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_input_array(tmpdir):
"""Test that ONNX model saves with_example_input_array and size is greater than 3 MB"""
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
file_path = os.path.join(tmpdir, "model.onnx")
model.to_onnx(file_path)
assert os.path.exists(file_path) is True
assert os.path.getsize(file_path) > 4e2
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")
model.to_onnx(file_path, verbose=True)
captured = capsys.readouterr()
assert "graph(%" in captured.out
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_model_saves_on_gpu(tmpdir):
"""Test that model saves on gpu"""
model = BoringModel()
trainer = Trainer(gpus=1, max_epochs=1)
trainer.fit(model)
file_path = os.path.join(tmpdir, "model.onnx")
input_sample = torch.randn((1, 32))
model.to_onnx(file_path, input_sample)
assert os.path.isfile(file_path)
assert os.path.getsize(file_path) > 4e2
def test_model_saves_with_input_sample(tmpdir):
"""Test that ONNX model saves with input sample and size is greater than 3 MB"""
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.to_onnx(file_path, input_sample)
assert os.path.isfile(file_path)
assert os.path.getsize(file_path) > 4e2
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
