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