Difference between revisions of "PyTorch"

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==PyTorch3D==
 
==PyTorch3D==
 
{{main | PyTorch3D}}
 
{{main | PyTorch3D}}
PyTorch3D is a library by Facebook AI Research which contains structures and differentiable mesh, point cloud renderers.   
+
[https://github.com/facebookresearch/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.
 
It is build with custom CUDA kernels.

Revision as of 10:04, 29 July 2020

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

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.