| import os | |
| import sys | |
| import torch | |
| sys.path.append(os.path.join(os.path.dirname(__file__), "..", "src")) | |
| from gliomasam3_moe.losses.brats_losses import LossComputer | |
| def test_loss_backward(): | |
| logits = torch.randn(2, 3, 8, 32, 32, requires_grad=True) | |
| labels = torch.randint(0, 4, (2, 1, 8, 32, 32)) | |
| # map {0,1,2,3} -> {0,1,2,4} | |
| labels = labels.clone() | |
| labels[labels == 3] = 4 | |
| aux = { | |
| "pi_et": torch.sigmoid(torch.randn(2)), | |
| "moe_gamma": torch.softmax(torch.randn(2, 5), dim=-1), | |
| } | |
| loss_fn = LossComputer() | |
| loss, _ = loss_fn(logits, aux, labels) | |
| loss.backward() | |