parent
1c10560531
commit
88f816ed06
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@ -292,6 +292,41 @@ class LoggerCollection(LightningLoggerBase):
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return '_'.join([str(logger.version) for logger in self._logger_iterable])
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class DummyExperiment(object):
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""" Dummy experiment """
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def nop(*args, **kw):
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pass
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def __getattr__(self, _):
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return self.nop
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class DummyLogger(LightningLoggerBase):
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""" Dummy logger for internal use. Is usefull if we want to disable users
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logger for a feature, but still secure that users code can run """
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def __init__(self):
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super().__init__()
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self._experiment = DummyExperiment()
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@property
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def experiment(self):
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return self._experiment
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def log_metrics(self, metrics, step):
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pass
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def log_hyperparams(self, params):
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pass
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@property
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def name(self):
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pass
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@property
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def version(self):
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pass
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def merge_dicts(
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dicts: Sequence[Mapping],
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agg_key_funcs: Optional[Mapping[str, Callable[[Sequence[float]], float]]] = None,
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@ -13,6 +13,7 @@ import os
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.loggers.base import DummyLogger
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from pytorch_lightning import _logger as log
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities import rank_zero_warn
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@ -133,7 +134,7 @@ class TrainerLRFinderMixin(ABC):
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progress_bar_refresh_rate=1)]
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# No logging
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self.logger = None
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self.logger = DummyLogger()
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# Max step set to number of iterations
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self.max_steps = num_training
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@ -12,6 +12,7 @@ from torch.utils.data import DataLoader
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from pytorch_lightning import _logger as log
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.callbacks import GradientAccumulationScheduler
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from pytorch_lightning.loggers.base import DummyLogger
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.memory import is_oom_error, garbage_collection_cuda
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@ -195,7 +196,7 @@ class TrainerTrainingTricksMixin(ABC):
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self.auto_scale_batch_size = None # prevent recursion
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self.max_steps = steps_per_trial # take few steps
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self.weights_summary = None # not needed before full run
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self.logger = None # not needed before full run
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self.logger = DummyLogger()
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self.callbacks = [] # not needed before full run
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self.checkpoint_callback = False # required for saving
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self.early_stop_callback = None
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