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()
