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(fast_dev_run=True)
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_tensorboard_log_graph(tmpdir, example_input_array):
"""test that log graph works with both model.example_input_array and if array is passed externally."""
model = BoringModel()
if example_input_array is not None:
model.example_input_array = None
logger = TensorBoardLogger(tmpdir, log_graph=True)
logger.log_graph(model, example_input_array)
def test_torchscript_device(device_str):
"""Test that scripted module is on the correct device."""
device = torch.device(device_str)
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_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_error_if_no_input(tmpdir):
"""Test that an error is thrown when there is no input tensor."""
model = BoringModel()
model.example_input_array = None
file_path = os.path.join(tmpdir, "model.onnx")
with pytest.raises(
ValueError,
match=r"Could not export to ONNX since neither `input_sample` nor"
r" `model.example_input_array` attribute is set.",
):
model.to_onnx(file_path)
def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
"""test that log graph throws warning if model.example_input_array is None."""
model = BoringModel()
model.example_input_array = None
logger = TensorBoardLogger(tmpdir, log_graph=True)
with pytest.warns(
UserWarning,
match=
"Could not log computational graph since the `model.example_input_array`"
" attribute is not set or `input_array` was not given",
):
logger.log_graph(model)
def test_verbose_param(tmpdir, capsys):
"""Test that output is present when verbose parameter is set."""
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
file_path = os.path.join(tmpdir, "model.onnx")
if _TORCH_GREATER_EQUAL_1_12:
with patch("torch.onnx.log", autospec=True) as test:
model.to_onnx(file_path, verbose=True)
args, kwargs = test.call_args
prefix, graph = args
assert prefix == "Exported graph: "
else:
model.to_onnx(file_path, verbose=True)
captured = capsys.readouterr()
assert "graph(%" in captured.out
def test_model_saves_on_multi_gpu(tmpdir):
"""Test that ONNX model saves on a distributed backend."""
tutils.set_random_main_port()
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=10,
limit_val_batches=10,
accelerator="gpu",
devices=[0, 1],
strategy="ddp_spawn",
enable_progress_bar=False,
)
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
tpipes.run_model_test(trainer_options, model, min_acc=0.08)
file_path = os.path.join(tmpdir, "model.onnx")
model.to_onnx(file_path)
assert os.path.exists(file_path) is True
