This article was compiled by a PhD student from Harbin Institute of Technology over the course of six months, consisting of a total of fourteen chapters. It starts with the basics of creating tensors and gradually deepens, covering almost all commonly used functions!
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Common Functions in PyTorch
1. Image and Vision Processing
torchvision.transforms.Resize(): Resize an image.
torchvision.transforms.Normalize(): Normalize image data.
torchvision.datasets: Provides common image dataset loading interfaces.
2. Neural Network Construction
torch.nn.Linear(): Fully connected layer.
torch.nn.Conv2d(): 2D convolutional layer.
torch.nn.ReLU(): ReLU activation function.
torch.nn.sigmoid(): Sigmoid activation function.
3. Training and Optimization
torch.optim.Adam(), torch.optim.SGD(): Common optimizers.
torch.autograd.backward(): Automatically compute gradients.
4. Model Saving and Loading
torch.save(), torch.load(): Used to save and load models or tensors.
5. Functional Interface
torch.nn.functional: Provides a series of functional APIs, such as
torch.nn.functional.relu(): Used for ReLU activation.
6. Data Processing and Transformation
torch.tensor(): Create a tensor.
torch.zeros(), torch.ones(): Create tensors filled with zeros or ones.
torch.randn(): Create a tensor with a normal distribution of random numbers.
torch.from_numpy(): Convert a NumPy array to a tensor.
torch.mean(), torch.std(): Compute the mean and standard deviation of a tensor.
torch.var(): Compute the variance of a tensor.
7. Advanced Data Processing and Transformation
torch.index_select(): Select data by specified dimensions and indices.
torch.masked_select(): Select elements based on a boolean mask.
torch.add(), torch.mul(), torch.div(): Addition, multiplication, and division operations on tensors.
torch.matmul(): Matrix multiplication of tensors.
8. Model Analysis and Debugging
torch.autograd.gradcheck(): Used to check the correctness of gradients. torch.autograd.profiler.profile(): Performance analysis tool for analyzing the time and memory consumption of models.
torchviz: An unofficial visualization tool for drawing the computational graph of models.
9. Advanced Network Construction
torch.nn.LSTM(): Long Short-Term Memory network layer.
torch.nn.GRU(): Gated Recurrent Unit layer.
torch.autograd.Function: Used to create custom differentiable operations.
torch.nn.Module: Base class for custom network layers or entire models.
10. Optimization and Hyperparameter Tuning
torch.optim.lr_scheduler: Learning rate scheduler for adjusting the learning rate. torch.optim.RMSprop: RMSprop optimizer, commonly used for training RNNs.
11. Special Purpose Functions and Utilities
torch.distributed: Module that supports distributed training.
torch.no_grad(): Disable gradient calculation to save computational resources during evaluation and inference.
12. Advanced Mathematical and Statistical Functions
torch.mean(), torch.std(): Compute the mean and standard deviation of a tensor.
torch.var(): Compute the variance of a tensor.
torch.exp(), torch.log(): Exponential and logarithmic operations.
torch.sin(), torch.cos(): Trigonometric functions.
13. Tensor Transformations and Operations
torch.split(): Split tensors by size or number of tensors.
torch.chunk(): Split a tensor into a specified number of chunks.
torch.sort(): Sort a tensor.
torch.topk(): Return the largest k elements in a tensor.
14. Data Loading and Processing
torch.utils.data.Dataset: Base class for custom datasets.
torch.utils.data.DataLoader: Tool for batch loading datasets.
15. Natural Language Processing
torchtext.data: Provides text preprocessing, loading, and other functionalities.
torch.nn.Embedding(): Used to create word embeddings.
16. Advanced Model Architectures
torch.nn.Transformer: Implementation of the Transformer model.
torch.nn.MultiheadAttention: Implementation of multi-head attention mechanism.
torch.utils.cpp_extension: Allows the use of C++ or CUDA to extend PyTorch.
17. Optimization and Debugging
torch.cuda.amp: Provides functionality for automatic mixed precision training to improve performance and efficiency.
torchsummary: Provides a detailed summary of model architecture and parameters (unofficial tool).
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