1. W&B简单介绍
Wandb是Weights & Biases的缩写，这款工具能够帮助跟踪你的机器学习项目。它能够自动记录模型训练过程中的超参数和输出指标，然后可视化和比较结果，并快速与同事共享结果。（感受到了yolov5作者对其极大的喜爱）
wandb和tensorboard最大区别是tensorboard的数据是存在本地的，wandb是存在wandb远端服务器，wandb会为开发真创建一个账户并生成登陆api的key。运行自己程序之前需要先登陆wandb。
在之前我也稍微介绍过Visdom与tensorboard的使用，见下面两个链接：
- Visdom的简单使用
- Tensorboard的简单使用
还介绍过普通的日志记录工具：
- MetricLogger日志工具代码调用
- python日志处理logging模块
如果是简单的想记录中间训练过程的结果，其实wandb和以上提到的两种可视化工具是差不多的，甚至还可以讲训练结果与中间过程结果保存的本地直接查看（logging日志处理），但是wandb好像可以提供更多强悍的功能。其功能如下：
- Dashboard：Track experiments（跟踪实验）, visualize results（可视化结果）；
- Reports：Save and share reproducible findings（分享和保存结果）；
- Sweeps：Optimize models with hyperparameter tuning（超参调优）；
- Artifacts：Dataset and model versioning, pipeline tracking（数据集和模型的版本控制）；
通过wandb，能够给你的机器学习项目带来强大的交互式可视化调试体验，能够自动化记录Python脚本中的图标，并且实时在网页仪表盘展示它的结果，例如，损失函数、准确率、召回率，它能够让你在最短的时间内完成机器学习项目可视化图片的制作。（这一点还是值得使用的，比自己记录数据然后matplotlib进行绘图要方便的多，还是推荐使用这些可视化的工具来减少不必要的代码编写，之前我就是憨批的自己matplotlib绘图的…）
- 核心优点
wandb并不单纯的是一款数据可视化工具。它具有更为强大的模型和数据版本管理。此外，还可以对你训练的模型进行调优。
wandb另外一大亮点的就是强大的兼容性，它能够和Jupyter、TensorFlow、Pytorch、Keras、Scikit、fast.ai、LightGBM、XGBoost一起结合使用。
因此，它不仅可以给你带来时间和精力上的节省，还能够给你的结果带来质的改变。
但是，wandb的高级功能对我来说暂时还用不上，等之后接触到的时候再查看，下面记录的是他的一些简单的可视化结果与保存结果的功能实现。
2. W&B快速入门
以下测试环境，全部是在本地远程调用服务器的jupyter notebook上进行。
- 安装库
pip install wandb
- 创建用户
wandb login
注册界面：https://wandb.ai/，然后把对应的key复制下来填写，就可以了
过程如下：
(yolo) [@localhost ~]$ wandb login wandb: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server) wandb: You can find your API key in your browser here: https://wandb.ai/authorize wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit: wandb: Appending key for api.wandb.ai to your netrc file: /home/xxx/.netrc
- 初始化
# Inside my model training code import wandb wandb.init(project="my-project")
此时，就会弹出云端的对应链接，所以其和jupyter是兼容的，可以直接内置查看这个网页
在wandb的home界面就会显示此时正在进行的进程
声明超参数
# config is a variable that holds and saves hyper parameters and inputs config = wandb.config # Initialize config config.batch_size = 4 # input batch size for training (default:64) config.test_batch_size = 10 # input batch size for testing(default:1000) config.epochs = 10 # number of epochs to train(default:10) config.lr = 0.1 # learning rate(default:0.01) config.momentum = 0.1 # SGD momentum(default:0.5) config.no_cuda = False # disables CUDA training config.seed = 42 # random seed(default:42) config.log_interval = 10 # how many batches to wait before logging training status
记录日志
# wandb.log用来记录一些日志(accuracy,loss and epoch), 便于随时查看网路的性能 def test(args, model, device, test_loader, classes): model.eval() # switch model to evaluation mode. # This is necessary for layers like dropout, batchNorm etc. which behave differently in training and evaluation mode test_loss = 0 correct = 0 example_images = [] with torch.no_grad(): for data, target in test_loader: # Load the input features and labels from the test dataset data, target = data.to(device), target.to(device) # Make predictions: Pass image data from test dataset, # make predictions about class image belongs to(0-9 in this case) output = model(data) # Compute the loss sum up batch loss test_loss += F.nll_loss(output, target, reduction='sum').item() # Get the index of the max log-probability pred = output.max(1, keepdim=True)[1] correct += pred.eq(target.view_as(pred)).sum().item() # Log images in your test dataset automatically, # along with predicted and true labels by passing pytorch tensors with image data into wandb. example_images.append(wandb.Image( data[0], caption="Pred:{} Truth:{}".format(classes[pred[0].item()], classes[target[0]]))) # wandb.log(a_dict) logs the keys and values of the dictionary passed in and associates the values with a step. # You can log anything by passing it to wandb.log(), # including histograms, custom matplotlib objects, images, video, text, tables, html, pointclounds and other 3D objects. # Here we use it to log test accuracy, loss and some test images (along with their true and predicted labels). wandb.log({ "Examples": example_images, "Test Accuracy": 100. * correct / len(test_loader.dataset), "Test Loss": test_loss })
其实，主要就是中间结果运行完之后。添加在wandb.log上，也就是最后的几行代码：
# 数据传入 wandb.log({ "Examples": example_images, "Test Accuracy": 100. * correct / len(test_loader.dataset), "Test Loss": test_loss }) # 图像传入 wandb.log({"examples" : [wandb.Image(i) for i in images]})
保存文件
# by default, this will save to a new subfolder for files associated # with your run, created in wandb.run.dir (which is ./wandb by default) wandb.save("mymodel.h5") # you can pass the full path to the Keras model API model.save(os.path.join(wandb.run.dir, "mymodel.h5"))
使用wandb以后，模型输出，log和要保存的文件将会同步到cloud。
3. W&B使用示例
以一个最简单的神经网络，进行一个cifar10的十分类任务为例来展示wandb的用法，代码来自参考资料3，亲测可用。代码比较简单，就不作解释了，使用的时候设置一下cifar10对应的数据集存放路径即可。
参考代码
from __future__ import print_function import argparse import random # to set the python random seed import numpy # to set the numpy random seed import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data import DataLoader # Ignore excessive warnings import logging logging.propagate = False logging.getLogger().setLevel(logging.ERROR) # WandB – Import the wandb library import wandb # WandB – Login to your wandb account so you can log all your metrics # 定义Convolutional Neural Network： class Net(nn.Module): def __init__(self): super(Net, self).__init__() # In our constructor, we define our neural network architecture that we'll use in the forward pass. # Conv2d() adds a convolution layer that generates 2 dimensional feature maps # to learn different aspects of our image. self.conv1 = nn.Conv2d(3, 6, kernel_size=5) self.conv2 = nn.Conv2d(6, 16, kernel_size=5) # Linear(x,y) creates dense, fully connected layers with x inputs and y outputs. # Linear layers simply output the dot product of our inputs and weights. self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): # Here we feed the feature maps from the convolutional layers into a max_pool2d layer. # The max_pool2d layer reduces the size of the image representation our convolutional layers learnt, # and in doing so it reduces the number of parameters and computations the network needs to perform. # Finally we apply the relu activation function which gives us max(0, max_pool2d_output) x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2(x), 2)) # Reshapes x into size (-1, 16 * 5 * 5) # so we can feed the convolution layer outputs into our fully connected layer. x = x.view(-1, 16 * 5 * 5) # We apply the relu activation function and dropout to the output of our fully connected layers. x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) # Finally we apply the softmax function to squash the probabilities of each class (0-9) # and ensure they add to 1. return F.log_softmax(x, dim=1) def train(config, model, device, train_loader, optimizer, epoch): # switch model to training mode. This is necessary for layers like dropout, batchNorm etc. # which behave differently in training and evaluation mode. model.train() # we loop over the data iterator, and feed the inputs to the network and adjust the weights. for batch_id, (data, target) in enumerate(train_loader): if batch_id > 20: break # Loop the input features and labels from the training dataset. data, target = data.to(device), target.to(device) # Reset the gradients to 0 for all learnable weight parameters optimizer.zero_grad() # Forward pass: Pass image data from training dataset, make predictions # about class image belongs to (0-9 in this case). output = model(data) # Define our loss function, and compute the loss loss = F.nll_loss(output, target) # Backward pass:compute the gradients of loss,the model's parameters loss.backward() # update the neural network weights optimizer.step() # wandb.log用来记录一些日志(accuracy,loss and epoch), 便于随时查看网路的性能 def test(args, model, device, test_loader, classes): model.eval() # switch model to evaluation mode. # This is necessary for layers like dropout, batchNorm etc. which behave differently in training and evaluation mode test_loss = 0 correct = 0 example_images = [] with torch.no_grad(): for data, target in test_loader: # Load the input features and labels from the test dataset data, target = data.to(device), target.to(device) # Make predictions: Pass image data from test dataset, # make predictions about class image belongs to(0-9 in this case) output = model(data) # Compute the loss sum up batch loss test_loss += F.nll_loss(output, target, reduction='sum').item() # Get the index of the max log-probability pred = output.max(1, keepdim=True)[1] correct += pred.eq(target.view_as(pred)).sum().item() # Log images in your test dataset automatically, # along with predicted and true labels by passing pytorch tensors with image data into wandb. example_images.append(wandb.Image( data[0], caption="Pred:{} Truth:{}".format(classes[pred[0].item()], classes[target[0]]))) # wandb.log(a_dict) logs the keys and values of the dictionary passed in and associates the values with a step. # You can log anything by passing it to wandb.log(), # including histograms, custom matplotlib objects, images, video, text, tables, html, pointclounds and other 3D objects. # Here we use it to log test accuracy, loss and some test images (along with their true and predicted labels). wandb.log({ "Examples": example_images, "Test Accuracy": 100. * correct / len(test_loader.dataset), "Test Loss": test_loss }) # 初始化一个wandb run, 并设置超参数 # Initialize a new run # wandb.init(project="pytorch-intro") wandb.init(project='test-project', entity='clichong') wandb.watch_called = False # Re-run the model without restarting the runtime, unnecessary after our next release # config is a variable that holds and saves hyper parameters and inputs config = wandb.config # Initialize config config.batch_size = 4 # input batch size for training (default:64) config.test_batch_size = 10 # input batch size for testing(default:1000) config.epochs = 10 # number of epochs to train(default:10) config.lr = 0.1 # learning rate(default:0.01) config.momentum = 0.1 # SGD momentum(default:0.5) config.no_cuda = False # disables CUDA training config.seed = 42 # random seed(default:42) config.log_interval = 10 # how many batches to wait before logging training status def main(): use_cuda = not config.no_cuda and torch.cuda.is_available() device = torch.device("cuda:0" if use_cuda else "cpu") kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} # Set random seeds and deterministic pytorch for reproducibility # random.seed(config.seed) # python random seed torch.manual_seed(config.seed) # pytorch random seed # numpy.random.seed(config.seed) # numpy random seed torch.backends.cudnn.deterministic = True # Load the dataset: We're training our CNN on CIFAR10. # First we define the transformations to apply to our images. transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) # Now we load our training and test datasets and apply the transformations defined above train_loader = DataLoader(datasets.CIFAR10( root='../../Classification/StageCNN/dataset/cifar10/', # 路径自行更改 train=True, download=False, transform=transform ), batch_size=config.batch_size, shuffle=True, **kwargs) test_loader = DataLoader(datasets.CIFAR10( root='../../Classification/StageCNN/dataset/cifar10/', # 路径自行更改 train=False, download=False, transform=transform ), batch_size=config.batch_size, shuffle=False, **kwargs) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') # Initialize our model, recursively go over all modules and convert their parameters # and buffers to CUDA tensors (if device is set to cuda) model = Net().to(device) optimizer = optim.SGD(model.parameters(), lr=config.lr, momentum=config.momentum) # wandb.watch() automatically fetches all layer dimensions, gradients, model parameters # and logs them automatically to your dashboard. # using log="all" log histograms of parameter values in addition to gradients wandb.watch(model, log="all") for epoch in range(1, config.epochs + 1): train(config, model, device, train_loader, optimizer, epoch) test(config, model, device, test_loader, classes) # Save the model checkpoint. This automatically saves a file to the cloud torch.save(model.state_dict(), 'model.h5') wandb.save('model.h5') if __name__ == '__main__': main()
Parameters
在运行当中，可以在其提供的链接中动态的查看训练过程与中间结果，wandb.watch(model, log="all") 可以自动获取所有层尺寸、梯度、模型参数，并将它们自动记录到云端的仪表板中。如下所示：
Chart & Media
在记录中间的测试准确率和测试损失时，还可以把测试的图像列表保存下来存放在云端，很方便。
# example_images.append(wandb.Image( # data[0], caption="Pred:{} Truth:{}".format(classes[pred[0].item()], classes[target[0]]))) wandb.log({ "Examples": example_images, "Test Accuracy": 100. * correct / len(test_loader.dataset), "Test Loss": test_loss })
云端结果显示如下：
还可以单独图表进行分析与平滑等处理：
上传的图像也可以进行设置：
Save
在模型训练完成保存在本地上时，还可以进行 wandb.save('model.h5') ，将模型保存在云端上，可以在相关路径下找到保存的模型。
4. W&B更多帮助
在W&B的官网中，还有更多的示例和更多的教程，更良心的是支持中文，简直爱了。
官方文档资料：https://docs.wandb.ai/v/zh-hans/examples
官方教程资料：https://wandb.ai/site/tutorials
参考资料：
1. wandb: 深度学习轻量级可视化工具入门教程
2. PyTorch 62.只需10分钟带你完美入门轻量级可视化工具wandb
3. wandb使用
4. W&B官网
