def _test_g(self, G: ModelTrainer, real):
gen_pred = torch.nn.functional.sigmoid(G.forward(real))
G.compute_and_update_test_loss("MSELoss", gen_pred, real)
metric = G.compute_metric("MeanSquaredError", gen_pred, real)
G.update_test_metric("MeanSquaredError", metric / 32768)
return gen_pred
def _valid_s(self, S: ModelTrainer, inputs, target):
target_ohe = to_onehot(torch.squeeze(target, dim=1).long(), num_classes=4)
target = torch.squeeze(target, dim=1).long()
seg_pred = torch.nn.functional.softmax(S.forward(inputs), dim=1)
loss_S = S.compute_loss("DiceLoss", seg_pred, target_ohe)
S.update_valid_loss("DiceLoss", loss_S.mean())
metrics = S.compute_metrics(seg_pred, target)
metrics["Dice"] = metrics["Dice"].mean()
metrics["IoU"] = metrics["IoU"].mean()
S.update_valid_metrics(metrics)
return seg_pred, loss_S
def _train_g(self, G: ModelTrainer, real, backward=True):
G.zero_grad()
gen_pred = torch.nn.functional.sigmoid(G.forward(real))
loss_G = G.compute_and_update_train_loss("MSELoss", gen_pred, real)
metric = G.compute_metric("MeanSquaredError", gen_pred, real)
G.update_train_metric("MeanSquaredError", metric / 32768)
if backward:
loss_G.backward()
G.step()
return gen_pred
def _train_s(self, S: ModelTrainer, inputs, target, backward=True):
S.zero_grad()
target_ohe = to_onehot(torch.squeeze(target, dim=1).long(), num_classes=4)
target = torch.squeeze(target, dim=1).long()
seg_pred = torch.nn.functional.softmax(S.forward(inputs), dim=1)
loss_S = S.compute_loss("DiceLoss", seg_pred, target_ohe)
S.update_train_loss("DiceLoss", loss_S.mean())
metrics = S.compute_metrics(seg_pred, target)
metrics["Dice"] = metrics["Dice"].mean()
metrics["IoU"] = metrics["IoU"].mean()
S.update_train_metrics(metrics)
if backward:
loss_S.mean().backward()
S.step()
return seg_pred, loss_S
