文章目录

  • 6.1 自定义损失函数
    • 6.1.1 以函数方式定义
    • 6.1.2 以类的方式定义
    • 6.1.3 继承`nn.autograd.Function`来实现
    • 6.1.4 案例
  • 6.2 动态调整学习率
    • 6.2.1 官方的scheduler
    • 6.2.2 自定义scheduler
  • 6.3 模型微调
    • 6.3.1 模型微调流程
    • 6.3.1 模型微调-torchvision
  • 6.3.2 模型微调-timm
  • 6.4 半精度训练
  • 6.5 数据增强
    • 6.5.1 imgaug简介与安装
    • 6.5.2 imgaug使用
      • 6.5.2.1 单图处理
    • 6.5.3 PyTorch与imgaug
  • 6.6 使用argparse调参
  • PyTorch模型定义与进阶训练技巧

6.1 自定义损失函数

6.1.1 以函数方式定义

# 以函数形式定义损失函数
def loss_func(output, target):# 均方误差loss = torch.mean((output-target)**2)return loss

6.1.2 以类的方式定义

虽然以函数定义的方式很简单，但是以类方式定义更加常用，在以类方式定义损失函数时，我们如果看每一个损失函数的继承关系我们就可以发现Loss函数部分继承自_loss, 部分继承自_WeightedLoss, 而_WeightedLoss继承自_loss， _loss继承自 nn.Module。我们可以将其当作神经网络的一层来对待，同样地，我们的损失函数类就需要继承自nn.Module类，在下面的例子中我们以DiceLoss为例向大家讲述。

Dice Loss是一种在分割领域常见的损失函数，定义如下：

DSC=2∣X∩Y∣∣X∣+∣Y∣DSC = \frac{2|X∩Y|}{|X|+|Y|} DSC=∣X∣+∣Y∣2∣X∩Y∣​

class DiceLoss(nn.Module):def __init(self, weight=None, size_average=True):super(DiceLoss, self).__init__()def forward(self, inputs, targets, smooth=1):    inputs = F.sigmoid(inputs)       inputs = inputs.view(-1)targets = targets.view(-1)intersection = (inputs * targets).sum()                   dice = (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)  return 1 - dice

在使用这个巡视函数的时候只需要如下即可：

criterion = DiceLoss()
loss = criterion(input, targets)

在自定义损失函数时，涉及到数学运算时，我们最好全程使用PyTorch提供的张量计算接口，这样就不需要我们实现自动求导功能并且我们可以直接调用cuda，使用numpy或者scipy的数学运算时，操作会有些麻烦，大家可以自己下去进行探索。关于PyTorch使用Class定义损失函数的原因，可以参考PyTorch的讨论区

其实在pytorch中很多损失函数都是定义好了的


class _Loss(Module):@weak_module
class L1Loss(_Loss):@weak_module
class NLLLoss(_WeightedLoss):@weak_module
class NLLLoss2d(NLLLoss):@weak_module
class PoissonNLLLoss(_Loss):@weak_module
class KLDivLoss(_Loss):@weak_module
class MSELoss(_Loss):@weak_module
class BCELoss(_WeightedLoss):@weak_module
class BCEWithLogitsLoss(_Loss):@weak_module
class HingeEmbeddingLoss(_Loss):@weak_module
class MultiLabelMarginLoss(_Loss):@weak_module
class SmoothL1Loss(_Loss):
# ————————————————
# 版权声明：本文为CSDN博主「LoveMIss-Y」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
# 原文链接：https://blog.csdn.net/qq_27825451/article/details/95165265

所有的类都继承自_Loss类，而——Loss类又继承自Module类

6.1.3 继承nn.autograd.Function来实现

Function类和Module类最大的区别是Function类多了一个backword()方法

# 定义一个继承了Function类的子类，实现y=f(x)的正向运算以及反向求导
class sqrt_and_inverse(torch.autograd.Function):'''本例子所采用的数学公式是：z=sqrt(x)+1/x+2*power(y,2)z是关于x,y的一个二元函数它的导数是z'(x)=1/(2*sqrt(x))-1/power(x,2)z'(y)=4*yforward和backward可以定义成静态方法，向定义中那样，也可以定义成实例方法'''# 前向运算def forward(self, input_x, input_y):'''self.save_for_backward(input_x,input_y) ,这个函数是定义在Function的父类_ContextMethodMixin中它是将函数的输入参数保存起来以便后面在求导时候再使用，起前向反向传播中协调作用'''self.save_for_backward(input_x, input_y)# 对输入和参数进行的操作，其实就是前向运算的函数表达式]output = torch.sqrt(input_x) + torch.reciprocal(input_x) + 2 * torch.pow(input_y, 2)return outputdef backward(self, grad_output):# 计算梯度是链式法则，输入的参数grad_output为反向传播上一级计算得到的梯度值input_x, input_y = self.saved_tensors  # 获取前面保存的参数,也可以使用self.saved_variables# 求函数forward(input)关于 parameters 的导数，其实就是反向运算的导数表达式# 这里上一级梯度值grad_output乘以当前级的梯度grad_x = grad_output * (torch.reciprocal(2 * torch.sqrt(input_x)) - torch.reciprocal(torch.pow(input_x, 2)))grad_y = grad_output * (4 * input_y)return grad_x, grad_y  # 需要注意的是，反向传播得到的结果需要与输入的参数相匹配# ————————————————
# 版权声明：本文为CSDN博主「豆豆小朋友小笔记」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
# 原文链接：https://blog.csdn.net/qq_40728805/article/details/103906140

6.1.4 案例

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# 第一步：自定义损失函数# 继承nn.Mdule
class My_loss(nn.Module):def __init__(self):super().__init__()def forward(self, x, y):return torch.mean(torch.pow((x - y), 2))

# 第二步：准备数据集，模拟一个线性拟合过程
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], [9.779], [6.182], [7.59], [2.167], [7.042], [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], [3.366], [2.596], [2.53], [1.221], [2.827], [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)# 将numpy数据转化为torch的张量
inputs = torch.from_numpy(x_train)
targets = torch.from_numpy(y_train)

input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001# 第三步： 构建模型，构建一个一层的网络模型
model = nn.Linear(input_size, output_size)# 与模型相关的配置、损失函数、优化方式
# 使用自定义函数，等价于criterion = nn.MSELoss()
criterion = My_loss()# 定义迭代优化算法， 使用的是随机梯度下降算法
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

loss_history = []
# 第四步：训练模型，迭代训练
for epoch in range(num_epochs):#  前向传播计算网络结构的输出结果outputs = model(inputs)# 计算损失函数loss = criterion(outputs, targets)# 反向传播更新参数，三步策略，归零梯度——>反向传播——>更新参数optimizer.zero_grad()loss.backward()optimizer.step()# 打印训练信息和保存lossloss_history.append(loss.item()) if (epoch+1) % 5 == 0:print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
Epoch [5/60], Loss: 2.8296
Epoch [10/60], Loss: 1.3841
Epoch [15/60], Loss: 0.7981
Epoch [20/60], Loss: 0.5603
Epoch [25/60], Loss: 0.4637
Epoch [30/60], Loss: 0.4242
Epoch [35/60], Loss: 0.4078
Epoch [40/60], Loss: 0.4008
Epoch [45/60], Loss: 0.3977
Epoch [50/60], Loss: 0.3960
Epoch [55/60], Loss: 0.3950
Epoch [60/60], Loss: 0.3943

# 第五步：结果展示。画出原y与x的曲线与网络结构拟合后的曲线
predicted = model(torch.from_numpy(x_train)).detach().numpy() #模型输出结果plt.plot(x_train, y_train, 'ro', label='Original data')       #原始数据
plt.plot(x_train, predicted, label='Fitted line')             #拟合之后的直线
plt.legend()
plt.show()# 画loss在迭代过程中的变化情况
plt.plot(loss_history, label='loss for every epoch')
plt.legend()
plt.show()

6.2 动态调整学习率

6.2.1 官方的scheduler

PyTorch在torch.optim.lr_scheduler中为我们封装好了一些动态调整学习率的方法：

  • lr_scheduler.LambdaLR
  • lr_scheduler.MultiplicativeLR
  • lr_scheduler.StepLR
  • lr_scheduler.MultiStepLR
  • lr_scheduler.ExponentialLR
  • lr_scheduler.CosineAnnealingLR
  • lr_scheduler.ReduceLROnPlateau
  • lr_scheduler.CyclicLR
  • lr_scheduler.OneCycleLR
  • lr_scheduler.CosineAnnealingWarmRestarts

PyTorch的官方代码：

# 选择一种优化器
optimizer = torch.optim.Adam(...)
# 选择上面提到的一种或多种动态调整学习率的方法
scheduler1 = torch.optim.lr_scheduler....
scheduler2 = torch.optim.lr_scheduler....
...
schedulern = torch.optim.lr_scheduler....
# 进行训练
for epoch in range(100):train(...)validate(...)optimizer.step()# 需要在优化器参数更新之后再动态调整学习率scheduler1.step() ...schedulern.step()

6.2.2 自定义scheduler

自定义函数adjust_learning_rate来改变param_group中lr的值

# 自定义学习率
# 学习率每39轮下降为原来的1/10
def adjust_learning_rate(optimizer, epoch):lr = args.lr * (0.1 ** (epoch // 30))for param_group in optimizer.param_groups:param_group['lr'] = lr

# 下面是训练脚本
optimizer = torch.optim.SGD(model.parameters(),lr = args.lr,momentum = 0.9)
for epoch in range(10):train(...)val(...)adjust_learning_rate(optimizer,epoch)

6.3 模型微调

6.3.1 模型微调流程

  1. 在源数据集(如ImageNet数据集)上预训练一个神经网络模型，即源模型。

  2. 创建一个新的神经网络模型，即目标模型。它复制了源模型上除了输出层外的所有模型设计及其参数。我们假设这些模型参数包含了源数据集上学习到的知识，且这些知识同样适用于目标数据集。我们还假设源模型的输出层跟源数据集的标签紧密相关，因此在目标模型中不予采用。

  3. 为目标模型添加一个输出⼤小为⽬标数据集类别个数的输出层，并随机初始化该层的模型参数。

  4. 在目标数据集上训练目标模型。我们将从头训练输出层，而其余层的参数都是基于源模型的参数微调得到的。

6.3.1 模型微调-torchvision

# 通过True或者False来决定是否使用预训练好的权重，
# 在默认状态下pretrained = False，意味着我们不使用预训练得到的权重，
# 当pretrained = True，意味着我们将使用在一些数据集上预训练得到的权重。import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
densenet = models.densenet161(pretrained=True)
inception = models.inception_v3(pretrained=True)
googlenet = models.googlenet(pretrained=True)
shufflenet = models.shufflenet_v2_x1_0(pretrained=True)
mobilenet_v2 = models.mobilenet_v2(pretrained=True)
mobilenet_v3_large = models.mobilenet_v3_large(pretrained=True)
mobilenet_v3_small = models.mobilenet_v3_small(pretrained=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=True)
mnasnet = models.mnasnet1_0(pretrained=True)

/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth0%|          | 0.00/44.7M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/alexnet-owt-7be5be79.pth" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth0%|          | 0.00/233M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=SqueezeNet1_0_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_0_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth" to /root/.cache/torch/hub/checkpoints/squeezenet1_0-b66bff10.pth0%|          | 0.00/4.78M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth0%|          | 0.00/528M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=DenseNet161_Weights.IMAGENET1K_V1`. You can also use `weights=DenseNet161_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/densenet161-8d451a50.pth" to /root/.cache/torch/hub/checkpoints/densenet161-8d451a50.pth0%|          | 0.00/110M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=Inception_V3_Weights.IMAGENET1K_V1`. You can also use `weights=Inception_V3_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth" to /root/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth0%|          | 0.00/104M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=GoogLeNet_Weights.IMAGENET1K_V1`. You can also use `weights=GoogLeNet_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/googlenet-1378be20.pth" to /root/.cache/torch/hub/checkpoints/googlenet-1378be20.pth0%|          | 0.00/49.7M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1`. You can also use `weights=ShuffleNet_V2_X1_0_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth" to /root/.cache/torch/hub/checkpoints/shufflenetv2_x1-5666bf0f80.pth0%|          | 0.00/8.79M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /root/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth0%|          | 0.00/13.6M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_large-8738ca79.pth0%|          | 0.00/21.1M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MobileNet_V3_Small_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V3_Small_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_small-047dcff4.pth0%|          | 0.00/9.83M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNeXt50_32X4D_Weights.IMAGENET1K_V1`. You can also use `weights=ResNeXt50_32X4D_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth" to /root/.cache/torch/hub/checkpoints/resnext50_32x4d-7cdf4587.pth0%|          | 0.00/95.8M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=Wide_ResNet50_2_Weights.IMAGENET1K_V1`. You can also use `weights=Wide_ResNet50_2_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth" to /root/.cache/torch/hub/checkpoints/wide_resnet50_2-95faca4d.pth0%|          | 0.00/132M [00:00<?, ?B/s]/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MNASNet1_0_Weights.IMAGENET1K_V1`. You can also use `weights=MNASNet1_0_Weights.DEFAULT` to get the most up-to-date weights.warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth" to /root/.cache/torch/hub/checkpoints/mnasnet1.0_top1_73.512-f206786ef8.pth0%|          | 0.00/16.9M [00:00<?, ?B/s]

6.3.2 模型微调-timm

6.4 半精度训练

通过抠浮点数存储位数来减少计算开销

# 设置方法
from torch.cuda.amp import autocast# 模型设置
@autocast()
def forward(self, x):...return x# 训练过程for x in train_loader:x = x.cuda()# 后面的都是autocastwith autocast():output = model(x)...

6.5 数据增强

6.5.1 imgaug简介与安装

imgaug是一个数据增强库

  1. Github地址：imgaug
  2. Readthedocs：imgaug
  3. 官方提供notebook例程：notebook

安装方法：

conda config --add channels conda-forge
conda install imgaug

6.5.2 imgaug使用

6.5.2.1 单图处理

import imageio
import imgaug as ia
%matplotlib inline# 图片的读取
img = imageio.imread("/content/Lenna.png")# 使用Image进行读取
# img = Image.open("./Lenna.jpg")
# image = np.array(img)
# ia.imshow(image)# 可视化图片
ia.imshow(img)


from imgaug import augmenters as iaa# 设置随机数种子
ia.seed(4)# 实例化方法
rotate = iaa.Affine(rotate=(-4,45))
img_aug = rotate(image=img)
ia.imshow(img_aug)

​

​

6.5.3 PyTorch与imgaug

6.6 使用argparse调参

总的来说，我们可以将argparse的使用归纳为以下三个步骤。

  • 创建ArgumentParser()对象

  • 调用add_argument()方法添加参数

  • 使用parse_args()解析参数 在接下来的内容中，我们将以实际操作来学习argparse的使用方法。

# demo.py
import argparse# 创建ArgumentParser()对象
parser = argparse.ArgumentParser()# 添加参数
parser.add_argument('-o', '--output', action='store_true', help="shows output")
# action = `store_true` 会将output参数记录为True
# type 规定了参数的格式
# default 规定了默认值
parser.add_argument('--lr', type=float, default=3e-5, help='select the learning rate, default=1e-3') parser.add_argument('--batch_size', type=int, required=True, help='input batch size')
# 使用parse_args()解析函数
args = parser.parse_args()if args.output:print("This is some output")print(f"learning rate:{args.lr}")

我们在命令行使用python demo.py --lr 3e-4 --batch_size 32，就可以看到以下的输出

This is some output
learning rate: 3e-4

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