code stringlengths 101 5.91M |
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class GINEConv(nn.Module):
def __init__(self, nin, nout, bias=True):
super().__init__()
self.nn = MLP(nin, nout, 2, False, bias=bias)
self.layer = gnn.GINEConv(self.nn, train_eps=True)
def reset_parameters(self):
self.layer.reset_parameters()
def forward(self, x, edge_index, ... |
def extract_feature(inception_model, images):
features = inception_model(images, output_logits=False)
features = features.detach().cpu().numpy()
assert ((features.ndim == 2) and (features.shape[1] == 2048))
return features |
def generate_codes_list(hwdb1x_codes_list: list, hwdb2x_train_codes_list: list, hwdb2x_test_codes_list: list):
codes_list = hwdb1x_codes_list
for code in hwdb2x_train_codes_list:
if (code not in codes_list):
codes_list.append(code)
for code in hwdb2x_test_codes_list:
if (code not... |
def show_sample(sample):
print(('==' * 20))
print('idx:', sample['idx'])
for key in ['type', 'level']:
if (key in sample):
print('{}: {}'.format(key, sample[key]))
print('question:', sample['question'])
if ('code' in sample):
for code in sample['code']:
print(... |
def add_parser_arguments(parser):
parser.add_argument('--last-epoch', type=int, default=(- 1), metavar='', help='lr scheduler - the index of last epoch required by [all]')
parser.add_argument('--step-size', type=int, default=(- 1), metavar='', help='lr scheduler - period (epoch) of learning rate decay required ... |
class DataParallelCriterion(DataParallel):
def forward(self, inputs, *targets, **kwargs):
if (not self.device_ids):
return self.module(inputs, *targets, **kwargs)
(targets, kwargs) = self.scatter(targets, kwargs, self.device_ids)
if (len(self.device_ids) == 1):
return... |
class ResNet(nn.Module):
def __init__(self, block, num_blocks, cfg, num_classes=10):
super(ResNet, self).__init__()
n = 2
self.in_planes = 64
self.cfg = cfg
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
... |
def log_box_proposal_results(results):
for dataset in results.keys():
keys = results[dataset]['box_proposal'].keys()
pad = max([len(k) for k in keys])
logger.info(dataset)
for (k, v) in results[dataset]['box_proposal'].items():
logger.info('{}: {:.3f}'.format(k.ljust(pad)... |
def torch_gc():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
elif torch.backends.mps.is_available():
try:
from torch.mps import empty_cache
empty_cache()
except Exception as e:
print(e)
print(' mac... |
class SynthPairTnf_pck(object):
def __init__(self, use_cuda=True, geometric_model='affine', crop_factor=(9 / 16), output_size=(240, 240), padding_factor=0.5):
assert isinstance(use_cuda, bool)
assert isinstance(crop_factor, float)
assert isinstance(output_size, tuple)
assert isinstan... |
class Caltech256(data.Dataset):
base_folder = '256_ObjectCategories'
url = '
filename = '256_ObjectCategories.tar'
tgz_md5 = '67b4f42ca05d46448c6bb8ecd2220f6d'
def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
self.root = os.path.expanduser(root)
... |
def base_kernels(dimensions=1, base_kernel_names='SE'):
for kernel in base_kernels_without_dimension(base_kernel_names):
if kernel.is_thunk:
(yield kernel)
else:
for dimension in range(dimensions):
k = kernel.copy()
k.dimension = dimension
... |
class ResNet_LandScape(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
super(ResNet_LandScape, self).__init__()
if (norm_layer is None):
norm_layer = nn.BatchNorm2... |
def create_target_connection(target: ConfigTarget):
if (target.os == 'linux'):
conn = get_ssh_connection(target)
conn.connect()
else:
conn = get_smb_connection(target)
conn.connect()
return conn |
_BOX_FEATURE_EXTRACTORS.register('FPN2MLPFeatureExtractor')
class FPN2MLPFeatureExtractor(nn.Module):
def __init__(self, cfg, in_channels):
super(FPN2MLPFeatureExtractor, self).__init__()
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
... |
def GetModelParser():
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('-L', '--lr', '--learning_rate', help='Learning rate to be used in algorithm.', type=float, default=0.001)
parser.add_argument('--model_directory', help='models directory', default='~/.costar/models')
parser.add_a... |
class NuSVR(SvmModel, RegressorMixin):
_impl = 'nu_svr'
def __init__(self, kernel='rbf', degree=3, gamma='auto', coef0=0.0, nu=0.5, C=1.0, tol=0.001, probability=False, shrinking=False, cache_size=None, verbose=False, max_iter=(- 1), n_jobs=(- 1), max_mem_size=(- 1), gpu_id=0):
super(NuSVR, self).__init... |
class BasicTextNormalizer():
def __init__(self, remove_diacritics: bool=False, split_letters: bool=False):
self.clean = (remove_symbols_and_diacritics if remove_diacritics else remove_symbols)
self.split_letters = split_letters
def __call__(self, s: str):
s = s.lower()
s = re.sub... |
class CategoricalParams(DistributionParams[Categorical]):
def __init__(self, n_categories, batch_shape: Size=torch.Size()):
super().__init__(batch_shape=torch.Size(batch_shape))
self.logits = nn.Parameter(torch.randn(*batch_shape, n_categories))
def get_distribution(self) -> Categorical:
... |
class CallerMutation(ExternalCallHandler):
def handle(self) -> None:
self.mutate_caller(should_propagate=True) |
class FlaxDiffusionPipeline(metaclass=DummyObject):
_backends = ['flax']
def __init__(self, *args, **kwargs):
requires_backends(self, ['flax'])
def from_config(cls, *args, **kwargs):
requires_backends(cls, ['flax'])
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls... |
def gumbel_softmax(logits, tau=1, hard=False, eps=1e-10):
y_soft = gumbel_softmax_sample(logits, tau=tau, eps=eps)
if hard:
shape = logits.size()
(_, k) = y_soft.data.max((- 1))
y_hard = torch.zeros(*shape)
if y_soft.is_cuda:
y_hard = y_hard.cuda()
y_hard = y_... |
def linkcode_resolve(domain, info):
if (domain != 'py'):
return None
if (not info['module']):
return None
filename = info['module'].replace('.', '/')
res = '{}/{}.py'.format(repo_url, filename)
return res |
def save(subdir, b, p, dp, faces, gt_mesh, image, duration=5, fps=50):
from scipy.misc import imsave
from util3d.mesh.obj_io import write_obj
imsave(os.path.join(subdir, 'image.png'), image)
(v, f) = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
write_obj(os.path.join(subdir, 'gt_mesh.obj'),... |
class Model(nn.Module):
def __init__(self, vsize, ncls):
super().__init__()
self.emb = nn.Embedding(vsize, 100)
self.rnn = nn.LSTM(100, 100, 1)
self.proj = nn.Linear(100, ncls)
def forward(self, input_):
emb_out = self.emb(input_)
(_, (h, c)) = self.rnn(emb_out)
... |
def _name_cleaner(agent_name):
rename_dict = {'correct_ts': 'Correct TS', 'kl_ucb': 'KL UCB', 'misspecified_ts': 'Misspecified TS', 'ucb1': 'UCB1', 'ucb-best': 'UCB-best', 'nonstationary_ts': 'Nonstationary TS', 'stationary_ts': 'Stationary TS', 'greedy': 'greedy', 'ts': 'TS', 'action_0': 'Action 0', 'action_1': 'A... |
(a='double', spline='Spline', returns='double')
def H(a=(- 1)):
if (not enable_Hubble):
return 0
if (a == (- 1)):
a = universals.a
spline = temporal_splines.a_H
if (spline is None):
abort('The function H(a) has not been tabulated. Have you called init_time?')
return (a(a) * s... |
def build_fake_yaml():
fake_yaml = "\n model:\n name: imagenet_prune\n framework: pytorch\n\n pruning:\n approach:\n weight_compression:\n initial_sparsity: 0.0\n target_sparsity: 0.97\n start_epoch: 0\n end_epoch: 3\n pruners:\n - ... |
def _is_valid_explainer(proposed_explainer, expected_explainer_type):
try:
explainer_type = proposed_explainer.explainer_type
available_explanations = proposed_explainer.available_explanations
if (explainer_type != expected_explainer_type):
_log.warning('Proposed explainer is not... |
class MBartTokenizer(metaclass=DummyObject):
_backends = ['sentencepiece']
def __init__(self, *args, **kwargs):
requires_backends(self, ['sentencepiece']) |
def weights_init_xavier(m):
classname = m.__class__.__name__
if (classname.find('Conv2d') != (- 1)):
init.xavier_normal(m.weight.data) |
def preload_training_data(cur_fraction, start_pos, end_pos):
input_spec = load_col_data(df_train, list(range(num_samples)), start_pos, end_pos, 'input_spec_path')
np.save(os.path.join(dataset_path, 'PreLoad Training Dataset', ('fraction_' + str(cur_fraction)), 'input_spec'), input_spec)
output_spec = load_c... |
class Step9_GenerateCleanDataset():
def __init__(self, savePath: str, infoFile: str, audioPersistenz: AudioPersistenz, transcriptsPersistenz: TranscriptsPersistenz, audioSamplingRateTransformer: AudioSamplingRateTransformer, transcriptsSelectionTransformer: TranscriptsSelectionTransformer, filter):
self.aud... |
def val_data():
(datasets, info) = tfds.load(name='beans', with_info=True, as_supervised=True, split=['train'])
valdataset = [scale(v, l) for (v, l) in datasets[(- 1)]]
return valdataset |
def parse_with_config(parser, cmds=None):
if (cmds is None):
args = parser.parse_args()
else:
args = parser.parse_args(cmds)
if (args.config is not None):
config_args = json.load(open(args.config))
override_keys = {arg[2:].split('=')[0] for arg in sys.argv[1:] if arg.startswi... |
def get_f1(file, task, iters):
f = open(file)
for line in f:
line = line.strip().replace(',', '').split()
if (int(line[1]) == iters):
if (line[3] == task):
acc = float(line[9])
break
f.close()
return acc |
def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor:
(cx, cy, w, h) = boxes.unbind((- 1))
x1 = (cx - (0.5 * w))
y1 = (cy - (0.5 * h))
x2 = (cx + (0.5 * w))
y2 = (cy + (0.5 * h))
boxes = torch.stack((x1, y1, x2, y2), dim=(- 1))
return boxes |
def main():
parser = ArgumentParser(description="This script computes the ASR-BLEU metric between model's generated audio and the text reference sequences.")
parser.add_argument('--lang', help='The target language used to initialize ASR model, see asr_model_cfgs.json for available languages', type=str)
pars... |
def main(cmdargs):
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='Compute the distortion matrix of the auto and cross-correlation of delta fields')
parser.add_argument('--out', type=str, default=None, required=True, help='Output file name')
parser.add_a... |
def main(dataset=None):
if (not dataset):
dataset = DatasetBuilder.build_kitti_dataset(DatasetBuilder.KITTI_TRAIN)
label_cluster_utils = dataset.kitti_utils.label_cluster_utils
print('Generating clusters in {}/{}'.format(label_cluster_utils.data_dir, dataset.data_split))
(clusters, std_devs) = d... |
_checkable
class JuEstimatorLike(EstimatorLikeFit1, Protocol):
def get_needed_types(self) -> ColumnTypes:
return ColumnTypes('placeholder')
def get_apply_to(self) -> ColumnTypes:
return ColumnTypes('placeholder') |
def rename_and_save_block(current_block, save_path):
current_block = rename_keys(current_block)
new_current_block = {}
for (k, v) in current_block.items():
new_current_block[k.replace('/', '.')] = v
current_block = new_current_block
torch.save(current_block, save_path) |
def register_agent(id=None, **kwargs):
if (id is None):
id = get_dynamic_name()
print(('Registering agent %s' % id))
def wrap(agent):
_agent_registry[id] = dict(agent=agent, **kwargs)
return agent
return wrap |
def test_synthesized_onnx_model(tmp_path):
d = (tmp_path / 'test_trt_onnx')
d.mkdir()
ONNXModel = Model.init('onnx')
factory = BackendFactory.init('tensorrt', target='cuda', optmax=True)
gen = model_gen(opset=auto_opset(ONNXModel, factory), seed=23132, max_nodes=1)
model = ONNXModel.from_gir(gen... |
def train():
depth = 6
filters = 25
block_filters = ([filters] * depth)
model = tcn.build_model(sequence_length=(28 * 28), channels=1, num_classes=10, filters=block_filters, kernel_size=8)
model.compile(optimizer='Adam', metrics=[metrics.SparseCategoricalAccuracy()], loss=losses.SparseCategoricalCro... |
def load_csv(data_dir):
sep = ('\t' if data_dir.endswith('.tsv') else ',')
import pandas as pd
try:
df = pd.read_csv(data_dir, sep=sep, header=0, encoding='utf-8')
except:
try:
sep = '\t'
df = pd.read_csv(data_dir, sep=sep, header=0, encoding='utf-8')
exce... |
class CelebAHQDatasetParams(util.Params):
def get_allowed_params_with_defaults(self):
return dict(values_range=((- 1.0), 1.0), img_side=128, data_dir=None, train_shuffle=True, gcs_bucket=None, tfrecord_dir=constants.NVIDIA_CELEBA_HQ_DATASET_PATH, random_flip=False, crop_at_center=False, restrict_to_num_imgs... |
class Meters():
def __init__(self):
self.meters = {}
def get_names(self):
return self.meters.keys()
def reset(self):
for (_, meter) in self.meters.items():
meter.reset()
def update(self, name, val):
if (name not in self.meters):
self.meters[name] =... |
class TFFunnelForQuestionAnswering():
def __init__(self, *args, **kwargs):
requires_tf(self)
def from_pretrained(self, *args, **kwargs):
requires_tf(self) |
def _check_and_coerce_cfg_value_type(value_a, value_b, key, full_key):
type_b = type(value_b)
type_a = type(value_a)
if (type_a is type_b):
return value_a
if isinstance(value_b, six.string_types):
value_a = str(value_a)
elif (isinstance(value_a, tuple) and isinstance(value_b, list)):... |
def scale(image, label):
w = 224
h = 224
class_num = 3
image = tf.cast(image, tf.float32)
image /= 255.0
return (tf.image.resize(image, [w, h]), tf.one_hot(label, class_num)) |
class FrontendCheckerResult(NamedTuple):
waiting_cells: Set[IdType]
ready_cells: Set[IdType]
new_ready_cells: Set[IdType]
forced_reactive_cells: Set[IdType]
forced_cascading_reactive_cells: Set[IdType]
typecheck_error_cells: Set[IdType]
unsafe_order_cells: Dict[(IdType, Set[Cell])]
unsaf... |
class AffineTransform3D(ImagePreprocessing3D):
def __init__(self, affine_mat, translation=np.zeros(3), clamp_mode='clamp', pad_val=0.0, bigdl_type='float'):
affine_mat_tensor = JTensor.from_ndarray(affine_mat)
translation_tensor = JTensor.from_ndarray(translation)
super(AffineTransform3D, se... |
def main_worker(gpu, args):
args.gpu = gpu
args.rank = gpu
print(f'Process Launching at GPU {gpu}')
if args.distributed:
torch.cuda.set_device(args.gpu)
dist.init_process_group(backend='nccl')
print(f'Building train loader at GPU {gpu}')
train_loader = get_loader(args, split=args... |
def string_sublength(args):
params = functionParams(args, ('s', 'i', 'len'))
s = params.get('s', '')
i = (int((params.get('i', 1) or 1)) - 1)
len = int((params.get('len', 1) or 1))
return s[i:(i + len)] |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', default='settings/pretrain.yaml', type=str, help='Setting files')
parser.add_argument('-n', '--exp_name', default='exp_name', type=str, help='name of this experiment.')
parser.add_argument('-l', '--lr', default=5e-05, t... |
def _post_command(self, cmd: str) -> None:
_deprecation("'post(cmd)' is deprecated. Use 'post.command(cmd)'.")
return post.command(cmd) |
def test_W_from_zZ():
shape = (3, 1, 5)
z = torch.tensor(np.random.rand(*shape))
Z = (z + torch.tensor(np.random.rand(*shape)))
box_W = SigmoidBoxTensor.W(z, Z)
eps = torch.finfo(z.dtype).tiny
w1 = inv_sigmoid(z.clamp(eps, (1.0 - eps)))
w2 = inv_sigmoid(((Z - z) / (1.0 - z)).clamp(eps, (1.0 ... |
def setup_imports():
root_folder = registry.get('pythia_root', no_warning=True)
if (root_folder is None):
root_folder = os.path.dirname(os.path.abspath(__file__))
root_folder = os.path.join(root_folder, '..')
environment_pythia_path = os.environ.get('PYTHIA_PATH')
if (environment... |
class PrintModelAnalysisHook(TrainingHook):
def __init__(self, params, model_dir, run_config):
super(PrintModelAnalysisHook, self).__init__(params, model_dir, run_config)
self._filename = os.path.join(self.model_dir, 'model_analysis.txt')
def default_params():
return {}
def begin(sel... |
def traverse(node, index):
queue = Queue()
queue.push(node)
result = []
while (not queue.isEmpty()):
node = queue.pop()
result.append(get_token(node, mode=token_mode))
result.append(index)
index += 1
for (child_name, child) in node.children():
queue.pu... |
def dsrla_mobilenetv2_k6():
print('Constructing dsrla_mobilenetv2_k6......')
model = dsRLA_MobileNetV2(rla_channel=6)
return model |
.skip()
def test_redwood_indoor_office1():
gt_prefix = 'RedwoodIndoorOffice1'
(_, gt_download_dir, gt_extract_dir) = get_test_data_dirs(gt_prefix)
dataset = o3d.data.RedwoodIndoorOffice1()
assert Path(gt_download_dir).is_dir()
assert Path(gt_extract_dir).is_dir()
pcd = o3d.io.read_point_cloud(da... |
class UCF101DataModule(pl.LightningDataModule):
def __init__(self, data_root, train_batch_size, test_batch_size, num_workers, scale_lower_bound, jitter_prob, greyscale_prob, solarize_prob, **kwargs):
super().__init__()
self.data_root = data_root
self.train_batch_size = train_batch_size
... |
def _get_qiskit_versions():
cmd = [sys.executable, '-m', 'pip', 'freeze']
reqs = subprocess.check_output(cmd)
reqs_dict = {}
for req in reqs.split():
req_parts = req.decode().split('==')
if ((len(req_parts) == 1) and req_parts[0].startswith('git')):
if ('qiskit' in req_parts[... |
_tf
def resnet152_v2_imagenet(tile_px, **kwargs):
return TensorflowImagenetLayerExtractor('resnet152_v2', tile_px, **kwargs) |
class _GatherShardDimWithReshuffleCheck(torch.autograd.Function):
def forward(ctx, input_, shard_dim, group=None, ranks=None):
ctx.group = group
ctx.ranks = ranks
ctx.shard_dim = shard_dim
return _gather_shard_dim_with_reshuffle_check(input_, shard_dim, group, ranks)
def backward... |
def train(args):
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if (args.dataset == 'Flythings3D'):
train_dataset = Flythings3D(npoints=args.npoints, root=args.root, train=True)
elif (args.dataset == 'Kitti'):
train_dataset = KittiSceneFlowDataset(args.root, args.npoints, True)
else:
... |
def run():
test_acc_results = []
for task_id in range(1, (20 + 1)):
print('-*_*_*_*_*_*_*_*_ Task', task_id)
if use_10k:
(train_data, test_data, vocab) = load_data('./data/tasks_1-20_v1-2/en-10k', 0, task_id)
else:
(train_data, test_data, vocab) = load_data('./dat... |
def fdmobilenet_wd2(**kwargs):
return get_mobilenet(version='fd', width_scale=0.5, model_name='fdmobilenet_wd2', **kwargs) |
def set_random_seed(seed):
np.random.seed(seed)
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False |
def _convert_output_type_range(img, dst_type):
if (dst_type not in (np.uint8, np.float32)):
raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}')
if (dst_type == np.uint8):
img = img.round()
else:
img /= 255.0
return img.astype(dst_type) |
class SendStat(Callback):
def __init__(self, command, stats):
self.command = command
if (not isinstance(stats, list)):
stats = [stats]
self.stats = stats
def _trigger_epoch(self):
holder = self.trainer.stat_holder
v = {k: holder.get_stat_now(k) for k in self.s... |
def parse_args():
parser = argparse.ArgumentParser(description='Gather benchmarked models')
parser.add_argument('root', type=str, help='root path of benchmarked models to be gathered')
parser.add_argument('out', type=str, help='output path of gathered models to be stored')
parser.add_argument('--best', ... |
def _parse_main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('data_dir')
parser.add_argument('--no-solve1', action='store_true', dest='no_solve1')
parser.add_argument('--sexp', action='store_true', dest='sexp')
return parser.parse_args() |
def set_save_name_log_nvdm(args):
args.save_name = os.path.join(args.root_path, args.exp_path, 'Data{}_Dist{}_Model{}_Emb{}_Hid{}_lat{}_lr{}_drop{}_kappa{}_auxw{}_normf{}'.format(args.data_name, str(args.dist), args.model, args.emsize, args.nhid, args.lat_dim, args.lr, args.dropout, args.kappa, args.aux_weight, str... |
class ResBlock(PlainNetBasicBlockClass):
def __init__(self, block_list, in_channels=None, stride=None, no_create=False, **kwargs):
super(ResBlock, self).__init__(**kwargs)
self.block_list = block_list
self.stride = stride
self.no_create = no_create
if (not no_create):
... |
def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
valid_mask = ((labels >= 0) & (labels != ignore_index))
inds = torch.nonzero((valid_mask & (labels < label_channels)), as_tuple=False)
if (inds.numel() > 0):
... |
class KnetD(nn.Module):
def __init__(self, inplanes, planes, dropout=0.0, norm='in', first=False):
super(KnetD, self).__init__()
self.first = first
self.maxpool = nn.MaxPool2d(2, 2)
self.dropout = dropout
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv3d(inplan... |
def binary_search_y1(x_minus, x_plus, y_minus, y_plus):
eps = 0.0001
y_lower = y_minus.data.clone()
y_upper = y_plus.data.clone()
y1 = ((y_lower + y_upper) / 2)
for i in range(10):
y1 = ((y_lower + y_upper) / 2)
g = estimate_gradient_upper(y1, eps, x_minus, x_plus, y_minus, y_plus)
... |
def remove_comments(original: str) -> str:
lines = original.splitlines()
c_lines = [x for x in lines if ((not x.rstrip().startswith('#')) and (not (x.strip() == '')))]
code = '\n'.join(c_lines)
try:
root = ast.parse(code)
PassRemoveDocstring().remove_docstring(root)
modified = as... |
class ContrastLoss(nn.Module):
def __init__(self, n_data):
super(ContrastLoss, self).__init__()
self.n_data = n_data
def forward(self, x):
bsz = x.shape[0]
m = (x.size(1) - 1)
Pn = (1 / float(self.n_data))
P_pos = x.select(1, 0)
P_pos[(P_pos == 0)] = eps
... |
def register_dataset(datasets_root: Optional[os.PathLike]=None):
def empty_load_callback():
pass
video_list_fpath = maybe_prepend_base_path(datasets_root, 'chimpnsee/cdna.eva.mpg.de/video_list.txt')
video_base_path = maybe_prepend_base_path(datasets_root, 'chimpnsee/cdna.eva.mpg.de')
DatasetCata... |
def test_purge(remove: MagicMock) -> None:
glob_result = ['1.cache_record.json', '2.cache_record.json']
glob_mock = MagicMock(return_value=glob_result)
mock_cache_record = {'expires': '3000-01-01', 'filename': 'df_cache.parquet'}
mock_load_json = MagicMock(return_value=mock_cache_record)
with patch(... |
class MetricLogger(object):
def __init__(self, delimiter='\t'):
self.meters = defaultdict(SmoothedValue)
self.delimiter = delimiter
def update(self, **kwargs):
for (k, v) in kwargs.items():
if isinstance(v, torch.Tensor):
v = v.item()
assert isinst... |
def decode_png(input: torch.Tensor, mode: ImageReadMode=ImageReadMode.UNCHANGED) -> torch.Tensor:
output = torch.ops.image.decode_png(input, mode.value)
return output |
def main(args):
set_logging(args.log_dir)
logger.setLevel(logging.INFO)
logger.info(f'Parameters: {args}')
logger.info('Reading data...')
with open(os.path.join(args.save_loc, f'train.json'), 'r', encoding='utf-8') as f:
train_data = json.load(f)
with open(os.path.join(args.save_loc, f'v... |
def torch_available():
try:
import torch
import torch.utils.dlpack
except ImportError:
return False
return True |
class CosineAnnealingScheduler(Callback):
def __init__(self, T_max, eta_max, eta_min=0, verbose=0, epoch_start=80, restart_epochs=None, gamma=1, expansion=1, flat_end=False):
super(CosineAnnealingScheduler, self).__init__()
self.epoch_start = epoch_start
self.expansion = expansion
se... |
(argument('id', help='id of instance to start/restart', type=int), usage='vast.py start instance <id> [--raw]', help='Start a stopped instance')
def start__instance(args):
url = apiurl(args, '/instances/{id}/'.format(id=args.id))
r = requests.put(url, json={'state': 'running'})
r.raise_for_status()
if (... |
def SEResNet18(input_shape=None, input_tensor=None, weights=None, classes=1000, stride_size=2, init_filters=64, include_top=False, repetitions=(2, 2, 2, 2), **kwargs):
return ResNet(MODELS_PARAMS['seresnet18'], input_shape=input_shape, input_tensor=input_tensor, include_top=include_top, classes=classes, stride_size... |
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--seed', type=int, help='seed', default=1)
parser.add_argument('--data-file', type=str, default='_output/data.pkl')
parser.add_argument('--out-file', type=str, default='_output/out.csv')
parser.add_argument(... |
def generate_model(input_shape_tra_cdr3, input_shape_tra_vgene, input_shape_tra_jgene, input_shape_trb_cdr3, input_shape_trb_vgene, input_shape_trb_jgene, num_outputs):
kmer_size = 4
features_tra_cdr3 = Input(shape=input_shape_tra_cdr3)
features_tra_vgene = Input(shape=input_shape_tra_vgene)
features_tr... |
def check_box_8c_format(input_data):
if isinstance(input_data, np.ndarray):
if (input_data.ndim == 3):
if (input_data.shape[1:] != (3, 8)):
raise TypeError('Given input does not have valid number of attributes. Should be N x 3 x 8 for box_8c.')
elif (input_data.ndim == 2)... |
class LayoutLMv2FeatureExtractor(LayoutLMv2ImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn('The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use LayoutLMv2ImageProcessor instead.', FutureWarning)
super().__init__(... |
def open_tsv(fname, folder):
print(('Opening %s Data File...' % fname))
df = pd.read_csv(fname, sep='\t', names=['caption', 'url'], usecols=range(1, 2))
df['folder'] = folder
print('Processing', len(df), ' Images:')
return df |
class BasicBlock(nn.Module):
def __init__(self, norm, in_channels):
super(BasicBlock, self).__init__()
self.norm = norm_layer(norm, in_channels)
self.dropout = SharedDropout()
def forward(self, x, edge_index, dropout_mask=None, edge_emb=None):
out = self.norm(x)
out = F.r... |
_module()
class GaussianFocalLoss(nn.Module):
def __init__(self, alpha=2.0, gamma=4.0, reduction='mean', loss_weight=1.0):
super(GaussianFocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
self.loss_weight = loss_weight
def forward... |
def test_reference_split_handles_repeated_fields():
ref_line = u'[20] A. Buchel, Finite temperature resolution of the Klebanov-Tseytlin singularity, Nucl. Phys. B 600, 219 (2001) [hep-th/0011146]. A. Buchel, C. P. Herzog, I. R. Klebanov, L. A. Pando Zayas and A. A. Tseytlin, Nonextremal gravity duals for fractional... |
class TestEmbeddings(unittest.TestCase):
def setUp(self):
self.emb_size = 10
self.vocab_size = 11
self.pad_idx = 1
seed = 42
torch.manual_seed(seed)
def test_size(self):
emb = Embeddings(embedding_dim=self.emb_size, vocab_size=self.vocab_size, padding_idx=self.pad... |
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