def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
""" Tests that the checkpoint saved as 'last.ckpt' contains the latest information. """
seed_everything(100)
model = EvalModelTemplate()
num_epochs = 3
model_checkpoint = ModelCheckpoint(filepath=tmpdir,
save_top_k=num_epochs,
save_last=True)
trainer = Trainer(
default_root_dir=tmpdir,
early_stop_callback=False,
checkpoint_callback=model_checkpoint,
max_epochs=num_epochs,
)
trainer.fit(model)
path_last_epoch = model_checkpoint.format_checkpoint_name(
num_epochs - 1, {}) # epoch=3.ckpt
path_last = str(tmpdir / ModelCheckpoint.CHECKPOINT_NAME_LAST) # last.ckpt
assert path_last_epoch != path_last
ckpt_last_epoch = torch.load(path_last_epoch)
ckpt_last = torch.load(path_last)
matching_keys = (
"epoch",
"global_step",
ModelCheckpoint.CHECKPOINT_STATE_BEST_SCORE,
ModelCheckpoint.CHECKPOINT_STATE_BEST_PATH,
)
for key in matching_keys:
assert ckpt_last_epoch[key] == ckpt_last[key]
# it is easier to load the model objects than to iterate over the raw dict of tensors
model_last_epoch = EvalModelTemplate.load_from_checkpoint(path_last_epoch)
model_last = EvalModelTemplate.load_from_checkpoint(path_last)
for w0, w1 in zip(model_last_epoch.parameters(), model_last.parameters()):
assert w0.eq(w1).all()
def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
"""Tests that the save_last checkpoint contains the latest information."""
seed_everything(100)
model = EvalModelTemplate()
num_epochs = 3
model_checkpoint = ModelCheckpoint(monitor='val_loss', filepath=tmpdir, save_top_k=num_epochs, save_last=True)
trainer = Trainer(
default_root_dir=tmpdir,
early_stop_callback=False,
checkpoint_callback=model_checkpoint,
max_epochs=num_epochs,
)
trainer.fit(model)
path_last_epoch = model_checkpoint.format_checkpoint_name(num_epochs - 1, {})
assert path_last_epoch != model_checkpoint.last_model_path
ckpt_last_epoch = torch.load(path_last_epoch)
ckpt_last = torch.load(model_checkpoint.last_model_path)
assert all(ckpt_last_epoch[k] == ckpt_last[k] for k in ("epoch", "global_step"))
# it is easier to load the model objects than to iterate over the raw dict of tensors
model_last_epoch = EvalModelTemplate.load_from_checkpoint(path_last_epoch)
model_last = EvalModelTemplate.load_from_checkpoint(model_checkpoint.last_model_path)
for w0, w1 in zip(model_last_epoch.parameters(), model_last.parameters()):
assert w0.eq(w1).all()
def test_strict_model_load(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv("TORCH_HOME", tmpdir)
model = EvalModelTemplate()
# Extra layer
model.c_d3 = torch.nn.Linear(model.hidden_dim, model.hidden_dim)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
)
result = trainer.fit(model)
# traning complete
assert result == 1
# save model
new_weights_path = os.path.join(tmpdir, "save_test.ckpt")
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, "hparams.yaml")
ckpt_path = (
f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}"
if url_ckpt
else new_weights_path
)
try:
EvalModelTemplate.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
)
except Exception:
failed = True
else:
failed = False
assert failed, "Model should not been loaded since the extra layer added."
failed = False
try:
EvalModelTemplate.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=False,
)
except Exception:
failed = True
assert not failed, "Model should be loaded due to strict=False."
def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
""" Tests that the save_last checkpoint contains the latest information. """
seed_everything(100)
model = EvalModelTemplate()
num_epochs = 3
model_checkpoint = ModelCheckpoint(monitor='early_stop_on',
dirpath=tmpdir,
save_top_k=num_epochs,
save_last=True)
trainer = Trainer(
default_root_dir=tmpdir,
checkpoint_callback=model_checkpoint,
max_epochs=num_epochs,
)
trainer.fit(model)
path_last_epoch = str(tmpdir / f"epoch={num_epochs - 1}.ckpt")
path_last = str(tmpdir / "last.ckpt")
assert path_last == model_checkpoint.last_model_path
ckpt_last_epoch = torch.load(path_last_epoch)
ckpt_last = torch.load(path_last)
assert all(ckpt_last_epoch[k] == ckpt_last[k]
for k in ("epoch", "global_step"))
ch_type = type(model_checkpoint)
assert ckpt_last["callbacks"][ch_type] == ckpt_last_epoch["callbacks"][
ch_type]
# it is easier to load the model objects than to iterate over the raw dict of tensors
model_last_epoch = EvalModelTemplate.load_from_checkpoint(path_last_epoch)
model_last = EvalModelTemplate.load_from_checkpoint(
model_checkpoint.last_model_path)
for w0, w1 in zip(model_last_epoch.parameters(), model_last.parameters()):
assert w0.eq(w1).all()
def test_strict_model_load_more_params(monkeypatch, tmpdir, tmpdir_server,
url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv('TORCH_HOME', tmpdir)
model = EvalModelTemplate()
# Extra layer
model.c_d3 = torch.nn.Linear(model.hidden_dim, model.hidden_dim)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir),
)
result = trainer.fit(model)
# traning complete
assert result == 1
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir),
'hparams.yaml')
hparams_url = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}'
ckpt_path = hparams_url if url_ckpt else new_weights_path
EvalModelTemplate.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=False,
)
with pytest.raises(
RuntimeError,
match=
r'Unexpected key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'):
EvalModelTemplate.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=True,
)
def test_no_val_end_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = EvalModelTemplate(tutils.get_default_hparams())
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
result = trainer.fit(model)
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
tags_path = tutils.get_data_path(logger, path_dir=tmpdir)
tags_path = os.path.join(tags_path, 'meta_tags.csv')
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=new_weights_path, tags_csv=tags_path)
model_2.eval()
def test_no_val_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = EvalModelTemplate(tutils.get_default_hparams())
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
# fit model
result = trainer.fit(model)
# training complete
assert result == 1, 'amp + ddp model failed to complete'
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# assert ckpt has hparams
ckpt = torch.load(new_weights_path)
assert 'hparams' in ckpt.keys(), 'hparams missing from checkpoints'
# won't load without hparams in the ckpt
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=new_weights_path, )
model_2.eval()
def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv('TORCH_HOME', tmpdir)
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
result = trainer.fit(model)
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
ckpt_path = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}' if url_ckpt else new_weights_path
model_2 = EvalModelTemplate.load_from_checkpoint(checkpoint_path=ckpt_path,
hparams_file=hparams_path)
model_2.eval()
def test_no_val_module(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
trainer = Trainer(max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir))
# fit model
result = trainer.fit(model)
# training complete
assert result == 1, 'amp + ddp model failed to complete'
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# assert ckpt has hparams
ckpt = torch.load(new_weights_path)
assert CHECKPOINT_KEY_MODULE_ARGS in ckpt.keys(
), 'module_arguments missing from checkpoints'
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=new_weights_path, hparams_file=hparams_path)
model_2.eval()
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,
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = EvalModelTemplate.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
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_model_saving_loading(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
trainer_options = dict(
max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(tmpdir)
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# traning complete
assert result == 1, 'amp + ddp model failed to complete'
# make a prediction
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
for batch in dataloader:
break
x, y = batch
x = x.view(x.size(0), -1)
# generate preds before saving model
model.eval()
pred_before_saving = model(x)
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=new_weights_path,
hparams_file=hparams_path
)
model_2.eval()
# make prediction
# assert that both predictions are the same
new_pred = model_2(x)
assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
"""Tests that the save_last checkpoint contains the latest information."""
seed_everything(100)
model = EvalModelTemplate()
num_epochs = 3
ModelCheckpoint.CHECKPOINT_NAME_LAST = 'last-{epoch}'
model_checkpoint = ModelCheckpoint(filepath=tmpdir, save_top_k=num_epochs, save_last=True)
trainer = Trainer(
default_root_dir=tmpdir,
early_stop_callback=False,
checkpoint_callback=model_checkpoint,
max_epochs=num_epochs,
)
trainer.fit(model)
last_filename = model_checkpoint._format_checkpoint_name(ModelCheckpoint.CHECKPOINT_NAME_LAST, num_epochs - 1, {})
path_last_epoch = model_checkpoint.format_checkpoint_name(num_epochs - 1, {}) # epoch=3.ckpt
path_last = str(tmpdir / f'{last_filename}.ckpt') # last-epoch=3.ckpt
assert path_last_epoch != path_last
ckpt_last_epoch = torch.load(path_last_epoch)
ckpt_last = torch.load(path_last)
trainer_keys = ("epoch", "global_step")
for key in trainer_keys:
assert ckpt_last_epoch[key] == ckpt_last[key]
checkpoint_callback_keys = ("best_model_score", "best_model_path")
for key in checkpoint_callback_keys:
assert (
ckpt_last["callbacks"][type(model_checkpoint)][key]
== ckpt_last_epoch["callbacks"][type(model_checkpoint)][key]
)
# it is easier to load the model objects than to iterate over the raw dict of tensors
model_last_epoch = EvalModelTemplate.load_from_checkpoint(path_last_epoch)
model_last = EvalModelTemplate.load_from_checkpoint(path_last)
for w0, w1 in zip(model_last_epoch.parameters(), model_last.parameters()):
assert w0.eq(w1).all()
ModelCheckpoint.CHECKPOINT_NAME_LAST = 'last'
def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir):
"""Verify `test()` on pretrained model."""
tutils.set_random_master_port()
model = EvalModelTemplate()
# 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,
limit_train_batches=0.4,
limit_val_batches=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend='ddp_spawn',
default_root_dir=tmpdir,
)
# 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 = EvalModelTemplate.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# run test set
new_trainer = Trainer(**trainer_options)
results = new_trainer.test(pretrained_model)
pretrained_model.cpu()
acc = results[0]['test_acc']
assert acc > 0.5, f"Model failed to get expected {0.5} accuracy. test_acc = {acc}"
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tpipes.run_prediction(dataloader, pretrained_model)
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 run_test_from_config(trainer_options):
"""Trains the default model with the given config."""
set_random_master_port()
reset_seed()
ckpt_path = trainer_options['weights_save_path']
trainer_options.update(checkpoint_callback=ModelCheckpoint(ckpt_path))
model = EvalModelTemplate()
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
# Horovod should be initialized following training. If not, this will raise an exception.
assert hvd.size() == 2
if trainer.global_rank > 0:
# on higher ranks the checkpoint location is unknown
# we want to test checkpointing on rank 0 only
assert not hasattr(trainer, 'ckpt_path')
assert not trainer.checkpoint_callback.best_model_path
return
# test model loading
pretrained_model = EvalModelTemplate.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# test new model accuracy
test_loaders = model.test_dataloader()
if not isinstance(test_loaders, list):
test_loaders = [test_loaders]
for dataloader in test_loaders:
run_prediction(dataloader, pretrained_model)
# test HPC loading / saving
trainer.hpc_save(ckpt_path, trainer.logger)
trainer.hpc_load(ckpt_path, on_gpu=args.on_gpu)
if args.on_gpu:
trainer = Trainer(gpus=1, distributed_backend='horovod', max_epochs=1)
# Test the root_gpu property
assert trainer.root_gpu == hvd.local_rank()
def run_test_from_config(trainer_options):
"""Trains the default model with the given config."""
set_random_master_port()
reset_seed()
ckpt_path = trainer_options['weights_save_path']
trainer_options.update(callbacks=[ModelCheckpoint(dirpath=ckpt_path)])
model = EvalModelTemplate()
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
# Horovod should be initialized following training. If not, this will raise an exception.
assert hvd.size() == 2
if trainer.global_rank > 0:
return
# test model loading
pretrained_model = EvalModelTemplate.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# test new model accuracy
test_loaders = model.test_dataloader()
if not isinstance(test_loaders, list):
test_loaders = [test_loaders]
for dataloader in test_loaders:
run_prediction(dataloader, pretrained_model)
# test HPC saving
trainer.checkpoint_connector.hpc_save(ckpt_path, trainer.logger)
# test HPC loading
checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder(
ckpt_path)
trainer.checkpoint_connector.hpc_load(checkpoint_path, on_gpu=args.on_gpu)
if args.on_gpu:
trainer = Trainer(gpus=1, accelerator='horovod', max_epochs=1)
# Test the root_gpu property
assert trainer.root_gpu == hvd.local_rank()
def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv("TORCH_HOME", str(tmpdir))
model = EvalModelTemplate()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
logger=logger,
checkpoint_callback=ModelCheckpoint(dirpath=tmpdir),
)
# fit model
result = trainer.fit(model)
# training complete
assert result == 1, "amp + ddp model failed to complete"
# save model
new_weights_path = os.path.join(tmpdir, "save_test.ckpt")
trainer.save_checkpoint(new_weights_path)
# assert ckpt has hparams
ckpt = torch.load(new_weights_path)
assert LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in ckpt.keys(), "module_arguments missing from checkpoints"
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, "hparams.yaml")
ckpt_path = (
f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}"
if url_ckpt
else new_weights_path
)
model_2 = EvalModelTemplate.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
)
model_2.eval()
def test_load_past_checkpoint(tmpdir, past_key):
model = EvalModelTemplate()
# verify we can train
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
trainer.fit(model)
# make sure the raw checkpoint saved the properties
raw_checkpoint_path = _raw_checkpoint_path(trainer)
raw_checkpoint = torch.load(raw_checkpoint_path)
raw_checkpoint[past_key] = raw_checkpoint[
LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
raw_checkpoint[past_key]['batch_size'] = -17
del raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]
# save back the checkpoint
torch.save(raw_checkpoint, raw_checkpoint_path)
# verify that model loads correctly
model2 = EvalModelTemplate.load_from_checkpoint(raw_checkpoint_path)
assert model2.hparams.batch_size == -17
def test_model_checkpoint_only_weights(tmpdir):
"""Tests use case where ModelCheckpoint is configured to save only model weights, and
user tries to load checkpoint to resume training.
"""
model = EvalModelTemplate()
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
checkpoint_callback=ModelCheckpoint(tmpdir,
monitor='early_stop_on',
save_weights_only=True),
)
# fit model
result = trainer.fit(model)
# training complete
assert result == 1, 'training failed to complete'
checkpoint_path = list(trainer.checkpoint_callback.best_k_models.keys())[0]
# assert saved checkpoint has no trainer data
checkpoint = torch.load(checkpoint_path)
assert 'optimizer_states' not in checkpoint, 'checkpoint should contain only model weights'
assert 'lr_schedulers' not in checkpoint, 'checkpoint should contain only model weights'
# assert loading model works when checkpoint has only weights
assert EvalModelTemplate.load_from_checkpoint(
checkpoint_path=checkpoint_path)
# directly save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path, weights_only=True)
# assert saved checkpoint has no trainer data
checkpoint = torch.load(new_weights_path)
assert 'optimizer_states' not in checkpoint, 'checkpoint should contain only model weights'
assert 'lr_schedulers' not in checkpoint, 'checkpoint should contain only model weights'
# assert restoring train state fails
with pytest.raises(KeyError, match='checkpoint contains only the model'):
trainer.checkpoint_connector.restore_training_state(checkpoint)
