| 10 | mx.set_default_device(mx.cpu) |
| 11 | |
| 12 | class BenchNetMLX(mlx.nn.Module): |
| 13 | # simple encoder-decoder net |
| 14 | |
| 15 | def __init__(self, in_channels, hidden_channels=32): |
| 16 | super().__init__() |
| 17 | |
| 18 | self.net = mlx.nn.Sequential( |
| 19 | mlx.nn.Conv2d(in_channels, hidden_channels, kernel_size=3, padding=1), |
| 20 | mlx.nn.ReLU(), |
| 21 | mlx.nn.Conv2d( |
| 22 | hidden_channels, 2 * hidden_channels, kernel_size=3, padding=1 |
| 23 | ), |
| 24 | mlx.nn.ReLU(), |
| 25 | mlx.nn.ConvTranspose2d( |
| 26 | 2 * hidden_channels, hidden_channels, kernel_size=3, padding=1 |
| 27 | ), |
| 28 | mlx.nn.ReLU(), |
| 29 | mlx.nn.ConvTranspose2d( |
| 30 | hidden_channels, in_channels, kernel_size=3, padding=1 |
| 31 | ), |
| 32 | ) |
| 33 | |
| 34 | def __call__(self, input): |
| 35 | return self.net(input) |
| 36 | |
| 37 | benchNet = BenchNetMLX(3) |
| 38 | mx.eval(benchNet.parameters()) |