PyTorch

PyTorch is a popular machine learning library developed by Facebook

Installation

See PyTorch Getting Started

# If using conda, python 3.5+, and CUDA 10.0 (+ compatible cudnn)
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

Getting Started

• PyTorch Tutorials

import torch
import torch.nn as nn

# Training
for epoch in range(epochs):
  running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

Importing Data

See Data Loading Tutorial

Usage

torch.nn.functional

PyTorch Documentation

F.grid_sample

Doc
This function allows you to perform interpolation on your input tensor.
It is very useful for resizing images or warping images.

Building a Model

To build a model, do the following:

• Create a class extending nn.Module.
• In your class include all other modules you need during init.
  • If you have a list of modules, make sure to wrap them in nn.ModuleList or nn.Sequential so they are properly recognized.
• Write a forward pass for your model.

Memory Usage

Reducing memory usage

• Save loss using .item() which returns a standard Python number
• For non-scalar items, use my_var.detach().cpu().numpy()

• detach() deletes the item from the autograd edge
• cpu() copies the tensor to the CPU
• numpy() returns a numpy view of the tensor

TensorBoard

Main article: TensorBoard

See PyTorch Docs: Tensorboard

from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(log_dir="./runs")

# Calculate loss. Increment the step.

writer.add_scalar("train_loss", loss.item(), step)

# Optionally flush e.g. at checkpoints
writer.flush()

# Close the writer (will flush)
writer.close()

PyTorch3D

Main article: PyTorch3D

PyTorch3D is a library by Facebook AI Research which contains differentiable renderers for meshes and point clouds.
It is build using custom CUDA kernels.