, omitted_labels=None, reduced_labels=None, reduced_labels_keep_num=None):
self.tokenizer = tokenizer
if (split == 'valid'):
file_prefix = 'train'
... |
def arg_parse():
parser = argparse.ArgumentParser(description='AD-GCL ZINC')
parser.add_argument('--dataset', type=str, default='zinc', help='Dataset')
parser.add_argument('--full', default=False, action='store_true', help='Flag to use full zinc dataset')
parser.add_argument('--model_lr', type=float, de... |
def multihead_attention(queries, keys, values, num_units=None, num_heads=1, dropout_keep_prob=1, is_training=True, has_residual=True):
if (num_units is None):
num_units = queries.get_shape().as_list[(- 1)]
Q = tf.layers.dense(queries, num_units, activation=tf.nn.relu)
K = tf.layers.dense(keys, num_u... |
class LeNet5(nn.Module):
def __init__(self, input_channels, imsize, output_dim):
super(LeNet5, self).__init__()
self.input_channels = input_channels
self.imsize = imsize
self.output_dim = output_dim
assert ((imsize % 2) == 0)
self.cnn = nn.Sequential(OrderedDict([('co... |
def eval_default_scale_factor(actf, lay):
if (actf in ('linear', 'relu')):
return 2.0
elif (actf in ('tanh', 'sigmoid')):
return (1.0 if (lay > 0) else 1.0)
elif (actf in ('sin', 'cos')):
return (2.0 if (lay > 0) else 2.0)
else:
return 1.0 |
class _data_matrix(spmatrix):
def __init__(self):
spmatrix.__init__(self)
def _get_dtype(self):
return self.data.dtype
def _set_dtype(self, newtype):
self.data.dtype = newtype
dtype = property(fget=_get_dtype, fset=_set_dtype)
def _deduped_data(self):
if hasattr(self,... |
def train(net, optimizer, trainloader):
net.train()
losses = AverageMeter()
torch.cuda.empty_cache()
loss_all = 0
for (data, labels, _) in tqdm(trainloader):
(data, labels) = (data.cuda(), labels.cuda())
optimizer.zero_grad()
(embedding, logits) = net(data, True)
(_, ... |
def rollout(env_name, num_steps=128, use_expert=False, seed=1):
env_fn = envs.create_fn(env_name)
env = env_fn(batch_size=1, episode_length=(num_steps * 2), auto_reset=False)
env.step = jax.jit(env.step)
if (not use_expert):
parametric_action_distribution = distribution.NormalTanhDistribution(ev... |
def test_contains():
proxy = tt.ObjectProxy([42])
assert (42 in proxy)
assert (int in tt.UsageTraceNode.from_proxy(proxy).children['__contains__'].arg_types[0]) |
def construct_beta_hats(opt_beta, sensitivity, eps_list, max_norm):
beta_hats = noise_reduc.gen_list(opt_beta, sensitivity, eps_list)
for i in range(len(beta_hats)):
beta_hats[i] = project.two_norm_project(beta_hats[i], max_norm)
return beta_hats |
class MyMultiSectionFactory(MultiSectionFactory):
def __init__(self, main_file_name, modules):
super(MyMultiSectionFactory, self).__init__()
self.main_file_name = main_file_name
self.main_sink = FileCodeSink(open(main_file_name, 'wt'))
self.header_name = 'ns3module.h'
header_... |
def test_gmm_wrong_descriptor_format_3():
with pytest.raises(DescriptorException):
learn_gmm([np.zeros((5, 10)), np.zeros((4, 10, 1))], n_modes=1) |
def new():
t_AND = '\\&'
t_ANDAND = '\\&\\&'
t_ANDEQ = '\\&='
t_BACKSLASH = '\\\\'
t_COLON = ':'
t_DIV = '\\/'
t_DIVEQ = '\\/='
t_DOT = '\\.'
t_DOTDIV = '\\./'
t_DOTDIVEQ = '\\./='
t_DOTEXP = '\\.\\^'
t_DOTMUL = '\\.\\*'
t_DOTMULEQ = '\\.\\*='
t_EQ = '='
t_EQE... |
def AUROC(open_set_preds, open_set_labels):
auroc = roc_auc_score(open_set_labels, open_set_preds)
return auroc |
def horizontally_flip_bbox(bbox: BoundingBox) -> BoundingBox:
return ((1 - (bbox[0] + bbox[2])), bbox[1], bbox[2], bbox[3]) |
class MatFile5Writer():
def __init__(self, file_stream, do_compression=False, unicode_strings=False, global_vars=None, long_field_names=False, oned_as='row'):
self.file_stream = file_stream
self.do_compression = do_compression
self.unicode_strings = unicode_strings
if global_vars:
... |
class ClusterGCN(GCN):
def __init__(self, layer_sizes, activations, generator, bias=True, dropout=0.0, kernel_initializer='glorot_uniform', kernel_regularizer=None, kernel_constraint=None, bias_initializer='zeros', bias_regularizer=None, bias_constraint=None):
warnings.warn('ClusterGCN has been replaced by ... |
def test_IndexedArray_RecordArray_NumpyArray():
a = ak.contents.indexedarray.IndexedArray(ak.index.Index(np.array([2, 2, 0, 1, 4, 5, 4])), ak.contents.recordarray.RecordArray([ak.contents.numpyarray.NumpyArray(np.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6]))], ['nest']))
assert (a.to_typetracer().form == a.form)
a... |
class AbstractLanguage(Parent):
def __init__(self, alphabet=None, category=None):
if isinstance(alphabet, (int, Integer)):
from sage.sets.integer_range import IntegerRange
alphabet = IntegerRange(1, (alphabet + 1))
elif ((alphabet == 'integers') or (alphabet == 'positive inte... |
class SimpleModel2(Model):
def __init__(self, output_dim=2, hidden_sizes=(4, 4), name=None):
super().__init__(name)
self._output_dim = output_dim
self._hidden_sizes = hidden_sizes
def _build(self, obs_input, name=None):
del name
action = mlp(obs_input, self._output_dim, s... |
class Parser(object):
def getParser(self):
parser = argparse.ArgumentParser()
parser.add_argument('--train', action='store_true')
parser.add_argument('--infer', action='store_true')
parser.add_argument('--verify', action='store_true')
parser.add_argument('--word_label', actio... |
def build_dataloader(dataset, samples_per_gpu, workers_per_gpu, num_gpus=1, dist=True, shuffle=True, seed=None, drop_last=False, pin_memory=True, persistent_workers=True, **kwargs):
(rank, world_size) = get_dist_info()
if dist:
sampler = DistributedSampler(dataset, world_size, rank, shuffle=shuffle)
... |
def _group_str(names: List[str]) -> str:
lcp = _longest_common_prefix_str(names)
rest = [x[len(lcp):] for x in names]
rest = (('{' + ','.join(rest)) + '}')
ret = (lcp + rest)
ret = ret.replace('bn_{beta,running_mean,running_var,gamma}', 'bn_*')
ret = ret.replace('bn_beta,bn_running_mean,bn_runni... |
def install_lightautoml():
os.system('curl -sSL | ../../bin/python -')
os.system('/root/.local/bin/poetry build')
os.system('../../bin/pip install ./dist/lightautoml-0.3.7.4-py3-none-any.whl') |
def test_custom_record():
behavior = {}
behavior[('__numba_typer__', 'Dummy')] = dummy_typer
behavior[('__numba_lower__', 'Dummy')] = dummy_lower
array = ak.highlevel.Array([{'x': 1.1, 'y': 100}, {'x': 2.2, 'y': 200}, {'x': 3.3, 'y': 300}], behavior=behavior, check_valid=True)
array.layout.parameter... |
class LatentWidget():
def __init__(self, viz):
self.viz = viz
self.latent = dnnlib.EasyDict(x=1, y=0, anim=False, speed=0.25)
self.latent_def = dnnlib.EasyDict(self.latent)
self.step_y = 100
def drag(self, dx, dy):
viz = self.viz
self.latent.x += ((dx / viz.font_s... |
class TestFFTShift(object):
def test_definition(self):
x = [0, 1, 2, 3, 4, (- 4), (- 3), (- 2), (- 1)]
y = [(- 4), (- 3), (- 2), (- 1), 0, 1, 2, 3, 4]
assert_array_almost_equal(fft.fftshift(x), y)
assert_array_almost_equal(fft.ifftshift(y), x)
x = [0, 1, 2, 3, 4, (- 5), (- 4)... |
def init_pos():
for (i, j) in ti.ndrange((N + 1), (N + 1)):
k = ((i * (N + 1)) + j)
pos[k] = (((ti.Vector([i, j]) / N) * 0.25) + ti.Vector([0.45, 0.45]))
vel[k] = ti.Vector([0, 0])
for i in range(NF):
(ia, ib, ic) = f2v[i]
(a, b, c) = (pos[ia], pos[ib], pos[ic])
B... |
class Bottleneck(nn.Module):
def __init__(self, tensor_shape):
super(Bottleneck, self).__init__()
(c, h, w) = tensor_shape
self.in_shape = tensor_shape
self.out_shape = tensor_shape
if config.refine_net_use_rnn:
rnn_cells = []
for i in range(config.ref... |
class CriterionDSN(nn.Module):
def __init__(self, ignore_index=255, use_weight=True, reduce=True):
super(CriterionDSN, self).__init__()
self.ignore_index = ignore_index
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=ignore_index, reduce=reduce)
if (not reduce):
p... |
def CremonaRichmondConfiguration():
from sage.graphs.generators.smallgraphs import TutteCoxeterGraph
from sage.combinat.designs.incidence_structures import IncidenceStructure
g = TutteCoxeterGraph()
H = IncidenceStructure([g.neighbors(v) for v in g.bipartite_sets()[0]])
H.relabel()
return H |
class CComplexBaseTypeNode(CBaseTypeNode):
child_attrs = ['base_type', 'declarator']
def analyse(self, env, could_be_name=False):
base = self.base_type.analyse(env, could_be_name)
(_, type) = self.declarator.analyse(base, env)
return type |
class TestParser(unittest.TestCase):
def test_unlabeled_unweighted(self):
self.stub_data_1 = 'stub_1.txt'
with open(self.stub_data_1, 'w') as text_file:
text_file.write('%stub\n1 3\n4 5\n0 2')
adjacency = parse.from_csv(self.stub_data_1)
self.assertTrue((adjacency.indices... |
def generate_categories(features, definition_df):
categories = {}
for feature in features:
if ('PUMA' in feature):
continue
coll_definition = definition_df[((definition_df[0] == 'VAL') & (definition_df[1] == feature))]
coll_type = coll_definition.iloc[0][2]
if (coll_t... |
class BaseResponse(object):
charset = 'utf-8'
default_status = 200
default_mimetype = 'text/plain'
implicit_sequence_conversion = True
autocorrect_location_header = True
automatically_set_content_length = True
max_cookie_size = 4093
def __init__(self, response=None, status=None, headers=... |
class PoseDataset(Dataset):
def __init__(self, pose: Pose):
super().__init__()
self.points = torch.tensor([p.flatten() for p in np.array(pose.body.data)], dtype=torch.float32)
self.confidence = torch.tensor([np.stack([c, c], axis=(- 1)).flatten() for c in np.array(pose.body.confidence)], dty... |
.mlir
def test_mlir_tasklet_no_entry():
A = dace.ndarray((1,), dace.int32)
B = dace.ndarray((1,), dace.int32)
C = dace.ndarray((1,), dace.int32)
A[:] = 5
B[:] = 2
C[:] = 15
with pytest.raises(SyntaxError):
mlir_tasklet_no_entry(A, B, C)
with pytest.raises(SyntaxError):
ml... |
class Logger():
def __init__(self, cfg):
self.path = path.join('..', 'experiment', cfg.save, cfg.ablation)
if cfg.reset:
if path.isdir(self.path):
response = input('Do you want to remove the existing directory? [Y/N]: ')
is_reset = (response.lower() == 'y'... |
def get_envs(variant):
from multiworld.core.image_env import ImageEnv
from railrl.envs.vae_wrappers import VAEWrappedEnv
from railrl.misc.asset_loader import load_local_or_remote_file
render = variant.get('render', False)
vae_path = variant.get('vae_path', None)
reproj_vae_path = variant.get('re... |
class NoFilter(FilterBase):
def __call__(self):
folder_path = (Path(self.root_folder) / self.folder_path)
assert folder_path.exists(), f'Folder {folder_path} does not exist'
files = sorted(list(folder_path.glob(self.extension)))
return files |
def test_export_sequence(exportable_test_case, tmp_path):
path = (tmp_path / 'generated.py')
exporter = export.PyTestChromosomeToAstVisitor()
exportable_test_case.accept(exporter)
exportable_test_case.accept(exporter)
export.save_module_to_file(exporter.to_module(), path)
assert (path.read_text(... |
class SimpleDicomReader(object):
def __init__(self, file):
if isinstance(file, str):
self._filename = file
self._file = open(file, 'rb')
else:
self._filename = '<unknown file>'
self._file = file
self._pixel_data_loc = None
self.is_impli... |
def convert_module_to_f16(l):
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.weight.data.half()
if (l.bias is not None):
l.bias.data = l.bias.data.half() |
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer |
def GenusSix():
L = ['014', '018', '023', '027', '036', '049', '056', '05b', '07a', '08a', '09b', '125', '126', '137', '139', '147', '15a', '16b', '18b', '19a', '23b', '248', '24a', '258', '269', '279', '2ab', '345', '34b', '35a', '367', '389', '38a', '459', '46a', '46b', '478', '568', '579', '57b', '67a', '689', '... |
class MinMaxResize():
def __init__(self, shorter=800, longer=1333):
self.min = shorter
self.max = longer
def __call__(self, x):
(w, h) = x.size
scale = (self.min / min(w, h))
if (h < w):
(newh, neww) = (self.min, (scale * w))
else:
(newh, n... |
def group_identifier(tlist):
def _consume_cycle(tl, i):
x = itertools.cycle(((lambda y: (y.match(T.Punctuation, '.') or (y.ttype in (T.Operator, T.Wildcard, T.Name)) or isinstance(y, sql.SquareBrackets))), (lambda y: ((y.ttype in (T.String.Symbol, T.Name, T.Wildcard, T.Literal.String.Single, T.Literal.Numbe... |
class TestMLPModel():
.parametrize('input_dim, output_dim, hidden_sizes', [(5, 1, (1,)), (5, 1, (2,)), (5, 2, (3,)), (5, 2, (1, 1)), (5, 3, (2, 2))])
def test_output_values(self, input_dim, output_dim, hidden_sizes):
input_val = torch.ones([1, input_dim], dtype=torch.float32)
module_with_nonline... |
class MetaDictSetting(Setting):
def __init__(self, meta_dict: dict, mandatory_fields: list=[]):
self.meta_dict = meta_dict
self.mandatory_fields = mandatory_fields |
def t5_3b_tied_lmheads_64_4_8p_bw12_async_squad1_mpipe():
return dict(model_type='t5_stateless', model_name_or_path='t5-3b', do_lower_case=False, output_past=False, output_attentions=False, output_hidden_states=False, do_resize_token_embedding=True, explicitly_set_dict={'output_only': True, 'output_attentions': Fal... |
def get_type_line(source):
lines = source.split('\n')
def strip_comment(line):
return line[:(line.index('#') if ('#' in line) else None)]
i = 0
while (not _def_end_regex.match(strip_comment(lines[i]))):
i += 1
i += 1
type_line = lines[i].strip()
if (not type_line.startswith('... |
def lift_to_sl2_Ok(N, c, d):
k = N.number_field()
if (c.is_zero() and d.is_zero()):
raise ValueError(('Cannot lift (%s, %s) to an element of Sl2(Ok).' % (c, d)))
if (not N.is_coprime(k.ideal(c, d))):
raise ValueError(('<%s> + <%s> and the %s are not coprime.' % (c, d, N)))
if ((c - 1) in... |
class DictAction(Action):
def _parse_int_float_bool(val):
try:
return int(val)
except ValueError:
pass
try:
return float(val)
except ValueError:
pass
if (val.lower() in ['true', 'false']):
return (True if (val.lower(... |
.parametrize('ratio, y, type, err_msg', [(0.5, binary_target, 'clean-sampling', "'clean-sampling' methods do let the user specify the sampling ratio"), (0.1, np.array((([0] * 10) + ([1] * 20))), 'over-sampling', 'remove samples from the minority class while trying to generate new'), (0.1, np.array((([0] * 10) + ([1] * ... |
.parametrize('dim_context, action_noise, reward_noise, min_action_value, max_action_value, random_state, err, description', invalid_input_of_init)
def test_synthetic_continuous_init_using_invalid_inputs(dim_context, action_noise, reward_noise, min_action_value, max_action_value, random_state, err, description):
wit... |
def unpickler(zone, utcoffset=None, dstoffset=None, tzname=None):
tz = pytz.timezone(zone)
if (utcoffset is None):
return tz
utcoffset = memorized_timedelta(utcoffset)
dstoffset = memorized_timedelta(dstoffset)
try:
return tz._tzinfos[(utcoffset, dstoffset, tzname)]
except KeyErr... |
def evaluate_interaction_sample(sample, model, max_generation_length, name='', gold_forcing=False, metrics=None, total_num=(- 1), database_username='', database_password='', database_timeout=0, use_predicted_queries=False, write_results=False, use_gpu=False, compute_metrics=False, bool_progressbar=True):
prediction... |
def get_map(num_classes=16):
if (num_classes == 16):
map_synthiaId_to_trainId = {3: 0, 4: 1, 2: 2, 21: 3, 5: 4, 7: 5, 15: 6, 9: 7, 6: 8, 1: 9, 10: 10, 17: 11, 8: 12, 19: 13, 12: 14, 11: 15}
else:
raise NotImplementedError(f'Not yet supported {num_classes} classes')
return map_synthiaId_to_tr... |
class D(nn.Module):
class Maxout(nn.Module):
def __init__(self, d_in, d_out, pool_size=5):
super().__init__()
(self.d_in, self.d_out, self.pool_size) = (d_in, d_out, pool_size)
self.lin = nn.Linear(d_in, (d_out * pool_size))
def forward(self, inputs):
... |
def load_model_config(config_f):
print(config_f)
with open(config_f, 'r') as f:
config = json.loads(f.read())
print(config)
return config |
def LF_non(x):
rgx = re.compile('(non)[-]*', re.I)
is_negated = (rgx.search(get_left_span(x.pain, window=2).text) is not None)
return (NEGATIVE if is_negated else ABSTAIN) |
class SyntheticProjectCheckout(ProjectCheckout):
def __init__(self, name: str, version: str, data_path: str, base_path: str):
super().__init__('-synthetic-', join(base_path, name), version)
self.name = name
self.version = version
self.data_path = data_path
def exists(self) -> boo... |
def _get_data(modality, output_folder_name, in_memory_directory):
data = {}
if output_folder_name:
for filename in os.listdir(output_folder_name):
if filename.endswith('.csv'):
table_name = Path(filename).stem
data_path = os.path.join(output_folder_name, filen... |
def add_roi_Xconv1fc_head(model, blob_in, dim_in, spatial_scale):
hidden_dim = cfg.FAST_RCNN.CONV_HEAD_DIM
roi_size = cfg.FAST_RCNN.ROI_XFORM_RESOLUTION
roi_feat = model.RoIFeatureTransform(blob_in, 'roi_feat', blob_rois='rois', method=cfg.FAST_RCNN.ROI_XFORM_METHOD, resolution=roi_size, sampling_ratio=cfg.... |
def get_elapsed_time():
if (os.name == 'nt'):
raise NotImplementedError('cannot use get_elapsed_time() on Windows')
return sum(os.times()[:4]) |
_model
def tf_efficientnet_b7_ns(pretrained=False, **kwargs):
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet('tf_efficientnet_b7_ns', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs)
return model |
class EmissionModel(nn.Module):
def __init__(self):
super().__init__()
self.distribution_function = tdist.normal.Normal
def sample(self, means, stds, sampling_temp=1.0):
return (self.distribution_function(means, (stds * sampling_temp)).sample() if (sampling_temp > 0) else means)
def ... |
(scope='module')
def source_2bin_2channel_coupledhistosys():
with open('validation/data/2bin_2channel_coupledhisto.json', encoding='utf-8') as read_json:
return json.load(read_json) |
class IntBlock(nn.Module):
def __init__(self, body, shortcut=None):
super(IntBlock, self).__init__()
self.body = body
self.residual_connection = (shortcut is None)
if (not self.residual_connection):
self.shortcut = shortcut
self.post_relu = nn.ReLU(inplace=True)
... |
def dataio_prepare(hparams):
logging.info('generating datasets...')
datasets = load_dataset('text', data_files={'train': hparams['lm_train_data'], 'valid': hparams['lm_valid_data'], 'test': hparams['lm_test_data']})
train_data = sb.dataio.dataset.DynamicItemDataset.from_arrow_dataset(datasets['train'])
... |
class TestDeriavtives(TestCase):
def setUp(self):
self.model = pin.buildSampleModelHumanoidRandom()
self.data = self.model.createData()
qmax = np.full((self.model.nq, 1), np.pi)
self.q = pin.randomConfiguration(self.model, (- qmax), qmax)
self.v = np.random.rand(self.model.nv... |
def build_processors(processors_config: DictConfig, registry_key: str=None, *args, **kwargs):
from mmf.datasets.processors.processors import Processor
processor_dict = {}
for (processor_key, processor_params) in processors_config.items():
if (not processor_params):
continue
proce... |
def hear_scene_trainvaltest(target_dir: str, cache_dir: str, dataset_root: str, get_path_only: bool=False):
target_dir = Path(target_dir)
resample_hear_corpus(dataset_root, target_sr=16000)
dataset_root = Path(dataset_root)
wav_root: Path = (dataset_root / '16000')
train_csv = (target_dir / 'train.c... |
def basic_blocks(dim, index, layers, pool_size=3, mlp_ratio=4.0, act_layer=nn.GELU, norm_layer=GroupNorm, drop_rate=0.0, drop_path_rate=0.0, use_layer_scale=True, layer_scale_init_value=1e-05):
blocks = []
for block_idx in range(layers[index]):
block_dpr = ((drop_path_rate * (block_idx + sum(layers[:ind... |
def calc_map_mesh(testfile, predfile):
with open(testfile, 'r') as ftest, open(predfile, 'r') as fpred:
data = []
pred = []
for line in ftest:
data.append(line.strip().split('\t'))
for line in fpred:
pred.append(float(line.strip()))
oneq = []
p... |
def apply_statistics_correction(transformed_graph: Graph, representative_data_gen: Callable, core_config: CoreConfig, fw_info: FrameworkInfo, fw_impl: FrameworkImplementation, tb_w: TensorboardWriter=None) -> Graph:
if core_config.quantization_config.weights_second_moment_correction:
transformed_graph = app... |
def test():
one = ak.highlevel.Array([[{'x': 1}], [], [{'x': 2}]], with_name='One')
two = ak.highlevel.Array([[{'x': 1.1}], [], [{'x': 2.2}]], with_name='Two')
assert (str(ak.operations.with_name(ak.operations.concatenate([one, two], axis=1), 'All').type) == '3 * var * All[x: float64]')
assert (str(ak.o... |
class AdaptiveAggregateAlarms(AggregateAlarms):
threshold_class = AdaptiveThreshold
def __init__(self, alm_threshold: float=None, abs_score=True, min_alm_in_window: int=2, alm_window_minutes: float=60, alm_suppress_minutes: float=120, bin_sz: int=10, default_hist_gap_thres: float=1.2):
super().__init__(... |
class consume():
def __init__(self, stream: Deque[T], processing_elements: int=1, condition: Optional[Callable[([], bool)]]=None):
self.stream = stream
self.pes = processing_elements
self.condition = (condition or (lambda : (len(stream) > 0)))
def __iter__(self) -> Generator[(T, None, No... |
def copy_conllu(tokenizer_dir, mwt_dir, short_name, dataset, particle):
input_conllu_tokenizer = f'{tokenizer_dir}/{short_name}.{dataset}.gold.conllu'
input_conllu_mwt = f'{mwt_dir}/{short_name}.{dataset}.{particle}.conllu'
shutil.copyfile(input_conllu_tokenizer, input_conllu_mwt) |
class BackendIPythonCommandline(BackendIPython):
def default_preferences(self):
from sage.repl.rich_output.preferences import DisplayPreferences
return DisplayPreferences(supplemental_plot='never')
def _repr_(self):
return 'IPython command line'
def supported_output(self):
re... |
def register_Ns3Object_methods(root_module, cls):
cls.add_constructor([])
cls.add_method('AggregateObject', 'void', [param('ns3::Ptr< ns3::Object >', 'other')])
cls.add_method('Dispose', 'void', [])
cls.add_method('GetAggregateIterator', 'ns3::Object::AggregateIterator', [], is_const=True)
cls.add_m... |
class FreeGradedModuleMorphism(FPModuleMorphism):
def __init__(self, parent, values):
from .free_homspace import FreeGradedModuleHomspace
if (not isinstance(parent, FreeGradedModuleHomspace)):
raise TypeError(('the parent (%s) must be a f.p. free module homset' % parent))
self._f... |
def upsample_bilinear(input, size=None, scale_factor=None):
warnings.warn('nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.')
return interpolate(input, size, scale_factor, mode='bilinear', align_corners=True) |
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