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 = BoringModel()
# Extra layer
model.c_d3 = torch.nn.Linear(32, 32)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=2,
limit_val_batches=2,
logger=logger,
callbacks=[ModelCheckpoint(dirpath=tmpdir)],
)
trainer.fit(model)
# traning complete
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
# 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
BoringModel.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"'):
BoringModel.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=True,
)
