(self, dev)
| 1219 | self._pow_helper(gpu_dev) |
| 1220 | |
| 1221 | def _clip_helper(self, dev): |
| 1222 | x = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, |
| 1223 | 0.9]).reshape(3, 2).astype(np.float32) |
| 1224 | |
| 1225 | x = tensor.from_numpy(x) |
| 1226 | min = -0.5 |
| 1227 | max = 0.5 |
| 1228 | x.to_device(dev) |
| 1229 | |
| 1230 | y = autograd.clip(x, min, max) |
| 1231 | |
| 1232 | # frontend |
| 1233 | model = sonnx.to_onnx([x, min, max], [y]) |
| 1234 | # print('The model is:\n{}'.format(model)) |
| 1235 | |
| 1236 | # backend |
| 1237 | sg_ir = sonnx.prepare(model, device=dev) |
| 1238 | sg_ir.is_graph = True |
| 1239 | y_t = sg_ir.run([x]) # min, max has been stored in model |
| 1240 | |
| 1241 | np.testing.assert_array_almost_equal(tensor.to_numpy(y), |
| 1242 | tensor.to_numpy(y_t[0]), |
| 1243 | decimal=5) |
| 1244 | |
| 1245 | def test_clip_cpu(self): |
| 1246 | self._clip_helper(cpu_dev) |
no test coverage detected