PyTorch深度学习必用库之PyTorch Lightning新手教程（附100行的完整深度学习项目代码模板） |
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文章目录
什么是PyTorch Lightning常用功能自动储存训练日志使用torchmetrics一行代码评估模型加载训练好的checkpoint定义模型运行的设备：CUDA or CPU?
深度学习实战项目模板
什么是PyTorch Lightning
PyTorch Lightning（PL）的主要优势包括： 训练自动化：PyTorch Lightning可以帮助开发者处理训练循环，包括数据加载、批次迭代、前向传播、损失计算和反向传播等。100行左右的代码就可以写出完整的深度学习项目。分布式训练支持：PyTorch Lightning支持分布式训练，可以在多个GPU或多台机器上进行训练，从而加快训练速度，而且配置特别简单。可复现性：PyTorch Lightning提供的API方便用户使用固定的随机种子和训练环境，确保每次运行的结果是可复现的。总之，PyTorch Lightning是一个强大而灵活的框架，可以帮助用户更高效地进行深度学习模型的训练和开发。它提供了许多易用的功能和工具，使用户可以更好地管理和组织训练代码，提高工作效率。 常用功能pl深度学习项目的基本思路： 定义PyTorch Lightning Module定义Trainer调用Trainer训练并检验深度学习模块 自动储存训练日志PL的便捷功能其中之一是在PyTorch中记录包括训练误差、测试误差的训练日志。PL默认使用Tensorboard来记录日志。 要查看日志，可以在终端中运行以下命令： tensorboard --logdir=lightning_logs/可以使用on_epoch参数来确定是否记录每个epoch的累积指标。 trainer = pl.Trainer(max_epochs=MAX_EPOCHS, num_sanity_val_steps=0, ) # num_sanity_val_steps=0 because of va_spo_list 使用torchmetrics一行代码评估模型torchmetrics是一个用于PyTorch深度学习库的指标计算和评估工具包。它提供了一系列常用的评估指标，用于衡量模型在不同任务上的性能，包括分类、回归、分割和生成等。 torchmetrics支持各种常见的评估指标，如准确率、精确度、召回率、F1分数、AUC、平均绝对误差、均方根误差等。它还提供了一些高级指标，如多类别混淆矩阵、Jaccard系数、Dice系数和IoU等。 torchmetrics的设计目标是提供一种简洁、灵活和可扩展的方式来计算和记录模型性能指标。它与PyTorch框架紧密集成，可以无缝地与PyTorch的训练和验证流程结合使用。这一点从本文文末提供的代码可以感受得到。 加载训练好的checkpoint # load the model CHECKPOINT_PATH = 'lightning_logs/version_9/checkpoints/epoch=59-step=120000.ckpt' TEMP_VIDEO_PATH = 'tmp_video' MODEL_TYPE = 'slowfast' classifier = SignLanguageClassifier.load_from_checkpoint(CHECKPOINT_PATH, strict=False, model_type=MODEL_TYPE) classifier.model_type = MODEL_TYPE trainer = pl.Trainer() # make inference trainer.test(classifier, test_dataloader) 定义模型运行的设备：CUDA or CPU? trainer = pl.Trainer(max_epochs=MAX_EPOCH, devices='auto', accelerator='auto', # 如果只用CPU，把'auto'改成'cpu'就行了 logger=tensorboard_logger) 深度学习实战项目模板下面是我在实战中用PL写的图片分类代码，对整个数据集进行5折交叉验证后汇报平均准确率和混淆矩阵。这个代码可以很直观地体现出PL的逻辑和整体流程。 """ a Python script to train ResNet-18 using PyTorch Lightning. The dataset includes 5 categories. Report the classification accuracy and confusion matrix with torch-metrics. Use 5-fold stratified sampling. Report the final average classification accuracies at the end of the program. """ import numpy as np import pytorch_lightning as pl from pytorch_lightning.loggers import TensorBoardLogger import torch from torch.nn import functional as F from torch.utils.data import DataLoader, TensorDataset from torchvision import models, transforms import torchmetrics from sklearn.model_selection import StratifiedKFold import seaborn import matplotlib.pyplot as plt MAX_EPOCH = 100 class Classifier(pl.LightningModule): def __init__(self, num_classes: int, model_type: str = 'resnet18'): super().__init__() self.model_type = model_type if model_type == 'resnet18': self.model = models.resnet18(pretrained=True) self.model.fc = torch.nn.Sequential( torch.nn.Linear(self.model.fc.in_features, 128), torch.nn.ReLU(), torch.nn.Linear(128, 64), torch.nn.ReLU(), torch.nn.Linear(64, num_classes) ) elif model_type == 'mlp': self.model = torch.nn.Sequential( torch.nn.Linear(40, 128), torch.nn.ReLU(), torch.nn.Linear(128, 64), torch.nn.ReLU(), torch.nn.Linear(64, num_classes) ) else: raise ValueError(f'Invalid model_type: {model_type}') self.accuracy = torchmetrics.classification.MulticlassAccuracy(num_classes) self.conf_mat = torchmetrics.classification.MulticlassConfusionMatrix(num_classes, normalize='true') def forward(self, x): if self.model_type == 'resnet18': x = x.view(x.size(0), 1, -1, 1) # Reshape 1D data into a single-channel "image" x = torch.repeat_interleave(x, repeats=3, dim=1) return self.model(x.float()) def training_step(self, batch, batch_idx): x, y = batch y_hat = self(x) loss = F.cross_entropy(y_hat, y.long()) self.log('train_loss', loss, ) return loss def validation_step(self, batch, batch_idx): x, y = batch y_hat = self(x) self.log('val_accuracy', self.accuracy, on_epoch=True, prog_bar=True) self.log('val_loss', F.cross_entropy(y_hat, y.long()), on_step=True, prog_bar=True) self.conf_mat.update(y_hat, y) self.accuracy.update(y_hat, y) def on_validation_end(self): conf_matrix = self.conf_mat.compute() print(conf_matrix) plt.figure() seaborn.heatmap(conf_matrix.cpu(), annot=True) plt.savefig(f'conf_mat_{fold_id}.png') accuracy_computed = self.accuracy.compute() print(f'Fold Accuracy={accuracy_computed}') def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.00001) # Load data and labels from .npy file data_and_labels = np.load('data/data_and_labels.npy', allow_pickle=True).item() X = data_and_labels['X'] y = data_and_labels['y'] # Prepare 5-fold stratified sampling skf = StratifiedKFold(n_splits=5, shuffle=True) # Initialize list for storing classification accuracies accuracies = [] fold_id = 0 # Perform 5-fold stratified sampling for train_index, val_index in skf.split(X, y): X_train, X_val = X[train_index], X[val_index] y_train, y_val = y[train_index], y[val_index] # Create TensorDatasets train_data = TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train)) val_data = TensorDataset(torch.from_numpy(X_val), torch.from_numpy(y_val)) # Create DataLoaders train_loader = DataLoader(train_data, batch_size=64, shuffle=True) val_loader = DataLoader(val_data, batch_size=64) # Model model = Classifier(num_classes=5) # Training tensorboard_logger = TensorBoardLogger(save_dir='.', version=fold_id) trainer = pl.Trainer(max_epochs=MAX_EPOCH, devices='auto', accelerator='auto', logger=tensorboard_logger) trainer.fit(model, train_loader, val_loader) fold_id += 1 |
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