henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

psayre23

Below are the Big O performance of common functions of different Java Collections.


List                 | Add  | Remove | Get  | Contains | Next | Data Structure
---------------------|------|--------|------|----------|------|---------------
ArrayList            | O(1) |  O(n)  | O(1) |   O(n)   | O(1) | Array
LinkedList           | O(1) |  O(1)  | O(n) |   O(n)   | O(1) | Linked List
CopyOnWriteArrayList | O(n) |  O(n)  | O(1) |   O(n)   | O(1) | Array

peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

francois-rozet

#!/usr/bin/env python

import math
import matplotlib.pyplot as plt
import torch
import torch.nn as nn

from sklearn.datasets import make_moons
from torch import Tensor
from tqdm import tqdm