def test_running_test_pretrained_model_cpu(tmpdir):
"""Verify test() on pretrained model."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(progress_bar_refresh_rate=0,
max_epochs=3,
limit_train_batches=0.4,
limit_val_batches=0.2,
checkpoint_callback=checkpoint,
logger=logger)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=EvalModelTemplate)
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
def test_running_test_pretrained_model_cpu(tmpdir):
"""Verify test() on pretrained model."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(progress_bar_refresh_rate=0,
max_epochs=4,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=LightningTestModel)
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
def test_running_test_after_fitting(tmpdir):
"""Verify test() on fitted model."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=8,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer, thr=0.5)
def test_running_test_no_val(tmpdir):
"""Verify `test()` works on a model with no `val_loader`."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(progress_bar_refresh_rate=0,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
early_stop_callback=False)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer)
def test_running_test_after_fitting(tmpdir):
"""Verify test() on fitted model."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=8,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer, thr=0.5)
def test_running_test_no_val(tmpdir):
"""Verify `test()` works on a model with no `val_loader`."""
tutils.reset_seed()
class CurrentTestModel(LightTrainDataloader, LightTestMixin,
TestModelBase):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(progress_bar_refresh_rate=0,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
early_stop_callback=False)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer)
def test_running_test_pretrained_model_distrib(tmpdir, backend):
"""Verify `test()` on pretrained model."""
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend=backend,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=LightningTestModel)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tutils.run_prediction(dataloader, pretrained_model)
def test_load_model_from_checkpoint(tmpdir):
"""Verify test() on pretrained model."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
show_progress_bar=False,
max_epochs=2,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
logger=False,
default_save_path=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
trainer.test()
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
# load last checkpoint
last_checkpoint = sorted(
glob.glob(os.path.join(trainer.checkpoint_callback.dirpath,
"*.ckpt")))[-1]
pretrained_model = LightningTestModel.load_from_checkpoint(last_checkpoint)
# test that hparams loaded correctly
for k, v in vars(hparams).items():
assert getattr(pretrained_model.hparams, k) == v
# assert weights are the same
for (old_name, old_p), (new_name,
new_p) in zip(model.named_parameters(),
pretrained_model.named_parameters()):
assert torch.all(torch.eq(
old_p,
new_p)), 'loaded weights are not the same as the saved weights'
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
def test_load_model_from_checkpoint(tmpdir):
"""Verify test() on pretrained model."""
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
limit_train_batches=0.4,
limit_val_batches=0.2,
checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
trainer.test(ckpt_path=None)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
# load last checkpoint
last_checkpoint = sorted(
glob.glob(os.path.join(trainer.checkpoint_callback.dirpath,
"*.ckpt")))[-1]
pretrained_model = EvalModelTemplate.load_from_checkpoint(last_checkpoint)
# test that hparams loaded correctly
for k, v in hparams.items():
assert getattr(pretrained_model, k) == v
# assert weights are the same
for (old_name, old_p), (new_name,
new_p) in zip(model.named_parameters(),
pretrained_model.named_parameters()):
assert torch.all(torch.eq(
old_p,
new_p)), 'loaded weights are not the same as the saved weights'
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
def test_running_test_pretrained_model_dp(tmpdir):
"""Verify test() on pretrained model."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(show_progress_bar=True,
max_epochs=4,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend='dp')
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=LightningTestModel)
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
