mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-09 11:32:07 +08:00
prune tests (#564)
* format docstring in tests * prune unused vars * optimize imports * drop duplicated var
This commit is contained in:
Jirka Borovec
committed by
William Falcon
William Falcon
parent
62f6f92fdf
commit
63717e8fda
@@ -1,17 +1,16 @@
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import os
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import logging
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import os
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import pytest
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import torch
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import tests.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.testing import LightningTestModel
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import tests.utils as tutils
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def test_running_test_pretrained_model_ddp(tmpdir):
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"""Verify test() on pretrained model"""
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"""Verify `test()` on pretrained model."""
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if not tutils.can_run_gpu_test():
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return
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@@ -21,10 +20,8 @@ def test_running_test_pretrained_model_ddp(tmpdir):
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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save_dir = tmpdir
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# exp file to get meta
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logger = tutils.get_test_tube_logger(save_dir, False)
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logger = tutils.get_test_tube_logger(tmpdir, False)
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# exp file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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@@ -68,10 +65,8 @@ def test_running_test_pretrained_model(tmpdir):
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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save_dir = tmpdir
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# logger file to get meta
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logger = tutils.get_test_tube_logger(save_dir, False)
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logger = tutils.get_test_tube_logger(tmpdir, False)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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@@ -109,8 +104,6 @@ def test_load_model_from_checkpoint(tmpdir):
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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save_dir = tmpdir
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trainer_options = dict(
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show_progress_bar=False,
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max_nb_epochs=1,
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@@ -118,7 +111,7 @@ def test_load_model_from_checkpoint(tmpdir):
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val_percent_check=0.2,
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checkpoint_callback=True,
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logger=False,
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default_save_path=save_dir
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default_save_path=tmpdir,
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)
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# fit model
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@@ -152,10 +145,8 @@ def test_running_test_pretrained_model_dp(tmpdir):
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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save_dir = tmpdir
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# logger file to get meta
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logger = tutils.get_test_tube_logger(save_dir, False)
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logger = tutils.get_test_tube_logger(tmpdir, False)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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@@ -189,10 +180,7 @@ def test_running_test_pretrained_model_dp(tmpdir):
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def test_dp_resume(tmpdir):
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"""
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Make sure DP continues training correctly
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:return:
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"""
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"""Make sure DP continues training correctly."""
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if not tutils.can_run_gpu_test():
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return
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@@ -208,10 +196,8 @@ def test_dp_resume(tmpdir):
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distributed_backend='dp',
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)
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save_dir = tmpdir
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# get logger
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logger = tutils.get_test_tube_logger(save_dir, debug=False)
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logger = tutils.get_test_tube_logger(tmpdir, debug=False)
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# exp file to get weights
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# logger file to get weights
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@@ -236,12 +222,12 @@ def test_dp_resume(tmpdir):
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# HPC LOAD/SAVE
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# ---------------------------
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# save
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trainer.hpc_save(save_dir, logger)
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trainer.hpc_save(tmpdir, logger)
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# init new trainer
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new_logger = tutils.get_test_tube_logger(save_dir, version=logger.version)
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new_logger = tutils.get_test_tube_logger(tmpdir, version=logger.version)
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trainer_options['logger'] = new_logger
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trainer_options['checkpoint_callback'] = ModelCheckpoint(save_dir)
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trainer_options['checkpoint_callback'] = ModelCheckpoint(tmpdir)
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trainer_options['train_percent_check'] = 0.2
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trainer_options['val_percent_check'] = 0.2
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trainer_options['max_nb_epochs'] = 1
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@@ -272,20 +258,15 @@ def test_dp_resume(tmpdir):
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def test_cpu_restore_training(tmpdir):
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"""
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Verify continue training session on CPU
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:return:
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"""
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"""Verify continue training session on CPU."""
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tutils.reset_seed()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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save_dir = tmpdir
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# logger file to get meta
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test_logger_version = 10
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logger = tutils.get_test_tube_logger(save_dir, False, version=test_logger_version)
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logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
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trainer_options = dict(
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max_nb_epochs=2,
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@@ -293,7 +274,7 @@ def test_cpu_restore_training(tmpdir):
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val_percent_check=0.2,
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train_percent_check=0.2,
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logger=logger,
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checkpoint_callback=ModelCheckpoint(save_dir)
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checkpoint_callback=ModelCheckpoint(tmpdir)
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)
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# fit model
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@@ -307,14 +288,14 @@ def test_cpu_restore_training(tmpdir):
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# wipe-out trainer and model
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# retrain with not much data... this simulates picking training back up after slurm
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# we want to see if the weights come back correctly
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new_logger = tutils.get_test_tube_logger(save_dir, False, version=test_logger_version)
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new_logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
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trainer_options = dict(
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max_nb_epochs=2,
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val_check_interval=0.50,
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val_percent_check=0.2,
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train_percent_check=0.2,
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logger=new_logger,
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checkpoint_callback=ModelCheckpoint(save_dir),
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checkpoint_callback=ModelCheckpoint(tmpdir),
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)
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trainer = Trainer(**trainer_options)
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model = LightningTestModel(hparams)
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@@ -338,24 +319,19 @@ def test_cpu_restore_training(tmpdir):
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def test_model_saving_loading(tmpdir):
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"""
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Tests use case where trainer saves the model, and user loads it from tags independently
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:return:
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"""
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"""Tests use case where trainer saves the model, and user loads it from tags independently."""
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tutils.reset_seed()
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hparams = tutils.get_hparams()
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model = LightningTestModel(hparams)
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save_dir = tmpdir
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# logger file to get meta
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logger = tutils.get_test_tube_logger(save_dir, False)
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logger = tutils.get_test_tube_logger(tmpdir, False)
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trainer_options = dict(
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max_nb_epochs=1,
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logger=logger,
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checkpoint_callback=ModelCheckpoint(save_dir)
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checkpoint_callback=ModelCheckpoint(tmpdir)
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)
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# fit model
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@@ -378,7 +354,7 @@ def test_model_saving_loading(tmpdir):
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pred_before_saving = model(x)
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# save model
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new_weights_path = os.path.join(save_dir, 'save_test.ckpt')
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new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
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trainer.save_checkpoint(new_weights_path)
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# load new model
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@@ -394,5 +370,5 @@ def test_model_saving_loading(tmpdir):
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assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
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if __name__ == '__main__':
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pytest.main([__file__])
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# if __name__ == '__main__':
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# pytest.main([__file__])
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