def __init__(self,
name: Optional[str] = None,
save_dir: Optional[str] = None,
offline: bool = False,
id: Optional[str] = None,
anonymous: bool = False,
version: Optional[str] = None,
project: Optional[str] = None,
log_model: bool = False,
experiment=None,
prefix: str = '',
**kwargs):
if wandb is None:
raise ImportError(
'You want to use `wandb` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install wandb`.')
super().__init__()
self._name = name
self._save_dir = save_dir
self._anonymous = 'allow' if anonymous else None
self._id = version or id
self._project = project
self._experiment = experiment
self._offline = offline
self._log_model = log_model
self._prefix = prefix
self._kwargs = kwargs
# logging multiple Trainer on a single W&B run (k-fold, resuming, etc)
self._step_offset = 0
self.warning_cache = WarningCache()
def __init__(self, trainer):
self.trainer = trainer
self.testing = False
self.outputs = []
self.step_metrics = []
self.predictions = None
self.max_batches = None
self.warning_cache = WarningCache()
def __init__(self, trainer):
self.trainer = trainer
self.early_stopping_accumulator = None
self.checkpoint_accumulator = None
self.accumulated_loss = None
self.warning_cache = WarningCache()
self._teardown_already_run = False
self.running_loss = TensorRunningAccum(window_length=20)
def __init__(self, trainer):
self.trainer = trainer
self.outputs = []
self.step_metrics = []
self.predictions = None
self.max_batches = None
self.warning_cache = WarningCache()
self.num_dataloaders = None
def __init__(self, trainer):
self.trainer = trainer
self.early_stopping_accumulator = None
self.checkpoint_accumulator = None
self.accumulated_loss = None
self.warning_cache = WarningCache()
self._teardown_already_run = False
self.running_loss = TensorRunningAccum(window_length=20)
self.automatic_optimization = True
self._curr_step_result = None
self._cur_grad_norm_dict = None
def __init__(self,
name: Optional[str] = None,
save_dir: Optional[str] = None,
offline: bool = False,
id: Optional[str] = None,
anonymous: bool = False,
version: Optional[str] = None,
project: Optional[str] = None,
log_model: bool = False,
experiment=None,
prefix: str = '',
**kwargs):
if wandb is None:
raise ImportError(
'You want to use `wandb` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install wandb`.')
if offline and log_model:
raise MisconfigurationException(
f'Providing log_model={log_model} and offline={offline} is an invalid configuration'
' since model checkpoints cannot be uploaded in offline mode.\n'
'Hint: Set `offline=False` to log your model.')
super().__init__()
self._name = name
self._save_dir = save_dir
self._anonymous = 'allow' if anonymous else None
self._id = version or id
self._project = project
self._experiment = experiment
self._offline = offline
self._log_model = log_model
self._prefix = prefix
self._kwargs = kwargs
# logging multiple Trainer on a single W&B run (k-fold, resuming, etc)
self._step_offset = 0
self.warning_cache = WarningCache()
def get_a_var(obj): # pragma: no-cover
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, (list, tuple)):
for result in map(get_a_var, obj):
if isinstance(result, torch.Tensor):
return result
if isinstance(obj, dict):
for result in map(get_a_var, obj.items()):
if isinstance(result, torch.Tensor):
return result
return None
warning_cache = WarningCache()
class LightningDataParallel(DataParallel):
"""
Override the forward call in lightning so it goes to training and validation step respectively
"""
def forward(self, *inputs, **kwargs):
if not self.device_ids:
return self.module(*inputs, **kwargs)
for t in chain(self.module.parameters(), self.module.buffers()):
if t.device != self.src_device_obj:
raise RuntimeError("module must have its parameters and buffers "
"on device {} (device_ids[0]) but found one of "