skeeet

skeeet

import mmap
import torch
import json
import os
from huggingface_hub import hf_hub_download


def load_file(filename, device):
    with open(filename, mode="r", encoding="utf8") as file_obj:
        with mmap.mmap(file_obj.fileno(), length=0, access=mmap.ACCESS_READ) as m:

skeeet

import torch

def gauss_kernel(size=5, device=torch.device('cpu'), channels=3):
    kernel = torch.tensor([[1., 4., 6., 4., 1],
                           [4., 16., 24., 16., 4.],
                           [6., 24., 36., 24., 6.],
                           [4., 16., 24., 16., 4.],
                           [1., 4., 6., 4., 1.]])
    kernel /= 256.
    kernel = kernel.repeat(channels, 1, 1, 1)

skeeet

import CoreML
import XCTest
@testable import mlsample

class mlsampleTests: XCTestCase {
    
    override func setUp() {
        super.setUp()
    }
    

skeeet

Ubuntu Grub Boot Kernel

Boot a specific installed Ubuntu kernel using grub-reboot and grub-set-default.

This allows you to pick what kernel you want to boot on next reboot, or set the default, without having to know much about how grub works or editing config files.

Usage

  Usage: boot-kernel [options] [kernel]

call grub-reboot or grub-set-default to boot the provided kernel.

skeeet

# Make mouse useful in copy mode
setw -g mode-mouse on

# Allow mouse to select which pane to use
set -g mouse-select-pane on

# Allow mouse dragging to resize panes
set -g mouse-resize-pane on

# Allow mouse to select windows

skeeet

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(cpp_shim)
set(CMAKE_PREFIX_PATH ../libtorch)
find_package(Torch REQUIRED)
find_package(OpenCV REQUIRED)

add_executable(testing main.cpp)

message(STATUS "OpenCV library status:")
message(STATUS "    config: ${OpenCV_DIR}")

skeeet

Danbooru Faces v0.1

Discription

This dataset contains ~443k anime face images of size 256x256 drawn by ~7,000 artists, obtained from Danbooru

Collection

We first downloaded JSON files of all existing posts numbered from 1 to 2,800,000 using their API. We filtered the posts by the following criteria:

skeeet

from torch.utils.data import DataLoader


class InfiniteDataLoader(DataLoader):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # Initialize an iterator over the dataset.
        self.dataset_iterator = super().__iter__()

    def __iter__(self):

skeeet

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