PyTorch

PyTorch is a popular machine learning library developed by Facebook

Installation

# 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.

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

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

PyTorch3D is a library by Facebook AI Research which contains structures and differentiable mesh, point cloud renderers.
It is build with custom CUDA kernels.