def train(opt):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
train_dataloader, val_dataloader = create_dataloader(opt)
net = Classification() # 定义训练的网络模型
net.to(device)
net.train()
loss_function = nn.CrossEntropyLoss() # 定义损失函数为交叉熵损失函数
optimizer = optim.Adam(net.parameters(), lr=0.001) # 定义优化器（训练参数，学习率）
for epoch in range(opt.num_epochs): # 一个epoch即对整个训练集进行一次训练
running_loss = 0.0
correct = 0
total = 0
time_start = time.perf_counter()
for step, data in enumerate(train_dataloader,
start=0): # 遍历训练集，step从0开始计算
inputs, labels = data # 获取训练集的图像和标签
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad() # 清除历史梯度
# forward + backward + optimize
# outputs = net(inputs.permute(0,1,3,2)) # 正向传播
outputs = net(inputs) # 正向传播
print('outputs.shape', outputs.shape, labels.shape)
loss = loss_function(outputs, labels) # 计算损失
loss.backward() # 反向传播
optimizer.step() # 优化器更新参数
predict_y = torch.max(outputs, dim=1)[1]
total += labels.size(0)
correct += (predict_y == labels).sum().item()
running_loss += loss.item()
# print statistics
# print('train_dataloader length: ', len(train_dataloader))
acc = correct / total
print('Train on epoch {}: loss:{}, acc:{}%'.format(epoch + 1, running_loss / total, 100 * correct / total))
# 保存训练得到的参数
if opt.model == 'basic':
save_weight_name = os.path.join(opt.save_path,
'Basic_Epoch_{0}_Accuracy_{1:.2f}.pth'.format(
epoch + 1,
acc))
elif opt.model == 'plus':
save_weight_name = os.path.join(opt.save_path,
'Plus_Epoch_{0}_Accuracy_{1:.2f}.pth'.format(
epoch + 1,
acc))
torch.save(net.state_dict(), save_weight_name)
print('Finished Training')
str(epoch) + ' Loss: ' + str(stats.mean(train_loss[-1:])))
loss_total.backward()
encoder_optim.step()
loss_optim.step()
classification_optim.step()
if epoch % 10 == 0:
root = Path(r'models')
enc_file = root / Path('encoder' + str(epoch) + '.wgt')
loss_file = root / Path('loss' + str(epoch) + '.wgt')
classification_loss_file = root / Path('classification_loss' +
str(epoch) + '.wgt')
enc_file.parent.mkdir(parents=True, exist_ok=True)
torch.save(encoder.state_dict(), str(enc_file))
torch.save(loss_fn.state_dict(), str(loss_file))
torch.save(classification.state_dict(),
str(classification_loss_file))
if epoch > 1:
with open('loss.pickle', 'rb') as handle:
loss_dict = pickle.load(handle)
loss_dict[str(epoch)] = stats.mean(train_loss[-20:])
with open('loss.pickle', 'wb') as handle:
pickle.dump(loss_dict,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
else:
with open('loss.pickle', 'wb') as handle:
loss_dict = {}
loss_dict[str(epoch)] = stats.mean(train_loss[-20:])
pickle.dump(loss_dict,