def test_torchcript_invalid_method(tmpdir):
"""Test that an error is thrown with invalid torchscript method"""
model = BoringModel()
model.train(True)
with pytest.raises(ValueError, match="only supports 'script' or 'trace'"):
model.to_torchscript(method='temp')
def test_torchscript_with_no_input(tmpdir):
"""Test that an error is thrown when there is no input tensor"""
model = BoringModel()
model.example_input_array = None
with pytest.raises(
ValueError,
match=
'requires either `example_inputs` or `model.example_input_array`'):
model.to_torchscript(method='trace')
def test_torchscript_retain_training_state():
""" Test that torchscript export does not alter the training mode of original model. """
model = BoringModel()
model.train(True)
script = model.to_torchscript()
assert model.training
assert not script.training
model.train(False)
_ = model.to_torchscript()
assert not model.training
assert not script.training
def test_torchscript_device(device):
""" Test that scripted module is on the correct device. """
model = BoringModel().to(device)
model.example_input_array = torch.randn(5, 32)
script = model.to_torchscript()
assert next(script.parameters()).device == device
script_output = script(model.example_input_array.to(device))
assert script_output.device == device
def test_torchscript_input_output_trace():
""" Test that traced LightningModule forward works with example_inputs """
model = BoringModel()
example_inputs = torch.randn(1, 32)
script = model.to_torchscript(example_inputs=example_inputs,
method='trace')
assert isinstance(script, torch.jit.ScriptModule)
model.eval()
with torch.no_grad():
model_output = model(example_inputs)
script_output = script(example_inputs)
assert torch.allclose(script_output, model_output)