code stringlengths 101 5.91M |
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def convert_ontonotes_file(filename, simplify, bigger_first):
assert ('en_ontonotes' in filename)
if (not os.path.exists(filename)):
raise FileNotFoundError(('Cannot convert missing file %s' % filename))
new_filename = filename.replace('en_ontonotes', 'en_ontonotes-multi')
with open(filename) as... |
class ChamferDistanceFunction(torch.autograd.Function):
def forward(ctx, xyz1, xyz2):
(batchsize, n, _) = xyz1.size()
(_, m, _) = xyz2.size()
xyz1 = xyz1.contiguous()
xyz2 = xyz2.contiguous()
dist1 = torch.zeros(batchsize, n)
dist2 = torch.zeros(batchsize, m)
... |
def build_transforms_head(cfg, is_train=True, PIXEL_MEAN=[0.485, 0.456, 0.406], PIXEL_STD=[0.229, 0.224, 0.225]):
normalize_transform = T.Normalize(mean=PIXEL_MEAN, std=PIXEL_STD)
if is_train:
transform = T.Compose([T.Resize([cfg.height, cfg.width]), T.Pad(10), T.RandomCrop([cfg.height, cfg.width]), T.C... |
def register_Ns3LteRrcSapPhysCellIdRange_methods(root_module, cls):
cls.add_constructor([])
cls.add_constructor([param('ns3::LteRrcSap::PhysCellIdRange const &', 'arg0')])
cls.add_instance_attribute('haveRange', 'bool', is_const=False)
cls.add_instance_attribute('range', 'uint16_t', is_const=False)
... |
_kl(ContinuousBernoulli, ContinuousBernoulli)
def _kl_continuous_bernoulli_continuous_bernoulli(p, q):
t1 = (p.mean * (p.logits - q.logits))
t2 = (p._cont_bern_log_norm() + torch.log1p((- p.probs)))
t3 = ((- q._cont_bern_log_norm()) - torch.log1p((- q.probs)))
return ((t1 + t2) + t3) |
def rule0(graph, nodes, sep_sets, knowledge, verbose):
reorientAllWith(graph, Endpoint.CIRCLE)
fci_orient_bk(knowledge, graph)
for node_b in nodes:
adjacent_nodes = graph.get_adjacent_nodes(node_b)
if (len(adjacent_nodes) < 2):
continue
cg = ChoiceGenerator(len(adjacent_n... |
class EfficientFormerModel(metaclass=DummyObject):
_backends = ['torch']
def __init__(self, *args, **kwargs):
requires_backends(self, ['torch']) |
.parametrize('dataset_type', [pytest.param('spark_dataframe_test', marks=pytest.mark.spark), pytest.param('pandas_dataframe_test', marks=pytest.mark.core)])
def test_with_session_ids(dataset_type, request):
log = request.getfixturevalue(dataset_type)
splitter = RandomSplitter(test_size=0.3, drop_cold_items=Fals... |
class _QPool2dBenchmarkBase(op_bench.TorchBenchmarkBase):
def setup(self, N, C, H, W, dtype, contig):
if (N == 0):
f_input = ((torch.rand(C, H, W) - 0.5) * 256)
else:
f_input = ((torch.rand(N, C, H, W) - 0.5) * 256)
scale = 1.0
zero_point = 0
self.q_in... |
class Mergeable(object):
def getTypeName(self) -> str:
raise NotImplementedError('getTypeName not implemented.')
def shouldMerge(self, other: Mergeable) -> bool:
raise NotImplementedError('equals not implemented.') |
def test_transformer_decoder():
decoder = NRTRDecoder(num_classes=37, padding_idx=36, max_seq_len=5)
decoder.init_weights()
decoder.train()
out_enc = torch.rand(1, 25, 512)
tgt_dict = {'padded_targets': torch.LongTensor([[1, 1, 1, 1, 36]])}
img_metas = [{'valid_ratio': 1.0}]
tgt_dict['padded... |
_to_string_io
def load_events(fhandle: TextIO) -> annotations.Events:
times = []
labels = []
confidence = []
reader = csv.reader(fhandle, delimiter='\t')
for line in reader:
times.append([float(line[0]), float(line[1])])
labels.append(line[2])
confidence.append(1.0)
event... |
class SimilarityEvaluator():
def __init__(self, model_path='models/sim/sim.pt', tokenizer_path='models/sim/sim.sp.30k.model', gpu=False):
self.model_path = model_path
self.tokenizer_path = tokenizer_path
self.tok = TreebankWordTokenizer()
kw = {}
if (not torch.cuda.is_availab... |
class DropoutAddLayerNormSubsetFn(torch.autograd.Function):
def forward(ctx, x0, residual, gamma, beta, colscale, x0_subset, out_subset, dropout_p, epsilon, rowscale_const, out_numrows, residual_in_fp32=False, prenorm=False, is_rms_norm=False, return_dmask=False):
x0 = maybe_align(x0.contiguous(), 16)
... |
def run_job_synchronously(shell_command, directory, valgrind, is_python, build_path=''):
suppressions_path = os.path.join(NS3_BASEDIR, VALGRIND_SUPPRESSIONS_FILE)
if is_python:
path_cmd = ((PYTHON[0] + ' ') + os.path.join(NS3_BASEDIR, shell_command))
elif len(build_path):
path_cmd = os.path.... |
class WordSplitter(Registrable):
default_implementation = 'spacy'
def split_words(self, sentence: str) -> List[Token]:
raise NotImplementedError
def from_params(cls, params: Params) -> 'WordSplitter':
choice = params.pop_choice('type', cls.list_available(), default_to_first_choice=True)
... |
def generate_test_cpp_sources(test_params, template):
(cpp_args_construction_stmts, _) = compute_cpp_args_construction_stmts_and_forward_arg_symbols(test_params)
test_cpp_sources = template.substitute(functional_variant_name=test_params.functional_variant_name, cpp_args_construction_stmts=';\n '.join(cpp_args_... |
('/response-conformance/missing-field', methods=['GET'])
def missing_field():
response_data = {'id': '123', 'name': 'Alice'}
return (jsonify(response_data), 200) |
def gs_link_prediction(g, edge_ids, edge_labels, num_samples, optimizer, batch_size=4, epochs=4, bias=True, dropout=0.0, normalize='l2', seed=0, shuffle=True):
set_seed(seed)
tf.random.set_seed(seed)
if shuffle:
random.seed(seed)
generator = GraphSAGELinkGenerator(g, batch_size, num_samples)
... |
def HAT(max_byte_size=, memory_estimate_period=1000000, grace_period=200, split_criterion='info_gain', split_confidence=1e-07, tie_threshold=0.05, binary_split=False, stop_mem_management=False, remove_poor_atts=False, no_preprune=False, leaf_prediction='nba', nb_threshold=0, nominal_attributes=None, bootstrap_sampling=... |
def load_data(path):
urls = {}
with open(path, 'r') as f:
for line in f:
(url, _) = line.split('\t')
vid = url[(- 11):]
urls[vid] = url
return urls |
class MovingAverageDict(object):
def __init__(self, decay=0.99):
self.decay = decay
self.ma_dict = {}
def __call__(self, value_dict):
for (key, val) in value_dict.items():
if (isinstance(val, (np.float32, np.float64, np.float16)) or (isinstance(val, np.ndarray) and (val.dtype... |
def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
requires_backends(get_polynomial_decay_schedule_with_warmup, ['torch']) |
def _is_this_machine(host):
try:
machine_ips = [addr[4][0] for addr in socket.getaddrinfo(socket.gethostname(), None)]
host_ip = socket.gethostbyname(host)
except socket.gaierror:
return False
return any(((host_ip == machine_ip) for machine_ip in machine_ips)) |
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.mha = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNor... |
def shfl_down_f32(mask, val, offset):
return impl.call_internal('cuda_shfl_down_sync_f32', mask, val, offset, 31, with_runtime_context=False) |
def _maybe_download_dataset(dataset_path):
dataset_folder = os.path.join(dataset_path, clrs.get_clrs_folder())
if os.path.isdir(dataset_folder):
logging.info('Dataset found at %s. Skipping download.', dataset_folder)
return dataset_folder
logging.info('Dataset not found in %s. Downloading...... |
def _cross_val_metrics(args_namespace):
return cross_val_metrics(args_namespace.dataset_path, args_namespace.output_path, args_namespace.config_path, args_namespace.nb_folds, args_namespace.train_size_ratio, args_namespace.exclude_slot_metrics, args_namespace.include_errors, args_namespace.verbosity) |
class RegNetForImageClassification(metaclass=DummyObject):
_backends = ['torch']
def __init__(self, *args, **kwargs):
requires_backends(self, ['torch']) |
class CamembertTokenizer():
def __init__(self, *args, **kwargs):
requires_sentencepiece(self)
def from_pretrained(self, *args, **kwargs):
requires_sentencepiece(self) |
def register_Ns3ObjectBase_methods(root_module, cls):
cls.add_constructor([])
cls.add_constructor([param('ns3::ObjectBase const &', 'arg0')])
cls.add_method('GetAttribute', 'void', [param('std::string', 'name'), param('ns3::AttributeValue &', 'value')], is_const=True)
cls.add_method('GetAttributeFailSaf... |
def gen_corrupt_batch_gpu(corruption, severity):
def corrupt_batch_gpu(images, model):
for i in range(images.size(0)):
corr_func = corruption_dict[corruption]
images[i] = corr_func(images[i], severity, gpu=True)
return images
return corrupt_batch_gpu |
def replicate_small_cp_cmd(src, dst, recursive=True) -> Optional[str]:
(provider_src, _, _) = parse_path(src)
(provider_dst, _, _) = parse_path(dst)
if ((provider_src == 'aws') and (provider_dst == 'aws')):
return fallback_cmd_s3_cp(src, dst, recursive)
elif ((provider_src == 'gcp') and (provide... |
def scheduled_sample(ground_truth_x, generated_x, batch_size, num_ground_truth):
idx = tf.random_shuffle(tf.range(int(batch_size)))
ground_truth_idx = tf.gather(idx, tf.range(num_ground_truth))
generated_idx = tf.gather(idx, tf.range(num_ground_truth, int(batch_size)))
ground_truth_examps = tf.gather(gr... |
class STSDataReader():
def __init__(self, dataset_folder, s1_col_idx=5, s2_col_idx=6, score_col_idx=4, delimiter='\t', quoting=csv.QUOTE_NONE, normalize_scores=True, min_score=0, max_score=5):
self.dataset_folder = dataset_folder
self.score_col_idx = score_col_idx
self.s1_col_idx = s1_col_id... |
def test_class_splitter_for_fold_overlaps():
class DemoTask(Task):
def __init__(self):
super(DemoTask, self).__init__(index=0, num_classes=None)
self._inputs = np.arange(10)
def __len__(self):
return len(self._inputs)
def __getitem__(self, index):
... |
def write_recip_lattice(f, recip_lattice):
f.write('begin recip_lattice\n')
for i in range(3):
a = recip_lattice[i]
f.write(' {0:>11.7f} {1:>11.7f} {2:>11.7f}\n'.format(*a))
f.write('end recip_lattice\n\n') |
class UnsetValue(object):
def __str__(self):
return '<unset value>'
def __repr__(self):
return '<unset value>'
def __bool__(self):
return False
def __nonzero__(self):
return False |
class RGBImgObsWrapper(gym.core.ObservationWrapper):
def __init__(self, env, tile_size=8):
super().__init__(env)
self.tile_size = tile_size
self.observation_space.spaces['image'] = spaces.Box(low=0, high=255, shape=((self.env.width * tile_size), (self.env.height * tile_size), 3), dtype='uint... |
def validate_cn_ric(df: Union[(str, pd.Series, dd.Series, pd.DataFrame, dd.DataFrame)], column: str='') -> Union[(bool, pd.Series, pd.DataFrame)]:
if isinstance(df, (pd.Series, dd.Series)):
return df.apply(ric.is_valid)
elif isinstance(df, (pd.DataFrame, dd.DataFrame)):
if (column != ''):
... |
class FlaxGPTNeoModel(metaclass=DummyObject):
_backends = ['flax']
def __init__(self, *args, **kwargs):
requires_backends(self, ['flax']) |
.parametrize('forest_cls, expected_oob_score', [(RandomSurvivalForest, 0.), (ExtraSurvivalTrees, 0.)])
def test_oob_score(make_whas500, forest_cls, expected_oob_score):
whas500 = make_whas500(to_numeric=True)
forest = forest_cls(oob_score=True, bootstrap=False, random_state=2)
with pytest.raises(ValueError,... |
def register_Ns3NoOpHandoverAlgorithm_methods(root_module, cls):
cls.add_constructor([param('ns3::NoOpHandoverAlgorithm const &', 'arg0')])
cls.add_constructor([])
cls.add_method('GetLteHandoverManagementSapProvider', 'ns3::LteHandoverManagementSapProvider *', [], is_virtual=True)
cls.add_method('GetTyp... |
def get_batch_size(tensor_shape):
tensor_shape.assert_has_rank(rank=4)
return tensor_shape[0].value |
def register_all_hico(root):
for (dataset_name, splits_per_dataset) in _PREDEFINED_SPLITS_HICO.items():
for (key, (image_root, json_file)) in splits_per_dataset.items():
register_hico_instances(key, _get_builtin_metadata(dataset_name), (os.path.join(root, json_file) if ('://' not in json_file) e... |
class ViTMAEModel(metaclass=DummyObject):
_backends = ['torch']
def __init__(self, *args, **kwargs):
requires_backends(self, ['torch']) |
class ENetMinus(ENet):
def __init__(self, n_classes=19, max_input_h=512, max_input_w=1024):
(h, w) = (max_input_h, max_input_w)
r = 0.86
nn.ModuleList.__init__(self, [Downsampler(3, 16), Bottleneck(16, 64, 0.01, downsample=True), Bottleneck(64, 64, 0.01), Bottleneck(64, 64, 0.01), Bottleneck... |
class STS():
def __init__(self, directory, train=True, seed=0):
cwd = os.getcwd().replace('dataset', '')
directory = path.join(cwd, directory)
ensure_dataset_exists(directory)
self._directory = directory
self._inner = 'Set{}'.format((1 + (((seed + 1) + int(train)) % 2)))
... |
def generate_datasets(data_root):
train_info = sio.loadmat(os.path.join(data_root, 'train_list.mat'))['file_list']
test_info = sio.loadmat(os.path.join(data_root, 'test_list.mat'))['file_list']
class_names = os.listdir(os.path.join(data_root, 'Images'))
class_names.sort()
train_dataset = []
test... |
def postprocess_one(pred_sql, schema):
pred_sql = pred_sql.replace('group_by', 'group by').replace('order_by', 'order by').replace('limit_value', 'limit 1').replace('_EOS', '').replace(' value ', ' 1 ').replace('distinct', '').strip(',').strip()
if pred_sql.endswith('value'):
pred_sql = (pred_sql[:(- le... |
class ResidualStack(nn.Module):
def __init__(self, in_channels, num_hiddens, num_residual_layers, num_residual_hiddens, use_kaiming_normal):
super(ResidualStack, self).__init__()
self._num_residual_layers = num_residual_layers
self._layers = nn.ModuleList(([Residual(in_channels, num_hiddens,... |
def read_train_split_to_str(dataset_dir):
train_dir = os.path.join(dataset_dir, 'bbox_train')
return read_train_test_directory_to_str(train_dir) |
def compute_on_dataset(model, data_loader, device, timer=None):
model.eval()
results_dict = {}
cpu_device = torch.device('cpu')
for (_, batch) in enumerate(tqdm(data_loader)):
(images_left, images_right, targets, calib, image_ids) = batch
with torch.no_grad():
if timer:
... |
def parse_notes_and_chords(stream: Stream, resolution: int=DEFAULT_RESOLUTION) -> Tuple[(List[Note], List[Chord])]:
notes: List[Note] = []
chords: List[Chord] = []
ties: Dict[(int, int)] = {}
for item in stream.flat.notesAndRests:
if ((not item.isNote) and (not item.isChord)):
contin... |
def sturm_bound(level, weight=2):
if is_ArithmeticSubgroup(level):
if level.is_congruence():
return level.sturm_bound(weight)
raise ValueError('no Sturm bound defined for noncongruence subgroups')
if isinstance(level, (int, Integer)):
return Gamma0(level).sturm_bound(weight) |
def visit_forward(variables, callback, fclosed=None):
if (fclosed is None):
fclosed = set()
stop = False
for v in variables:
stop |= v.stop
f = v.parent
if (f is None):
continue
if (f in fclosed):
continue
fclosed.add(f)
stop_f ... |
class Partition3(nn.Module):
LAYER_SCOPES = ['BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/BertLayer[8]/BertAttention[attention]/BertSelfOutput[output]/LayerNorm[LayerNorm]', 'BertForQuestionAnswering/BertModel[bert]/BertEncoder[encoder]/BertLayer[8]/BertIntermediate[intermediate]/Linear[dense]', '... |
class TestConstant(unittest.TestCase):
def test_objective_function(self):
obj = objective.Constant(1)
self.assertEqual(obj.calculate_objective_function(None), 1)
val = 2
obj = objective.Constant(val)
val = 3
self.assertEqual(obj.calculate_objective_function(None), 2)
... |
def rotx(t):
c = np.cos(t)
s = np.sin(t)
return np.array([[1, 0, 0], [0, c, (- s)], [0, s, c]]) |
class TextCapsCapEvalDataset(CaptionEvalDataset):
def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
BaseDataset.__init__(self, vis_processor, text_processor, vis_root, ann_paths)
self.annotation = self.annotation[3]['data']
self.annotation = [ann for ann in self.annotat... |
def main(args):
mimic_notes_fpath = args.mimic_notes
anno_data_path = args.anno_data
outputdir = args.outputdir
dataset = {'gold': 'gold.pain_complications.mimic.row_ids.tsv', 'unlabeled': 'unlabeled.pain_complications.mimic.row_ids.tsv'}
print('Loading MIMIC-III notes ...')
for name in dataset:... |
def main():
try:
(opts, args) = getopt.getopt(sys.argv[1:], '')
except:
usage(sys.argv[0])
for (opt, arg) in opts:
usage(sys.argv[0])
if (len(args) != 2):
usage(sys.argv[0])
waves = int(args[0])
seeds = int(args[1])
print((((('Conditional estimation on snowbal... |
class GooglenetModel(model.Model):
def __init__(self):
super(GooglenetModel, self).__init__('googlenet', 224, 32, 0.005)
def add_inference(self, cnn):
def inception_v1(cnn, k, l, m, n, p, q):
cols = [[('conv', k, 1, 1)], [('conv', l, 1, 1), ('conv', m, 3, 3)], [('conv', n, 1, 1), ('c... |
.script
def batch_select(data, mask, dims, dim, index):
data = data.select(dim, index)
if dims[(dim - 1)]:
mask = mask.select(dim, 0)
else:
mask = mask.select(dim, index)
dims = torch.cat((dims[:(dim - 1)], dims[dim:dims.size(0)]))
return (data, mask, dims) |
class IBertForMaskedLM():
def __init__(self, *args, **kwargs):
requires_pytorch(self)
def from_pretrained(self, *args, **kwargs):
requires_pytorch(self) |
def test_version_1_point_10():
assert_((NumpyVersion('1.9.0') < '1.10.0'))
assert_((NumpyVersion('1.11.0') < '1.11.1'))
assert_((NumpyVersion('1.11.0') == '1.11.0'))
assert_((NumpyVersion('1.99.11') < '1.99.12')) |
def calc_wer_on_dataset(dataset, refs, options, hyps):
assert (dataset or refs)
start_time = time.time()
seq_len_stats = {'refs': Stats(), 'hyps': Stats()}
seq_idx = options.startseq
if (options.endseq < 0):
options.endseq = float('inf')
wer = 1.0
remaining_hyp_seq_tags = set(hyps.ke... |
def _get_long_description():
with open(str((Path(__file__).parent / 'README.md')), 'r') as f:
return f.read() |
def depthwise_net_for_pruning(image, threshold, with_bias=False, channel_last=False, name_scope='net1'):
with nn.parameter_scope(name_scope):
h = image
h /= 255.0
h = PF.convolution(h, 16, kernel=(3, 3), pad=(1, 1), with_bias=False, channel_last=channel_last, name='conv')
inputs = h.... |
class Layout(Enum):
alignEU = 0
compact = 1
offset = 2
stride = 3
matrix = 10
matrix2 = 11
_64IC = 20
_32IC = 21
_1IC = 22
_16IC = 23
T3 = 30
T4 = 31
T5 = 32
DMAstride = 40
DMA4Bank = 41
DMAmatrix = 42
DMAlinear = 43
alignEU_XN = 50
compact_XN ... |
class LassoBenchmark(Predictor, Estimator, Benchmark):
param_names = ['representation', 'precompute']
params = (['dense', 'sparse'], [True, False])
def setup_cache(self):
super().setup_cache()
def make_data(self, params):
(representation, precompute) = params
if (representation =... |
class Datagen_set():
def __init__(self, X, Y, batch_size, code_dic, nl_dic, train=True):
self.X = X
self.Y = Y
self.batch_size = batch_size
self.code_dic = code_dic
self.nl_dic = nl_dic
self.train = train
def __len__(self):
return len(range(0, len(self.X),... |
class PairedEvaluationDataset(Dataset):
def __init__(self, pair_file_list, image_size=512):
self.image_size = image_size
self.pair_file_list = pair_file_list
def __len__(self):
return len(self.pair_file_list)
def __getitem__(self, item):
(pred_file, ref_file) = self.pair_file... |
class ScipyLBFGSBTuner(Tuner):
def tune_impl(self, **kwargs):
if ('init_method' in kwargs):
init_method = kwargs['init_method']
else:
init_method = 'average'
if (self.start_config is not None):
config = self.start_config
elif (init_method is 'avera... |
(ipyvuetify=_HAS_IPYVUETIFY, IPython=_HAS_IPYTHON)
def init_filename_textfield():
return v.TextField(class_='ml-3 pl-3', style_='max-width: 600px', v_model=str(Path.cwd().joinpath('plot.pdf')), label='Save As') |
class SwitchWhiten2d(Module):
def __init__(self, num_features, num_pergroup=16, sw_type=2, T=5, tie_weight=False, eps=1e-05, momentum=0.99, affine=True):
super(SwitchWhiten2d, self).__init__()
if (sw_type not in [2, 3, 5]):
raise ValueError('sw_type should be in [2, 3, 5], but got {}'.fo... |
def run_full_influence_functions(mode: str, num_examples_to_test: int, s_test_num_samples: int=1000) -> Dict[(int, Dict[(str, Any)])]:
if (mode not in ['only-correct', 'only-incorrect']):
raise ValueError(f'Unrecognized mode {mode}')
(tokenizer, model) = misc_utils.create_tokenizer_and_model(constants.M... |
_INGREDIENT.capture
def build_model(graph_adj, node_features, labels, dataset_indices_placeholder, train_feed, trainval_feed, val_feed, test_feed, weight_decay, normalize_features, num_layers, hidden_size, dropout_prob):
dropout = tf.placeholder(dtype=tf.float32, shape=[])
train_feed[dropout] = dropout_prob
... |
(precision=4)
def backward_difference():
(X, _, _) = get_mushroom_data()
print(X.info())
enc = ce.BackwardDifferenceEncoder()
enc.fit(X, None)
out = enc.transform(X)
print(out.info())
del enc, _, X, out |
class UpSample(nn.Module):
def __init__(self, layer_type):
super().__init__()
self.layer_type = layer_type
def forward(self, x):
if (self.layer_type == 'none'):
return x
elif (self.layer_type == 'timepreserve'):
return F.interpolate(x, scale_factor=(2, 1),... |
def get_rotated_mnist_loaders(angle, data_path, model_class='LeNet', download=False):
if (model_class == 'MLP'):
shift_tforms = transforms.Compose([RotationTransform(angle), transforms.ToTensor(), ReshapeTransform(((- 1),))])
else:
shift_tforms = transforms.Compose([RotationTransform(angle), tra... |
def to_gif(video, duration, event):
output = '/tmp/processed-{}.gif'.format(os.path.basename(video))
call_ffmpeg(['-i', video, '-t', '{0}'.format(duration), '-vf', 'fps=10,scale=320:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse', '-loop', '0', output])
return output |
def load_checkpoints(directory, is_gpu=True):
checkpoints = []
for (root, _, filenames) in os.walk(directory):
for filename in filenames:
results = re.search('.*?-([0-9].*?).pt', filename)
if (results is not None):
epoch_idx = int(results.group(1))
... |
_model
def hrnet_w18_small(pretrained=True, **kwargs):
return _create_model('hrnet_w18_small', pretrained, kwargs) |
_utils.test()
def test_nested_subscript():
x = ti.field(ti.i32)
y = ti.field(ti.i32)
ti.root.dense(ti.i, 1).place(x)
ti.root.dense(ti.i, 1).place(y)
x[0] = 0
def inc():
for i in range(1):
x[x[i]] += 1
inc()
assert (x[0] == 1) |
def test_mixed(spark_session):
_run_job(spark=spark_session, name='mixed', data_cols=['Weekly_Sales', 'Temperature', 'CPI'], hierarchical=True, agg_dict={'Weekly_Sales': 'sum'}, predict_on_train=True, robust=False) |
class LightConv3x3(nn.Module):
def __init__(self, in_channels, out_channels):
super(LightConv3x3, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=False, grou... |
class TestAI21TokenCounter():
def setup_method(self, method):
self.token_counter = AI21TokenCounter()
def test_count_tokens(self):
request = Request(model='openai/text-davinci-002', model_deployment='openai/text-davinci-002', prompt='The Center for Research on Foundation Models (CRFM) is an inte... |
def clean_graph_item(graph_item):
clean_graph_item = copy.deepcopy(graph_item)
if ('optional' in clean_graph_item):
del clean_graph_item['optional']
if ('required' in clean_graph_item):
del clean_graph_item['required']
for (index, ii) in enumerate(clean_graph_item['objects']):
if... |
def getConnection(db=None, driver=None, user=None, password=None, host=None):
conn_str = ('dbname=%s host=%s' % (db, host))
if (user is not None):
conn_str = (conn_str + (' user=%s' % user))
if (password is not None):
conn_str = (conn_str + (' password=%s' % password))
conn = psycopg2.co... |
def _is_stopped(demo, i, obs, stopped_buffer, delta=0.1):
next_is_not_final = (i == (len(demo) - 2))
gripper_state_no_change = ((i < (len(demo) - 2)) and ((obs.gripper_open == demo[(i + 1)].gripper_open) and (obs.gripper_open == demo[(i - 1)].gripper_open) and (demo[(i - 2)].gripper_open == demo[(i - 1)].grippe... |
def truncate_seq_pair(tokens_a, tokens_b, max_length):
while True:
total_length = (len(tokens_a) + len(tokens_b))
if (total_length <= max_length):
break
if (len(tokens_a) > len(tokens_b)):
tokens_a.pop(0)
else:
tokens_b.pop(0) |
class A002113(SloaneSequence):
def __init__(self):
SloaneSequence.__init__(self, offset=0)
def _repr_(self):
return 'Palindromes in base 10.'
def _precompute(self, how_many=150):
try:
self._b
self._n
except AttributeError:
self._b = []
... |
class Decoder(layers.Layer):
def __init__(self, original_dim, intermediate_dim=600, name='decoder', regularization_lambda=0.01, random_seed=42, **kwargs):
super().__init__(name=name, **kwargs)
tf.random.set_seed(random_seed)
self.dense_proj = layers.Dense(intermediate_dim, activation='tanh',... |
def save_models(path: str, net, *, write_layers=True, file_format=None):
os.makedirs(path, exist_ok=True)
net_name = net.get_name()
_save_net_file(path, net_name, net, file_format=file_format)
if write_layers:
models = bb.get_model_list(net, flatten=True)
fname_list = []
for (i, ... |
_properties
class StencilTiling(transformation.SubgraphTransformation):
debug = Property(desc='Debug mode', dtype=bool, default=False)
prefix = Property(dtype=str, default='stencil', desc='Prefix for new inner tiled range symbols')
strides = ShapeProperty(dtype=tuple, default=(1,), desc='Tile stride')
s... |
class TFConvBertForTokenClassification():
def __init__(self, *args, **kwargs):
requires_tf(self)
def from_pretrained(self, *args, **kwargs):
requires_tf(self) |
def test_recordarray_7():
def test_recordarray_7(x):
return ((2 * x.y[(2, 0, 1)]) + 10)
(value_jvp, jvp_grad) = jax.jvp(test_recordarray_7, (test_recordarray,), (test_recordarray_tangent,))
(value_vjp, vjp_func) = jax.vjp(test_recordarray_7, test_recordarray)
assert (ak.to_list(value_jvp) == 14.... |
def load_dataset(choice, data_dir='./data/'):
if (choice == 'mnist2d'):
from datasets.mnist import mnist2d_10class
return mnist2d_10class(data_dir)
if (choice == 'mnist2d_2class'):
from datasets.mnist import mnist2d_2class
return mnist2d_2class(data_dir)
if (choice == 'mnistv... |
class Sampler(torch.utils.data.Sampler):
def __init__(self, buckets, batch_size, shuffle=False, distributed=False, evaluate=False):
self.batch_size = batch_size
self.shuffle = shuffle
(self.sizes, self.buckets) = zip(*[(size, bucket) for (size, bucket) in buckets.items()])
self.chunk... |
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