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Alex Shevchenko skeeet

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skeeet / .tmux.conf
Created Nov 3, 2019 — forked from paulodeleo/.tmux.conf
Tmux configuration to enable mouse scroll and mouse panel select, taken from:
View .tmux.conf
# 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 / CMakeLists.txt
Created Apr 1, 2019 — forked from zeryx/CMakeLists.txt
minimal pytorch 1.0 pytorch -> C++ full example demo image at:
View CMakeLists.txt
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
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 /
Created Feb 20, 2019 — forked from stormraiser/
Danbooru Faces dataset

Danbooru Faces v0.1


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


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 /
Created Feb 20, 2019 — forked from MFreidank/
A pytorch DataLoader that generates an unbounded/infinite number of minibatches from the dataset.
from 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 /
Created Oct 6, 2018 — forked from peteflorence/
Bilinear interpolation in PyTorch, and benchmarking vs. numpy

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

View ycbcr-to-rgb.metal
fragment float4 capturedImageFragmentShader(ImageColorInOut in [[stage_in]],
texture2d<float, access::sample> capturedImageTextureY [[ texture(kTextureIndexY) ]],
texture2d<float, access::sample> capturedImageTextureCbCr [[ texture(kTextureIndexCbCr) ]]) {
constexpr sampler colorSampler(mip_filter::linear,
const float4x4 ycbcrToRGBTransform = float4x4(
float4(+1.0000f, +1.0000f, +1.0000f, +0.0000f),
skeeet / std.cpp
Created Apr 19, 2018 — forked from mahuna13/std.cpp
standard library functions for Halide
View std.cpp
#include "std_try.h"
#include <math.h>
using namespace Halide;
#define PI 3.14159
skeeet / GeneticAlgorithm.swift
Created Apr 9, 2018 — forked from tombaranowicz/GeneticAlgorithm.swift
Simple Starter for experiments with Genetic Algorithms in Swift
View GeneticAlgorithm.swift
//: Simple Genetic Algorithm Starter in Swift 3
import UIKit
import Foundation
let AVAILABLE_GENES:[Int] = Array(1...100)
let DNA_LENGTH = 6
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers=1):
super(RNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
skeeet / neural.c
Created Jan 9, 2018 — forked from hollance/neural.c
Playing with BNNS on macOS 10.12. The "hello world" of neural networks.
View neural.c
The "hello world" of neural networks: a simple 3-layer feed-forward
network that implements an XOR logic gate.
The first layer is the input layer. It has two neurons a and b, which
are the two inputs to the XOR gate.
The middle layer is the hidden layer. This has two neurons h1, h2 that
will learn what it means to be an XOR gate.
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