Meta (formerly known as Facebook) operates both PyTorch and Convolutional Architecture for Fast Feature Embedding (Caffe2), but models defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange (ONNX) project was created by Meta and Microsoft in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018.[23] In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation.[24]
PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable NVIDIA GPU. PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm[26] and Apple's Metal Framework.[27]
PyTorch supports various sub-types of Tensors.[28]
Note that the term "tensor" here does not carry the same meaning as tensor in mathematics or physics. The meaning of the word in machine learning is only superficially related to its original meaning as a certain kind of object in linear algebra. Tensors in PyTorch are simply multi-dimensional arrays.
PyTorch defines a module called nn (torch.nn) to describe neural networks and to support training. This module offers a comprehensive collection of building blocks for neural networks, including various layers and activation functions, enabling the construction of complex models. Networks are built by inheriting from the torch.nn module and defining the sequence of operations in the forward() function.
Example
The following program shows the low-level functionality of the library with a simple example.
importtorchdtype=torch.floatdevice=torch.device("cpu")# Execute all calculations on the CPU# device = torch.device("cuda:0") # Executes all calculations on the GPU# Create a tensor and fill it with random numbersa=torch.randn(2,3,device=device,dtype=dtype)print(a)# Output: tensor([[-1.1884, 0.8498, -1.7129],# [-0.8816, 0.1944, 0.5847]])b=torch.randn(2,3,device=device,dtype=dtype)print(b)# Output: tensor([[ 0.7178, -0.8453, -1.3403],# [ 1.3262, 1.1512, -1.7070]])print(a*b)# Output: tensor([[-0.8530, -0.7183, 2.58],# [-1.1692, 0.2238, -0.9981]])print(a.sum())# Output: tensor(-2.1540)print(a[1,2])# Output of the element in the third column of the second row (zero based)# Output: tensor(0.5847)print(a.max())# Output: tensor(0.8498)
The following code-block defines a neural network with linear layers using the nn module.
importtorchfromtorchimportnn# Import the nn sub-module from PyTorchclassNeuralNetwork(nn.Module):# Neural networks are defined as classesdef__init__(self):# Layers and variables are defined in the __init__ methodsuper().__init__()# Must be in every network.self.flatten=nn.Flatten()# Construct a flattening layer.self.linear_relu_stack=nn.Sequential(# Construct a stack of layers.nn.Linear(28*28,512),# Linear Layers have an input and output shapenn.ReLU(),# ReLU is one of many activation functions provided by nnnn.Linear(512,512),nn.ReLU(),nn.Linear(512,10),)defforward(self,x):# This function defines the forward pass.x=self.flatten(x)logits=self.linear_relu_stack(x)returnlogits
^Mannes, John. "Facebook and Microsoft collaborate to simplify conversions from PyTorch to Caffe2". TechCrunch. Retrieved 2017-12-18. FAIR is accustomed to working with PyTorch – a deep learning framework optimized for achieving state of the art results in research, regardless of resource constraints. Unfortunately in the real world, most of us are limited by the computational capabilities of our smartphones and computers.
