PYTORCH CHEAT SHEET[1]
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 import Tensor # tensor node in the 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, nn, functional and optim
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
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
Vision
from torchvision import datasets, models, transforms # vision datasets,
# architectures &
# transforms
import torchvision.transforms as transforms # composable transforms
See torchvision
Distributed Training
import torch.distributed as dist # distributed communication
from torch.multiprocessing import Process # memory sharing processes
See distributed and multiprocessing
Tensors
Creation
x = torch.randn(*size) # tensor with independent N(0,1) entries
x = torch.[ones|zeros](*size) # tensor with all 1's [or 0's]
x = torch.tensor(L) # create tensor from [nested] list or ndarray L
y = 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
x = torch.tensor([1.0])
x.item() #1.0 Returns the value of this tensor as a standard Python number.
See tensor
Dimensionality
x.size() # return tuple-like object of dimensions
x = torch.cat(tensor_seq, dim=0) # concatenates tensors along dim
y = x.view(a,b,...) # reshapes x into size (a,b,...)
y = x.view(-1,a) # reshapes x into size (b,a) for some b
y = x.transpose(a,b) # swaps dimensions a and b
y = x.permute(*dims) # permutes dimensions
y = x.unsqueeze(dim) # tensor with added axis
y = x.unsqueeze(dim=2) # (a,b,c) tensor -> (a,b,1,c) tensor
y = x.squeeze() # removes all dimensions of size 1 (a,1,b,1) -> (a,b)
y = x.squeeze(dim=1) # removes specified dimension of size 1 (a,1,b,1) -> (a,b,1)
See tensor
Algebra
ret = A.mm(B) # matrix multiplication
ret = A.mv(x) # matrix-vector multiplication
x = x.t() # matrix transpose
See math operations
GPU Usage
torch.cuda.is_available # check for cuda
x = x.cuda() # move x's data from
# CPU to GPU and return new object
x = 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
args.device = torch.device('cuda') # and modularity
else: #
args.device = torch.device('cpu') #
net.to(device) # recursively convert their
# parameters and buffers to
# device specific tensors
x = x.to(device) # copy your tensors to a device
# (gpu, cpu)
See cuda
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.BatchNormXd # 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
Loss Functions
nn.X # where X is L1Loss, MSELoss, CrossEntropyLoss
# CTCLoss, NLLLoss, PoissonNLLLoss,
# KLDivLoss, BCELoss, BCEWithLogitsLoss,
# MarginRankingLoss, HingeEmbeddingLoss,
# MultiLabelMarginLoss, SmoothL1Loss,
# SoftMarginLoss, MultiLabelSoftMarginLoss,
# CosineEmbeddingLoss, MultiMarginLoss,
# or TripletMarginLoss
See loss functions
Activation Functions
nn.X # where X is ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU,
# RReLu, CELU, GELU, Threshold, Hardshrink, HardTanh,
# Sigmoid, LogSigmoid, Softplus, SoftShrink,
# Softsign, Tanh, TanhShrink, Softmin, Softmax,
# Softmax2d, LogSoftmax or AdaptiveSoftmaxWithLoss
Optimizers
opt = optim.x(model.parameters(), ...) # create optimizer
opt.step() # update weights
optim.X # where X is SGD, Adadelta, Adagrad, Adam,
# AdamW, SparseAdam, Adamax, ASGD,
# LBFGS, RMSprop or Rprop
See optimizers
Learning rate scheduling
scheduler = optim.X(optimizer,...) # create lr scheduler
scheduler.step() # update lr after optimizer updates weights
optim.lr_scheduler.X # where X is LambdaLR, MultiplicativeLR,
# StepLR, MultiStepLR, ExponentialLR,
# CosineAnnealingLR, ReduceLROnPlateau, CyclicLR,
# OneCycleLR, CosineAnnealingWarmRestarts,
Data Utilities
Datasets
Dataset # abstract class representing dataset
TensorDataset # labelled dataset in the form of tensors
Concat Dataset # concatenation of Datasets
See datasets
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 ... # Sequential, Random, SubsetRandom,
# WeightedRandom, Batch, Distributed
See dataloader
Also see
- Deep Learning with PyTorch: A 60 Minute Blitz (pytorch.org)
- PyTorch Forums (discuss.pytorch.org)
- PyTorch for Numpy users (github.com/wkentaro/pytorch-for-numpy-users)
Reference:
[1] https://pytorch.org/tutorials/beginner/ptcheat.html
