Source code for power_cogs.config.torch.torch_config

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/master/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa

from pydantic.dataclasses import dataclass
from omegaconf import MISSING  # Do not confuse with dataclass.MISSING
from typing import Dict, Any, Optional, List

"""
    Optimizer + Scheduler config
"""
[docs]@dataclass class AdamConf: _target_: str = "torch.optim.adam.Adam" params: Any = MISSING lr: Any = 0.001 betas: Any = (0.9, 0.999) eps: Any = 1e-08 weight_decay: Any = 0 amsgrad: Any = False
[docs]@dataclass class LambdaLRConf: _target_: str = "torch.optim.lr_scheduler.LambdaLR" optimizer: Any = MISSING lr_lambda: Any = MISSING last_epoch: Any = -1
[docs]@dataclass class MultiplicativeLRConf: _target_: str = "torch.optim.lr_scheduler.MultiplicativeLR" optimizer: Any = MISSING lr_lambda: Any = MISSING last_epoch: Any = -1
[docs]@dataclass class StepLRConf: _target_: str = "torch.optim.lr_scheduler.StepLR" optimizer: Any = MISSING step_size: Any = 0.1 gamma: Any = 0.1 last_epoch: Any = -1
[docs]@dataclass class MultiStepLRConf: _target_: str = "torch.optim.lr_scheduler.MultiStepLR" optimizer: Any = MISSING milestones: Any = MISSING gamma: Any = 0.1 last_epoch: Any = -1
[docs]@dataclass class ExponentialLRConf: _target_: str = "torch.optim.lr_scheduler.ExponentialLR" optimizer: Any = MISSING gamma: Any = 0.9999 last_epoch: Any = -1
[docs]@dataclass class CosineAnnealingLRConf: _target_: str = "torch.optim.lr_scheduler.CosineAnnealingLR" optimizer: Any = MISSING T_max: Any = MISSING eta_min: Any = 0 last_epoch: Any = -1
[docs]@dataclass class ReduceLROnPlateauConf: _target_: str = "torch.optim.lr_scheduler.ReduceLROnPlateau" optimizer: Any = MISSING 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
[docs]@dataclass class CyclicLRConf: _target_: str = "torch.optim.lr_scheduler.CyclicLR" optimizer: Any = MISSING base_lr: Any = MISSING max_lr: Any = MISSING 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
[docs]@dataclass class CosineAnnealingWarmRestartsConf: _target_: str = "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts" optimizer: Any = MISSING T_0: Any = MISSING T_mult: Any = 1 eta_min: Any = 0 last_epoch: Any = -1
[docs]@dataclass class OneCycleLRConf: _target_: str = "torch.optim.lr_scheduler.OneCycleLR" optimizer: Any = MISSING max_lr: Any = MISSING 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
""" Dataset + Dataloader Config """
[docs]@dataclass class DataLoaderConf: _target_: str = "torch.utils.data.dataloader.DataLoader" dataset: Any = MISSING 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
[docs]@dataclass class DatasetConf: _target_: str = "torch.utils.data.dataset.Dataset"
[docs]@dataclass class ChainDatasetConf: _target_: str = "torch.utils.data.dataset.ChainDataset" datasets: Any = MISSING
[docs]@dataclass class ConcatDatasetConf: _target_: str = "torch.utils.data.dataset.ConcatDataset" datasets: Any = MISSING
[docs]@dataclass class IterableDatasetConf: _target_: str = "torch.utils.data.dataset.IterableDataset"
[docs]@dataclass class TensorDatasetConf: _target_: str = "torch.utils.data.dataset.TensorDataset" tensors: Any = MISSING
[docs]@dataclass class SubsetConf: _target_: str = "torch.utils.data.dataset.Subset" dataset: Any = MISSING indices: Any = MISSING