code stringlengths 17 6.64M |
|---|
def xavier_uniform_init(module, gain=1.0):
if (isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d)):
nn.init.xavier_uniform_(module.weight.data, gain)
nn.init.constant_(module.bias.data, 0)
return module
|
def adjust_lr(optimizer, init_lr, timesteps, max_timesteps):
lr = (init_lr * (1 - (timesteps / max_timesteps)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return optimizer
|
def get_n_params(model):
return (str(np.round((np.array([p.numel() for p in model.parameters()]).sum() / 1000000.0), 3)) + ' M params')
|
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), (- 1))
|
class MlpModel(nn.Module):
def __init__(self, input_dims=4, hidden_dims=[64, 64], **kwargs):
'\n input_dim: (int) number of the input dimensions\n hidden_dims: (list) list of the dimensions for the hidden layers\n use_batchnorm: (bool) whether to use batchnorm\n '
... |
class NatureModel(nn.Module):
def __init__(self, in_channels, **kwargs):
'\n input_shape: (tuple) tuple of the input dimension shape (channel, height, width)\n filters: (list) list of the tuples consists of (number of channels, kernel size, and strides)\n use_batchnorm: (bool)... |
class ResidualBlock(nn.Module):
def __init__(self, in_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, ke... |
class ImpalaBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ImpalaBlock, self).__init__()
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
self.res1 = ResidualBlock(out_channels)
self.res2 = Residu... |
class ImpalaModel(nn.Module):
def __init__(self, in_channels, **kwargs):
super(ImpalaModel, self).__init__()
self.block1 = ImpalaBlock(in_channels=in_channels, out_channels=16)
self.block2 = ImpalaBlock(in_channels=16, out_channels=32)
self.block3 = ImpalaBlock(in_channels=32, out... |
class GRU(nn.Module):
def __init__(self, input_size, hidden_size):
super(GRU, self).__init__()
self.gru = orthogonal_init(nn.GRU(input_size, hidden_size), gain=1.0)
def forward(self, x, hxs, masks):
if (x.size(0) == hxs.size(0)):
masks = masks.unsqueeze((- 1))
... |
class CategoricalPolicy(nn.Module):
def __init__(self, embedder, recurrent, action_size):
'\n embedder: (torch.Tensor) model to extract the embedding for observation\n action_size: number of the categorical actions\n '
super(CategoricalPolicy, self).__init__()
self.em... |
def load_model(args):
if (args.model == 'clip_vis'):
model = CLIP_Visual(classes=classes, device=device, inet=(args.dataset == 'imagenet')).to(device)
elif (args.model == 'clip_zero'):
model = CLIP_Zero_Shot(classes=classes, prompt=prompt, device=device).to(device)
else:
raise Valu... |
def predict(image):
global model, zero_shot_model, preprocess, device
image = Image.fromarray(image.astype('uint8'), 'RGB')
input_tensor = preprocess(image)
input_batch = input_tensor.unsqueeze(0)
input_batch = input_batch.to(device)
model = model.to(device)
zero_shot_model = zero_shot_mod... |
def sample_assumed_distribution(dist_parameters, num_samples):
dist_type = dist_parameters['dist_type']
if (dist_type == 'gaussian'):
distribution = torch.distributions.Normal(loc=dist_parameters['mean'], scale=dist_parameters['std'])
sample = distribution.sample([num_samples])
sample ... |
class DictX(dict):
'\n Taken From https://dev.to/0xbf/use-dot-syntax-to-access-dictionary-key-python-tips-10ec\n '
def __getattr__(self, key):
try:
return self[key]
except KeyError as k:
raise AttributeError(k)
def __setattr__(self, key, value):
self... |
def save_experiment_hyper_params(args, exp_dir, verbose=True):
with open(join(exp_dir, f'args.txt'), 'w+') as f:
f.write('\n\n\n')
f.write('Experiment Args:\n\n')
for k in args:
f.write(f''' {k}: {args[k]}
''')
f.write('\n\n\n')
if verbose:
with open(join(e... |
def verify_token(headers, path):
token = headers.get('authorization', '')[7:]
if (os.environ['SYSTEM_TOKEN'] == token):
return True
elif ((not path.startswith('/upload_video')) and (os.environ['USER_TOKEN'] == token)):
return True
else:
return False
|
@app.get('/jobid/{task_id}')
def check_job(task_id: str) -> str:
res = celery_workers.AsyncResult(task_id)
if (res.state == states.PENDING):
reserved_tasks = celery_workers.control.inspect().reserved()
tasks = []
if reserved_tasks:
tasks_per_worker = reserved_tasks.values()... |
def fix_obj(parent_obj):
for obj in parent_obj.children:
fix_obj(obj)
parent_obj.rotation_euler.x = 0
if (parent_obj.name in ['pCube0', 'pCube1', 'pCube2']):
parent_obj.location.y = (- 13)
if (parent_obj.name == 'pCube3'):
parent_obj.location.y = (- 10)
if (parent_obj.name ... |
class TaskFailure(Exception):
pass
|
def validate_bvh_file(bvh_file):
MAX_NUMBER_FRAMES = int(os.environ['MAX_NUMBER_FRAMES'])
FRAME_TIME = (1.0 / float(os.environ['RENDER_FPS']))
file_content = bvh_file.decode('utf-8')
mocap = Bvh(file_content)
counter = None
for line in file_content.split('\n'):
if ((counter is not None... |
@celery.task(name='tasks.render', bind=True, hard_time_limit=WORKER_TIMEOUT)
def render(self, bvh_file_uri: str) -> str:
HEADERS = {'Authorization': (f'Bearer ' + os.environ['SYSTEM_TOKEN'])}
API_SERVER = os.environ['API_SERVER']
logger.info('rendering..')
self.update_state(state='PROCESSING')
bvh... |
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
|
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
|
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
|
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
|
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
|
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
|
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
|
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
|
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
|
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
|
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
|
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
|
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
|
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
|
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
|
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
|
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
|
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
|
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
|
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
|
class ThresholdParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _THRESHOLDPARAMETER
|
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
|
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
|
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
|
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
|
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
|
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
|
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
|
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
|
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
|
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
|
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
|
class ReLUParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _RELUPARAMETER
|
class SigmoidParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SIGMOIDPARAMETER
|
class SliceParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SLICEPARAMETER
|
class SoftmaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOFTMAXPARAMETER
|
class TanHParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TANHPARAMETER
|
class WindowDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _WINDOWDATAPARAMETER
|
class V0LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _V0LAYERPARAMETER
|
class BlobShape(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBSHAPE
|
class BlobProto(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTO
|
class BlobProtoVector(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _BLOBPROTOVECTOR
|
class Datum(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATUM
|
class FillerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FILLERPARAMETER
|
class NetParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETPARAMETER
|
class SolverParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERPARAMETER
|
class SolverState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _SOLVERSTATE
|
class NetState(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATE
|
class NetStateRule(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _NETSTATERULE
|
class ParamSpec(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PARAMSPEC
|
class LayerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LAYERPARAMETER
|
class TransformationParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _TRANSFORMATIONPARAMETER
|
class LossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOSSPARAMETER
|
class AccuracyParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ACCURACYPARAMETER
|
class ArgMaxParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ARGMAXPARAMETER
|
class ConcatParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONCATPARAMETER
|
class ContrastiveLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONTRASTIVELOSSPARAMETER
|
class ConvolutionParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _CONVOLUTIONPARAMETER
|
class DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DATAPARAMETER
|
class DropoutParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DROPOUTPARAMETER
|
class DummyDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _DUMMYDATAPARAMETER
|
class EltwiseParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _ELTWISEPARAMETER
|
class EmbedParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EMBEDPARAMETER
|
class ExpParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _EXPPARAMETER
|
class FlattenParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _FLATTENPARAMETER
|
class HDF5DataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5DATAPARAMETER
|
class HDF5OutputParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HDF5OUTPUTPARAMETER
|
class HingeLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _HINGELOSSPARAMETER
|
class ImageDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _IMAGEDATAPARAMETER
|
class InfogainLossParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INFOGAINLOSSPARAMETER
|
class InnerProductParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _INNERPRODUCTPARAMETER
|
class LogParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LOGPARAMETER
|
class LRNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _LRNPARAMETER
|
class MemoryDataParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MEMORYDATAPARAMETER
|
class MVNParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _MVNPARAMETER
|
class PoolingParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POOLINGPARAMETER
|
class PowerParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _POWERPARAMETER
|
class PythonParameter(message.Message):
__metaclass__ = reflection.GeneratedProtocolMessageType
DESCRIPTOR = _PYTHONPARAMETER
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.