code stringlengths 101 5.91M |
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_numpy_output(check_dtype=True)
def test_ufunc_expm1_c(A: dace.complex64[10]):
return np.expm1(A) |
class TestContainsNaNTest():
def test_policy(self):
data = np.array([1, 2, 3, np.nan])
(contains_nan, nan_policy) = _contains_nan(data, nan_policy='propagate')
assert contains_nan
assert (nan_policy == 'propagate')
(contains_nan, nan_policy) = _contains_nan(data, nan_policy='... |
def create_batches(sampler, shuffle=True, cache_dir='cache'):
batches_dict = defaultdict((lambda : []))
for (i, batch) in enumerate(tqdm(sampler, desc='Creating batches for training')):
for (k, v) in batch.items():
batches_dict[k].append(v)
batches = Dataset.from_dict(batches_dict)
r... |
class TestMediumLevelActionManagerSimple(unittest.TestCase):
def test_simple_mdp_without_start_orientations(self):
print('Simple - no start orientations (& shared motion goals)')
mlam = ml_action_manager_simple
self.simple_mpd_empty_hands(mlam)
self.simple_mdp_deliver_soup(mlam)
... |
def get_dataset_details(dataset):
if (dataset == 'mnist'):
(input_nc, input_width, input_height) = (1, 28, 28)
classes = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
elif (dataset == 'cifar10'):
(input_nc, input_width, input_height) = (3, 32, 32)
classes = ('plane', 'car', 'bird', 'cat', 'deer... |
def writetab(llargs, fout, kset):
t = Texttable()
t.set_max_width(500)
info = ['dataset', 'al_type', 'knn', 'clustering']
restricted = ['knn', 'clustering']
header = []
format_row = []
for k in info:
if (k not in ['exp_fd']):
header.append(k)
format_row.append... |
def test_trigamma():
x = Symbol('x')
assert (trigamma((- 2)) == zoo)
assert (trigamma(x) == polygamma(1, x)) |
class GroupAll(nn.Module):
def __init__(self, use_xyz: bool=True):
super().__init__()
self.use_xyz = use_xyz
def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor=None):
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if (features is not None):
... |
def verify_dir_exists(filename):
if (not os.path.exists(os.path.dirname(filename))):
try:
os.makedirs(os.path.dirname(filename))
except OSError as exc:
if (exc.errno != errno.EEXIST):
raise |
def get_types(entity: str) -> List[str]:
query = (('\n PREFIX rdf: < PREFIX rdfs: < PREFIX : < \n SELECT (?x0 AS ?value) WHERE {\n SELECT DISTINCT ?x0 WHERE {\n :' + entity) + ' :type.object.type ?x0 . \n }\n }\n ')
sparql.setQuery(query)
try:
results = sparql.query().con... |
def euler_xyz_to_R(euler):
return ((_Rz(np.deg2rad(euler[2])) * _Ry(np.deg2rad(euler[1]))) * _Rx(np.deg2rad(euler[0]))) |
def test_Updater_GradientDescent():
with make_scope() as session:
from returnn.tf.network import TFNetwork, ExternData
from returnn.config import Config
config = Config()
network = TFNetwork(extern_data=ExternData(), train_flag=True)
network.add_layer(name='output', layer_cla... |
def plot_embedding_as_heatmap(embed, ax=None, title='', shape=None, color_range=(0, 0.3)):
if (ax is None):
ax = plt.gca()
if (shape is None):
height = int(np.sqrt(len(embed)))
shape = (height, (- 1))
embed = embed.reshape(shape)
cmap = cm.get_cmap()
mappable = ax.imshow(embe... |
def test_pytest_parametrize(testdir):
testdir.make_test('\.parametrize("param", ("A", "B"))\()\ndef test_(request, param, case):\n request.config.HYPOTHESIS_CASES += 1\n assert case.full_path == "/v1/users"\n assert case.method in ("GET", "POST")\n', paths={'/users': {'get': {'responses': {'200': {'descrip... |
def nonsaturating_hinge_gan_losses(discriminator_real_outputs, discriminator_fake_outputs):
generator_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(discriminator_fake_outputs), discriminator_fake_outputs)
discriminator_loss = (tf.reduce_mean(tf.nn.relu((1.0 - discriminator_real_outputs))) + tf.reduce_mean... |
class ParameterNamer(object):
def __call__(self, graph):
for node in graph.nodes:
if (node.data is None):
continue
if (node.kind in (NodeKind.Convolution, NodeKind.InnerProduct)):
names = ('weights',)
if node.parameters.bias_term:
... |
def group_together(file_paths, num_samples):
for i in range(1, len(num_samples)):
num_samples[i] *= num_samples[(i - 1)]
all_lines = []
for file_path in file_paths:
lines = []
with open(file_path) as f:
for line in f:
lines.append(line.strip())
all... |
class RefAdagrad(RefSolver):
def __init__(self, lr, eps):
super().__init__()
self.lr = lr
self.eps = eps
self.G = {}
def _set_state_impl(self, key, param):
self.G[key] = np.zeros_like(param)
def _update_impl(self, key, p, g):
_update_adagrad(p, g, self.G[key],... |
def test_test_case_equals_on_different_prim(simple_test_case: dtc.DefaultTestCase, constructor_mock):
cloned = simple_test_case.clone()
simple_test_case.add_statement(st.ConstructorStatement(simple_test_case, constructor_mock, {'y': simple_test_case.statements[0].ret_val}))
cloned.add_statement(st.Construct... |
def test_format_tags():
tags = ['tag_1', 'tag_2', 'tag_3']
assert (format_tags(tags) == '[tag_1,tag_2,tag_3]') |
class ThresholdedImprovementScoringFunction(MoleculewiseScoringFunction):
def __init__(self, objective, constraint, threshold, offset):
super().__init__()
self.objective = objective
self.constraint = constraint
self.threshold = threshold
self.offset = offset
def raw_score... |
def test_for_one_epoch(model, loss, test_loader, epoch_number):
model.eval()
loss.eval()
data_time_meter = utils.AverageMeter()
batch_time_meter = utils.AverageMeter()
loss_meter = utils.AverageMeter(recent=100)
top1_meter = utils.AverageMeter(recent=100)
top5_meter = utils.AverageMeter(rece... |
class ContinualScaler():
state_dict_key = 'amp_scaler'
def __init__(self, disable_amp):
self._scaler = torch.cuda.amp.GradScaler(enabled=(not disable_amp))
def __call__(self, loss, optimizer, model_without_ddp, clip_grad=None, clip_mode='norm', parameters=None, create_graph=False, hook=True):
... |
def index_predicates(es, KB):
file_name = ('%s_predicates' % KB)
file_path = ('../data/%s.txt' % file_name)
ns_filter = None
index_name = ('%sp' % KB)
start_indexing(es, index_name, file_path, ns_filter) |
def run(*args, env=None):
args = list(map(str, args))
if (env is None):
return subprocess.Popen(args).wait()
else:
e = os.environ.copy()
e.update(env)
return subprocess.Popen(args, env=e).wait() |
class Tanh_DenseNet(nn.Module):
def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):
super(Tanh_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, bias=... |
class Config(NamedTuple):
name: str
settings_train: str
settings_eval: str
rendering_mode: LoadedModel.EvaluationMode
args: List[str] |
class Dict(TokenConverter):
def __init__(self, expr):
super(Dict, self).__init__(expr)
self.saveAsList = True
def postParse(self, instring, loc, tokenlist):
for (i, tok) in enumerate(tokenlist):
if (len(tok) == 0):
continue
ikey = tok[0]
... |
class ginn_autoencoder(nn.Module):
def __init__(self, g, mask, in_feats, h_feats, activation, dropout):
super(ginn_autoencoder, self).__init__()
self.mask = mask
self.masked_gcn = GCL(g, in_feats, h_feats, activation, dropout)
self.output_gcn = GCL(g, h_feats, in_feats, torch.sigmoid... |
def width_to_lifetime(Gamma):
if (Gamma <= 0.0):
raise ValueError('Input provided, %s <= 0!'.format(Gamma))
return (hbar / float((Gamma / MeV))) |
def get_train_transformers(args):
img_tr = [transforms.RandomResizedCrop(int(args.image_size), (args.min_scale, args.max_scale))]
if (args.flip > 0.0):
img_tr.append(transforms.RandomHorizontalFlip(args.flip))
if (args.jitter > 0.0):
img_tr.append(transforms.ColorJitter(brightness=args.jitte... |
class FunnelTokenizerFast(PreTrainedTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
slow_tokenizer_class = FunnelTokenizer
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDI... |
def jamendo_resampler(track_id):
audio_path = os.path.join(DATASET, 'mtg', 'raw30s', track_id)
(src, _) = load_audio(path=audio_path, ch_format=STR_CH_FIRST, sample_rate=MUSIC_SAMPLE_RATE, downmix_to_mono=True)
save_name = os.path.join(DATASET, 'mtg', 'npy', track_id.replace('.mp3', '.npy'))
if (not os.... |
class TimeSplitter(Splitter):
_init_arg_names = ['time_threshold', 'drop_cold_users', 'drop_cold_items', 'query_column', 'item_column', 'timestamp_column', 'session_id_column', 'session_id_processing_strategy', 'time_column_format']
def __init__(self, time_threshold: Union[(datetime, str, int, float)], query_co... |
def get_image_ocrs_from_path(pdf_file_path: str, ocr_file_path: str, resize_scale=resize_scale):
reader = PdfReader(pdf_file_path)
img_list = []
for i in range(len(reader.pages)):
page = reader.pages[i]
for image_file_object in page.images:
stream = io.BytesIO(image_file_object.d... |
_torch
_sigopt
class TrainerHyperParameterSigOptIntegrationTest(unittest.TestCase):
def setUp(self):
args = TrainingArguments('..')
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
def test_hyperparameter_search(self):
class MyTrialShortNamer(TrialSho... |
def tplquad(func, a, b, gfun, hfun, qfun, rfun, args=(), epsabs=1.49e-08, epsrel=1.49e-08):
def ranges0(*args):
return [(qfun(args[1], args[0]) if callable(qfun) else qfun), (rfun(args[1], args[0]) if callable(rfun) else rfun)]
def ranges1(*args):
return [(gfun(args[0]) if callable(gfun) else gf... |
class Renderer(object):
MAX_FBO_WIDTH = 2000
MAX_FBO_HEIGHT = 2000
def __init__(self, models_cad_files, samples=1, vertex_tmp_store_folder='.', clamp=False, vertex_scale=1.0):
self._samples = samples
self._context = gu.OffscreenContext()
(W, H) = (Renderer.MAX_FBO_WIDTH, Renderer.MAX... |
def evaluate_mislabeling_patch(target_class, rois, detections_adv, detections_rand, iou_thresh=0.5):
(score_adv, score_rand) = (0, 0)
for (_, roi_obj_bbox, _, _) in rois:
found = False
for detection in detections_adv:
det_obj_bbox = tuple(map(float, detection[(- 4):]))
de... |
def Train(model, x, adj, A, optimizer):
max_epochs = 100
min_loss = 100
for epoch in range(max_epochs):
Y = model(x, adj)
loss = CutLoss.apply(Y, A)
print('Epoch {}: Loss = {}'.format(epoch, loss.item()))
if (loss < min_loss):
min_loss = loss.item()
... |
def rand_v_diffusion(shape, sigma_data=1.0, min_value=0.0, max_value=float('inf'), device='cpu', dtype=torch.float32):
min_cdf = ((math.atan((min_value / sigma_data)) * 2) / math.pi)
max_cdf = ((math.atan((max_value / sigma_data)) * 2) / math.pi)
u = ((torch.rand(shape, device=device, dtype=dtype) * (max_cd... |
class RepeatFactorTrainingSampler(Sampler):
def __init__(self, dataset, config, num_replicas=None, rank=None, shuffle=True):
self.shuffle = shuffle
self.config = config
if (num_replicas is None):
if (not dist.is_available()):
raise RuntimeError('Requires distribut... |
class TrafficControlPredictTrafficCongestion(VirtualFunctionTool):
name = 'TrafficControlPredictTrafficCongestion'
summary = 'Predicts traffic congestion at a specific road or intersection in the future based on historical data and current conditions.'
parameters: List[ArgParameter] = [{'name': 'location_id... |
def get_activations(images, sess, batch_size=50, verbose=False):
inception_layer = _get_inception_layer(sess)
d0 = images.shape[0]
if (batch_size > d0):
print('warning: batch size is bigger than the data size. setting batch size to data size')
batch_size = d0
n_batches = (d0 // batch_siz... |
def test_evaluation():
table = pd.DataFrame({'id': [0, 1, 2, 3], 'col': [1, 2, 3, 4]})
slightly_different_table = pd.DataFrame({'id': [0, 1, 2, 3], 'col': [1, 2, 3, 3.5]})
data = {'table1': table, 'table2': table}
samples = {'table1': table, 'table2': slightly_different_table}
metadata = MultiTableM... |
def late_import():
if ('GF2' in globals()):
return
global Cache_ntl_gf2e, GF, GF2
import sage.rings.finite_rings.element_ntl_gf2e
Cache_ntl_gf2e = sage.rings.finite_rings.element_ntl_gf2e.Cache_ntl_gf2e
import sage.rings.finite_rings.finite_field_constructor
GF = sage.rings.finite_rings.... |
def first_bn_multiplier_weighting_fn(orig_bn_stats_holder: KerasOriginalBNStatsHolder, **kwargs) -> Dict[(str, float)]:
num_bn_layers = orig_bn_stats_holder.get_num_bn_layers()
layer_weighting_dict = {orig_bn_stats_holder.get_bn_layer_names()[0]: (10 / num_bn_layers)}
layer_weighting_dict.update({bn_layer_n... |
class _FilePersistence(_ConcretePersistence):
def __init__(self, data_filename, data_store, configurator, ui):
super(_FilePersistence, self).__init__(data_store, ui)
if (not data_filename):
raise ValueError(('DataPointPersistence expects a filename ' + ('for data_filename, but got: %s' %... |
class Swish(nn.Module):
def __init__(self, inplace: bool=False):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
return swish(x, self.inplace) |
def TupleSort(name, sorts, ctx=None):
tuple = Datatype(name, ctx)
projects = [(('project%d' % i), sorts[i]) for i in range(len(sorts))]
tuple.declare(name, *projects)
tuple = tuple.create()
return (tuple, tuple.constructor(0), [tuple.accessor(0, i) for i in range(len(sorts))]) |
def magnet_loss(features, labels, margin=1.0, unique_labels=None):
nil = tf.constant(0.0, tf.float32)
one = tf.constant(1.0, tf.float32)
minus_two = tf.constant((- 2.0), tf.float32)
eps = tf.constant(0.0001, tf.float32)
margin = tf.constant(margin, tf.float32)
num_per_class = None
if (unique... |
def _has_only_empty_bbox(anno):
return all((any(((o <= 1) for o in obj['bbox'][2:])) for obj in anno)) |
def model_parameters(model):
return (sum([np.prod(p.size()) for p in model.parameters()]) / 1000000.0) |
def gradients_speed(ys, xs, grad_ys=None, **kwargs):
return gradients(ys, xs, grad_ys, checkpoints='speed', **kwargs) |
def get_arg_parser():
from snips_nlu.cli.download import add_download_parser, add_download_all_languages_parser
from snips_nlu.cli.download_entity import add_download_entity_parser, add_download_language_entities_parser
from snips_nlu.cli.generate_dataset import add_generate_dataset_subparser
from snips... |
def get_args_EBM():
parser = argparse.ArgumentParser(description='Concept argparse.')
parser.add_argument('--exp_id', type=str, help='Experiment id')
parser.add_argument('--date_time', type=str, help='date and time')
parser.add_argument('--exp_name', default='None', help='If not "None", will use asynchr... |
def generate_png(all_iter, net, gt_hsi, Dataset, device, total_indices, path):
pred_test = []
for (X, y) in all_iter:
X = X.to(device)
net.eval()
pred_test.extend(net(X).cpu().argmax(axis=1).detach().numpy())
gt = gt_hsi.flatten()
x_label = np.zeros(gt.shape)
for i in range(l... |
def parse_flags(line):
d = {'include_dirs': [], 'library_dirs': [], 'libraries': [], 'macros': [], 'ignored': []}
flags = (' ' + line).split(' -')
for flag in flags:
flag = ('-' + flag)
if (len(flag) > 0):
if flag.startswith('-I'):
d['include_dirs'].append(flag[2:... |
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('dump_dir')
parser.add_argument('start', type=int)
parser.add_argument('end', type=int)
return parser.parse_args() |
def _apply_chi(dwg, g, meld, offset):
tile = Meld.target(meld)
if (Meld.action(meld) == Action.CHI_L):
tile1 = tile
tile2 = (tile + 1)
tile3 = (tile + 2)
elif (Meld.action(meld) == Action.CHI_M):
tile1 = tile
tile2 = (tile - 1)
tile3 = (tile + 1)
else:
... |
def register_Ns3Icmpv6TimeExceeded_methods(root_module, cls):
cls.add_constructor([param('ns3::Icmpv6TimeExceeded const &', 'arg0')])
cls.add_constructor([])
cls.add_method('Deserialize', 'uint32_t', [param('ns3::Buffer::Iterator', 'start')], is_virtual=True)
cls.add_method('GetInstanceTypeId', 'ns3::Ty... |
def is_external_stream(node: dace.sdfg.nodes.Node, subgraph: Union[(dace.sdfg.SDFGState, ScopeSubgraphView)]):
external = False
if (isinstance(node, dace.nodes.AccessNode) and isinstance(node.desc(subgraph), dt.Stream)):
for nn in subgraph.nodes():
if ((nn != node) and isinstance(nn, dace.no... |
class LinUCBVI(UCBVI):
def __init__(self, mdp, n_episodes=1, init_state=0, reg_factor=1.0, confidence_scaling_factor=(- 1.0), bound_theta=1.0, throttle=int(100.0)):
self.bound_theta = bound_theta
super().__init__(mdp, n_episodes=n_episodes, init_state=init_state, reg_factor=reg_factor, confidence_sc... |
.hypothesis_nested
.operations('custom_format')
def test_schema_query_hook(wsgi_app_schema, schema_url):
_app_schema.hook
def filter_query(context, query):
return (query['id'].isdigit() and query['id'].isascii())
strategy = wsgi_app_schema['/custom_format']['GET'].as_strategy()
(case=strategy)
... |
_metric
def fid50k_full(opts):
opts.dataset_kwargs.update(max_size=None)
opts.dataset_kwargs.cfg.update(mirror=False)
fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000)
return dict(fid50k_full=fid) |
def get_command_args():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', help='pattern config', type=str, default='meta_infos/configs/dataset_config.yaml')
parser.add_argument('--out', '-o', help='folder to save generated patterns', type=str, default='test/outputs')
args = parser... |
class MobileInvertedResidualBlock(MyModule):
def __init__(self, mobile_inverted_conv, shortcut):
super(MobileInvertedResidualBlock, self).__init__()
self.mobile_inverted_conv = mobile_inverted_conv
self.shortcut = shortcut
def forward(self, x):
if ((self.mobile_inverted_conv is N... |
def _update_command_info():
global _command_info_cache
if (_command_info_cache is not None):
return
cache = {}
with open(os.path.join(SAGE_LOCAL, 'share/qepcad', 'qepcad.help')) as help:
assert (help.readline().strip() == '')
while True:
cmd_line = help.readline()
... |
('/annotator', methods=['GET'])
def annotator():
cad_type = request.args.get('cad', '')
if (cad_type == '0'):
condition = ''
else:
condition = f'and source = {cad_type}'
keyword = request.args.get('keyword', '')
img_info = get_cad_imgs(keyword, condition)
print(f'{len(img_info)} ... |
def _digit_span_to_special_tag(span):
if ((span[0] == '0') and (len(span) > 2)):
return '<NUM>'
decimal_point_count = 0
for (idx, char) in enumerate(span):
if ((char == '.') or (char == '.') or (char == '')):
decimal_point_count += 1
if ((span[(- 1)] == '.') or (span[(- 1)] =... |
def elog(x):
if ((x <= 0.0) or (x >= 1.0)):
return 0
else:
return (x * log(x)) |
def build_input_fn(builder, is_training):
def _input_fn(params):
preprocess_fn_pretrain = get_preprocess_fn(is_training, is_pretrain=True)
preprocess_fn_finetune = get_preprocess_fn(is_training, is_pretrain=False)
num_classes = builder.info.features['label'].num_classes
def map_fn(im... |
def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[(int, float)]:
if (args.local_rank in [(- 1), 0]):
tb_writer = SummaryWriter()
args.train_batch_size = (args.per_gpu_train_batch_size * max(1, args.n_gpu))
def collate(examples: List[torch.Tensor]):
... |
def is_valid_profile(profile, truncation_type, p=2, generic=None):
from sage.rings.infinity import Infinity
if (generic is None):
generic = (p != 2)
if (not generic):
pro = (list(profile) + ([truncation_type] * len(profile)))
r = 0
for pro_r in pro:
r += 1
... |
class TomlEncoder(object):
def __init__(self, _dict=dict, preserve=False):
self._dict = _dict
self.preserve = preserve
self.dump_funcs = {str: _dump_str, unicode: _dump_str, list: self.dump_list, bool: (lambda v: unicode(v).lower()), int: (lambda v: v), float: _dump_float, Decimal: _dump_flo... |
class PNW(BenchmarkDataset):
def __init__(self, **kwargs):
citation = 'Ni, Y., Hutko, A., Skene, F., Denolle, M., Malone, S., Bodin, P., Hartog, R., & Wright, A. (2023).Curated Pacific Northwest AI-ready Seismic Dataset. Seismica, 2(1).
license = 'CC BY 4.0'
super().__init__(citation=citati... |
class Task(object):
def __init__(self, config):
self.config = config
self.cli = Client(config)
def exec_sql(self, code, output=sys.stdout, resultful=False):
(task_id, status) = self.cli.create_sql_task(code)
return self._tracking(task_id, status, output, resultful)
def exec_p... |
class blis_info(blas_info):
section = 'blis'
dir_env_var = 'BLIS'
_lib_names = ['blis']
notfounderror = BlasNotFoundError
def calc_info(self):
lib_dirs = self.get_lib_dirs()
opt = self.get_option_single('blis_libs', 'libraries')
blis_libs = self.get_libs(opt, self._lib_names)... |
class Brick(PhysicalObject):
def __init__(self, *args, **kwargs):
self.row = kwargs.pop('row')
self.column = kwargs.pop('column')
kwargs['color'] = self.get_color()
super(Brick, self).__init__('brick.png', *args, **kwargs)
def get_color(self):
colors = {0: (255, 0, 0), 1:... |
def create_model(cfg, device):
cfg = copy.deepcopy(cfg)
cfg.freeze()
model = build_detection_model(cfg)
model = model.to(device)
return model |
class Singular(Executable):
def __init__(self):
Executable.__init__(self, 'singular', SINGULAR_BIN, spkg='singular', type='standard') |
class RandomIdentitySamplerAdv(Sampler):
def __init__(self, data_source, batch_size, num_instances):
self.data_source = data_source
self.batch_size = batch_size
self.num_instances = num_instances
self.num_pids_per_batch = (self.batch_size // self.num_instances)
self.index_dic... |
class ChineseCLIPVisionConfig(PretrainedConfig):
model_type = 'chinese_clip_vision_model'
def __init__(self, hidden_size=768, intermediate_size=3072, projection_dim=512, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=32, hidden_act='quick_gelu', layer_norm_eps=1e-05, at... |
class FalseConditionElimination(transformation.MultiStateTransformation):
state_a = transformation.PatternNode(sdfg.SDFGState)
state_b = transformation.PatternNode(sdfg.SDFGState)
def expressions(cls):
return [sdutil.node_path_graph(cls.state_a, cls.state_b)]
def can_be_applied(self, graph: SDFG... |
def check_all_models_are_tested():
modules = get_model_modules()
test_files = get_model_test_files()
failures = []
for module in modules:
test_file = [file for file in test_files if (f"test_{module.__name__.split('.')[(- 1)]}.py" in file)]
if (len(test_file) == 0):
failures.a... |
def get_score(submission_folder):
FLAGS(['eval.py'])
if (FLAGS.hint_mode == 'encoded_decoded'):
encode_hints = True
decode_hints = True
elif (FLAGS.hint_mode == 'decoded_only'):
encode_hints = False
decode_hints = True
elif (FLAGS.hint_mode == 'none'):
encode_hint... |
def init_weights(net, init_type='normal', init_gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if (hasattr(m, 'weight') and ((classname.find('Conv') != (- 1)) or (classname.find('Linear') != (- 1)))):
if (init_type == 'normal'):
init.normal_(m.weight.data, ... |
class VoVNet(Backbone):
def __init__(self, cfg, input_ch, out_features=None):
super(VoVNet, self).__init__()
global _NORM
_NORM = cfg.MODEL.VOVNET.NORM
stage_specs = _STAGE_SPECS[cfg.MODEL.VOVNET.CONV_BODY]
stem_ch = stage_specs['stem']
config_stage_ch = stage_specs['... |
def find_library_location(lib_name: str) -> Path:
torch_root = Path(torch.__file__).resolve().parent
path = ((torch_root / 'lib') / lib_name)
if os.path.exists(path):
return path
torch_root = Path(__file__).resolve().parent.parent.parent
return (((torch_root / 'build') / 'lib') / lib_name) |
.parametrize('inspecs', inspecs_params())
.parametrize('op', ['logical_and_scalar', 'logical_or_scalar', 'logical_xor_scalar', 'greater_scalar', 'greater_equal_scalar', 'less_scalar', 'less_equal_scalar', 'equal_scalar', 'not_equal_scalar'])
def test_scalar_logical(inspecs, op, nnabla_opts):
func = getattr(F, op)
... |
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
if (args.work_dir is not None):
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
json_file = osp.join(args.work_dir, f'fps_{timestamp}.json')
else:
w... |
class _ContextMethodMixin(object):
def save_for_backward(self, *tensors):
self.to_save = tensors
def mark_dirty(self, *args):
self.dirty_tensors = args
def mark_shared_storage(self, *pairs):
warnings.warn('mark_shared_storage is deprecated. Tensors with shared storages are automatica... |
class BMProfileParserPerfAI(BMProfileParser):
def __init__(self):
super().__init__()
self.gdma_cmd = []
self.bd_cmd = []
self.bd_monitor = []
self.gdma_monitor = []
self.in_dir = None
self.out_dir = None
def parse(self, in_dir):
self.in_dir = in_di... |
def add_dataset_arguments(parser):
parser.add_argument('--train-examples-paths', nargs='*', default=[], help='Input training examples')
parser.add_argument('--test-examples-paths', nargs='*', default=[], help='Input test examples')
parser.add_argument('--train-max-examples', type=int, help='Maximum number o... |
class WeightRing(CombinatorialFreeModule):
def __classcall__(cls, parent, prefix=None):
return super().__classcall__(cls, parent, prefix=prefix)
def __init__(self, parent, prefix):
self._parent = parent
self._style = parent._style
self._prefix = prefix
self._space = paren... |
class RegLog(nn.Module):
def __init__(self, num_labels, arch='resnet50', global_avg=False, use_bn=True):
super(RegLog, self).__init__()
self.bn = None
if global_avg:
if (arch == 'resnet50'):
s = 2048
elif (arch == 'resnet50w2'):
s = 409... |
def write_prediction_result(prediction_result: PredictionResult, output_dir) -> None:
input_path = prediction_result.example
representation = prediction_result.inference
input_filename = os.path.basename(input_path.replace('gs://', ''))
output_filename = input_filename.replace('.wav', '.npy')
if out... |
def _unique_python(values, *, return_inverse, return_counts):
try:
uniques_set = set(values)
(uniques_set, missing_values) = _extract_missing(uniques_set)
uniques = sorted(uniques_set)
uniques.extend(missing_values.to_list())
uniques = np.array(uniques, dtype=values.dtype)
... |
class Transformer_exp(FNN_exp):
def __init__(self, data_path, param_dict, config):
super().__init__(data_path, param_dict, config)
def load_model(self):
model = TransformerNet(hidden_size=self.param_dict['hidden_size'], num_layers=self.param_dict['num_layers'], dropout=self.param_dict['dropout']... |
def job_fssdq_opt(p, data_source, tr, te, r, J, null_sim=None):
if (null_sim is None):
null_sim = gof.FSSDH0SimCovObs(n_simulate=2000, seed=r)
Xtr = tr.data()
with util.ContextTimer() as t:
n_gwidth_cand = 5
gwidth_factors = (2.0 ** np.linspace((- 3), 3, n_gwidth_cand))
med2 ... |
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