code stringlengths 101 5.91M |
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class FileDataset():
def __init__(self, path, tokenizer=nltk.RegexpTokenizer('\\b[a-zA-Z]{2,}\\b')):
self.path = path
self.tokenizer = tokenizer
def __iter__(self):
lines = list(open(self.path))
lines_tok = self.tokenizer.tokenize_sents(map(str.lower, lines))
return iter(... |
def IntSort(ctx=None):
ctx = _get_ctx(ctx)
return ArithSortRef(Z3_mk_int_sort(ctx.ref()), ctx) |
class SNodeHostAccessor():
def __init__(self, snode):
if _ti_core.is_real(snode.data_type()):
write_func = snode.write_float
read_func = snode.read_float
else:
def write_func(key, value):
if (value >= 0):
snode.write_uint(key, v... |
class _SplitDataset(torch.utils.data.Dataset):
def __init__(self, underlying_dataset, keys):
super(_SplitDataset, self).__init__()
self.underlying_dataset = underlying_dataset
self.keys = keys
def __getitem__(self, key):
return self.underlying_dataset[self.keys[key]]
def __le... |
def retrive_var(scopes):
var = []
for scope in scopes:
var += tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)
return var |
def sdfg_with_children(A: dp.float32[(N, N)], B: dp.float32[(N, N)]):
def elements(i: _[0:N], j: _[0:N]):
def init():
(inp << A[(i, j)])
(out >> B[(i, j)])
out = inp
for k in range(4):
def do():
(inp << A[(i, j)])
(oin <... |
def init_test_mot16():
config['resume'] = '/home/ssm/ssj/weights/MOT17/weights0326-I50k-M80-G30/ssj300_0712_80000.pth'
config['mot_root'] = '/home/ssm/ssj/dataset/MOT16'
config['batch_size'] = 1
config['write_file'] = True
config['tensorboard'] = True
config['save_combine'] = False
config['t... |
def export_onnx(pretrained):
net = SYEISPNetS(channels=12)
checkpoint = torch.load(pretrained)
net.load_state_dict(checkpoint)
net.eval()
net = net.slim().eval()
net.body.block1.weight = nn.Parameter(net.body.block1.weight.reshape((3, 4, 12, 3, 3)).permute([1, 0, 2, 3, 4]).reshape((12, 12, 3, 3)... |
_level_function()
def from_avro_file(file, limit_entries=None, *, debug_forth=False, highlevel=True, behavior=None, attrs=None):
import awkward._connect.avro
if isinstance(file, (str, bytes, PathLike)):
file = fsdecode(file)
with open(file, 'rb') as opened_file:
(form, length, contai... |
class Transition(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition, self).__init__()
self.bn = nn.BatchNorm2d(in_planes)
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)
self.pau = PAU()
def forward(self, x):
out = self.conv(se... |
class ProcessGroupRpcAgentTestFixture(RpcAgentTestFixture):
def rpc_backend(self):
return rpc.backend_registry.BackendType['PROCESS_GROUP']
def rpc_backend_options(self):
try:
return self._rpc_backend_options
except AttributeError:
return rpc.backend_registry.cons... |
def train(args, model, classifier, train_loader, criterion, optimizer, epoch):
model.train()
classifier.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
total_feats = []
total_targets = []
end = time.time()
for (batch_idx, (input1, target)) in en... |
class TFLxmertMainLayer(metaclass=DummyObject):
_backends = ['tf']
def __init__(self, *args, **kwargs):
requires_backends(self, ['tf']) |
def _add_category_id_to_contiguous_id_maps_to_metadata(merged_categories: _MergedCategoriesT):
merged_categories_per_dataset = {}
for (contiguous_cat_id, cat_id) in enumerate(sorted(merged_categories.keys())):
for cat in merged_categories[cat_id]:
if (cat.dataset_name not in merged_categorie... |
def run_ddp(rank, world_size, prepared):
ddp_setup(rank, world_size)
prepared.cuda()
prepared = torch.nn.parallel.DistributedDataParallel(prepared, device_ids=[rank])
prepared.to(rank)
model_with_ddp = prepared
optimizer = torch.optim.SGD(model_with_ddp.parameters(), lr=0.0001)
train_one_epo... |
def write_embeddings(filename, dict, embeddings):
with open(filename, 'w') as file:
for i in range(len(embeddings)):
str = dict.idxToLabel[i].encode('utf-8')
for j in range(len(embeddings[0])):
str = (str + (' %5f' % embeddings[i][j]))
file.write((str + '\... |
class MovingAverageActionWrapperActorPolicy(BaseActorPolicy):
def __init__(self, policy, widow_size=8, initial_value=0):
super(BaseActorPolicy, self).__init__()
self.__widow_size = widow_size
self.__buffer = ([(initial_value / widow_size)] * widow_size)
self.__avg = initial_value
... |
def average_time_of_func(func, num_iter=10000, ms=False):
duration = timeit.timeit(func, number=num_iter)
avg_time = (duration / num_iter)
if ms:
avg_time *= 1000
logger.info('{} {} ms/iter'.format(func.__name__, avg_time))
else:
logger.info('{} {} s/iter'.format(func.__name__, a... |
class MapTilingWithOverlapTest(unittest.TestCase):
def semantic_eq(self, tile_sizes):
A = np.random.rand(16, 16).astype(np.float32)
B1 = np.zeros((16, 16), dtype=np.float32)
B2 = np.zeros((16, 16), dtype=np.float32)
sdfg = copy.to_sdfg()
sdfg(inp=A, out=B1, I=A.shape[0], J=A.... |
def get_utterances_from_file(dialog_csv_file, dialog_csv_filename):
reader = csv.DictReader(dialog_csv_file)
path = dialog_csv_filename.split('\\')
return [_dict_to_dialog_utterance(du_dict, path[(- 1)]) for du_dict in reader] |
def test_weka(filename):
(data, meta) = loadarff(filename)
print(len(data.dtype))
print(data.size)
for i in meta:
print_attribute(i, meta[i], data[i]) |
def check_in_repo():
if (not os.path.isfile('setup.py')):
return 'Not in root-level PyTorch repo, no setup.py found'
with open('setup.py') as f:
s = f.read()
if ('PyTorch' not in s):
return "Not in PyTorch repo, 'PyTorch' not found in setup.py" |
class ImageNetDataset(Dataset):
def __init__(self, split, bucket_name, streaming=True, data_download_dir=None, transform=None):
assert (split in ['train', 'validation']), 'split {} not in (train, validation)'.format(split)
self._split = split
self._bucket_name = bucket_name
self._tar... |
def test_reduce_1d_strided():
non_contiguous_array = np.arange(64, dtype=np.int64)[::3]
layout = ak.contents.NumpyArray(non_contiguous_array)
assert (not layout.is_contiguous)
assert (ak.sum(layout, axis=(- 1)) == np.sum(non_contiguous_array, axis=(- 1))) |
def read_concode_examples(filename, data_num):
examples = []
with open(filename) as f:
for (idx, line) in enumerate(f):
x = json.loads(line)
examples.append(Example(idx=idx, source=x['nl'].strip(), target=x['code'].strip()))
idx += 1
if (idx == data_num):
... |
def test_module_parameter_path():
x = nn.Variable((4, 3, 32, 32))
e = Example()
h = e(x)
e2 = Example()
assert (not e2.get_parameters()), "It doesn't have any parameters so far."
e2.set_parameters(e.get_parameters())
assert (e.get_parameters() == e2.get_parameters()), 'They have the same par... |
def numpy_set_unused(v):
if (v is None):
return
assert isinstance(v, numpy.ndarray)
if isinstance(v.base, SharedNumpyArray):
assert v.base.is_in_use()
v.base.set_unused() |
class Decoder(DecoderBase):
tiu_head_length = 50
dma_head_length = 39
def decode_tiu_cmd(self, reg_buf: memoryview, *, offset, subnet_id) -> BaseTpuCmd:
head = TiuHead.from_buffer(reg_buf, offset)
op_info = tiu_index.get((bool(head.cmd_short), head.tsk_typ, head.tsk_eu_typ), None)
as... |
def test_mrmr_regression_with_scores():
(selected_features, relevance, redundancy) = mrmr.polars.mrmr_regression(df=df_polars, K=4, target_column=target_column_regression, features=features, denominator='mean', only_same_domain=False, return_scores=True, show_progress=True)
assert (set(selected_features) == set... |
def validate(val_loader, model, criterion, args, epoch, tb_logger):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('', ':6.2f')
top5 = AverageMeter('', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_time, losses, top1, top5], prefix='T... |
def test_gc_head():
head = GCHead(in_channels=4, channels=4, num_classes=19)
assert (len(head.convs) == 2)
assert hasattr(head, 'gc_block')
inputs = [torch.randn(1, 4, 23, 23)]
if torch.cuda.is_available():
(head, inputs) = to_cuda(head, inputs)
outputs = head(inputs)
assert (outputs... |
def _evalcode_python(executor, code, input_type):
global_dict = gdb.parse_and_eval('PyEval_GetGlobals()')
local_dict = gdb.parse_and_eval('PyEval_GetLocals()')
if ((pointervalue(global_dict) == 0) or (pointervalue(local_dict) == 0)):
raise gdb.GdbError('Unable to find the locals or globals of the mo... |
class WSGenerator(dataset.Generator):
def __init__(self, sizes, density_alpha=1.3, rewire_alpha=2, rewire_beta=2, **kwargs):
super(WSGenerator, self).__init__(sizes, **kwargs)
self.density_alpha = density_alpha
self.rewire_alpha = rewire_alpha
self.rewire_beta = rewire_beta
def g... |
def _get_repo(repo):
assert isinstance(repo, str)
obj = _repo_cache.get(repo)
if obj:
return obj
obj = _Repo(repo)
_repo_cache[repo] = obj
return obj |
_numpy_output(check_dtype=True)
def test_ufunc_square_f(A: dace.float32[10]):
return np.square(A) |
_paths
def parse_args(args=None, namespace=None):
parser = argparse.ArgumentParser(description='Extract audio from videos.')
parser.add_argument('-i', '--in_dir', default=pathlib.Path('data/vggsound/vggsound/'), type=pathlib.Path, help='input directory')
parser.add_argument('-o', '--out_dir', default=pathli... |
class XLMRobertaConverter(SpmConverter):
def vocab(self, proto):
vocab = [('<s>', 0.0), ('<pad>', 0.0), ('</s>', 0.0), ('<unk>', 0.0)]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [('<mask>', 0.0)]
return vocab
def unk_id(self, proto):
unk_... |
def converId(img_id):
img_id = img_id.split('-')
if ('train' in img_id[0]):
new_id = int(img_id[1])
elif ('val' in img_id[0]):
new_id = (int(img_id[1]) + 1000000)
elif ('test' in img_id[0]):
new_id = (int(img_id[1]) + 2000000)
else:
pdb.set_trace()
return new_id |
def layout():
children_list = [html.Div([html.H2('Daily proportion of women quoted'), html.Div(dcc.Markdown('\n The below charts showcase a 7-day moving average of the daily\n proportion of women quoted for each outlet since October 2018.\n The pi... |
def convert_examples_to_features(examples, label_list, label_list_tagging, max_seq_length, tokenizer, sel_prob, train_type='train'):
label_map = {label: i for (i, label) in enumerate(label_list)}
label_tagging_map = {label: i for (i, label) in enumerate(label_list_tagging)}
features = []
for (ex_index, ... |
class _PackageResourceReader():
def __init__(self, importer, fullname):
self.importer = importer
self.fullname = fullname
def open_resource(self, resource):
from io import BytesIO
return BytesIO(self.importer.load_binary(self.fullname, resource))
def resource_path(self, resou... |
class MLPMergeModel(Model):
def __init__(self, output_dim, name='MLPMergeModel', hidden_sizes=(32, 32), concat_layer=(- 2), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_... |
class LnStructured(BasePruningMethod):
PRUNING_TYPE = 'structured'
def __init__(self, amount, n, dim=(- 1)):
_validate_pruning_amount_init(amount)
self.amount = amount
self.n = n
self.dim = dim
def compute_mask(self, t, default_mask):
_validate_structured_pruning(t)
... |
def get_logger(logpath, filepath, package_files=[], displaying=True, saving=True, debug=False):
logger = logging.getLogger()
if debug:
level = logging.DEBUG
else:
level = logging.INFO
logger.setLevel(level)
if saving:
info_file_handler = logging.FileHandler(logpath, mode='a')... |
class DirectoryLocator(Locator):
def __init__(self, path, **kwargs):
self.recursive = kwargs.pop('recursive', True)
super(DirectoryLocator, self).__init__(**kwargs)
path = os.path.abspath(path)
if (not os.path.isdir(path)):
raise DistlibException(('Not a directory: %r' % ... |
def get_scores_for_imputer(imputer, X_missing, y_missing):
estimator = make_pipeline(imputer, regressor)
impute_scores = cross_val_score(estimator, X_missing, y_missing, scoring='neg_mean_squared_error', cv=N_SPLITS)
return impute_scores |
def fit_str(string, colwidth=16):
if (len(string) < colwidth):
return (((colwidth - len(string)) * ' ') + string)
else:
return string[:colwidth] |
def boomerang_force_calculator(location, orientation):
gravity = np.array([0.0, 0.0, ((- 1.0) * sum(M))])
r_vectors = get_boomerang_r_vectors_15(location[0], orientation[0])
repulsion = 0.0
for k in range(len(r_vectors)):
h = r_vectors[k][2]
repulsion += np.array([0.0, 0.0, (((REPULSION_... |
def test_nested_default_arg_reuse_2():
class MyClass():
def __call__(self, arr: dace.float64[20], qmin: float=0.0):
self.nested(arr, qmin)
def nested(self, arr: dace.float64[20], qmin: float):
arr[:] = qmin
a = MyClass()
def tester(arr: dace.float64[20], arr2: dace.fl... |
class AdainResBlk(nn.Module):
def __init__(self, dim_in, dim_out, style_dim=64, w_hpf=0, actv=nn.LeakyReLU(0.2), upsample='none'):
super().__init__()
self.w_hpf = w_hpf
self.actv = actv
self.upsample = UpSample(upsample)
self.learned_sc = (dim_in != dim_out)
self._bui... |
class SELayer(nn.Module):
def __init__(self, channels, ratio=16, conv_cfg=None, act_cfg=(dict(type='ReLU'), dict(type='HSigmoid', bias=3.0, divisor=6.0))):
super(SELayer, self).__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert (len(act_cfg) == 2)
... |
def get_padded_batch(file_list, batch_size, input_size, output_size, num_enqueuing_threads=4, num_epochs=1, shuffle=True):
file_queue = tf.train.string_input_producer(file_list, num_epochs=num_epochs, shuffle=shuffle)
reader = tf.TFRecordReader()
(_, serialized_example) = reader.read(file_queue)
sequenc... |
class _grid_encode(Function):
_fwd
def forward(ctx, inputs, embeddings, offsets, per_level_scale, base_resolution, calc_grad_inputs=False, gridtype=0, align_corners=False, interpolation=0):
inputs = inputs.contiguous()
(B, D) = inputs.shape
L = (offsets.shape[0] - 1)
C = embeddin... |
class UnetSkipConnectionBlock(nn.Module):
def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if (type(norm_layer) == ... |
class BoundingBox():
def __init__(self, image_name, class_id=None, coordinates=None, type_coordinates=CoordinatesType.ABSOLUTE, img_size=None, bb_type=BBType.GROUND_TRUTH, confidence=None, format=BBFormat.XYWH):
self._image_name = image_name
self._type_coordinates = type_coordinates
self._co... |
def set_separate_embeddings(model, mapping):
model.set_input_embeddings(MyEmbedding2(model.transformer.wte, mapping)) |
class SemEvalHook(Hook):
def __init__(self, batcher, placeholders, at_every_epoch):
self.batcher = batcher
self.placeholders = placeholders
self.at_every_epoch = at_every_epoch
def __call__(self, sess, epoch, iteration, model, loss):
if ((iteration == 0) and ((epoch % self.at_eve... |
def test_ngrams_for_evaluation():
from speechbrain.lm.counting import ngrams_for_evaluation
assert (list(ngrams_for_evaluation(['a', 'b', 'c'], max_n=3)) == [('b', ('a',)), ('c', ('a', 'b'))])
assert (list(ngrams_for_evaluation(['a', 'b', 'c'], max_n=3, predict_first=True)) == [('a', ()), ('b', ('a',)), ('c... |
def test_hist():
hist = glob.glob('test_trainer_outputs/history.npy')
assert (len(hist) == 1) |
def execute_onnx(model, input_dict, return_full_exec_context=False, start_node=None, end_node=None):
if (not model.check_all_tensor_shapes_specified()):
raise Exception('Found unspecified tensor shapes, try infer_shapes')
ret = model.analysis(ta.nodes_topologically_sorted)
assert (ret['nodes_topolog... |
def normalize_args_vectorspace(*args, **kwds):
from sage.rings.integer_ring import ZZ
if (len(args) == 1):
V = args[0]
try:
degree = V.dimension_relative()
except AttributeError:
degree = V.dimension()
ring = V.base_ring()
if (len(args) == 2):
... |
def test_conv1d_same_out():
time_dim = Dim(Tensor('time', [batch_dim], dtype='int32'))
in_dim = Dim(7, name='in')
extern_data = TensorDict({'data': Tensor('data', [batch_dim, time_dim, in_dim], dtype='float32')})
class _Net(rf.Module):
def __init__(self):
super().__init__()
... |
def make_cc_vector(sequence_list, lag, phyche_value, k):
phyche_values = list(phyche_value.values())
len_phyche_value = len(phyche_values[0])
vec_cc = []
for sequence in sequence_list:
len_seq = len(sequence)
each_vec = []
for temp_lag in range(1, (lag + 1)):
for i1 i... |
def _default_template_ctx_processor():
reqctx = _request_ctx_stack.top
appctx = _app_ctx_stack.top
rv = {}
if (appctx is not None):
rv['g'] = appctx.g
if (reqctx is not None):
rv['request'] = reqctx.request
rv['session'] = reqctx.session
return rv |
def parse_args():
parser = argparse.ArgumentParser(description='None')
parser.add_argument('--config', help='config file path')
parser.add_argument('--seed', type=int, default=42, help='random seed')
parser.add_argument('--phase', choices=['test', 'train'], default='test')
parser.add_argument('--wor... |
class Runner():
def __init__(self):
self.t0 = time()
self.hours_pretrained = 0
if c.TEST_ONLY:
print('TESTING ONLY')
self.tst_file_list = []
for data_dir in c.TEST_DIRS:
self.tst_file_list += sorted(glob.glob((data_dir + '/**/*.wav'), recur... |
def edit_distance(str1, str2):
try:
import Levenshtein
d = (Levenshtein.distance(str1, str2) / float(max(len(str1), len(str2))))
except:
d = (1.0 - SequenceMatcher((lambda x: (x == ' ')), str1, str2).ratio())
return d |
def normal_prior(prior_std):
def prior_fn(dtype, shape, name, trainable, add_variable_fn):
tfd = tfp.distributions
dist = tfd.Normal(loc=tf.zeros(shape, dtype), scale=dtype.as_numpy_dtype(prior_std))
batch_ndims = tf.size(input=dist.batch_shape_tensor())
return tfd.Independent(dist, ... |
class RecencyWeightedVariance(Variance):
mean_class = ExponentialMovingAverage
def __init__(self, recency_weight: float, **kwargs):
super().__init__(**kwargs)
self.recency_weight = recency_weight
def recency_weight(self):
return self._recency_weight
_weight.setter
def recency... |
def Cyclic(R, n=None, homog=False, singular=None):
from .rational_field import RationalField
if n:
if (n > R.ngens()):
raise ArithmeticError('n must be <= R.ngens()')
else:
n = R.ngens()
if (singular is None):
from sage.interfaces.singular import singular as singular_... |
class AdvContrastiveNMT(nn.Module):
def __init__(self, args):
super(AdvContrastiveNMT, self).__init__()
self.tau = args.tau
self.pos_eps = args.pos_eps
self.neg_eps = args.neg_eps
self.t5_model = T5ForConditionalGeneration.from_pretrained(args.t5_model)
self.projectio... |
def main():
save_dir = f'exp/{args.probe_type}/{args.eval_dataset}/{args.framework}_{args.text_type}_{args.text_rep}/{args.lr}_{args.batch_size}'
(pretrain_model, _, config) = get_model(args)
(task_type, output_dim, loss_fn) = get_cls_config(args)
model = CLSLayer(audio_encoder=pretrain_model.audio_enco... |
_module()
class mit_b1(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b1, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-06), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], **k... |
def main(file_name, starting_value):
file_name = file_name
starting_value = starting_value
training_data = []
for i in list(range(4))[::(- 1)]:
print((i + 1))
time.sleep(1)
last_time = time.time()
paused = False
print('STARTING!!!')
while True:
if (not paused):
... |
class NystromformerForMultipleChoice(metaclass=DummyObject):
_backends = ['torch']
def __init__(self, *args, **kwargs):
requires_backends(self, ['torch']) |
def hop(entities, constraints, top_predicates, verbose=False, max_triples=500000, bl_p=[68655]):
n_constraints = len(constraints)
if entities:
n_constraints += 1
top_entities = (entities + constraints)
all_entities_ids = [_id for e in top_entities for _id in e]
top_predicates_ids = [_id for ... |
def _reject_cdef_modifier_in_py(s, name):
if ((s.sy == 'IDENT') and (name in _CDEF_MODIFIERS)):
s.error(("Cannot use cdef modifier '%s' in Python function signature. Use a decorator instead." % name), fatal=False)
return p_ident(s)
return name |
class OneOf():
def __init__(self, contents):
self._contents = contents
def contents(self):
return self._contents
def __eq__(self, other):
if isinstance(other, OneOf):
return (set(self._contents) == set(other._contents))
else:
return False
def __rep... |
def run_ml_pipeline(nlpPipelineDF, num_topics, max_iterations, vocabSize, minDF, maxDF):
cv = CountVectorizer(inputCol='allTokens', outputCol='features', vocabSize=vocabSize, minDF=minDF, maxDF=maxDF, minTF=1.0)
idf = IDF(inputCol='features', outputCol='idf')
lda = LDA(k=num_topics, maxIter=max_iterations, ... |
def main():
args = get_args()
out = args.out
(max_peaks, min_inten) = (args.max_peaks, args.min_inten)
(num_bins, upper_limit) = (args.num_bins, args.upper_limit)
num_workers = args.num_workers
form_folder = Path(args.form_folder)
form_files = list(form_folder.glob('*.json'))
if (out is ... |
def meta_training(train_dataset, valid_dataset, model, classifier, lr=None, optimizer=None, epochs=100, episodes=1000, ways=5, shots=5, query_num=15, report_epoch=1, lr_step_epoch=10, save_model_epoch=20, save_model_root='~/trained_models'):
lr = (0.001 if (lr is None) else lr)
if (optimizer is None):
o... |
class AdjacencyField(Field[torch.Tensor]):
_already_warned_namespaces: Set[str] = set()
def __init__(self, indices: List[Tuple[(int, int)]], sequence_field: SequenceField, labels: List[str]=None, label_namespace: str='labels', padding_value: int=(- 1)) -> None:
self.indices = indices
self.labels... |
class TestThresholdedRelu(serial.SerializedTestCase):
(input=hu.tensor(), engine=st.sampled_from(['', 'CUDNN']), **hu.gcs)
def test_thresholded_relu_1(self, input, gc, dc, engine):
X = input
op = core.CreateOperator('ThresholdedRelu', ['X'], ['Y'], engine=engine)
def defaultRef(X):
... |
class ArrayStreamer(BaseStreamer):
def __init__(self, shuffle=False):
self.shuffle = shuffle
def iter(self, X, y=None):
indices = list(range(len(X)))
if self.shuffle:
np.random.shuffle(indices)
if (y is None):
for i in indices:
(yield X[i])... |
def do_naive_bayes_prediction(X, observed_class_distribution: dict, attribute_observers: dict):
observed_class_sum = sum(observed_class_distribution.values())
if ((observed_class_distribution == {}) or (observed_class_sum == 0.0)):
return {0: 0.0}
votes = {}
for (class_index, observed_class_val)... |
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
cfgs = []
data_cfg = config.DataConfig(args['data_config'])
cfgs.append(data_cfg)
if (args['lm_model_config'] is None):
model_cfg = config.ModelConfig(args['model_config'], input_dim=len(alphabet), num_... |
class VectorField(MultivectorField):
def __init__(self, vector_field_module, name=None, latex_name=None):
MultivectorField.__init__(self, vector_field_module, 1, name=name, latex_name=latex_name)
MultivectorField._init_derived(self)
self._init_dependencies()
def _repr_(self):
des... |
def latent_optimise(zs, fake_labels, gen_model, dis_model, conditional_strategy, latent_op_step, latent_op_rate, latent_op_alpha, latent_op_beta, trans_cost, default_device):
batch_size = zs.shape[0]
for step in range(latent_op_step):
drop_mask = (torch.FloatTensor(batch_size, 1).uniform_() > (1 - laten... |
def test_estimate_bandwidth():
bandwidth = estimate_bandwidth(X, n_samples=200)
assert (0.9 <= bandwidth <= 1.5) |
class ExecutorBase():
def __init__(self, n_workers: int, verbose: bool=False):
self.verbose = verbose
self.n_workers = n_workers
self.n_free_workers = n_workers
self.n_busy_workers = 0
self._queue = []
self._running_tasks = []
self._completed_tasks = []
de... |
def parameter_parser():
parser = argparse.ArgumentParser(description='Run GETNext.')
parser.add_argument('--seed', type=int, default=42, help='Random seed')
parser.add_argument('--device', type=str, default=device, help='')
parser.add_argument('--data-adj-mtx', type=str, default='dataset/NYC/graph_A.csv... |
('/list_sessions', methods=['GET'])
def list_sessions():
limit = request.args.get('limit', None)
skip = request.args.get('skip', None)
return api.get_all_sessions(limit, skip) |
def test_nothing_on_after_test_case_execution():
stopping = DummyStopping()
stopping.after_test_case_execution_inside_thread(None, None) |
class Discriminator(nn.Module):
def __init__(self, nc):
super(Discriminator, self).__init__()
self.enc = Encoder(nc)
self.dec = Decoder(nc, True)
def forward(self, input):
return self.dec(self.enc(input)) |
def test_chrono_duration_subtraction_equivalence():
date1 = datetime.datetime.today()
date2 = datetime.datetime.today()
diff = (date2 - date1)
cpp_diff = m.test_chrono4(date2, date1)
assert (cpp_diff.days == diff.days)
assert (cpp_diff.seconds == diff.seconds)
assert (cpp_diff.microseconds =... |
def test_isinstance():
objects = ([tuple(), dict(), m.Pet('Polly', 'parrot')] + ([m.Dog('Molly')] * 4))
expected = (True, True, True, True, True, False, False)
assert (m.check_instances(objects) == expected) |
def ascii_art(*obj, **kwds):
(separator, baseline, sep_baseline) = _ascii_art_factory.parse_keywords(kwds)
if kwds:
raise ValueError('unknown keyword arguments: {0}'.format(list(kwds)))
if (len(obj) == 1):
return _ascii_art_factory.build(obj[0], baseline=baseline)
if (not isinstance(sepa... |
def collate(samples, pad_idx, eos_idx, left_pad_source=False, left_pad_target=False):
if (len(samples) == 0):
return {}
def merge(key, left_pad, move_eos_to_beginning=False):
return data_utils.collate_tokens([s[key] for s in samples], pad_idx, eos_idx, left_pad, move_eos_to_beginning)
id = n... |
def qa_for_label_file(dict_paragraphs: dict, label_file2: str, output_dir: str, separate=True):
with open(label_file2, 'r') as fp:
labels = json.load(fp)
dict_paragraphs_train = {}
for key in labels.keys():
dict_paragraphs_train.update({key: dict_paragraphs.get(key)})
base_case_to_qa_fil... |
class OracleSelectionMethod(SelectionMethod):
name = 'test-domain validation set (oracle)'
def run_acc(self, run_records):
run_records = run_records.filter((lambda r: (len(r['args']['test_envs']) == 1)))
if (not len(run_records)):
return None
test_env = run_records[0]['args']... |
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