5. 模型训练和测试

分类模型训练代码

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i ,(images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        # Backward and optimizer
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print('Epoch: [{}/{}], Step: [{}/{}], Loss: {}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

分类模型测试代码

# Test the model
model.eval()  # eval mode(batch norm uses moving mean/variance 
              #instead of mini-batch mean/variance)
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Test accuracy of the model on the 10000 test images: {} %'
          .format(100 * correct / total))

自定义loss

继承torch.nn.Module类写自己的loss。

class MyLoss(torch.nn.Moudle):
    def __init__(self):
        super(MyLoss, self).__init__()
    def forward(self, x, y):
        loss = torch.mean((x - y) ** 2)
        return loss

标签平滑（label smoothing）

写一个label_smoothing.py的文件，然后在训练代码里引用，用LSR代替交叉熵损失即可。label_smoothing.py内容如下：

import torch
import torch.nn as nn
class LSR(nn.Module):
    def __init__(self, e=0.1, reduction='mean'):
        super().__init__()
        self.log_softmax = nn.LogSoftmax(dim=1)
        self.e = e
        self.reduction = reduction
    def _one_hot(self, labels, classes, value=1):
        """
            Convert labels to one hot vectors
        Args:
            labels: torch tensor in format [label1, label2, label3, ...]
            classes: int, number of classes
            value: label value in one hot vector, default to 1
        Returns:
            return one hot format labels in shape [batchsize, classes]
        """
        one_hot = torch.zeros(labels.size(0), classes)
        #labels and value_added  size must match
        labels = labels.view(labels.size(0), -1)
        value_added = torch.Tensor(labels.size(0), 1).fill_(value)
        value_added = value_added.to(labels.device)
        one_hot = one_hot.to(labels.device)
        one_hot.scatter_add_(1, labels, value_added)
        return one_hot
    def _smooth_label(self, target, length, smooth_factor):
        """convert targets to one-hot format, and smooth
        them.
        Args:
            target: target in form with [label1, label2, label_batchsize]
            length: length of one-hot format(number of classes)
            smooth_factor: smooth factor for label smooth
        Returns:
            smoothed labels in one hot format
        """
        one_hot = self._one_hot(target, length, value=1 - smooth_factor)
        one_hot += smooth_factor / (length - 1)
        return one_hot.to(target.device)
    def forward(self, x, target):
        if x.size(0) != target.size(0):
            raise ValueError('Expected input batchsize ({}) to match target batch_size({})'
                    .format(x.size(0), target.size(0)))
        if x.dim() < 2:
            raise ValueError('Expected input tensor to have least 2 dimensions(got {})'
                    .format(x.size(0)))
        if x.dim() != 2:
            raise ValueError('Only 2 dimension tensor are implemented, (got {})'
                    .format(x.size()))
        smoothed_target = self._smooth_label(target, x.size(1), self.e)
        x = self.log_softmax(x)
        loss = torch.sum(- x * smoothed_target, dim=1)
        if self.reduction == 'none':
            return loss
        elif self.reduction == 'sum':
            return torch.sum(loss)
        elif self.reduction == 'mean':
            return torch.mean(loss)
        else:
            raise ValueError('unrecognized option, expect reduction to be one of none, mean, sum')

或者直接在训练文件里做label smoothing

for images, labels in train_loader:
    images, labels = images.cuda(), labels.cuda()
    N = labels.size(0)
    # C is the number of classes.
    smoothed_labels = torch.full(size=(N, C), fill_value=0.1 / (C - 1)).cuda()
    smoothed_labels.scatter_(dim=1, index=torch.unsqueeze(labels, dim=1), value=0.9)
    score = model(images)
    log_prob = torch.nn.functional.log_softmax(score, dim=1)
    loss = -torch.sum(log_prob * smoothed_labels) / N
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Mixup训练

beta_distribution = torch.distributions.beta.Beta(alpha, alpha)
for images, labels in train_loader:
    images, labels = images.cuda(), labels.cuda()
    # Mixup images and labels.
    lambda_ = beta_distribution.sample([]).item()
    index = torch.randperm(images.size(0)).cuda()
    mixed_images = lambda_ * images + (1 - lambda_) * images[index, :]
    label_a, label_b = labels, labels[index]
    # Mixup loss.
    scores = model(mixed_images)
    loss = (lambda_ * loss_function(scores, label_a)
            + (1 - lambda_) * loss_function(scores, label_b))
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

L1 正则化

l1_regularization = torch.nn.L1Loss(reduction='sum')
loss = ...  # Standard cross-entropy loss
for param in model.parameters():
    loss += torch.sum(torch.abs(param))
loss.backward()

不对偏置项进行权重衰减（weight decay）

pytorch里的weight decay相当于l2正则

bias_list = (param for name, param in model.named_parameters() if name[-4:] == 'bias')
others_list = (param for name, param in model.named_parameters() if name[-4:] != 'bias')
parameters = [{'parameters': bias_list, 'weight_decay': 0},                
              {'parameters': others_list}]
optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)

梯度裁剪（gradient clipping）

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=20)

得到当前学习率

# If there is one global learning rate (which is the common case).
lr = next(iter(optimizer.param_groups))['lr']
# If there are multiple learning rates for different layers.
all_lr = []
for param_group in optimizer.param_groups:
    all_lr.append(param_group['lr'])

另一种方法，在一个batch训练代码里，当前的lr是optimizer.param_groups[0]['lr']

学习率衰减

# Reduce learning rate when validation accuarcy plateau.
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=5, verbose=True)
for t in range(0, 80):
    train(...)
    val(...)
    scheduler.step(val_acc)
# Cosine annealing learning rate.
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=80)
# Reduce learning rate by 10 at given epochs.
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 70], gamma=0.1)
for t in range(0, 80):
    scheduler.step()    
    train(...)
    val(...)
# Learning rate warmup by 10 epochs.
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda t: t / 10)
for t in range(0, 10):
    scheduler.step()
    train(...)
    val(...)

优化器链式更新

从1.4版本开始，torch.optim.lr_scheduler 支持链式更新（chaining），即用户可以定义两个 schedulers，并交替在训练中使用。

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import ExponentialLR, StepLR
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = SGD(model, 0.1)
scheduler1 = ExponentialLR(optimizer, gamma=0.9)
scheduler2 = StepLR(optimizer, step_size=3, gamma=0.1)
for epoch in range(4):
    print(epoch, scheduler2.get_last_lr()[0])
    optimizer.step()
    scheduler1.step()
    scheduler2.step()

模型训练可视化

PyTorch可以使用tensorboard来可视化训练过程。

安装和运行TensorBoard。

pip install tensorboard
tensorboard --logdir=runs

使用SummaryWriter类来收集和可视化相应的数据，放了方便查看，可以使用不同的文件夹，比如’Loss/train’和’Loss/test’。

from torch.utils.tensorboard import SummaryWriter
import numpy as np
writer = SummaryWriter()
for n_iter in range(100):
    writer.add_scalar('Loss/train', np.random.random(), n_iter)
    writer.add_scalar('Loss/test', np.random.random(), n_iter)
    writer.add_scalar('Accuracy/train', np.random.random(), n_iter)
    writer.add_scalar('Accuracy/test', np.random.random(), n_iter)

保存与加载断点

注意为了能够恢复训练，我们需要同时保存模型和优化器的状态，以及当前的训练轮数。

start_epoch = 0
# Load checkpoint.
if resume: # resume为参数，第一次训练时设为0，中断再训练时设为1
    model_path = os.path.join('model', 'best_checkpoint.pth.tar')
    assert os.path.isfile(model_path)
    checkpoint = torch.load(model_path)
    best_acc = checkpoint['best_acc']
    start_epoch = checkpoint['epoch']
    model.load_state_dict(checkpoint['model'])
    optimizer.load_state_dict(checkpoint['optimizer'])
    print('Load checkpoint at epoch {}.'.format(start_epoch))
    print('Best accuracy so far {}.'.format(best_acc))
# Train the model
for epoch in range(start_epoch, num_epochs): 
    ... 
    # Test the model
    ...
    # save checkpoint
    is_best = current_acc > best_acc
    best_acc = max(current_acc, best_acc)
    checkpoint = {
        'best_acc': best_acc,
        'epoch': epoch + 1,
        'model': model.state_dict(),
        'optimizer': optimizer.state_dict(),
    }
    model_path = os.path.join('model', 'checkpoint.pth.tar')
    best_model_path = os.path.join('model', 'best_checkpoint.pth.tar')
    torch.save(checkpoint, model_path)
    if is_best:
        shutil.copy(model_path, best_model_path)

提取 ImageNet 预训练模型某层的卷积特征

# VGG-16 relu5-3 feature.
model = torchvision.models.vgg16(pretrained=True).features[:-1]
# VGG-16 pool5 feature.
model = torchvision.models.vgg16(pretrained=True).features
# VGG-16 fc7 feature.
model = torchvision.models.vgg16(pretrained=True)
model.classifier = torch.nn.Sequential(*list(model.classifier.children())[:-3])
# ResNet GAP feature.
model = torchvision.models.resnet18(pretrained=True)
model = torch.nn.Sequential(collections.OrderedDict(
    list(model.named_children())[:-1]))
with torch.no_grad():
    model.eval()
    conv_representation = model(image)

提取 ImageNet 预训练模型多层的卷积特征

class FeatureExtractor(torch.nn.Module):
    """Helper class to extract several convolution features from the given
    pre-trained model.
    Attributes:
        _model, torch.nn.Module.
        _layers_to_extract, list<str> or set<str>
    Example:
        >>> model = torchvision.models.resnet152(pretrained=True)
        >>> model = torch.nn.Sequential(collections.OrderedDict(
                list(model.named_children())[:-1]))
        >>> conv_representation = FeatureExtractor(
                pretrained_model=model,
                layers_to_extract={'layer1', 'layer2', 'layer3', 'layer4'})(image)
    """
    def __init__(self, pretrained_model, layers_to_extract):
        torch.nn.Module.__init__(self)
        self._model = pretrained_model
        self._model.eval()
        self._layers_to_extract = set(layers_to_extract)
    def forward(self, x):
        with torch.no_grad():
            conv_representation = []
            for name, layer in self._model.named_children():
                x = layer(x)
                if name in self._layers_to_extract:
                    conv_representation.append(x)
            return conv_representation

微调全连接层

model = torchvision.models.resnet18(pretrained=True)
for param in model.parameters():
    param.requires_grad = False
model.fc = nn.Linear(512, 100)  # Replace the last fc layer
optimizer = torch.optim.SGD(model.fc.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)

以较大学习率微调全连接层，较小学习率微调卷积层

model = torchvision.models.resnet18(pretrained=True)
finetuned_parameters = list(map(id, model.fc.parameters()))
conv_parameters = (p for p in model.parameters() if id(p) not in finetuned_parameters)
parameters = [{'params': conv_parameters, 'lr': 1e-3}, 
              {'params': model.fc.parameters()}]
optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)

6. 其他注意事项

不要使用太大的线性层。因为nn.Linear(m,n)使用的是的内存，线性层太大很容易超出现有显存。

不要在太长的序列上使用RNN。因为RNN反向传播使用的是BPTT算法，其需要的内存和输入序列的长度呈线性关系。

model(x) 前用 model.train() 和 model.eval() 切换网络状态。

不需要计算梯度的代码块用 with torch.no_grad() 包含起来。

model.eval() 和 torch.no_grad() 的区别在于，model.eval() 是将网络切换为测试状态，例如 BN 和dropout在训练和测试阶段使用不同的计算方法。torch.no_grad() 是关闭 PyTorch 张量的自动求导机制，以减少存储使用和加速计算，得到的结果无法进行 loss.backward()。

model.zero_grad()会把整个模型的参数的梯度都归零, 而optimizer.zero_grad()只会把传入其中的参数的梯度归零.

torch.nn.CrossEntropyLoss 的输入不需要经过 Softmax。torch.nn.CrossEntropyLoss 等价于 torch.nn.functional.log_softmax + torch.nn.NLLLoss。

loss.backward() 前用 optimizer.zero_grad() 清除累积梯度。

torch.utils.data.DataLoader 中尽量设置 pin_memory=True，对特别小的数据集如 MNIST 设置 pin_memory=False 反而更快一些。num_workers 的设置需要在实验中找到最快的取值。

用 del 及时删除不用的中间变量，节约 GPU 存储。

使用 inplace 操作可节约 GPU 存储，如

x = torch.nn.functional.relu(x, inplace=True)

减少 CPU 和 GPU 之间的数据传输。例如如果你想知道一个 epoch 中每个 mini-batch 的 loss 和准确率，先将它们累积在 GPU 中等一个 epoch 结束之后一起传输回 CPU 会比每个 mini-batch 都进行一次 GPU 到 CPU 的传输更快。

使用半精度浮点数 half() 会有一定的速度提升，具体效率依赖于 GPU 型号。需要小心数值精度过低带来的稳定性问题。

时常使用 assert tensor.size() == (N, D, H, W) 作为调试手段，确保张量维度和你设想中一致。

除了标记 y 外，尽量少使用一维张量，使用n*1 的二维张量代替，可以避免一些意想不到的一维张量计算结果。

统计代码各部分耗时

with torch.autograd.profiler.profile(enabled=True, use_cuda=False) as profile:    ...print(profile)# 或者在命令行运行python -m torch.utils.bottleneck main.py

使用TorchSnooper来调试PyTorch代码，程序在执行的时候，就会自动 print 出来每一行的执行结果的 tensor 的形状、数据类型、设备、是否需要梯度的信息。

# pip install torchsnooperimport torchsnooper# 对于函数，使用修饰器@torchsnooper.snoop()# 如果不是函数，使用 with 语句来激活 TorchSnooper，把训练的那个循环装进 with 语句中去。with torchsnooper.snoop():    原本的代码

https://github.com/zasdfgbnm/TorchSnoopergithub.com

模型可解释性，使用captum库：https://captum.ai/captum.ai

参考资料

张皓：PyTorch Cookbook（常用代码段整理合集），https://zhuanlan.zhihu.com/p/59205847?

PyTorch官方文档和示例

https://pytorch.org/docs/stable/notes/faq.html

https://github.com/szagoruyko/pytorchviz

https://github.com/sksq96/pytorch-summary

其他