| # RUN: %PYTHON %s | FileCheck %s |
| |
| import torch |
| import numpy as np |
| |
| from mpact.mpactbackend import mpact_jit |
| |
| from mpact.models.kernels import FeatureScale |
| |
| net = FeatureScale() |
| |
| # Get random (but reproducible) matrices. |
| torch.manual_seed(0) |
| features = torch.rand(7, 7) |
| |
| # |
| # CHECK: pytorch |
| # CHECK: tensor({{\[}}[0.1702, 0.2634, 0.0303, 0.0453, 0.1054, 0.2174, 0.1680], |
| # CHECK: [0.3064, 0.1557, 0.2161, 0.1192, 0.1373, 0.0076, 0.0577], |
| # CHECK: [0.0856, 0.1510, 0.2031, 0.2329, 0.0469, 0.0822, 0.1984], |
| # CHECK: [0.2207, 0.0957, 0.2108, 0.1011, 0.1333, 0.2297, 0.0087], |
| # CHECK: [0.0774, 0.1561, 0.1275, 0.3896, 0.0735, 0.1128, 0.0630], |
| # CHECK: [0.0093, 0.0611, 0.2731, 0.2124, 0.2180, 0.1546, 0.0716], |
| # CHECK: [0.2026, 0.0115, 0.0481, 0.0839, 0.2826, 0.2749, 0.0964]{{\]}}) |
| # |
| # TODO: first row? |
| # |
| # CHECK: mpact |
| # CHECK: {{\[}}[0. 0. 0. 0. 0. 0. |
| # CHECK: 0. ] |
| # CHECK: [0.30635384 0.15570773 0.21608633 0.11923195 0.13728413 0.00762967 |
| # CHECK: 0.05770639] |
| # CHECK: [0.08555716 0.15095268 0.20310582 0.23290026 0.04687909 0.08217437 |
| # CHECK: 0.19843069] |
| # CHECK: [0.22065267 0.09574053 0.2107584 0.10111907 0.13330552 0.22970453 |
| # CHECK: 0.00871931] |
| # CHECK: [0.07743214 0.15609969 0.12754099 0.3896042 0.07353575 0.11279855 |
| # CHECK: 0.06298868] |
| # CHECK: [0.00931544 0.06112389 0.2730649 0.2123639 0.21801054 0.15456341 |
| # CHECK: 0.07155795] |
| # CHECK: [0.20259099 0.01148908 0.04807246 0.08394676 0.28260148 0.2748705 |
| # CHECK: 0.09642864]] |
| # |
| |
| # Run it with PyTorch. |
| print("pytorch") |
| res = net(features) |
| print(res) |
| |
| # Run it with MPACT. |
| print("mpact") |
| res = mpact_jit(net, features) |
| print(res) |
