img, label, label_rotate = data
img, label, label_rotate = img.to(opt.device), label.to(opt.device), label_rotate.to(opt.device)
optimizer.zero_grad()
optimizer_rotate.zero_grad()
x, feat = net(img, label, is_train=True)
loss_info = criterion(x, label)
x_rotate = fc(feat)
loss_rotate = criterion_rotate(x_rotate, label_rotate)
loss = (loss_info[0] + loss_rotate).mean()
loss.backward()
optimizer.step()
optimizer_rotate.step()
log.update(x, label)
log.log_train(optimizer, loss_info, opt, i_epoch, i_iter, trainloader_len)
# -------------------- 测试 -------------------------
print("测试...")
with torch.no_grad():
net.eval()
fc.eval()
log.init()
for data in testloader:
img, label = data
img, label = img.to(opt.device), label.to(opt.device)
x = net(img, label, is_train=False)[0]
log.update(x, label)
log.log_test(net, opt, i_epoch, trainloader_len)
