|
484 | 484 | "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n", |
485 | 485 | "device = torch.device(\"cuda:0\")\n", |
486 | 486 | "\n", |
487 | | - "UNet_meatdata = {\n", |
| 487 | + "UNet_metadata = {\n", |
488 | 488 | " \"spatial_dims\": 3,\n", |
489 | 489 | " \"in_channels\": 1,\n", |
490 | 490 | " \"out_channels\": 2,\n", |
|
494 | 494 | " \"norm\": Norm.BATCH,\n", |
495 | 495 | "}\n", |
496 | 496 | "\n", |
497 | | - "model = UNet(**UNet_meatdata).to(device)\n", |
| 497 | + "model = UNet(**UNet_metadata).to(device)\n", |
498 | 498 | "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n", |
499 | 499 | "loss_type = \"DiceLoss\"\n", |
500 | 500 | "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n", |
|
539 | 539 | "# initialize a new Aim Run\n", |
540 | 540 | "aim_run = aim.Run()\n", |
541 | 541 | "# log model metadata\n", |
542 | | - "aim_run[\"UNet_meatdata\"] = UNet_meatdata\n", |
| 542 | + "aim_run[\"UNet_metadata\"] = UNet_metadata\n", |
543 | 543 | "# log optimizer metadata\n", |
544 | 544 | "aim_run[\"Optimizer_metadata\"] = Optimizer_metadata\n", |
545 | 545 | "\n", |
|
0 commit comments