def test_if_inference_output_is_valid(tmpdir):
"""Test that the output inferred from ONNX model is same as from PyTorch"""
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
trainer = Trainer(max_epochs=2)
trainer.fit(model)
model.eval()
with torch.no_grad():
torch_out = model(model.example_input_array)
file_path = os.path.join(tmpdir, "model.onnx")
model.to_onnx(file_path, model.example_input_array, export_params=True)
ort_session = onnxruntime.InferenceSession(file_path)
def to_numpy(tensor):
return tensor.detach().cpu().numpy(
) if tensor.requires_grad else tensor.cpu().numpy()
# compute ONNX Runtime output prediction
ort_inputs = {
ort_session.get_inputs()[0].name: to_numpy(model.example_input_array)
}
ort_outs = ort_session.run(None, ort_inputs)
# compare ONNX Runtime and PyTorch results
assert np.allclose(to_numpy(torch_out),
ort_outs[0],
rtol=1e-03,
atol=1e-05)
def test_model_saves_with_example_output(tmpdir):
"""Test that ONNX model saves when provided with example output"""
model = BoringModel()
trainer = Trainer(max_epochs=1)
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_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)
