Pytorch模型的保存/复用/迁移实现代码
作者:信海
本文整理了Pytorch框架下模型的保存、复用、推理、再训练和迁移等实现,本文通过实例代码给大家介绍的非常详细,对大家的学习或工作具有一定的参考借鉴价值,需要的朋友可以参考下
本文整理了Pytorch框架下模型的保存、复用、推理、再训练和迁移等实现。
模型的保存与复用
模型定义和参数打印
# 定义模型结构 class LenNet(nn.Module): def __init__(self): super(LenNet, self).__init__() self.conv = nn.Sequential( # [batch, 1, 28, 28] nn.Conv2d(1, 8, 5, 2), # [batch, 1, 28, 28] nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [batch, 8, 14, 14] nn.Conv2d(8, 16, 5), # [batch, 16, 10, 10] nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [batch, 16, 5, 5] ) self.fc = nn.Sequential( nn.Flatten(), nn.Linear(16*5*5, 128), nn.ReLU(inplace=True), nn.Linear(128, 64), nn.ReLU(inplace=True), nn.Linear(64, 10) ) def forward(self, X): return self.fc(self.conv(X))
# 查看模型参数 # 网络模型中的参数model.state_dict()是以字典形式保存(实质上是collections模块中的OrderedDict) model = LenNet() print("Model's state_dict:") for param_tensor in model.state_dict(): print(param_tensor, "\t", model.state_dict()[param_tensor].size()) # 参数名中的fc和conv前缀是根据定义nn.Sequential()时的名字所确定。 # 参数名中的数字表示每个Sequential()中网络层所在的位置。 print(model.state_dict().keys()) # 打印键 print(model.state_dict().values()) # 打印值 # 优化器optimizer的参数打印类似 optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) print("Optimizer's state_dict:") for var_name in optimizer.state_dict(): print(var_name, "\t", optimizer.state_dict()[var_name])
模型保存
import os # 指定保存的模型名称时Pytorch官方建议的后缀为.pt或者.pth model_save_dir = './model_logs/' model_save_path = os.path.join(model_save_dir, 'LeNet.pt') torch.save(model.state_dict(), model_save_path) # 在训练过程中保存某个条件下的最优模型,可以如下操作 best_model_state = deepcopy(model.state_dict()) torch.save(best_model_state, model_save_path) # 下面这种方法是错误的,因为best_model_state只是model.state_dict()的引用,会随着训练的改变而改变 best_model_state = model.state_dict() torch.save(best_model_state, model_save_path)
模型推理
def inference(data_iter, device, model_save_dir): model = LeNet() # 初始化现有模型的权重参数 model.to(device) model_save_path = os.path.join(model_save_dir, 'LeNet.pt') # 如果本地存在模型,则加载本地模型参数覆盖原有模型 if os.path.exists(model_save_path): loaded_paras = torch.load(model_save_path) model.load_state_dict(loaded_paras) model.eval() with torch.no_grad(): # 开始推理 acc_sum, n = 0., 0 for x, y in data_iter: x, y = x.to(device), y.to(device) logits = model(x) acc_sum += (logits.argmax(1) == y).float().sum().item() n += len(y) print("Accuracy in test data is : ", acc_sum / n)
模型再训练
class MyModel: def __init__(self, batch_size=64, epochs=5, learning_rate=0.001, model_save_dir='./MODEL'): self.batch_size = batch_size self.epochs = epochs self.learning_rate = learning_rate self.model_save_dir = model_save_dir self.model = LeNet() def train(self): train_iter, test_iter = load_dataset(self.batch_size) # 在训练过程中只保存网络权重,在再训练时只载入网络权重参数初始化网络训练。这里是核心部分,开始。 if not os.path.exists(self.model_save_dir): os.makedirs(self.model_save_dir) model_save_path = os.path.join(self.model_save_dir, 'model.pt') if os.path.exists(model_save_path): loaded_paras = torch.load(model_save_path) self.model.load_state_dict(loaded_paras) print("#### 成功载入已有模型,进行再训练...") # 结束 optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.model.to(device) for epoch in range(self.epochs): for i, (x, y) in enumerate(train_iter): x, y = x.to(device), y.to(device) loss, logits = self.model(x) optimizer.zero_grad() loss.backward() optimizer.step() if i % 100 == 0: acc = (logits.argmax(1) == y).float().mean() print("Epochs[{}/{}]---batch[{}/{}]---acc {:.4}---loss {:.4}".format( epoch, self.epochs, len(train_iter), i, acc, loss.item())) print("Epochs[{}/{}]--acc on test {:.4}".format(epoch, self.epochs, self.evaluate(test_iter, self.model, device))) torch.save(self.model.state_dict(), model_save_path) @staticmethod def evaluate(data_iter, model, device): with torch.no_grad(): acc_sum, n = 0.0, 0 for x, y in data_iter: x, y = x.to(device), y.to(device) logits = model(x) acc_sum += (logits.argmax(1) == y).float().sum().item() n += len(y) return acc_sum / n
# 在保存参数的时候,将优化器参数、损失值等可一同保存,然后在恢复模型时连同其它参数一起恢复 model_save_path = os.path.join(model_save_dir, 'LeNet.pt') torch.save({ 'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'loss': loss, ... }, model_save_path) # 加载方式如下 checkpoint = torch.load(model_save_path) model.load_state_dict(checkpoint['model_state_dict']) optimizer.load_state_dict(checkpoint['optimizer_state_dict']) epoch = checkpoint['epoch'] loss = checkpoint['loss']
模型迁移
# 定义新模型NewLeNet 和LeNet区别在于新增了一个全连接层 class NewLenNet(nn.Module): def __init__(self): super(NewLenNet, self).__init__() self.conv = nn.Sequential( # [batch, 1, 28, 28] nn.Conv2d(1, 8, 5, 2), # [batch, 1, 28, 28] nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [batch, 8, 14, 14] nn.Conv2d(8, 16, 5), # [batch, 16, 10, 10] nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [batch, 16, 5, 5] ) self.fc = nn.Sequential( nn.Flatten(), nn.Linear(16*5*5, 128), nn.ReLU(inplace=True), nn.Linear(128, 64), # 这层以前和LeNet结构一致 可以用LeNet的参数来进行替换 nn.ReLU(inplace=True), nn.Linear(64, 32), nn.ReLU(inplace=True), nn.Linear(32, 10) ) def forward(self, X): return self.fc(self.conv(X))
# 定义替换函数 匹配两个网络 size相同处地方进行参数替换 def para_state_dict(model, model_save_dir): state_dict = deepcopy(model.state_dict()) model_save_path = os.path.join(model_save_dir, 'model.pt') if os.path.exists(model_save_path): loaded_paras = torch.load(model_save_path) for key in state_dict: # 在新的网络模型中遍历对应参数 if key in loaded_paras and state_dict[key].size() == loaded_paras[key].size(): print("成功初始化参数:", key) state_dict[key] = loaded_paras[key] return state_dict
# 更新一下模型迁移后的训练代码 def train(self): train_iter, test_iter = load_dataset(self.batch_size) if not os.path.exists(self.model_save_dir): os.makedirs(self.model_save_dir) model_save_path = os.path.join(self.model_save_dir, 'model_new.pt') old_model = os.path.join(self.model_save_dir, 'LeNet.pt') if os.path.exists(old_model): state_dict = para_state_dict(self.model, self.model_save_dir) # 调用迁移代码 将LeNet的前几层参数迁移到NewLeNet self.model.load_state_dict(state_dict) print("#### 成功载入已有模型,进行再训练...") optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.model.to(device) for epoch in range(self.epochs): for i, (x, y) in enumerate(train_iter): x, y = x.to(device), y.to(device) loss, logits = self.model(x) optimizer.zero_grad() loss.backward() optimizer.step() if i % 100 == 0: acc = (logits.argmax(1) == y).float().mean() print("Epochs[{}/{}]---batch[{}/{}]---acc {:.4}---loss {:.4}".format( epoch, self.epochs, len(train_iter), i, acc, loss.item())) print("Epochs[{}/{}]--acc on test {:.4}".format(epoch, self.epochs, self.evaluate(test_iter, self.model, device))) torch.save(self.model.state_dict(), model_save_path)
# 这里更新未进行训练的推理 def inference(data_iter, device, model_save_dir='./MODEL'): model = NewLeNet() # 初始化现有模型的权重参数 print("初始化参数 conv.0.bias 为:", model.state_dict()['conv.0.bias']) model.to(device) state_dict = para_state_dict(model, model_save_dir) # 迁移模型参数 model.load_state_dict(state_dict) model.eval() print("载入本地模型重新初始化 conv.0.bias 为:", model.state_dict()['conv.0.bias']) with torch.no_grad(): acc_sum, n = 0.0, 0 for x, y in data_iter: x, y = x.to(device), y.to(device) logits = model(x) acc_sum += (logits.argmax(1) == y).float().sum().item() n += len(y) print("Accuracy in test data is :", acc_sum / n)
参考文献
[1] https://github.com/moon-hotel/DeepLearningWithMe
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