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class RoFormerConverter(Converter):
def converted(self) -> Tokenizer:
from .models.roformer.tokenization_utils import JiebaPreTokenizer
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
strip_accents = Fals... |
class Writer():
def __init__(self, width=None, height=None, size=None, greyscale=Default, alpha=False, bitdepth=8, palette=None, transparent=None, background=None, gamma=None, compression=None, interlace=False, planes=None, colormap=None, maxval=None, chunk_limit=(2 ** 20), x_pixels_per_unit=None, y_pixels_per_unit... |
def add_T_label(img, label, bbox, draw_bg=True, text_bg_color=(255, 255, 255), text_color=(0, 0, 0)):
text_width = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)[0][0]
text_height = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)[0][1]
x_center = ((bbox[0] + bbox[2]) // 2)
y_top = (bbox[1... |
def get_word_idx(context, wordss, idx):
spanss = get_2d_spans(context, wordss)
idx = spanss[idx[0]][idx[1]][0]
return idx |
def main(argv=None, **kw):
from setuptools import setup
from setuptools.dist import Distribution
class DistributionWithoutHelpCommands(Distribution):
common_usage = ''
def _show_help(self, *args, **kw):
with _patch_usage():
Distribution._show_help(self, *args, **k... |
.parametrize('device', ['cpu', 'cuda'])
.parametrize('seed', [[[(- 0.5), 0, 0.5], [1, (- 2), 1]], [[3, 0, 1, 2, 0], [0, (- 1), 0]], [2, 3]])
def test_compatibility(device, seed, T=100, D=2):
mlpg = diffsptk.MaximumLikelihoodParameterGeneration(T, seed=seed)
if U.is_array(seed[0]):
opt = ' '.join([('-d '... |
.parametrize('indices', [None, [1, 3]])
def test_check_method_params(indices):
X = np.random.randn(4, 2)
_params = {'list': [1, 2, 3, 4], 'array': np.array([1, 2, 3, 4]), 'sparse-col': sp.csc_matrix([1, 2, 3, 4]).T, 'sparse-row': sp.csc_matrix([1, 2, 3, 4]), 'scalar-int': 1, 'scalar-str': 'xxx', 'None': None}
... |
class TwoAlignedDataset():
def initialize(self, opt):
assert (opt.isTrain == True)
opt1 = opt
opt1.phase = opt.phase1
opt1.dataset_model = 'aligned'
self.dataset1 = AlignedDataset()
self.dataset1.initialize(opt1)
opt2 = opt
opt2.phase = opt.phase2
... |
def test_pixel_size_setter():
persimgr = PersistenceImager(birth_range=(0, 1), pers_range=(0, 2), pixel_size=1)
persimgr.pixel_size = 0.75
np.testing.assert_equal(persimgr.pixel_size, 0.75)
np.testing.assert_equal(persimgr._pixel_size, 0.75)
np.testing.assert_equal(persimgr.birth_range, ((- 0.25), 1... |
def minimal_grid(x, y, tol=1e-06, error_scale=1.0, y_reference=None):
import numpy as np
from scipy.interpolate import CubicSpline as spline
from scipy.signal import find_peaks
deg = 3
if (y_reference is None):
y_reference = y
error_scale = np.asarray(error_scale)
if (np.ndim(error_s... |
def do_learning(extractor, model, optimizer, loader, k_step, device):
model.train()
acc_list = []
loss_list = []
for (idx, (img, mask, lbl_1, lbl_2, lbl_1_oh, lbl_2_oh)) in enumerate(loader):
if (idx >= k_step):
break
if (mask.sum() == 0):
continue
img = i... |
class TestConsumeOp(unittest.TestCase):
def test_jit_consume_op(self):
iters = 6
def foo(x):
for i in range(iters):
result = torch.ops.operator_benchmark._consume(torch.sum(x))
return result
r = torch.jit.trace(foo, torch.rand(2, 2))
graph = st... |
def to_float(x, name='ToFloat'):
try:
return tf.to_float(x, name)
except AttributeError:
return tf.compat.v1.to_float(x, name) |
def test_grad_add_check_numerics_ops():
with make_scope() as session:
x = tf.Variable(initial_value=0.0, name='x')
session.run(x.initializer)
y = (1.0 / x)
grad_x = tf.gradients(y, x)[0]
print('grad_x:', grad_x.eval())
assert_equal(str(float('-inf')), '-inf')
... |
def get_aircraft_datasets(train_transform, test_transform, train_classes=range(60), open_set_classes=range(60, 100), balance_open_set_eval=False, split_train_val=True, seed=0):
np.random.seed(seed)
train_dataset_whole = FGVCAircraft(root=aircraft_root, transform=train_transform, split='trainval')
train_data... |
def import_module_error_class(module_name):
def decorate(cls):
def import_error_init(*args, **kwargs):
raise ImportError(f'Please install {module_name} to use {cls.__name__}.')
cls.__init__ = MethodType(import_error_init, cls)
return cls
return decorate |
class HybridQA_Dataset():
def __init__(self, config):
print('Loading HybridQA ')
self.table_dir = config.table_dir
self.text_dir = config.text_dir
self.table_id_list = []
g = os.walk(self.table_dir)
for (_, _, file_list) in g:
for file_name in file_list:
... |
def generate_ChangePoint(inter_prob, intra_prob, alpha):
cps = [15, 30, 60, 75, 90, 105, 135]
fname = (((((('ChangePoint_' + str(inter_prob)) + '_') + str(intra_prob)) + '_') + str(alpha)) + '.txt')
cps_sizes = []
cps_probs = []
sizes_1 = [250, 250]
probs_1 = construct_SBM_block(sizes_1, inter_p... |
class DefaultDict(dict):
def __getitem__(self, item):
try:
return dict.__getitem__(self, item)
except KeyError:
value = self[item] = type(self)()
return value |
def _linear_normalize(weights):
weights = torch.max(weights, torch.zeros_like(weights))
if (torch.sum(weights) > 1e-08):
return (weights / torch.sum(weights))
return torch.zeros_like(weights) |
class Trie():
def __init__(self, eos):
self.root = TreeNode()
self.eos = eos
def insert(self, word):
cur = self.root
for c in word:
cur = cur.child[c]
def get_next_layer(self, word):
cur = self.root
for c in word:
cur = cur.child.get(c)... |
(config_path='../hydra_config', config_name='black_box_opt')
def main(config):
random.seed(None)
log_config = flatten_config(OmegaConf.to_container(config, resolve=True), sep='/')
log_config = {'/'.join(('config', key)): val for (key, val) in log_config.items()}
wandb.login(host=config.wandb_host)
w... |
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) |
class OrderedEnqueuer(SequenceEnqueuer):
def __init__(self, sequence, use_multiprocessing=False, shuffle=False):
self.sequence = sequence
self.use_multiprocessing = use_multiprocessing
self.shuffle = shuffle
self.workers = 0
self.executor = None
self.queue = None
... |
(scope='module')
def nlp_pipeline():
nlp = stanza.Pipeline(dir=TEST_MODELS_DIR, lang='en')
return nlp |
class ActionNormWrapper(gym.Wrapper):
def __init__(self, env):
super().__init__(env)
assert isinstance(env.action_space, gym.spaces.Dict), env.action_space
ac_space = []
self._low = {}
self._high = {}
for (k, space) in env.action_space.spaces.items():
if i... |
def load_data():
X_df = pd.read_csv(('/projects/leelab2/data/AD_DATA/Nicasia/processed' + '/PCG_normalized/no_covar_correction/MSBB_RNA.tsv'), sep='\t')
y_df = pd.read_csv(('/projects/leelab2/data/AD_DATA/Nicasia/processed' + '/samples_neuropath_prenorm/MSBB_RNA.tsv'), sep='\t')
X_df = X_df.T
X_df.colum... |
class NumericTestCase(TorchTestCase):
def testNumericBatchNorm(self):
a = torch.rand(16, 10)
bn = nn.BatchNorm2d(10, momentum=1, eps=1e-05, affine=False)
bn.train()
a_var1 = Variable(a, requires_grad=True)
b_var1 = bn(a_var1)
loss1 = b_var1.sum()
loss1.backwar... |
class TestCharSvm(unittest.TestCase):
def test_charsvm(self):
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
run_experiment(predictor_fn=(lambda : CharSvmPredictor()), output_dir=temp_path, n_examples=4, n_trials=1, dataset='gnad10', config=None, n_test_ex... |
class Triples():
def __init__(self, ranking, seed=12345):
random.seed(seed)
self.seed = seed
ranking = Ranking.cast(ranking)
self.ranking_provenance = ranking.provenance()
self.qid2rankings = ranking.todict()
def create(self, positives, depth):
assert all(((len(x)... |
def generate_jsfile(dirpath, name, out_path):
tiuProcessor = TIU(dirpath)
tiu_instance = tiuProcessor.process_file()
gdmaProcessor = DMA(dirpath, 'GDMA')
gdma_instances = gdmaProcessor.process_file()
sdmaProcessor = DMA(dirpath, 'SDMA')
sdma_instances = sdmaProcessor.process_file()
cdmaProce... |
def _recalculateCenters(y, balancedCluster, k):
Centers = []
kAux = 0
while (kAux < k):
vectorAux = np.zeros(len(y))
for i in range(0, len(balancedCluster)):
if (int(kAux) == int(balancedCluster[i])):
for j in range(0, len(y)):
vectorAux[j] += ... |
def convert_vi_vsfc(paths, dataset_name, *args):
in_directory = os.path.join(paths['SENTIMENT_BASE'], 'vietnamese', '_UIT-VSFC')
out_directory = paths['SENTIMENT_DATA_DIR']
process_vsfc_vietnamese.main(in_directory, out_directory, dataset_name) |
_params({'y_true': ['array-like'], 'y_pred': ['array-like'], 'sample_weight': ['array-like', None]}, prefer_skip_nested_validation=True)
def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
(_, y_true, y_pred) = _check_targets(y_true, y_pred)
if (sample_weight is not None):
sam... |
def _get_boolean_value(value):
if (value.lower() == TRUE):
return True
else:
return False |
def thwart_lemma_3_5(k, n, m, a, b, c, d=0, complement=False, explain_construction=False):
from sage.arith.misc import is_prime_power
from sage.rings.finite_rings.finite_field_constructor import FiniteField as GF
if complement:
(a, b, c) = ((n - a), (n - b), (n - c))
if explain_construction:
... |
def _called_with_cfg(*args, **kwargs):
if (len(args) and isinstance(args[0], _CfgNode)):
return True
if isinstance(kwargs.pop('cfg', None), _CfgNode):
return True
return False |
def test_wrap_experiment_invalid_options():
prefix = 'wrap_exp_invalid_options'
exp_path = pathlib.Path(os.getcwd(), 'data/local', prefix)
_hard_rmtree(exp_path)
logdir = 'data/local/wrap_exp_invalid_options/test_exp'
_experiment(prefix=prefix)
def test_exp(ctxt):
del ctxt
with pytes... |
class ProcessContext():
def __init__(self, processes, error_queues):
_python_version_check()
self.error_queues = error_queues
self.processes = processes
self.sentinels = {process.sentinel: index for (index, process) in enumerate(processes)}
def pids(self):
return [int(pro... |
def yiq_to_rgb(y, i, q):
r = ((y + (0. * i)) + (0. * q))
g = ((y - (0. * i)) - (0. * q))
b = ((y - (1. * i)) + (1. * q))
if (r < 0.0):
r = 0.0
if (g < 0.0):
g = 0.0
if (b < 0.0):
b = 0.0
if (r > 1.0):
r = 1.0
if (g > 1.0):
g = 1.0
if (b > 1.0):... |
def get_project_path(ExpID):
full_path = glob.glob(('Experiments/*%s*' % ExpID))
assert (len(full_path) == 1), 'There should be only ONE folder with <ExpID> in its name'
return full_path[0] |
def to_type(handle: int) -> Object:
t = sim.simGetObjectType(handle)
if (t == sim.sim_object_shape_type):
return Shape(handle)
elif (t == sim.sim_object_dummy_type):
return Dummy(handle)
elif (t == sim.sim_object_path_type):
return CartesianPath(handle)
elif (t == sim.sim_obj... |
class BUDUDrp1mat(SpectralMatrix):
def assemble(self, method):
(test, trial) = (self.testfunction, self.trialfunction)
assert isinstance(test[0], UD)
assert isinstance(trial[0], UD)
assert (test[0].quad == 'LG')
k = np.arange((test[0].N - 1))
d = {0: ((2 * k) + 2)}
... |
def main(args=None):
args = parse_args(args=args)
utils.set_random_seed(args['seed'])
logger.info('Running tagger in {} mode'.format(args['mode']))
if (args['mode'] == 'train'):
train(args)
else:
evaluate(args) |
(name='random_basis_func_cls', params=[RandomFourierFeatures, RandomFourierFeaturesCosine])
def _random_basis_func_cls_fixture(request):
return request.param |
def create_model_single_conv2d(input_shape):
inputs = Input(shape=input_shape)
outputs = Conv2D(2, 3)(inputs)
return keras.Model(inputs=inputs, outputs=outputs) |
class Registry(Printable):
__objects: Dict[(Tuple[(str, str, str)], Registrable)]
def __init__(self):
self.__objects = {}
def register(self, scope: str, type: str, name: str, obj: Registrable) -> Registrable:
assert ((scope, type, name) not in self.__objects), 'object with name {} already ex... |
.parametrize('valid_index', [[[0, 1]]])
def test_find_lambda_control_star_output(valid_index: List[List[int]]) -> None:
assert find_lambda_control_star(r_hat, valid_index, lambdas) |
class CC3MDataset(BaseDataset):
def __init__(self, *args, split='', **kwargs):
assert (split in ['train', 'val', 'test'])
self.split = split
self.metadata = None
self._load_metadata()
if (split == 'train'):
names = ['cc3m_train']
elif (split == 'val'):
... |
def register_Ns3EpcUeNas_methods(root_module, cls):
cls.add_constructor([param('ns3::EpcUeNas const &', 'arg0')])
cls.add_constructor([])
cls.add_method('ActivateEpsBearer', 'void', [param('ns3::EpsBearer', 'bearer'), param('ns3::Ptr< ns3::EpcTft >', 'tft')])
cls.add_method('Connect', 'void', [])
cl... |
class FullyShardedDataParallel(FSDP):
def __init__(self, *args, use_sharded_state: bool=False, **kwargs):
if (not has_FSDP):
raise ImportError('Cannot find FullyShardedDataParallel. Please install fairscale with: pip install fairscale')
super().__init__(*args, **kwargs)
self.use_... |
def get_chinese_rouge_function(rouge_type: str) -> Callable[([str, str], float)]:
char_tokenizer = ChineseTokenizer()
scorer = rouge_scorer.RougeScorer([rouge_type], use_stemmer=True, tokenizer=char_tokenizer)
return partial(rouge_score, scorer=scorer, rouge_type=rouge_type) |
def _check_lat_long(val: Any, clean: bool) -> Any:
if (val in NULL_VALUES):
return ((((None,) * 8) + (0,)) if clean else False)
mch = re.match(LAT_LONG_PATTERN, re.sub("''", '"', str(val)))
if (not mch):
return ((((None,) * 8) + (1,)) if clean else False)
if ((not mch.group('deg')) or (n... |
def remove_output_labels(s) -> str:
label = re.compile('^o+[0-9]+ (=|:) |^ *')
lines = s.split('\n')
matches = [label.match(l) for l in lines if l]
if (not matches):
return s
n = min(((m.end() - m.start()) for m in matches))
return '\n'.join((l[n:] for l in lines)) |
class WidthSelFunc(Protocol):
def __call__(self, table: Table, attrs: List[str], centers: List[Any], params: Dict[(str, Any)]) -> Query:
... |
class DefaultWorker(Worker):
def __init__(self, *, seed, max_path_length, worker_number):
super().__init__(seed=seed, max_path_length=max_path_length, worker_number=worker_number)
self.agent = None
self.env = None
self._observations = []
self._last_observations = []
s... |
def _save(im, fp, filename):
try:
rawmode = RAWMODE[im.mode]
except KeyError:
raise OSError(('cannot write mode %s as JPEG' % im.mode))
info = im.encoderinfo
dpi = [round(x) for x in info.get('dpi', (0, 0))]
quality = info.get('quality', (- 1))
subsampling = info.get('subsampling... |
def test():
form = ak.forms.from_dict({'class': 'RecordArray', 'fields': ['muon', 'jet'], 'contents': [{'class': 'ListOffsetArray', 'offsets': 'i64', 'content': {'class': 'RecordArray', 'fields': ['pt', 'eta', 'phi', 'crossref'], 'contents': [{'class': 'NumpyArray', 'primitive': 'int64', 'inner_shape': [], 'paramet... |
class SpectralNormalization(tf.keras.layers.Wrapper):
def __init__(self, layer, power_iterations=1, **kwargs):
super(SpectralNormalization, self).__init__(layer, **kwargs)
if (power_iterations <= 0):
raise ValueError('`power_iterations` should be greater than zero, got `power_iterations=... |
def vgg_a(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.5, spatial_squeeze=True, scope='vgg_a', fc_conv_padding='VALID', global_pool=False):
with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
end_points_collection = (sc.original_name_scope + '_end_points')
with slim.arg_scope(... |
class RefBox():
"Ray doesn't dereference ObjectRefs if they're nested in another object. So we use this to take advantage of that.\n
ref: ray.ObjectRef |
()
('model_path', type=str)
('dataset_name', type=str)
('--im-size', default=None, type=int)
('--multiscale/--singlescale', default=False, is_flag=True)
('--blend/--no-blend', default=True, is_flag=True)
('--window-size', default=None, type=int)
('--window-stride', default=None, type=int)
('--window-batch-size', defaul... |
(name='versions')
def _versions() -> list[dict[(str, Any)]]:
with open(VERSIONS) as f:
return json.load(f) |
def test_bbox_mask():
cfg = dict(img_shape=(256, 256), max_bbox_shape=100, max_bbox_delta=10, min_margin=10)
bbox = random_bbox(**cfg)
mask_bbox = bbox2mask(cfg['img_shape'], bbox)
assert (mask_bbox.shape == (256, 256, 1))
zero_area = np.sum((mask_bbox == 0).astype(np.uint8))
ones_area = np.sum(... |
class KLDivTeacherList(nn.Module):
def __init__(self):
super(KLDivTeacherList, self).__init__()
self.kl = torch.nn.KLDivLoss(reduction='batchmean')
def forward(self, scores, labels):
loss = self.kl(scores.softmax((- 1)), labels.softmax((- 1)))
return loss |
def test_NumpyArray():
a = ak.contents.numpyarray.NumpyArray(np.array([0.0, 1.1, 2.2, 3.3], dtype=np.float64))
assert (a.to_typetracer().form == a.to_typetracer(forget_length=True).form)
assert is_unknown_length(a.to_typetracer(forget_length=True).length)
b = ak.contents.numpyarray.NumpyArray(np.arange(... |
(scope='module')
def test_data_xy_dict(test_data_xy):
return {'x': test_data_xy[0], 'y': test_data_xy[1]} |
def add_bootstrap_config(cfg: CN):
_C = cfg
_C.BOOTSTRAP_DATASETS = []
_C.BOOTSTRAP_MODEL = CN()
_C.BOOTSTRAP_MODEL.WEIGHTS = ''
_C.BOOTSTRAP_MODEL.DEVICE = 'cuda' |
class SPC(Model):
def __init__(self, cfg, emb_dim):
super().__init__(name=cfg['name'])
cfg['num_inputs'] = emb_dim
self.minion = minion_maker(cfg)
self.loss = self.minion.loss
self.loss_weight = self.minion.loss_weight
def forward(self, x, alpha=1, device=None):
y... |
def get_ann_ids(anno_path):
ids = list()
for p in anno_path.iterdir():
ids.append(p.name.split('.')[0])
return ids |
def matrix_centralizer_cardinalities_length_two(n, q=None, selftranspose=False, invertible=False):
if (q is None):
q = FractionField(QQ['q']).gen()
for tau in SimilarityClassTypes(n):
for pair in ext_orbit_centralizers(tau, q=q, selftranspose=selftranspose):
(yield (((q ** tau.centra... |
def annotate_hop_ids(hop):
samples = mongo.get_sample(train=False, limit=limit)
count = 0
for doc in samples:
(e, p) = doc[hop]
e_ids = []
for uri in e:
try:
e_ids.append(e_index.look_up_by_uri(uri)[0]['_source']['id'])
except:
... |
class DivisorGroup_curve(DivisorGroup_generic):
def _element_constructor_(self, x, check=True, reduce=True):
if isinstance(x, Divisor_curve):
P = x.parent()
if (P is self):
return x
elif (P == self):
return Divisor_curve(x._data, check=Fals... |
class VenmoAddMoney(VirtualFunctionTool):
name = 'VenmoAddMoney'
summary = "Add money to the User's Venmo balance from a linked bank account."
parameters: List[ArgParameter] = [{'name': 'amount', 'type': 'number', 'description': 'The amount of money to add, must be positive.', 'required': True}, {'name': 'a... |
class ClassGroup(AbelianGroupWithValues_class):
Element = FractionalIdealClass
def __init__(self, gens_orders, names, number_field, gens, proof=True):
AbelianGroupWithValues_class.__init__(self, gens_orders, names, gens, values_group=number_field.ideal_monoid())
self._proof_flag = proof
... |
class AttrDict(dict):
def __init__(self, init={}):
dict.__init__(self, init)
def __getitem__(self, name):
return super(AttrDict, self).__getitem__(name.lower())
def __setitem__(self, key, value):
return super(AttrDict, self).__setitem__(key.lower(), value)
__getattr__ = __getitem... |
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default='cfgs/kitti_models/ptt_best.yaml', help='specify the config for demo')
parser.add_argument('--data_path', type=str, default=None, help='specify the point cloud data file or dire... |
class HubregtsenEncodingCircuit(EncodingCircuitBase):
def __init__(self, num_qubits: int, num_features: int, num_layers: int=1, closed: bool=True, final_encoding=False) -> None:
super().__init__(num_qubits, num_features)
self.num_layers = num_layers
self.closed = closed
self.final_en... |
def adjust_learning_rate(optimizer, epoch, gammas, schedule):
lr = args.learning_rate
assert (len(gammas) == len(schedule)), 'length of gammas and schedule should be equal'
for (gamma, step) in zip(gammas, schedule):
if (epoch >= step):
lr = (lr * gamma)
else:
break
... |
def test_mmhash3_bytes():
assert (murmurhash3_32(b'foo', 0) == (- ))
assert (murmurhash3_32(b'foo', 42) == (- ))
assert (murmurhash3_32(b'foo', 0, positive=True) == )
assert (murmurhash3_32(b'foo', 42, positive=True) == ) |
class Swish_DenseNet(nn.Module):
def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=100):
super(Swish_DenseNet, self).__init__()
self.growth_rate = growth_rate
num_planes = (2 * growth_rate)
self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bi... |
def short_path(path, cwd=None):
if (not isinstance(path, str)):
return path
if (cwd is None):
cwd = os.getcwd()
abspath = os.path.abspath(path)
relpath = os.path.relpath(path, cwd)
if (len(abspath) <= len(relpath)):
return abspath
return relpath |
def test_orthogonal_procrustes_ndim_too_small():
np.random.seed(1234)
A = np.random.randn(3)
B = np.random.randn(3)
assert_raises(ValueError, orthogonal_procrustes, A, B) |
def resnet152_csn_ir(**kwargs):
model = ResNet(Bottleneck_depthwise_ir, [3, 8, 36, 3], **kwargs)
model.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False)
return model |
(autouse=True)
def add_dataset(doctest_namespace):
columns = ['query_id', 'item_id', 'timestamp']
data = [(1, 1, '01-01-2020'), (1, 2, '02-01-2020'), (1, 3, '03-01-2020'), (1, 4, '04-01-2020'), (1, 5, '05-01-2020'), (2, 1, '06-01-2020'), (2, 2, '07-01-2020'), (2, 3, '08-01-2020'), (2, 9, '09-01-2020'), (2, 10, ... |
class Queue(deque, object):
def seeleft(self):
if self:
return self[0]
else:
return None |
def batch_step(pbar, net, image, optimizers, label, criterion, gamma, gamma_target, gamma_rate, amp_flag, working_device):
pbar.update(1)
image = image.to(working_device)
label = label.to(working_device)
prediction = net(image)
(correct, total) = common.accuracy_factor(prediction, label)
l = cri... |
def parse_package(line: str) -> Tuple[(str, Optional[str])]:
parts = re.split('(==|>=|<=|>|<)', line)
module = parts[0]
version = line.replace(module, '')
return (module, version) |
def build_model(model_opt, opt, fields, checkpoint):
print('Building model...')
model = onmt.ModelConstructor.make_base_model(model_opt, fields, use_gpu(opt), checkpoint)
if (len(opt.gpuid) > 1):
print('Multi gpu training: ', opt.gpuid)
model = nn.DataParallel(model, device_ids=opt.gpuid, di... |
def test_QSDetectorPolarization_set_basis_list():
tl = Timeline()
qsdetector = QSDetectorPolarization('qsd', tl)
basis_list = []
start_time = 0
frequency = 1000000.0
qsdetector.set_basis_list(basis_list, start_time, frequency)
assert ((qsdetector.splitter.basis_list == basis_list) and (qsdet... |
class FailedBuilding(Exception):
def __init__(self, name, build_command):
super(FailedBuilding, self).__init__()
self._name = name
self._build_command = build_command |
def test_sbottom_regionC_1600_850_60(get_json_from_tarfile):
sbottom_archive = data_path('pyhf-ins1748602-probability-models.tar.gz')
sbottom_regionC_bkgonly_json = get_json_from_tarfile(sbottom_archive, 'RegionC/BkgOnly.json')
sbottom_regionC_1600_850_60_patch_json = get_json_from_tarfile(sbottom_archive, ... |
.parametrize('task_name', [tn for tn in (all_tasks - julia_tasks)])
def test_obtain_prior_samples_from_task(task_name):
task = get_task(task_name)
prior = task.get_prior()
nsamples = 10
thetas = prior(num_samples=nsamples)
assert (thetas.shape[0] == nsamples) |
def reset():
for i in range(n_particles):
x[i] = [(((ti.random() * 0.2) + 0.3) + (0.1 * (i // group_size))), (((ti.random() * 0.2) + 0.05) + (0.32 * (i // group_size)))]
material[i] = (i // group_size)
v[i] = [0, 0]
F[i] = ti.Matrix([[1, 0], [0, 1]])
Jp[i] = 1
C[i] = ... |
def get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, convert=True):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
if ('fixsize' in opt.preprocess):
transform_list.append(transforms.Resize((opt.crop_size, opt.load_size), method))
if... |
def main(_config):
_config = copy.deepcopy(_config)
pl.seed_everything(_config['seed'])
dm = MTDataModule(_config, dist=True)
model = AllinoneTransformerSS(_config)
exp_name = f"{_config['exp_name']}"
os.makedirs(_config['log_dir'], exist_ok=True)
checkpoint_callback = pl.callbacks.ModelChec... |
def get_amr_match(cur_amr1, cur_amr2, sent_num=1, justinstance=False, justattribute=False, justrelation=False):
amr_pair = []
for (i, cur_amr) in ((1, cur_amr1), (2, cur_amr2)):
try:
amr_pair.append(amr.AMR.parse_AMR_line(cur_amr))
except Exception as e:
print(('Error in ... |
def test_image_to_tensor():
ori_results = dict(img=np.random.randn(256, 256, 3))
keys = ['img']
to_float32 = False
image_to_tensor = ImageToTensor(keys)
results = image_to_tensor(ori_results)
assert (results['img'].shape == torch.Size([3, 256, 256]))
assert isinstance(results['img'], torch.T... |
class Object3dCaptionDataset(BaseDataset, __DisplMixin):
def __init__(self, **kwargs):
super().__init__(kwargs['vis_processor'], kwargs['text_processor'], kwargs['vis_root'], kwargs['ann_paths'])
self.modalities = kwargs['modalities']
self.npoints = 8192
self.sample_points_num = self... |
.spark
def test_refit(fitted_model, log_ucb, log_ucb2):
fitted_model.seed = 123
fitted_model.sample = True
equality_check = (sparkDataFrameNotEqual if (fitted_model.sample and (fitted_model.seed is None)) else sparkDataFrameEqual)
dataset = create_dataset(log_ucb)
dataset2 = create_dataset(log_ucb2)... |
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