As configured in my dotfiles.
start new:
tmux
start new with session name:
As configured in my dotfiles.
start new:
tmux
start new with session name:
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 | |
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
#!/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 |