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/*
  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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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

//: Simple Genetic Algorithm Starter in Swift 3

import UIKit
import Foundation

let AVAILABLE_GENES:[Int] = Array(1...100)
let DNA_LENGTH = 6

let TOURNAMENT_SIZE = 5
let MAX_GENERATIONS_COUNT = 100

skeeet

#include "std_try.h"
#include <math.h>

using namespace Halide;

#define PI 3.14159
/*
  Interpolations
*/

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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,
                                   mag_filter::linear,
                                   min_filter::linear);
    
    const float4x4 ycbcrToRGBTransform = float4x4(
        float4(+1.0000f, +1.0000f, +1.0000f, +0.0000f),

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

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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):

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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:

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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}")

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# 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