power_cogs.config.torch package¶
Submodules¶
power_cogs.config.torch.torch_config module¶
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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:
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amsgrad= False¶
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betas= (0.9, 0.999)¶
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eps= 1e-08¶
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lr= 0.001¶
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params= '???'¶
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weight_decay= 0¶
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class
power_cogs.config.torch.torch_config.ChainDatasetConf(_target_: str = 'torch.utils.data.dataset.ChainDataset', datasets: Any = '???')[source]¶ Bases:
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datasets= '???'¶
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class
power_cogs.config.torch.torch_config.ConcatDatasetConf(_target_: str = 'torch.utils.data.dataset.ConcatDataset', datasets: Any = '???')[source]¶ Bases:
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datasets= '???'¶
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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:
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T_max= '???'¶
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eta_min= 0¶
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last_epoch= -1¶
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optimizer= '???'¶
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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:
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T_0= '???'¶
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T_mult= 1¶
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eta_min= 0¶
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last_epoch= -1¶
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optimizer= '???'¶
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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:
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base_lr= '???'¶
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base_momentum= 0.8¶
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cycle_momentum= True¶
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gamma= 1.0¶
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last_epoch= -1¶
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max_lr= '???'¶
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max_momentum= 0.9¶
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mode= 'triangular'¶
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optimizer= '???'¶
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scale_fn= None¶
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scale_mode= 'cycle'¶
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step_size_down= None¶
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step_size_up= 2000¶
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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:
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batch_sampler= None¶
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batch_size= 1¶
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collate_fn= None¶
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dataset= '???'¶
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drop_last= False¶
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generator= None¶
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multiprocessing_context= None¶
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num_workers= 0¶
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pin_memory= False¶
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sampler= None¶
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shuffle= False¶
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timeout= 0¶
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worker_init_fn= None¶
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class
power_cogs.config.torch.torch_config.DatasetConf(_target_: str = 'torch.utils.data.dataset.Dataset')[source]¶ Bases:
object
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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:
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gamma= 0.9999¶
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last_epoch= -1¶
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optimizer= '???'¶
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class
power_cogs.config.torch.torch_config.IterableDatasetConf(_target_: str = 'torch.utils.data.dataset.IterableDataset')[source]¶ Bases:
object
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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:
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last_epoch= -1¶
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lr_lambda= '???'¶
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optimizer= '???'¶
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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:
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gamma= 0.1¶
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last_epoch= -1¶
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milestones= '???'¶
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optimizer= '???'¶
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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:
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last_epoch= -1¶
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lr_lambda= '???'¶
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optimizer= '???'¶
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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:
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anneal_strategy= 'cos'¶
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base_momentum= 0.85¶
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cycle_momentum= True¶
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div_factor= 25.0¶
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epochs= None¶
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final_div_factor= 10000.0¶
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last_epoch= -1¶
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max_lr= '???'¶
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max_momentum= 0.95¶
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optimizer= '???'¶
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pct_start= 0.3¶
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steps_per_epoch= None¶
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total_steps= None¶
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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:
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cooldown= 0¶
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eps= 1e-08¶
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factor= 0.1¶
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min_lr= 0¶
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mode= 'min'¶
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optimizer= '???'¶
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patience= 10¶
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threshold= 0.0001¶
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threshold_mode= 'rel'¶
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verbose= False¶
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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:
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gamma= 0.1¶
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last_epoch= -1¶
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optimizer= '???'¶
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step_size= 0.1¶
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