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test_datamodules.py
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import pickle
from argparse import ArgumentParser
import torch
import pytest
from pytorch_lightning import Trainer
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'
reset_seed()
dm = TrialMNISTDataModule(tmpdir)
model = EvalModelTemplate()
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=3,
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