指南

• Cifar 数据集上的 ResNet
  • Cifar10 上的 ReNet18
    • Cutout Cifar10 训练
    • 性能度量
  • Cifar10 上的 ReNet18(SiLU)
    • 性能度量
  • Cifar10 上的 ReNet18 SiLU 线性化
• PyTorch 基准测试
  • 定义同一功能两种不同实现以备后续测试
  • timeit 测试
  • 使用 torch.utils.benchmark.Timer 测试
  • 使用Blocked Autorange 进行基准测试
  • 比较基准测试结果
  • 保存/加载基准测试结果
  • 生成带有模糊参数的输入
  • 收集带有Callgrind的指令计数
• 激活函数
  • Sigmoid 量化
    • 区间分划
    • uint8 量化
    • Sigmoid 观测器
  • silu 函数
  • tanh 函数
• 计算机视觉
  • 转换和增强图像
    • 支持的输入类型和约定
    • 数据类型和预期值范围
    • 性能考虑
    • Torchscript 支持
  • 模型和预训练权重
    • 关于预训练权重的一般信息
    • 初始化预训练模型
    • 使用预训练模型
    • 列出和检索可用的模型
    • 使用来自 Hub 的模型
  • 快速上手 torchvision.transforms.v2
    • v2 基础
    • 简单的分类管道
    • 检测、分割、视频
    • TVTensors 是什么？
    • 需要传递什么作为输入？
    • 转换和数据集的兼容性
      • 内置的数据集
  • Illustration of transforms
    • Geometric Transforms
      • Pad
      • Resize
      • CenterCrop
      • FiveCrop
      • RandomPerspective
      • RandomRotation
      • RandomAffine
      • ElasticTransform
      • RandomCrop
      • RandomResizedCrop
    • Photometric Transforms
      • Grayscale
      • ColorJitter
      • GaussianBlur
      • RandomInvert
      • RandomPosterize
      • RandomSolarize
      • RandomAdjustSharpness
      • RandomAutocontrast
      • RandomEqualize
    • Augmentation Transforms
      • AutoAugment
      • RandAugment
      • TrivialAugmentWide
      • AugMix
    • Randomly-applied Transforms
      • RandomHorizontalFlip
      • RandomVerticalFlip
      • RandomApply
  • Transforms v2: End-to-end object detection/segmentation example
    • Dataset preparation
    • Transforms
    • Data loading and training loop
    • Training References
  • How to use CutMix and MixUp
    • Pre-processing pipeline
    • Where to use MixUp and CutMix
      • After the DataLoader
      • As part of the collation function
    • Non-standard input format
  • How to write your own v2 transforms
    • Just create a nn.Module and override the forward method
    • Supporting arbitrary input structures
  • TVTensors FAQ
    • What are TVTensors?
    • What can I do with a TVTensor?
    • How do I construct a TVTensor?
      • Using the constructor
      • Using tv_tensors.wrap()
    • I had a TVTensor but now I have a Tensor. Help!
      • But I want a TVTensor back!
      • Why is this happening?
      • Exceptions
  • How to write your own TVTensor class
    • Parameter forwarding, and ensuring future compatibility of your kernels
  • Optical Flow: Predicting movement with the RAFT model
    • Reading Videos Using Torchvision
    • Estimating Optical flow using RAFT
    • Visualizing predicted flows
    • Bonus: Creating GIFs of predicted flows
  • Repurposing masks into bounding boxes
    • Masks
    • Converting Masks to Bounding Boxes
    • Converting Segmentation Dataset to Detection Dataset
  • Torchscript support
  • Video API
    • 1. Introduction: building a new video object and examining the properties
    • 2. Building a sample read_video function
    • 3. Building an example randomly sampled dataset (can be applied to training dataset of kinetics400)
    • 4. Data Visualization
  • Visualization utilities
    • Visualizing a grid of images
    • Visualizing bounding boxes
    • Visualizing segmentation masks
      • Semantic segmentation models
      • Instance segmentation models
    • Visualizing keypoints
  • feature_extraction