def test_compare_ort_pytorch_outputs_no_raise_with_acceptable_error_percentage(
self, ):
ort_outs = [np.array([[1.0, 2.0], [3.0, 4.0]])]
pytorch_outs = [torch.tensor([[1.0, 2.0], [3.0, 1.0]])]
verification._compare_ort_pytorch_outputs(
ort_outs,
pytorch_outs,
rtol=1e-5,
atol=1e-6,
check_shape=True,
check_dtype=False,
acceptable_error_percentage=0.3,
)
def test_compare_ort_pytorch_outputs_raise_without_acceptable_error_percentage(
self, ):
ort_outs = [np.array([[1.0, 2.0], [3.0, 4.0]])]
pytorch_outs = [torch.tensor([[1.0, 2.0], [3.0, 1.0]])]
with self.assertRaises(AssertionError):
verification._compare_ort_pytorch_outputs(
ort_outs,
pytorch_outs,
rtol=1e-5,
atol=1e-6,
check_shape=True,
check_dtype=False,
acceptable_error_percentage=None,
)
def run_test(self, graph_ir, example_inputs):
graph = parse_ir(graph_ir)
jit_outs = torch._C._jit_interpret_graph(graph, example_inputs)
onnx_proto = _jit_graph_to_onnx_model(
graph, torch.onnx.OperatorExportTypes.ONNX, self.opset_version)
ort_sess = onnxruntime.InferenceSession(onnx_proto,
providers=self.ort_providers)
ort_outs = verification._run_ort(ort_sess, example_inputs)
verification._compare_ort_pytorch_outputs(ort_outs,
jit_outs,
rtol=1e-3,
atol=1e-7)