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This commit is contained in:
William Falcon
2019-10-19 00:39:30 +02:00
committed by GitHub
parent e1b45ca492
commit 6111edaf82
1 changed files with 30 additions and 21 deletions
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pytorch_lightning/trainer/trainer.py
+30 -21
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@@ -597,6 +597,9 @@ class Trainer(TrainerIOMixin):
model.zero_grad()
model.eval()
# copy properties for forward overrides
self.__copy_trainer_model_properties(model)
# disable gradients to save memory
torch.set_grad_enabled(False)
@@ -842,6 +845,24 @@ class Trainer(TrainerIOMixin):
self.__run_pretrain_routine(model)
def __copy_trainer_model_properties(self, model):
if isinstance(model, LightningDataParallel):
ref_model = model.module
elif isinstance(model, LightningDistributedDataParallel):
ref_model = model.module
else:
ref_model = model
for m in [model, ref_model]:
m.trainer = self
m.on_gpu = self.on_gpu
m.use_dp = self.use_dp
m.use_ddp2 = self.use_ddp2
m.use_ddp = self.use_ddp
m.use_amp = self.use_amp
m.testing = self.testing
m.single_gpu = self.single_gpu
def ddp_train(self, gpu_nb, model):
"""
Entry point into a DP thread
@@ -890,13 +911,7 @@ class Trainer(TrainerIOMixin):
model.cuda(gpu_nb)
# set model properties before going into wrapper
model.trainer = self
model.on_gpu = self.on_gpu
model.use_dp = self.use_dp
model.use_ddp2 = self.use_ddp2
model.use_ddp = self.use_ddp
model.use_amp = self.use_amp
model.testing = self.testing
self.__copy_trainer_model_properties(model)
# override root GPU
self.root_gpu = gpu_nb
@@ -989,13 +1004,7 @@ class Trainer(TrainerIOMixin):
ref_model.trainer = self
# set local properties on the model
ref_model.on_gpu = self.on_gpu
ref_model.single_gpu = self.single_gpu
ref_model.use_dp = self.use_dp
ref_model.use_ddp = self.use_ddp
ref_model.use_ddp2 = self.use_ddp2
ref_model.use_amp = self.use_amp
ref_model.testing = self.testing
self.__copy_trainer_model_properties(ref_model)
# link up experiment object
if self.logger is not None:
@@ -1199,8 +1208,8 @@ class Trainer(TrainerIOMixin):
self.logger.save()
def test(self, model=None):
self.testing = True
if model is not None:
self.testing = True
self.fit(model)
else:
self.__run_evaluation(test=True)
@@ -1305,7 +1314,7 @@ class Trainer(TrainerIOMixin):
if k not in ['progress_bar', 'log']:
callback_metrics[k] = v
if train and self.use_dp or self.use_ddp2:
if train and (self.use_dp or self.use_ddp2):
nb_gpus = self.num_gpus
callback_metrics = reduce_distributed_output(callback_metrics, nb_gpus)
@@ -1319,7 +1328,7 @@ class Trainer(TrainerIOMixin):
progress_output = output['progress_bar']
# reduce progress metrics for tqdm when using dp
if train and self.use_dp or self.use_ddp2:
if train and (self.use_dp or self.use_ddp2):
nb_gpus = self.num_gpus
progress_output = reduce_distributed_output(progress_output, nb_gpus)
@@ -1517,13 +1526,13 @@ class Trainer(TrainerIOMixin):
model.on_pre_performance_check()
# select dataloaders
dataloaders = self.get_val_dataloaders()
max_batches = self.nb_val_batches
# calculate max batches to use
if test:
dataloaders = self.get_test_dataloaders()
max_batches = self.nb_test_batches
else:
# val
dataloaders = self.get_val_dataloaders()
max_batches = self.nb_val_batches
# cap max batches to 1 when using fast_dev_run
if self.fast_dev_run: