opset_version=self.opset_version, training=torch.onnx.TrainingMode.TRAINING) ort_sess = onnxruntime.InferenceSession(f.getvalue()) ort_inputs = {ort_sess.get_inputs()[0].name: x.cpu().numpy()} ort_outs = ort_sess.run(None, ort_inputs) assert x != ort_outs[0] # opset 10 tests TestUtilityFuns_opset10 = type( str("TestUtilityFuns_opset10"), (TestCase, ), dict(TestUtilityFuns.__dict__, opset_version=10)) # opset 11 tests TestUtilityFuns_opset11 = type( str("TestUtilityFuns_opset11"), (TestCase, ), dict(TestUtilityFuns.__dict__, opset_version=11)) # opset 12 tests TestUtilityFuns_opset12 = type( str("TestUtilityFuns_opset12"), (TestCase, ), dict(TestUtilityFuns.__dict__, opset_version=12)) # opset 12tests TestUtilityFuns_opset12 = type( str("TestUtilityFuns_opset12"), (TestCase, ), dict(TestUtilityFuns.__dict__, opset_version=12)) if __name__ == '__main__': run_tests()
assert isinstance(x, torch._C.Value) assert isinstance(y[0], torch._C.Value) assert isinstance(y[1], torch._C.Value) return g.op('Sum', x, y[0], y[1]), ( g.op('Neg', x), g.op('Neg', y[0])) @torch.onnx.symbolic_override_first_arg_based(symb) def foo(x, y): return x + y[0] + y[1], (-x, -y[0]) class BigModule(torch.nn.Module): def forward(self, x, y): return foo(x, y) inp = (Variable(torch.FloatTensor([1])), (Variable(torch.FloatTensor([2])), Variable(torch.FloatTensor([3])))) BigModule()(*inp) self.assertONNX(BigModule(), inp) if __name__ == '__main__': onnx_test_flag = '--onnx-test' _onnx_test = onnx_test_flag in common.UNITTEST_ARGS if onnx_test_flag in common.UNITTEST_ARGS: common.UNITTEST_ARGS.remove(onnx_test_flag) if _onnx_test: for d in glob.glob(os.path.join(test_onnx_common.pytorch_operator_dir, "test_operator_*")): shutil.rmtree(d) run_tests()