def test_model_saves_with_example_output(tmpdir):
"""Test that ONNX model saves when provided with example output."""
model = BoringModel()
trainer = Trainer(fast_dev_run=True)
trainer.fit(model)
file_path = os.path.join(tmpdir, "model.onnx")
input_sample = torch.randn((1, 32))
model.eval()
example_outputs = model.forward(input_sample)
model.to_onnx(file_path, input_sample, example_outputs=example_outputs)
assert os.path.exists(file_path) is True
def test_eval_loop_config(tmpdir):
"""When either eval step or eval data is missing."""
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
# has val step but no val data
model = BoringModel()
model.val_dataloader = None
with pytest.raises(MisconfigurationException,
match=r"No `val_dataloader\(\)` method defined"):
trainer.validate(model)
# has test data but no val step
model = BoringModel()
model.validation_step = None
with pytest.raises(MisconfigurationException,
match=r"No `validation_step\(\)` method defined"):
trainer.validate(model)
# has test loop but no test data
model = BoringModel()
model.test_dataloader = None
with pytest.raises(MisconfigurationException,
match=r"No `test_dataloader\(\)` method defined"):
trainer.test(model)
# has test data but no test step
model = BoringModel()
model.test_step = None
with pytest.raises(MisconfigurationException,
match=r"No `test_step\(\)` method defined"):
trainer.test(model)
# has predict step but no predict data
model = BoringModel()
model.predict_dataloader = None
with pytest.raises(MisconfigurationException,
match=r"No `predict_dataloader\(\)` method defined"):
trainer.predict(model)
# has predict data but no predict_step
model = BoringModel()
model.predict_step = None
with pytest.raises(MisconfigurationException,
match=r"`predict_step` cannot be None."):
trainer.predict(model)
# has predict data but no forward
model = BoringModel()
model.forward = None
with pytest.raises(MisconfigurationException,
match=r"requires `forward` method to run."):
trainer.predict(model)
