316 lines
7.2 KiB
Python
316 lines
7.2 KiB
Python
import pickle
|
|
from argparse import ArgumentParser
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from pytorch_lightning import Trainer, seed_everything
|
|
from tests.base import EvalModelTemplate
|
|
from tests.base.datamodules import TrialMNISTDataModule
|
|
from tests.base.develop_utils import reset_seed
|
|
|
|
|
|
def test_can_prepare_data(tmpdir):
|
|
|
|
dm = TrialMNISTDataModule()
|
|
trainer = Trainer()
|
|
trainer.datamodule = dm
|
|
|
|
# 1 no DM
|
|
# prepare_data_per_node = True
|
|
# local rank = 0 (True)
|
|
trainer.prepare_data_per_node = True
|
|
trainer.local_rank = 0
|
|
assert trainer.can_prepare_data()
|
|
|
|
# local rank = 1 (False)
|
|
trainer.local_rank = 1
|
|
assert not trainer.can_prepare_data()
|
|
|
|
# prepare_data_per_node = False (prepare across all nodes)
|
|
# global rank = 0 (True)
|
|
trainer.prepare_data_per_node = False
|
|
trainer.node_rank = 0
|
|
trainer.local_rank = 0
|
|
assert trainer.can_prepare_data()
|
|
|
|
# global rank = 1 (False)
|
|
trainer.node_rank = 1
|
|
trainer.local_rank = 0
|
|
assert not trainer.can_prepare_data()
|
|
trainer.node_rank = 0
|
|
trainer.local_rank = 1
|
|
assert not trainer.can_prepare_data()
|
|
|
|
# 2 dm
|
|
# prepar per node = True
|
|
# local rank = 0 (True)
|
|
trainer.prepare_data_per_node = True
|
|
trainer.local_rank = 0
|
|
|
|
# is_overridden prepare data = True
|
|
# has been called
|
|
# False
|
|
dm._has_prepared_data = True
|
|
assert not trainer.can_prepare_data()
|
|
|
|
# has not been called
|
|
# True
|
|
dm._has_prepared_data = False
|
|
assert trainer.can_prepare_data()
|
|
|
|
# is_overridden prepare data = False
|
|
# True
|
|
dm.prepare_data = None
|
|
assert trainer.can_prepare_data()
|
|
|
|
|
|
def test_base_datamodule(tmpdir):
|
|
dm = TrialMNISTDataModule()
|
|
dm.prepare_data()
|
|
dm.setup()
|
|
|
|
|
|
def test_base_datamodule_with_verbose_setup(tmpdir):
|
|
dm = TrialMNISTDataModule()
|
|
dm.prepare_data()
|
|
dm.setup('fit')
|
|
dm.setup('test')
|
|
|
|
|
|
def test_data_hooks_called(tmpdir):
|
|
dm = TrialMNISTDataModule()
|
|
assert dm.has_prepared_data is False
|
|
assert dm.has_setup_fit is False
|
|
assert dm.has_setup_test is False
|
|
|
|
dm.prepare_data()
|
|
assert dm.has_prepared_data is True
|
|
assert dm.has_setup_fit is False
|
|
assert dm.has_setup_test is False
|
|
|
|
dm.setup()
|
|
assert dm.has_prepared_data is True
|
|
assert dm.has_setup_fit is True
|
|
assert dm.has_setup_test is True
|
|
|
|
|
|
def test_data_hooks_called_verbose(tmpdir):
|
|
dm = TrialMNISTDataModule()
|
|
assert dm.has_prepared_data is False
|
|
assert dm.has_setup_fit is False
|
|
assert dm.has_setup_test is False
|
|
|
|
dm.prepare_data()
|
|
assert dm.has_prepared_data is True
|
|
assert dm.has_setup_fit is False
|
|
assert dm.has_setup_test is False
|
|
|
|
dm.setup('fit')
|
|
assert dm.has_prepared_data is True
|
|
assert dm.has_setup_fit is True
|
|
assert dm.has_setup_test is False
|
|
|
|
dm.setup('test')
|
|
assert dm.has_prepared_data is True
|
|
assert dm.has_setup_fit is True
|
|
assert dm.has_setup_test is True
|
|
|
|
|
|
def test_data_hooks_called_with_stage_kwarg(tmpdir):
|
|
dm = TrialMNISTDataModule()
|
|
dm.prepare_data()
|
|
assert dm.has_prepared_data is True
|
|
|
|
dm.setup(stage='fit')
|
|
assert dm.has_setup_fit is True
|
|
assert dm.has_setup_test is False
|
|
|
|
dm.setup(stage='test')
|
|
assert dm.has_setup_fit is True
|
|
assert dm.has_setup_test is True
|
|
|
|
|
|
def test_dm_add_argparse_args(tmpdir):
|
|
parser = ArgumentParser()
|
|
parser = TrialMNISTDataModule.add_argparse_args(parser)
|
|
args = parser.parse_args(['--data_dir', './my_data'])
|
|
assert args.data_dir == './my_data'
|
|
|
|
|
|
def test_dm_init_from_argparse_args(tmpdir):
|
|
parser = ArgumentParser()
|
|
parser = TrialMNISTDataModule.add_argparse_args(parser)
|
|
args = parser.parse_args(['--data_dir', './my_data'])
|
|
dm = TrialMNISTDataModule.from_argparse_args(args)
|
|
dm.prepare_data()
|
|
dm.setup()
|
|
|
|
|
|
def test_dm_pickle_after_init(tmpdir):
|
|
dm = TrialMNISTDataModule()
|
|
pickle.dumps(dm)
|
|
|
|
|
|
def test_train_loop_only(tmpdir):
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
model.validation_step = None
|
|
model.validation_step_end = None
|
|
model.validation_epoch_end = None
|
|
model.test_step = None
|
|
model.test_step_end = None
|
|
model.test_epoch_end = None
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=3,
|
|
weights_summary=None,
|
|
)
|
|
|
|
# fit model
|
|
result = trainer.fit(model, dm)
|
|
assert result == 1
|
|
assert trainer.callback_metrics['loss'] < 0.6
|
|
|
|
|
|
def test_train_val_loop_only(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
model.validation_step = None
|
|
model.validation_step_end = None
|
|
model.validation_epoch_end = None
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=3,
|
|
weights_summary=None,
|
|
)
|
|
|
|
# fit model
|
|
result = trainer.fit(model, dm)
|
|
assert result == 1
|
|
assert trainer.callback_metrics['loss'] < 0.6
|
|
|
|
|
|
def test_test_loop_only(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=3,
|
|
weights_summary=None,
|
|
)
|
|
trainer.test(model, datamodule=dm)
|
|
|
|
|
|
def test_full_loop(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=3,
|
|
weights_summary=None,
|
|
)
|
|
|
|
# fit model
|
|
result = trainer.fit(model, dm)
|
|
assert result == 1
|
|
|
|
# test
|
|
result = trainer.test(datamodule=dm)
|
|
result = result[0]
|
|
assert result['test_acc'] > 0.8
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="test requires multi-GPU machine")
|
|
def test_full_loop_single_gpu(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=3,
|
|
weights_summary=None,
|
|
gpus=1
|
|
)
|
|
|
|
# fit model
|
|
result = trainer.fit(model, dm)
|
|
assert result == 1
|
|
|
|
# test
|
|
result = trainer.test(datamodule=dm)
|
|
result = result[0]
|
|
assert result['test_acc'] > 0.8
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
|
|
def test_full_loop_dp(tmpdir):
|
|
reset_seed()
|
|
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=3,
|
|
weights_summary=None,
|
|
distributed_backend='dp',
|
|
gpus=2
|
|
)
|
|
|
|
# fit model
|
|
result = trainer.fit(model, dm)
|
|
assert result == 1
|
|
|
|
# test
|
|
result = trainer.test(datamodule=dm)
|
|
result = result[0]
|
|
assert result['test_acc'] > 0.8
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
|
|
def test_full_loop_ddp_spawn(tmpdir):
|
|
import os
|
|
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
|
|
|
|
seed_everything(1234)
|
|
|
|
dm = TrialMNISTDataModule(tmpdir)
|
|
|
|
model = EvalModelTemplate()
|
|
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
max_epochs=5,
|
|
weights_summary=None,
|
|
distributed_backend='ddp_spawn',
|
|
gpus=[0, 1]
|
|
)
|
|
|
|
# fit model
|
|
result = trainer.fit(model, dm)
|
|
assert result == 1
|
|
|
|
# test
|
|
result = trainer.test(datamodule=dm)
|
|
result = result[0]
|
|
assert result['test_acc'] > 0.8
|