power_cogs.config.torch package

Submodules

power_cogs.config.torch.torch_config module

class power_cogs.config.torch.torch_config.AdamConf(_target_: str = 'torch.optim.adam.Adam', params: Any = '???', lr: Any = 0.001, betas: Any = (0.9, 0.999), eps: Any = 1e-08, weight_decay: Any = 0, amsgrad: Any = False)[source]

Bases: object

amsgrad = False
betas = (0.9, 0.999)
eps = 1e-08
lr = 0.001
params = '???'
weight_decay = 0
class power_cogs.config.torch.torch_config.ChainDatasetConf(_target_: str = 'torch.utils.data.dataset.ChainDataset', datasets: Any = '???')[source]

Bases: object

datasets = '???'
class power_cogs.config.torch.torch_config.ConcatDatasetConf(_target_: str = 'torch.utils.data.dataset.ConcatDataset', datasets: Any = '???')[source]

Bases: object

datasets = '???'
class power_cogs.config.torch.torch_config.CosineAnnealingLRConf(_target_: str = 'torch.optim.lr_scheduler.CosineAnnealingLR', optimizer: Any = '???', T_max: Any = '???', eta_min: Any = 0, last_epoch: Any = -1)[source]

Bases: object

T_max = '???'
eta_min = 0
last_epoch = -1
optimizer = '???'
class power_cogs.config.torch.torch_config.CosineAnnealingWarmRestartsConf(_target_: str = 'torch.optim.lr_scheduler.CosineAnnealingWarmRestarts', optimizer: Any = '???', T_0: Any = '???', T_mult: Any = 1, eta_min: Any = 0, last_epoch: Any = -1)[source]

Bases: object

T_0 = '???'
T_mult = 1
eta_min = 0
last_epoch = -1
optimizer = '???'
class power_cogs.config.torch.torch_config.CyclicLRConf(_target_: str = 'torch.optim.lr_scheduler.CyclicLR', optimizer: Any = '???', base_lr: Any = '???', max_lr: Any = '???', step_size_up: Any = 2000, step_size_down: Any = None, mode: Any = 'triangular', gamma: Any = 1.0, scale_fn: Any = None, scale_mode: Any = 'cycle', cycle_momentum: Any = True, base_momentum: Any = 0.8, max_momentum: Any = 0.9, last_epoch: Any = -1)[source]

Bases: object

base_lr = '???'
base_momentum = 0.8
cycle_momentum = True
gamma = 1.0
last_epoch = -1
max_lr = '???'
max_momentum = 0.9
mode = 'triangular'
optimizer = '???'
scale_fn = None
scale_mode = 'cycle'
step_size_down = None
step_size_up = 2000
class power_cogs.config.torch.torch_config.DataLoaderConf(_target_: str = 'torch.utils.data.dataloader.DataLoader', dataset: Any = '???', batch_size: Any = 1, shuffle: Any = False, sampler: Any = None, batch_sampler: Any = None, num_workers: Any = 0, collate_fn: Any = None, pin_memory: Any = False, drop_last: Any = False, timeout: Any = 0, worker_init_fn: Any = None, multiprocessing_context: Any = None, generator: Any = None)[source]

Bases: object

batch_sampler = None
batch_size = 1
collate_fn = None
dataset = '???'
drop_last = False
generator = None
multiprocessing_context = None
num_workers = 0
pin_memory = False
sampler = None
shuffle = False
timeout = 0
worker_init_fn = None
class power_cogs.config.torch.torch_config.DatasetConf(_target_: str = 'torch.utils.data.dataset.Dataset')[source]

Bases: object

class power_cogs.config.torch.torch_config.ExponentialLRConf(_target_: str = 'torch.optim.lr_scheduler.ExponentialLR', optimizer: Any = '???', gamma: Any = 0.9999, last_epoch: Any = -1)[source]

Bases: object

gamma = 0.9999
last_epoch = -1
optimizer = '???'
class power_cogs.config.torch.torch_config.IterableDatasetConf(_target_: str = 'torch.utils.data.dataset.IterableDataset')[source]

Bases: object

class power_cogs.config.torch.torch_config.LambdaLRConf(_target_: str = 'torch.optim.lr_scheduler.LambdaLR', optimizer: Any = '???', lr_lambda: Any = '???', last_epoch: Any = -1)[source]

Bases: object

last_epoch = -1
lr_lambda = '???'
optimizer = '???'
class power_cogs.config.torch.torch_config.MultiStepLRConf(_target_: str = 'torch.optim.lr_scheduler.MultiStepLR', optimizer: Any = '???', milestones: Any = '???', gamma: Any = 0.1, last_epoch: Any = -1)[source]

Bases: object

gamma = 0.1
last_epoch = -1
milestones = '???'
optimizer = '???'
class power_cogs.config.torch.torch_config.MultiplicativeLRConf(_target_: str = 'torch.optim.lr_scheduler.MultiplicativeLR', optimizer: Any = '???', lr_lambda: Any = '???', last_epoch: Any = -1)[source]

Bases: object

last_epoch = -1
lr_lambda = '???'
optimizer = '???'
class power_cogs.config.torch.torch_config.OneCycleLRConf(_target_: str = 'torch.optim.lr_scheduler.OneCycleLR', optimizer: Any = '???', max_lr: Any = '???', total_steps: Any = None, epochs: Any = None, steps_per_epoch: Any = None, pct_start: Any = 0.3, anneal_strategy: Any = 'cos', cycle_momentum: Any = True, base_momentum: Any = 0.85, max_momentum: Any = 0.95, div_factor: Any = 25.0, final_div_factor: Any = 10000.0, last_epoch: Any = -1)[source]

Bases: object

anneal_strategy = 'cos'
base_momentum = 0.85
cycle_momentum = True
div_factor = 25.0
epochs = None
final_div_factor = 10000.0
last_epoch = -1
max_lr = '???'
max_momentum = 0.95
optimizer = '???'
pct_start = 0.3
steps_per_epoch = None
total_steps = None
class power_cogs.config.torch.torch_config.ReduceLROnPlateauConf(_target_: str = 'torch.optim.lr_scheduler.ReduceLROnPlateau', optimizer: Any = '???', mode: Any = 'min', factor: Any = 0.1, patience: Any = 10, verbose: Any = False, threshold: Any = 0.0001, threshold_mode: Any = 'rel', cooldown: Any = 0, min_lr: Any = 0, eps: Any = 1e-08)[source]

Bases: object

cooldown = 0
eps = 1e-08
factor = 0.1
min_lr = 0
mode = 'min'
optimizer = '???'
patience = 10
threshold = 0.0001
threshold_mode = 'rel'
verbose = False
class power_cogs.config.torch.torch_config.StepLRConf(_target_: str = 'torch.optim.lr_scheduler.StepLR', optimizer: Any = '???', step_size: Any = 0.1, gamma: Any = 0.1, last_epoch: Any = -1)[source]

Bases: object

gamma = 0.1
last_epoch = -1
optimizer = '???'
step_size = 0.1
class power_cogs.config.torch.torch_config.SubsetConf(_target_: str = 'torch.utils.data.dataset.Subset', dataset: Any = '???', indices: Any = '???')[source]

Bases: object

dataset = '???'
indices = '???'
class power_cogs.config.torch.torch_config.TensorDatasetConf(_target_: str = 'torch.utils.data.dataset.TensorDataset', tensors: Any = '???')[source]

Bases: object

tensors = '???'

Module contents