code stringlengths 101 5.91M |
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class EndSignal(object):
def __init__(self, id, errno=0, errmsg=''):
self.id = id
self.errno = errno
self.errmsg = errmsg |
def flash_save_checkpoint(checkpointer, step, model, optimizer, save_memory_interval, save_storage_interval, checkpoint_dir):
saved = False
if (((step % save_memory_interval) != 0) and ((step % save_storage_interval) != 0)):
return saved
with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_D... |
class MixtureTable(Layer):
def __init__(self, dim=INTMAX, bigdl_type='float'):
super(MixtureTable, self).__init__(None, bigdl_type, dim) |
def get_parser():
parser = argparse.ArgumentParser(description='Command-line script for BLEU scoring.')
parser.add_argument('-s', '--sys', default='-', help='system output')
parser.add_argument('-r', '--ref', required=True, help='references')
parser.add_argument('-o', '--order', default=4, metavar='N', ... |
def clean_nyt_nursinghomes(data_dir='../../raw/nyt_nursinghomes/', out_dir='.'):
df = load_nyt_nursinghomes(data_dir=data_dir)
cols = (['Name', 'City', 'State'] + [col for col in list(df.columns) if (col not in ['Name', 'City', 'State'])])
df = df[cols]
df.to_csv(oj(out_dir, 'nyt_nursinghomes.csv'), hea... |
def split_next_chamber(state: MazeGenerationState) -> MazeGenerationState:
(chambers, chamber) = stack_pop(state.chambers)
(*_, width, height) = chamber
new_state: MazeGenerationState = jax.lax.cond((width >= height), split_horizontally, split_vertically, MazeGenerationState(state.maze, chambers, state.key)... |
def set_regularization(model, kernel_regularizer=None, bias_regularizer=None):
for layer in model.layers:
if ((kernel_regularizer is not None) and hasattr(layer, 'kernel_regularizer')):
layer.kernel_regularizer = kernel_regularizer
if ((bias_regularizer is not None) and hasattr(layer, 'b... |
def validate(valloader, model, criterion, epoch, use_cuda, mode):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.eval()
end = time.time()
bar = Bar(f'{mode}', max=len(valloader))
with torch.no_grad():
... |
class DIV2K(srdata.SRData):
def __init__(self, args, train=True):
super(DIV2K, self).__init__(args, train)
self.repeat = (args.test_every // (args.n_train // args.batch_size))
def _scan(self):
list_hr = []
list_lr = [[] for _ in self.scale]
if self.train:
idx_... |
class ResUNetSP(ME.MinkowskiNetwork):
NORM_TYPE = 'BN'
BLOCK_NORM_TYPE = 'BN'
CHANNELS = [None, 32, 64, 128]
TR_CHANNELS = [None, 32, 64, 64]
DEPTHS = [None, 1, 1, 1, 1, 1, None]
REGION_TYPE = ME.RegionType.HYPER_CUBE
def __init__(self, in_channels=3, out_channels=32, bn_momentum=0.1, conv1_... |
def _color(img, magnitude):
return ImageEnhance.Color(img).enhance((1 + (magnitude * random.choice([(- 1), 1])))) |
class ZeroBridge(Bridge):
def default_params():
return {}
def _create(self):
zero_state = nest.map_structure((lambda x: tf.zeros([self.batch_size, x], dtype=tf.float32)), self.decoder_state_size)
return zero_state |
class GroupViTTextModel(metaclass=DummyObject):
_backends = ['torch']
def __init__(self, *args, **kwargs):
requires_backends(self, ['torch']) |
def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=(7.0 / 16.0), last_epoch=(- 1)):
def _lr_lambda(current_step):
if (current_step < num_warmup_steps):
return (float(current_step) / float(max(1, num_warmup_steps)))
no_progress = (float((curren... |
def to_katakana(str):
str = str.lower()
str = normalize_double_n(str)
tmp = ROMPAT.sub((lambda x: ROMKAN[x.group(0)]), str)
return tmp |
def create_data():
seq_len = 400
data = np.random.rand(seq_len)
horizon = np.random.randint(2, 50)
validation_data = np.random.rand(horizon)
return (data, validation_data) |
.parametrize('arch, expected_out_shape', [('resnet', 512), ('shufflenet', 1024), ('resnext', 2048), ('wide_resnet', 2048), ('regnet', 912), ('mobilenet', 1280), ('mnasnet', 1280), ('squeezenet', 512), ({'shufflenet': ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1}, 1024), ({'resnext': ResNeXt50_32X4D_Weights.IMAGENET1K_V2}, ... |
class Bottleneck(nn.Module):
expansion = 4
num_layers = 3
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if (norm_layer is None):
norm_layer = nn.BatchNorm2d
width =... |
def is_2d_tensor(x_tensor):
return (isinstance(x_tensor, torch.Tensor) and (len(x_tensor.shape) == 2)) |
class DGN(nn.Module):
def __init__(self, n_agent, num_inputs, hidden_dim, num_actions):
super(DGN, self).__init__()
self.encoder = Encoder(num_inputs, hidden_dim)
self.att_1 = AttModel(n_agent, hidden_dim, hidden_dim, hidden_dim)
self.att_2 = AttModel(n_agent, hidden_dim, hidden_dim,... |
def get_net_instance(net_type, net_name, *args, **kwargs):
a = try_get_net_instance(net_type, net_name, *args, **kwargs)
assert (a is not None), 'Cannot find such a net'
return a |
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