(self, dev)
| 1010 | self._Sqrt_helper(gpu_dev) |
| 1011 | |
| 1012 | def _Greater_helper(self, dev): |
| 1013 | x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, |
| 1014 | 0.9]).reshape(3, 2).astype(np.float32) |
| 1015 | x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, |
| 1016 | 2).astype(np.float32) |
| 1017 | x0 = tensor.from_numpy(x0) |
| 1018 | x1 = tensor.from_numpy(x1) |
| 1019 | x0.to_device(cpu_dev) |
| 1020 | x1.to_device(cpu_dev) |
| 1021 | |
| 1022 | y = autograd.greater(x0, x1) |
| 1023 | |
| 1024 | # frontend |
| 1025 | model = sonnx.to_onnx([x0, x1], [y]) |
| 1026 | # print('The model is:\n{}'.format(model)) |
| 1027 | |
| 1028 | # backend |
| 1029 | sg_ir = sonnx.prepare(model, device=dev) |
| 1030 | sg_ir.is_graph = True |
| 1031 | y_t = sg_ir.run([x0, x1]) |
| 1032 | |
| 1033 | np.testing.assert_array_almost_equal(tensor.to_numpy(y), |
| 1034 | tensor.to_numpy(y_t[0]), |
| 1035 | decimal=5) |
| 1036 | |
| 1037 | def test_Greater_cpu(self): |
| 1038 | self._Greater_helper(cpu_dev) |
no test coverage detected