code stringlengths 101 5.91M |
|---|
class FairseqEncoderModel(BaseFairseqModel):
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
assert isinstance(self.encoder, FairseqEncoder)
def forward(self, src_tokens, src_lengths, **kwargs):
return self.encoder(src_tokens, src_lengths, **kwargs)
def... |
_grad()
def show_flops_params(model, device, input_shape=[1, 3, 1024, 2048], logger=None):
input = torch.randn(*input_shape).to(torch.device(device))
(flops, params) = profile(model, inputs=(input,), verbose=False)
if (logger is not None):
logger.info('{} flops: {:.3f}G input shape is {}, params: {:... |
class SubGaussianInverseProbabilityWeightingTuning(BaseOffPolicyEstimatorTuning):
estimator_name: str = 'sg-ipw'
def __post_init__(self) -> None:
self.base_ope_estimator = SubGaussianInverseProbabilityWeighting
super()._check_lambdas(max_val=1.0)
super()._check_init_inputs()
self... |
def _render(template, context, app):
before_render_template.send(app, template=template, context=context)
rv = template.render(context)
template_rendered.send(app, template=template, context=context)
return rv |
def _expmap0(u, c):
sqrt_c = (c ** 0.5)
u_norm = torch.clamp_min(u.norm(dim=(- 1), p=2, keepdim=True), 1e-05)
gamma_1 = ((tanh((sqrt_c * u_norm)) * u) / (sqrt_c * u_norm))
return gamma_1 |
class Assign(FunctionAssignment):
def __str__(self):
return ('%s := %s' % (self.fluent, self.expression)) |
def test_root_get_distance(branchless_codeobject_goal, mocker):
mock = mocker.patch('pynguin.ga.coveragegoals.cfd.get_root_control_flow_distance', return_value=42)
distance = branchless_codeobject_goal.get_distance(MagicMock(), MagicMock())
assert (distance == 42)
mock.assert_called_once() |
def test_line_str_parser():
data_ret = ['sample1.jpg hello\n', 'sample2.jpg world']
keys = ['filename', 'text']
keys_idx = [0, 1]
separator = ' '
with pytest.raises(AssertionError):
parser = LineStrParser('filename', keys_idx, separator)
with pytest.raises(AssertionError):
parser... |
def main(unused_argv):
config = configs.load_config(save_config=False)
config.render_path = True
dataset = datasets.load_dataset('test', config.data_dir, config)
(model, init_variables) = models.construct_mipnerf(random.PRNGKey(), dataset.peek()['rays'], config)
optimizer = flax.optim.Adam(config.lr... |
class PitchExtractionInterface(metaclass=ABCMeta):
def calc_prob(self, x):
def calc_embed(self, x):
def calc_pitch(self, x): |
.imputils.lower_constant(ArrayBuilderType)
def lower_const_ArrayBuilder(context, builder, arraybuildertype, arraybuilder):
layout = arraybuilder._layout
attrs = arraybuilder._attrs
rawptr = context.get_constant(numba.intp, arraybuilder._layout._ptr)
proxyout = context.make_helper(builder, arraybuilderty... |
def _load_video_1cam(idx: int, paths: List[str], poses: torch.Tensor, out_h: int, out_w: int, load_every: int=1):
filters = [('scale', f'w={out_w}:h={out_h}')]
all_frames = iio.imread(paths[idx], plugin='pyav', format='rgb24', constant_framerate=True, thread_count=2, filter_sequence=filters)
(imgs, timestam... |
class _UniformExpertAssignment(torch.autograd.Function):
def forward(ctx, x, num_experts):
out = torch.arange(x.numel(), dtype=x.dtype, device=x.device)
out = torch.remainder(out, num_experts)
return out.view(x.shape) |
def test_synthetic_sample_results_in_sampled_delay_with_weighted_delays_per_arm():
n_actions = 3
delay_function = ExponentialDelaySampler(max_scale=100.0, min_scale=10.0, random_state=12345).exponential_delay_function_expected_reward_weighted
dataset = BanditEnvironmentSimulator(n_actions=n_actions, reward_... |
_model
def densenetblur121d(pretrained=False, **kwargs):
model = _densenet('densenetblur121d', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep', aa_layer=BlurPool2d, **kwargs)
return model |
def check_label_existence(value_label, usr_utt_tok):
(in_usr, usr_pos) = get_token_pos(usr_utt_tok, value_label)
if ((not in_usr) and (value_label in LABEL_MAPS)):
for value_label_variant in LABEL_MAPS[value_label]:
(in_usr, usr_pos) = get_token_pos(usr_utt_tok, value_label_variant)
... |
class SympyPredictingOptimizer(ABC):
def step(self):
pass
def prediction(self, nsteps):
pass |
def set_learning_rate(optim, lr):
for param_group in optim.param_groups:
param_group['lr'] = lr |
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('l', help=((('Location whose data needs to be trained/tested with' + 'Values can be one of [Bondville, Boulder, Desert_Rock,') + 'Fort_Peck,Goodwin_Creek, Penn_State,') + 'Sioux_Falls]'))
parser.add_argument('y', help='4 digit Test ... |
class Trainer(DefaultTrainer):
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if (output_folder is None):
output_folder = os.path.join(cfg.OUTPUT_DIR, 'inference')
if ('coco' in dataset_name):
return COCOEvaluator(dataset_name, cfg, True, output_folder)
... |
def make_batches(lines, args, task, max_positions, encode_fn):
tokens = [task.source_dictionary.encode_line(encode_fn(src_str), add_if_not_exist=False).long() for src_str in lines]
lengths = [t.numel() for t in tokens]
itr = task.get_batch_iterator(dataset=task.build_dataset_for_inference(tokens, lengths), ... |
class BaseTestCase(unittest.TestCase):
def test_base_correct(self):
query = ''
(corrected_sent, detail) = m.correct(query)
print(corrected_sent, detail)
self.assertEqual(corrected_sent, '')
self.assertEqual(detail, [('', '', 0, 2), ('', '', 9, 11)])
def test_base_demos(se... |
def build_argparse():
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='data/constituency', help='Directory of constituency data.')
parser.add_argument('--wordvec_dir', type=str, default='extern_data/wordvec', help='Directory of word vectors')
parser.add_argument('-... |
_interface.register_fl_task(model='net_model', data_loader='val_loader', device='device')
def validate(net_model, val_loader, device):
device = torch.device('cuda')
if (not torch.cuda.is_available()):
device = 'cpu'
net_model.eval()
net_model.to(device)
val_loader = tqdm.tqdm(val_loader, des... |
class SMPBlock(nn.Module):
def __init__(self, in_channels, dw_channels, lk_size, drop_path, n_points=None, n_points_divide=4):
super().__init__()
self.pw1 = conv_bn_relu(in_channels, dw_channels, 1, 1, 0, groups=1)
self.pw2 = conv_bn(dw_channels, in_channels, 1, 1, 0, groups=1)
self.... |
def potential_neighbors(dom1, dom2):
if (isinstance(dom1, DOMElementPAD) or isinstance(dom2, DOMElementPAD)):
return False
if (dom1.ref == dom2.ref):
return False
return True |
class IntegerVectorsModPermutationGroup(UniqueRepresentation):
def __classcall__(cls, G, sum=None, max_part=None, sgs=None):
if ((sum is None) and (max_part is None)):
if G.domain():
return IntegerVectorsModPermutationGroup_All(G, sgs=sgs)
else:
return... |
class MutableString(UserString):
def __init__(self, string=''):
self.data = string
def __hash__(self):
raise TypeError('unhashable type (it is mutable)')
def __setitem__(self, index, sub):
if (index < 0):
index += len(self.data)
if ((index < 0) or (index >= len(se... |
def quantize_model_(model, p=0.2, bits=8, update_step=3000):
quantized_layers = get_layers(model, '(.*?)')
for layer in quantized_layers:
is_master_process = ((not dist.is_initialized()) or (dist.is_initialized() and (dist.get_rank() == 0)))
module = attrgetter(layer)(model)
if is_master... |
def get_dataset(dataset, batch_size=256, augment=False):
mean = (0.5, 0.5, 0.5)
std = (0.5, 0.5, 0.5)
num_workers = 4
if (dataset in ['mnist', 'kmnist', 'fashionmnist']):
if augment:
transform_train = transforms.Compose([transforms.RandomCrop(28, padding=4), transforms.RandomHorizont... |
def type_check(param_name, param_schema, input_params):
type_check_error = []
doc_type = convert_type(param_schema.get('type', ''))
if (doc_type and (not isinstance(input_params[param_name], doc_type))):
type_error = True
if (((doc_type in [int, float, bool]) and (type(input_params[param_nam... |
(scope='module')
.usefixtures('columns')
def simple_dataframe_pandas(columns):
data = [(1, 2, 19842), (1, 4, 19844), (1, 3, 19843), (1, 5, 19845), (1, 6, 19846), (1, 7, 19847), (2, 1, 19841), (2, 2, 19842), (2, 3, 19843), (2, 4, 19844), (3, 10, 19844), (4, 11, 19843), (4, 12, 19845), (1, 1, 19841)]
return pd.Da... |
def _check_likelihood(model: GPModel, classification: bool, likelihood_variance: Optional[float], empirical_variance: Optional[TensorType], trainable_likelihood: bool) -> None:
if classification:
assert isinstance(model.likelihood, gpflow.likelihoods.Bernoulli)
else:
assert isinstance(model.like... |
class OrderedMultisetPartitionsIntoSets_n_constraints(OrderedMultisetPartitionsIntoSets):
def __init__(self, n, **constraints):
self._n = n
OrderedMultisetPartitionsIntoSets.__init__(self, True, size=n, **constraints)
def _repr_(self):
cdict = dict(self.constraints)
cdict.pop('si... |
class RawExplorationStrategy(ExplorationStrategy, metaclass=abc.ABCMeta):
def get_action_from_raw_action(self, action, **kwargs):
pass
def get_actions_from_raw_actions(self, actions, **kwargs):
raise NotImplementedError()
def get_action(self, t, policy, *args, **kwargs):
(action, age... |
class SAGE(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers):
super().__init__()
self.num_layers = num_layers
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, hidden_channels))
for _ in range((num_layers - 2... |
.parametrize('tifunc,npfunc', [((lambda x: ti.tanh(x)), (lambda x: np.tanh(x))), ((lambda x: ti.sin(x)), (lambda x: np.sin(x))), ((lambda x: ti.cos(x)), (lambda x: np.cos(x))), ((lambda x: ti.acos(x)), (lambda x: np.arccos(x))), ((lambda x: ti.asin(x)), (lambda x: np.arcsin(x)))])
_has_autograd
_utils.test()
def test_t... |
.pairing
.dependency()
def test_build_and_search():
global _tempdir
conf = utils.relative_file('spacegraphcats/conf/twofoo-short.yaml')
target = 'search'
status = run_snakemake(conf, verbose=True, outdir=_tempdir, extra_args=[target])
assert (status == 0)
output_files = ['twofoo-short_k31/bcalm.... |
def handle_sampling_error(is_tmp_file, output_file_path, sampling_error):
if ('Unable to sample any rows for the given conditions' in str(sampling_error)):
raise sampling_error
error_msg = None
if is_tmp_file:
error_msg = f'Error: Sampling terminated. Partial results are stored in a temporar... |
def validate_before_compute_similarity(float_tensor: Any, fxp_tensor: Any):
assert isinstance(float_tensor, np.ndarray)
assert isinstance(fxp_tensor, np.ndarray)
assert (float_tensor.shape == fxp_tensor.shape) |
def fake_colmap_normal(in_depth_path, out_normal_path):
depth_image = read_gipuma_dmb(in_depth_path)
image_shape = np.shape(depth_image)
normal_image = np.ones_like(depth_image)
normal_image = np.reshape(normal_image, (image_shape[0], image_shape[1], 1))
normal_image = np.tile(normal_image, [1, 1, 3... |
class Embeder(nn.Module):
def __init__(self, conf, fields):
super(Embeder, self).__init__()
self.conf = conf
if ('pos' in fields.inputs):
self.pos_emb = nn.Embedding(fields.get_vocab_size('pos'), conf.n_pos_embed)
else:
self.pos_emb = None
if ('char' i... |
class FocalLoss(nn.Module):
def __init__(self, focusing_param=2, balance_param=0.25):
super(FocalLoss, self).__init__()
self.focusing_param = focusing_param
self.balance_param = balance_param
def forward(self, output, target, reduction='mean'):
cross_entropy = F.cross_entropy(out... |
def _merge_a_into_b(a, b, stack=None):
assert isinstance(a, AttrDict), '`a` (cur type {}) must be an instance of {}'.format(type(a), AttrDict)
assert isinstance(b, AttrDict), '`b` (cur type {}) must be an instance of {}'.format(type(b), AttrDict)
for (k, v_) in a.items():
full_key = ((('.'.join(stac... |
def distshift_detector_preprocess(data: List[pd.DataFrame], domain_index: Union[(List[int], np.ndarray)], domain_index_name: str='domain_index', n_states: int=2):
df = pd.concat(data)
domain_index = np.array(domain_index)
df[domain_index_name] = domain_index
data_array = df.to_numpy()
var_names = df... |
def logits_to_probs(logits, is_binary=False):
if is_binary:
return torch.sigmoid(logits)
return F.softmax(logits, dim=(- 1)) |
def unregister_compiled_sdfg_call_hook(hook_id: int):
if (hook_id >= len(_COMPILED_SDFG_CALL_HOOKS)):
raise ValueError('Invalid hook ID')
_COMPILED_SDFG_CALL_HOOKS[hook_id] = None |
('This function is now called SE3ToXYZQUAT. Please change for this new signature to delete this warning.')
def se3ToXYZQUAT(M):
return pin.SE3ToXYZQUAT(M) |
def main():
p = argparse.ArgumentParser()
p.add_argument('node_mh_pickle')
p.add_argument('lca_db')
args = p.parse_args()
node_mhs = pickle.load(open(args.node_mh_pickle, 'rb'))
lca_obj = LCA_Database()
lca_obj.load(args.lca_db)
databases = ((lca_obj, args.lca_db, 'LCA'),)
d = {}
... |
def GenCircle_PUNGraph(Nodes, NodeOutDeg=1, IsDir=True):
return _snap.GenCircle_PUNGraph(Nodes, NodeOutDeg, IsDir) |
def process_constraints(constraints, columns, slots):
slot_values = {}
skip_db_with_one_table = False
for constraint in constraints:
if ('P0==' == constraint):
assert ('{OP0}' in slots)
slot_values['{OP0}'] = '='
elif ('P1==' == constraint):
assert ('{OP1}... |
class Token(object):
def __init__(self, start_mark, end_mark):
self.start_mark = start_mark
self.end_mark = end_mark
def __repr__(self):
attributes = [key for key in self.__dict__ if (not key.endswith('_mark'))]
attributes.sort()
arguments = ', '.join([('%s=%r' % (key, ge... |
class CondBatchNorm3d(_CondBatchNorm):
def _check_input_dim(self, input):
if (input.dim() != 5):
raise ValueError('expected 5D input (got {}D input)'.format(input.dim())) |
class FairseqAdamConfig(FairseqDataclass):
adam_betas: Any = field(default=(0.9, 0.999), metadata={'help': 'betas for Adam optimizer'})
adam_eps: float = field(default=1e-08, metadata={'help': 'epsilon for Adam optimizer'})
weight_decay: float = field(default=0.0, metadata={'help': 'weight decay'})
use_... |
class RegressionLoss(object):
def __call__(self, pred, gtruth):
loss = self.criterion(pred, gtruth)
return loss |
def resnet_mark_before_relu(model):
if isinstance(model, DataParallel):
model.module.conv1.before_relu = True
else:
model.conv1.before_relu = True
mark_bottlenetck_before_relu(model)
mark_basicblock_before_relu(model) |
def test_top_selector_find_top_k_binary_values_none():
ts = TopSelector()
tst = TopSelectorTorch()
with pytest.raises(TypeError):
ts.find_top_k_binary(None, k)
with pytest.raises(TypeError):
tst.find_top_k_binary(None, k) |
class MaxMarginRankingLoss(nn.Module):
def __init__(self, margin=1.0, negative_weighting=False, batch_size=1, n_pair=1, hard_negative_rate=0.5):
super(MaxMarginRankingLoss, self).__init__()
self.margin = margin
self.n_pair = n_pair
self.batch_size = batch_size
easy_negative_r... |
def action_open_cache_dir():
d = misc.get_cache_home()
misc.info(f'Opening cache directory: {d}')
if (sys.platform == 'win32'):
os.startfile(d)
elif (sys.platform == 'darwin'):
subprocess.Popen(['open', d])
else:
subprocess.Popen(['xdg-open', d]) |
def load_data(args):
if (args.dataset_str == 'dblp'):
(adj_list, features, train_data, train_label, test_data, test_label) = load_data_dblp('dataset/DBLP4057_GAT_with_idx_tra200_val_800.mat')
node_size = features.shape[0]
node_embedding = features.shape[1]
class_size = train_label.sh... |
def visualize_heatmap(image, mask):
masks = norm_image(mask).astype(np.uint8)
heatmap = cv2.applyColorMap(masks, cv2.COLORMAP_JET)
heatmap = np.float32(heatmap)
heatmap = cv2.resize(heatmap, (image.shape[1], image.shape[0]))
cam = ((0.4 * heatmap) + (0.6 * np.float32(image)))
return cam |
.lower_builtin('end_tuple', ArrayBuilderType)
def lower_endtuple(context, builder, sig, args):
(arraybuildertype,) = sig.args
(arraybuilderval,) = args
proxyin = context.make_helper(builder, arraybuildertype, arraybuilderval)
call(context, builder, libawkward.ArrayBuilder_endtuple, (proxyin.rawptr,))
... |
class TFRobertaModel(metaclass=DummyObject):
_backends = ['tf']
def __init__(self, *args, **kwargs):
requires_backends(self, ['tf']) |
def create_cumprod_input(rng, shape, axis, with_mask, with_random_zero_pos, zero_pos):
x = rng.randn(*shape).astype(np.float32)
if with_mask:
if with_random_zero_pos:
mask = (rng.rand(*shape) > (1.0 / shape[axis]))
x = (x * mask)
else:
x.swapaxes(0, axis)[zero... |
def load_config(path, default_path=None):
with open(path, 'r') as f:
cfg_special = yaml.load(f, Loader=yaml.CLoader)
if (default_path is not None):
with open(default_path, 'r') as f:
cfg_default = yaml.load(f, Loader=yaml.CLoader)
else:
cfg_default = dict()
cfg_import... |
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--in_data', type=str)
parser.add_argument('--nb_train', type=int, default=(- 1))
parser.add_argument('--nb_jobs', type=int, default=8)
parser.add_argument('--nb_splits', type=int, default=5)
parser.add_argument('--eval_split... |
_spec([HookScope.GLOBAL])
def after_call(context: HookContext, case: Case, response: GenericResponse) -> None: |
def is_square(input_matrix: Union[(sparse.csr_matrix, np.ndarray)]) -> bool:
return (input_matrix.shape[0] == input_matrix.shape[1]) |
class MatFile5Reader(MatFileReader):
def __init__(self, mat_stream, byte_order=None, mat_dtype=False, squeeze_me=False, chars_as_strings=True, matlab_compatible=False, struct_as_record=True, verify_compressed_data_integrity=True, uint16_codec=None):
super(MatFile5Reader, self).__init__(mat_stream, byte_orde... |
def ismatrix(t):
return ((isinstance(t, (list, tuple)) and (len(t) > 0) and issequence(t[0])) or (isinstance(t, np.ndarray) and (t.ndim == 2))) |
def test_ListOffset():
builder = ListOffset('int64', Numpy('float64', ''), '')
subbuilder = builder.begin_list()
subbuilder.append(1.1)
subbuilder.append(2.2)
subbuilder.append(3.3)
builder.end_list()
subbuilder = builder.begin_list()
builder.end_list()
subbuilder = builder.begin_lis... |
class ReActNetBlock(nn.Module):
def __init__(self, inplanes, planes, stride=1):
super(ReActNetBlock, self).__init__()
norm_layer = nn.BatchNorm2d
self.move11 = LearnableBias(inplanes)
self.binary_activation1 = BinaryActivation()
self.binary_3x3 = conv3x3(inplanes, inplanes, s... |
def get_quantizers(cfg, test, pname, with_bias=True):
if (cfg.w_quantize in ['fp', 'parametric_fp_b_xmax', 'parametric_fp_d_xmax', 'parametric_fp_d_b', 'pow2', 'parametric_pow2_b_xmax', 'parametric_pow2_b_xmin', 'parametric_pow2_xmin_xmax']):
if (pname in nn.get_parameters()):
delta = find_delta... |
.parametrize('grid', [1, 3, 6])
.parametrize('block_size, context', [(40, 0), (55, 3), (80, 10), (128, 17), (256, 80), (512, 93)])
def test_cover2D(block_size, context, grid):
lbl = real_image2d()[1]
lbl = lbl.astype(np.int32)
max_sizes = tuple(calculate_extents(lbl, func=np.max))
min_overlap = tuple(((... |
class Normal_Loader(Dataset):
def __init__(self, is_train=1, path='/workspace/DATA/UCF-Crime/'):
super(Normal_Loader, self).__init__()
self.is_train = is_train
self.path = path
if (self.is_train == 1):
data_list = os.path.join(path, 'train_normal.txt')
with op... |
class MBInvertedConvLayer(MyModule):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, expand_ratio=6, mid_channels=None, act_func='relu6', use_se=False):
super(MBInvertedConvLayer, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
... |
def import_cves():
cf.logger.info(('-' * 70))
if db.table_exists('cve'):
cf.logger.warning('The cve table already exists, loading and continuing extraction...')
else:
for year in range(initYear, (currentYear + 1)):
extract_target = (('nvdcve-1.1-' + str(year)) + '.json')
... |
def dump_paths(Graph, rating_pair, maxLen, sample_size, fw_file):
for pair in rating_pair:
user_id = pair[0]
movie_id = pair[1]
user_node = ('u' + user_id)
movie_node = ('i' + movie_id)
if (Graph.has_node(user_node) and Graph.has_node(movie_node)):
mine_paths_betw... |
class FrameLevel(nn.Module):
def __init__(self, input_dim, output_dim, hiddens=None, activation='ReLU', **kwargs):
super().__init__()
latest_dim = input_dim
self.hiddens = []
if (hiddens is not None):
for dim in hiddens:
self.hiddens += [nn.Linear(latest_d... |
def load_bootstrap_config(cfg: CN):
if (not cfg.BOOTSTRAP_DATASETS):
return
bootstrap_datasets_cfgnodes = []
for dataset_cfg in cfg.BOOTSTRAP_DATASETS:
_C = get_bootstrap_dataset_config().clone()
_C.merge_from_other_cfg(CN(dataset_cfg))
bootstrap_datasets_cfgnodes.append(_C)
... |
def get_solver(solver_, warm_start_, num_cpu_, default_action_):
if (solver_ == 'random_shooting'):
mpc_solver = RandomShooting(dynamical_model=dynamical_model, reward_model=reward_model, horizon=HORIZON, gamma=1.0, num_samples=NUM_SAMPLES, num_elites=NUM_ELITES, termination=termination, terminal_reward=val... |
def compatible_system_lift(compatible_system, split_primes_list):
if (len(split_primes_list) != len(compatible_system)):
raise ValueError('The number of primes does not match the length of the given exponent vectors.')
exponent_vector_lift = [ZZ(compatible_system[0][0][0])]
complement_vector_lift = ... |
class EarlyStopping():
def __init__(self, early_stopping_ns: SimpleNamespace, validation_metric: str, validation_k: int, cutoffs: t.List, simple_metrics: t.List):
self.logger = logging.get_logger(self.__class__.__name__, pylog.DEBUG)
self.validation_metric = validation_metric
self.validation... |
()
def get(key: str):
try:
value = cloud_config.get_flag(key)
console.print(f'[bold][blue]{key}[/blue] = [italic][green]{value}[/italic][/green]')
except KeyError:
console.print(f'[red][bold]{key}[/bold] is not a valid config key[/red]') |
class JonesDatabase():
def __init__(self):
self.root = None
def __repr__(self):
return "John Jones's table of number fields with bounded ramification and degree <= 6"
def _load(self, path, filename):
print(filename)
i = 0
while filename[i].isalpha():
i += ... |
class Max(Module):
def __init__(self, dimension=0):
super(Max, self).__init__()
self.dimension = dimension
self._output = None
self._indices = None
def _getPositiveDimension(self, input):
dimension = self.dimension
if (dimension < 0):
dimension = (inpu... |
def ResNet18(num_classes=10):
return ResNet(BasicBlock, layers=[2, 2, 2, 2], filters=[64, 128, 256, 512], num_classes=num_classes) |
class CPPInliner():
def __init__(self, inline_target, inline_val):
self.inline_target = inline_target
self.inline_val = inline_val
def inline(self, code: str):
return re.sub(('\\b%s\\b' % re.escape(self.inline_target)), (('(' + self.inline_val) + ')'), code) |
def test_interval_real_not_int():
constraint = Interval(RealNotInt, 0, 1, closed='both')
assert constraint.is_satisfied_by(1.0)
assert (not constraint.is_satisfied_by(1)) |
class TFViTMAEPreTrainedModel(metaclass=DummyObject):
_backends = ['tf']
def __init__(self, *args, **kwargs):
requires_backends(self, ['tf']) |
def max_pool_3d(input, ds, ignore_border=False):
if (input.ndim < 3):
raise NotImplementedError('max_pool_3d requires a dimension >= 3')
vid_dim = input.ndim
if ((ds[1] > 1) or (ds[2] > 1)):
frame_shape = input.shape[(- 2):]
batch_size = tensor.prod(input.shape[:(- 2)])
batch... |
def simplify_onnx_model(model: onnx.ModelProto, auto_merge: bool) -> onnx.ModelProto:
(model, check) = onnxsim.simplify(model, skip_fuse_bn=True)
if (not check):
raise RuntimeError('onnx-simplifier optimizations failed')
return model |
def load_frames_directory_dict(directory: str, pattern: str) -> OpenPoseFrames:
frames = {}
with os.scandir(directory) as entry_iterator:
for entry in entry_iterator:
with open(entry.path, 'r') as f:
frame_id = get_frame_id(entry.name, pattern=pattern)
frame_d... |
def _reconstitute(form, length, container, getkey, backend, byteorder, simplify):
if isinstance(form, ak.forms.EmptyForm):
if (length != 0):
raise ValueError(f'EmptyForm node, but the expected length is {length}')
return ak.contents.EmptyArray()
elif isinstance(form, ak.forms.NumpyFo... |
class Polynomial_generic_sparse_cdvf(Polynomial_generic_sparse_cdv, Polynomial_generic_cdvf):
pass |
def test_serialize_int_float():
obj = MyObject(1.0)
assert (obj.float_prop == 1.0)
json_obj = obj.to_json()
json_obj['float_prop'] = int(json_obj['float_prop'])
obj = MyObject.from_json(json_obj)
assert (obj.float_prop == 1.0) |
class TarIO(ContainerIO.ContainerIO):
def __init__(self, tarfile, file):
self.fh = open(tarfile, 'rb')
while True:
s = self.fh.read(512)
if (len(s) != 512):
raise OSError('unexpected end of tar file')
name = s[:100].decode('utf-8')
i = ... |
class BasicBlock(nn.Sequential):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, conv3x3=default_conv, norm=default_norm, act=default_act):
modules = []
modules.append(conv3x3(in_channels, out_channels, kernel_size, stride=stride, bias=bias))
if (norm is not N... |
def iter_rows(rows, col_count):
for row in rows:
row = tuple(row)
(yield (row + (('',) * (col_count - len(row))))) |
def _assert_override(spy, arg, original, overridden):
for (key, value) in {**original, **overridden}.items():
kwargs = spy.call_args[1]
assert (kwargs[arg][key] == value)
assert all(((key not in kwargs) for key in overridden)) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.