| --- |
| Title: PyTorch Cheat Sheet |
| PyTorch version: 1.0Pre |
| Date updated: 7/30/18 |
|
|
| --- |
| |
| # Imports |
| --------------- |
| ### General |
|
|
| ``` |
| import torch # root package |
| from torch.utils.data import Dataset, DataLoader # dataset representation and loading |
| ``` |
|
|
| ### Neural Network API |
|
|
| ``` |
| import torch.autograd as autograd # computation graph |
| from torch.autograd import Variable # variable node in computation graph |
| import torch.nn as nn # neural networks |
| import torch.nn.functional as F # layers, activations and more |
| import torch.optim as optim # optimizers e.g. gradient descent, ADAM, etc. |
| from torch.jit import script, trace # hybrid frontend decorator and tracing jit |
| ``` |
| See [autograd](https://pytorch.org/docs/stable/autograd.html), [nn](https://pytorch.org/docs/stable/nn.html), [functional](https://pytorch.org/docs/stable/nn.html#torch-nn-functional) and [optim](https://pytorch.org/docs/stable/optim.html) |
|
|
| ### Torchscript and JIT |
|
|
| ``` |
| torch.jit.trace() # takes your module or function and an example data input, and traces the computational steps that the data encounters as it progresses through the model |
| @script # decorator used to indicate data-dependent control flow within the code being traced |
| ``` |
| See [Torchscript](https://pytorch.org/docs/stable/jit.html) |
|
|
| ### ONNX |
|
|
| ``` |
| torch.onnx.export(model, dummy data, xxxx.proto) # exports an ONNX formatted model using a trained model, dummy data and the desired file name |
| model = onnx.load("alexnet.proto") # load an ONNX model |
| onnx.checker.check_model(model) # check that the model IR is well formed |
| onnx.helper.printable_graph(model.graph) # print a human readable representation of the graph |
| ``` |
| See [onnx](https://pytorch.org/docs/stable/onnx.html) |
|
|
| ### Vision |
|
|
| ``` |
| from torchvision import datasets, models, transforms # vision datasets, architectures & transforms |
| import torchvision.transforms as transforms # composable transforms |
| ``` |
| See [torchvision](https://pytorch.org/docs/stable/torchvision/index.html) |
|
|
| ### Distributed Training |
|
|
| ``` |
| import torch.distributed as dist # distributed communication |
| from multiprocessing import Process # memory sharing processes |
| ``` |
| See [distributed](https://pytorch.org/docs/stable/distributed.html) and [multiprocessing](https://pytorch.org/docs/stable/multiprocessing.html) |
|
|
|
|
| # Tensors |
| -------------------- |
|
|
| ### Creation |
|
|
| ``` |
| torch.randn(*size) # tensor with independent N(0,1) entries |
| torch.[ones|zeros](*size) # tensor with all 1's [or 0's] |
| torch.Tensor(L) # create tensor from [nested] list or ndarray L |
| x.clone() # clone of x |
| with torch.no_grad(): # code wrap that stops autograd from tracking tensor history |
| requires_grad=True # arg, when set to True, tracks computation history for future derivative calculations |
| ``` |
| See [tensor](https://pytorch.org/docs/stable/tensors.html) |
|
|
| ### Dimensionality |
|
|
| ``` |
| x.size() # return tuple-like object of dimensions |
| torch.cat(tensor_seq, dim=0) # concatenates tensors along dim |
| x.view(a,b,...) # reshapes x into size (a,b,...) |
| x.view(-1,a) # reshapes x into size (b,a) for some b |
| x.transpose(a,b) # swaps dimensions a and b |
| x.permute(*dims) # permutes dimensions |
| x.unsqueeze(dim) # tensor with added axis |
| x.unsqueeze(dim=2) # (a,b,c) tensor -> (a,b,1,c) tensor |
| ``` |
| See [tensor](https://pytorch.org/docs/stable/tensors.html) |
|
|
| ### Algebra |
|
|
| ``` |
| A.mm(B) # matrix multiplication |
| A.mv(x) # matrix-vector multiplication |
| x.t() # matrix transpose |
| ``` |
| See [math operations](https://pytorch.org/docs/stable/torch.html?highlight=mm#math-operations) |
|
|
| ### GPU Usage |
|
|
| ``` |
| torch.cuda.is_available # check for cuda |
| x.cuda() # move x's data from CPU to GPU and return new object |
| x.cpu() # move x's data from GPU to CPU and return new object |
| |
| if not args.disable_cuda and torch.cuda.is_available(): # device agnostic code and modularity |
| args.device = torch.device('cuda') # |
| else: # |
| args.device = torch.device('cpu') # |
| |
| net.to(device) # recursively convert their parameters and buffers to device specific tensors |
| mytensor.to(device) # copy your tensors to a device (gpu, cpu) |
| ``` |
| See [cuda](https://pytorch.org/docs/stable/cuda.html) |
|
|
|
|
| # Deep Learning |
| ``` |
| nn.Linear(m,n) # fully connected layer from m to n units |
| nn.ConvXd(m,n,s) # X dimensional conv layer from m to n channels where X⍷{1,2,3} and the kernel size is s |
| nn.MaxPoolXd(s) # X dimension pooling layer (notation as above) |
| nn.BatchNorm # batch norm layer |
| nn.RNN/LSTM/GRU # recurrent layers |
| nn.Dropout(p=0.5, inplace=False) # dropout layer for any dimensional input |
| nn.Dropout2d(p=0.5, inplace=False) # 2-dimensional channel-wise dropout |
| nn.Embedding(num_embeddings, embedding_dim) # (tensor-wise) mapping from indices to embedding vectors |
| ``` |
| See [nn](https://pytorch.org/docs/stable/nn.html) |
|
|
| ### Loss Functions |
|
|
| ``` |
| nn.X # where X is BCELoss, CrossEntropyLoss, L1Loss, MSELoss, NLLLoss, SoftMarginLoss, MultiLabelSoftMarginLoss, CosineEmbeddingLoss, KLDivLoss, MarginRankingLoss, HingeEmbeddingLoss or CosineEmbeddingLoss |
| ``` |
| See [loss functions](https://pytorch.org/docs/stable/nn.html#loss-functions) |
|
|
| ### Activation Functions |
|
|
| ``` |
| nn.X # where X is ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU, Threshold, HardTanh, Sigmoid, Tanh, LogSigmoid, Softplus, SoftShrink, Softsign, TanhShrink, Softmin, Softmax, Softmax2d or LogSoftmax |
| ``` |
| See [activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) |
|
|
| ### Optimizers |
|
|
| ``` |
| opt = optim.x(model.parameters(), ...) # create optimizer |
| opt.step() # update weights |
| optim.X # where X is SGD, Adadelta, Adagrad, Adam, SparseAdam, Adamax, ASGD, LBFGS, RMSProp or Rprop |
| ``` |
| See [optimizers](https://pytorch.org/docs/stable/optim.html) |
|
|
| ### Learning rate scheduling |
|
|
| ``` |
| scheduler = optim.X(optimizer,...) # create lr scheduler |
| scheduler.step() # update lr at start of epoch |
| optim.lr_scheduler.X # where X is LambdaLR, StepLR, MultiStepLR, ExponentialLR or ReduceLROnPLateau |
| ``` |
| See [learning rate scheduler](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) |
|
|
|
|
| # Data Utilities |
|
|
| ### Datasets |
|
|
| ``` |
| Dataset # abstract class representing dataset |
| TensorDataset # labelled dataset in the form of tensors |
| ConcatDataset # concatenation of Datasets |
| ``` |
| See [datasets](https://pytorch.org/docs/stable/data.html?highlight=dataset#torch.utils.data.Dataset) |
|
|
| ### Dataloaders and DataSamplers |
|
|
| ``` |
| DataLoader(dataset, batch_size=1, ...) # loads data batches agnostic of structure of individual data points |
| sampler.Sampler(dataset,...) # abstract class dealing with ways to sample from dataset |
| sampler.XSampler # where X is Sequential, Random, Subset, WeightedRandom or Distributed |
| ``` |
| See [dataloader](https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader) |
|
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|
|
| ## Also see |
|
|
| * [Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) _(pytorch.org)_ |
| * [PyTorch Forums](https://discuss.pytorch.org/) _(discuss.pytorch.org)_ |
| * [PyTorch for Numpy users](https://github.com/wkentaro/pytorch-for-numpy-users) _(github.com/wkentaro/pytorch-for-numpy-users)_ |
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