def _get_loss(F, model, batch, forward_barch_fun, **kws):
device = torch.device(F.device)
y = batch['label'].to(device)
y_ = forward_barch_fun(F, model, batch, **kws)
loss = BCELoss()(y_, y)
sc = {}
sc['loss'] = loss.item()
sc['auc'] = np.mean([
calc_auc(y_.cpu().detach().numpy()[:, i],
y.cpu().detach().numpy()[:, i]) for i in range(y.shape[1])
])
sc['score'] = scoring(sc['loss'])
return loss, sc
label_fake_G_var = Variable(onehot[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else onehot[label_fake])
label_fake_D_var = Variable(fill[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else fill[label_fake])
g_result = NetG(img_fake_var, label_fake_G_var)
d_result = NetD(g_result, label_fake_D_var)
d_result = d_result.squeeze()
D_LOSS_FAKE = BCELoss()(d_result, label_false_var)
D_train_loss = D_LOSS_REAL + D_LOSS_FAKE
D_train_loss.backward()
D_optimizer.step()
NetG.zero_grad()
img_fake = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)
label_fake = (torch.rand(mini_batch, 1) * 10).type(torch.LongTensor).squeeze()
img_fake_var = Variable(img_fake.cuda() if CONFIG["GPU_NUMS"] > 0 else img_fake)
label_fake_G_var = Variable(onehot[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else onehot[label_fake])
label_fake_D_var = Variable(fill[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else fill[label_fake])
g_result = NetG(img_fake_var, label_fake_G_var)
d_result = NetD(g_result, label_fake_D_var)
d_result = d_result.squeeze()
G_train_loss= BCELoss()(d_result, label_true_var)
G_train_loss.backward()
G_optimizer.step()
bar.show(epoch, D_train_loss.item(), G_train_loss.item())
test_images = NetG(fixed_z_, fixed_y_label_)
torchvision.utils.save_image(test_images.data[:100],'outputs/mnist_%03d.png' % (epoch),nrow=10,
normalize=True,range=(-1,1), padding=0)
