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Change nb to num in ABCs, comments, and tqdm logging #613

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Dec 9, 2019
Merged

Change nb to num in ABCs, comments, and tqdm logging #613

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9ec361c
Change nb to num in ABCs, comments, and tqdm logging
elliotwaite Dec 8, 2019
d515dec
Fix warnings text
elliotwaite Dec 8, 2019
a2765ca
Make warnings one line
elliotwaite Dec 8, 2019
ca6ce47
Change num to number in comments
elliotwaite Dec 8, 2019
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4 changes: 2 additions & 2 deletions pytorch_lightning/trainer/evaluation_loop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,8 +145,8 @@ def __init__(self):
self.single_gpu = None
self.data_parallel_device_ids = None
self.model = None
self.nb_test_batches = None
self.nb_val_batches = None
self.num_test_batches = None
self.num_val_batches = None
self.fast_dev_run = None
self.process_position = None
self.show_progress_bar = None
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2 changes: 1 addition & 1 deletion pytorch_lightning/trainer/logging.py
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Expand Up @@ -177,7 +177,7 @@ def reduce_distributed_output(self, output, num_gpus):
elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
pass

# reduce only metrics that have the same nb of gpus
# reduce only metrics that have the same number of gpus
elif output[k].size(0) == num_gpus:
reduced = torch.mean(output[k])
output[k] = reduced
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2 changes: 1 addition & 1 deletion pytorch_lightning/trainer/trainer.py
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Expand Up @@ -346,7 +346,7 @@ def training_tqdm_dict(self):
tqdm_dict['split_idx'] = self.split_idx

if self.logger is not None and self.logger.version is not None:
tqdm_dict['v_nb'] = self.logger.version
tqdm_dict['v_num'] = self.logger.version

tqdm_dict.update(self.tqdm_metrics)

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31 changes: 25 additions & 6 deletions pytorch_lightning/trainer/training_loop.py
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Expand Up @@ -151,6 +151,7 @@ def training_step(self, batch, batch_idx):

import inspect
from abc import ABC, abstractmethod
import warnings

import numpy as np

Expand All @@ -169,22 +170,22 @@ class TrainerTrainLoopMixin(ABC):
def __init__(self):
# this is just a summary on variables used in this abstract class,
# the proper values/initialisation should be done in child class
self.max_nb_epochs = None
self.max_epochs = None
self.min_epochs = None
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is the min needed here in this class?

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yes, it is used in the training loop.

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self.min_nb_epochs was there before, just lower down in the list, so I moved it up so it would be next to self.max_epochs.

self.use_ddp = None
self.use_dp = None
self.use_ddp2 = None
self.single_gpu = None
self.data_parallel_device_ids = None
self.check_val_every_n_epoch = None
self.nb_training_batches = None
self.num_training_batches = None
self.val_check_batch = None
self.nb_val_batches = None
self.num_val_batches = None
self.fast_dev_run = None
self.is_iterable_train_dataloader = None
self.main_progress_bar = None
self.accumulation_scheduler = None
self.lr_schedulers = None
self.min_nb_epochs = None
self.enable_early_stop = None
self.early_stop_callback = None
self.callback_metrics = None
Expand All @@ -194,7 +195,7 @@ def __init__(self):
self.log_save_interval = None
self.proc_rank = None
self.row_log_interval = None
self.total_batch_nb = None
self.total_batches = None
self.truncated_bptt_steps = None
self.optimizers = None
self.accumulate_grad_batches = None
Expand All @@ -207,6 +208,24 @@ def __init__(self):
self.get_train_dataloader = None
self.reduce_lr_on_plateau_scheduler = None

@property
def max_nb_epochs(self):
"""
.. warning:: `max_nb_epochs` is deprecated and will be removed in v0.8.0, use `max_epochs` instead.
"""
warnings.warn("`max_nb_epochs` is deprecated and will be removed in "
"v0.8.0, use `max_epochs` instead.", DeprecationWarning)
return self.max_epochs

@property
def min_nb_epochs(self):
"""
.. warning:: `min_nb_epochs` is deprecated and will be removed in v0.8.0, use `min_epochs` instead.
"""
warnings.warn("`min_nb_epochs` is deprecated and will be removed in "
"v0.8.0, use `min_epochs` instead.", DeprecationWarning)
return self.min_epochs

@abstractmethod
def get_model(self):
# this is just empty shell for code from other class
Expand Down Expand Up @@ -391,7 +410,7 @@ def run_training_epoch(self):
if early_stop_epoch or self.fast_dev_run:
break

# stop epoch if we limited nb batches
# stop epoch if we limited the number of training batches
met_batch_limit = batch_idx >= self.num_training_batches
if met_batch_limit:
break
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