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Logger default (#351)

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* weights go into default logger folder

* weights go into default logger folder

* weights go into default logger folder

* weights go into default logger folder

* weights go into default logger folder

* weights go into default logger folder

* ckpt callback in pretrain routine so exp already has version

* ckpt callback in pretrain routine so exp already has version

* ckpt callback in pretrain routine so exp already has version
This commit is contained in:
William Falcon
2019-10-09 17:46:27 -04:00
committed by GitHub
parent d95e693598
commit 453568179b
1 changed files with 22 additions and 20 deletions
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pytorch_lightning/trainer/trainer.py
+22 -20
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@@ -197,20 +197,8 @@ class Trainer(TrainerIO):
# configure checkpoint callback
self.checkpoint_callback = checkpoint_callback
if self.checkpoint_callback is None:
if isinstance(self.logger, TestTubeLogger):
ckpt_path = '{}/{}/{}/{}'.format(self.default_save_path,
self.logger.name,
self.logger.version, 'checkpoints')
else:
ckpt_path = self.default_save_path
self.checkpoint_callback = ModelCheckpoint(
filepath=ckpt_path
)
# configure weights save path
self.__configure_weights_path(checkpoint_callback, weights_save_path)
self.weights_save_path = weights_save_path
# accumulated grads
self.__configure_accumulated_gradients(accumulate_grad_batches)
@@ -262,22 +250,33 @@ class Trainer(TrainerIO):
job_id = None
return job_id
def __configure_weights_path(self, checkpoint_callback, weights_save_path):
def __configure_checkpoint_callback(self):
"""
Weight path set in this priority:
Checkpoint_callback's path (if passed in).
User provided weights_saved_path
Otherwise use os.getcwd()
"""
self.weights_save_path = weights_save_path
if self.checkpoint_callback is None:
# init a default one
if isinstance(self.logger, TestTubeLogger):
ckpt_path = '{}/{}/version_{}/{}'.format(self.default_save_path,
self.logger.experiment.name,
self.logger.experiment.version, 'checkpoints')
else:
ckpt_path = self.default_save_path
if self.checkpoint_callback is not None:
self.checkpoint_callback.save_function = self.save_checkpoint
self.checkpoint_callback = ModelCheckpoint(
filepath=ckpt_path
)
# if checkpoint callback used, then override the weights path
self.weights_save_path = self.checkpoint_callback.filepath
# set the path for the callbacks
self.checkpoint_callback.save_function = self.save_checkpoint
# if weights_save_path is still none here, set to current workingdir
# if checkpoint callback used, then override the weights path
self.weights_save_path = self.checkpoint_callback.filepath
# if weights_save_path is still none here, set to current working dir
if self.weights_save_path is None:
self.weights_save_path = self.default_save_path
@@ -982,6 +981,9 @@ class Trainer(TrainerIO):
ref_model.use_amp = self.use_amp
ref_model.testing = self.testing
# set up checkpoint callback
self.__configure_checkpoint_callback()
# register auto-resubmit when on SLURM
self.register_slurm_signal_handlers()