def test_strict_model_load_less_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()
# 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
class CurrentModel(BoringModel):
def __init__(self):
super().__init__()
self.c_d3 = torch.nn.Linear(7, 7)
CurrentModel.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=False,
)
with pytest.raises(
RuntimeError,
match=r'Missing key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'
):
CurrentModel.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=True,
)
def test_model_saving_loading(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = BoringModel()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
max_epochs=1,
limit_train_batches=2,
limit_val_batches=2,
logger=logger,
callbacks=[ModelCheckpoint(dirpath=tmpdir)],
default_root_dir=tmpdir,
)
trainer.fit(model)
# traning complete
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
# make a prediction
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
batch = next(iter(dataloaders[0]))
# generate preds before saving model
model.eval()
pred_before_saving = model(batch)
# 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 = BoringModel.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(batch)
assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir):
"""Verify `test()` on pretrained model."""
tutils.set_random_master_port()
dm = ClassifDataModule()
model = ClassificationModel()
# 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=2,
limit_val_batches=2,
callbacks=[checkpoint],
logger=logger,
gpus=[0, 1],
accelerator='ddp_spawn',
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
trainer.fit(model, datamodule=dm)
log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
# correct result and ok accuracy
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
pretrained_model = ClassificationModel.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
pretrained_model.cpu()
dataloaders = dm.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tpipes.run_prediction_eval_model_template(pretrained_model,
dataloader,
min_acc=0.1)
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.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)
