NeRF Attempt 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e506bc24",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Global Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f6ddf43",
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Device to be used\n",
    "def_device = 'cuda:1'\n",
    "\n",
    "#Verbosity of functions in this notebook.\n",
    "#1 - verbose\n",
    "#0 - for the most part quiet\n",
    "spammyness = 1\n",
    "\n",
    "#Whether to use weird extensions that allow collapsing?\n",
    "weird_extensions = True "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c169a97e",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enabling notebook extension codefolding/main...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n",
      "Enabling notebook extension collapsableheadings/main...\n",
      "      - Validating: problems found:\n",
      "        - require? \u001b[31m X\u001b[0m collapsableheadings/main\n"
     ]
    }
   ],
   "source": [
    "if def_device[:4] == 'cuda':\n",
    "    using_gpu = True\n",
    "else:\n",
    "    using_gpu = False\n",
    "\n",
    "if weird_extensions:\n",
    "    !jupyter nbextension enable codefolding/main\n",
    "    !jupyter nbextension enable collapsableheadings/main\n",
    "else:\n",
    "    !jupyter nbextension disable codefolding/main\n",
    "    !jupyter nbextension disable collapsableheadings/main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80b83232",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as f\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import random\n",
    "import time\n",
    "import json\n",
    "import PIL\n",
    "import gc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4637d4b3",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Informative Stuff"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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59Pbr5emTVZ27ZlUEOhDoNk7rvd28PV168ka584+flV//0FtTB2feIzYi0HWctt+eLl++XD777DPfNTVycC1z+QJdx2n77Wn977e/f1r+1ntCAnULdB+n7bens39yV/fWWR2BZQt0H6f19m5+rWDzxfiyt9zTEWhDQJzEqY2TapXdCYiTOHV36D1wGwLiJE5tnFSr7E5AnLrbcg9MoA0BcWpjn6ySQHcC4tTdlntgAm0IiFMb+2SVBLoTEKfuttwDE2hDQJza2CerJNCdgDh1t+UemEAbAuLUxj5ZJYHuBMSpuy33wATaEBCnNvbJKgl0JyBO3W25BybQhoA4tbFPVkmgOwFx6m7LPTCBNgT+A6//eqAK7Jt+AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "0c2c929a",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Coordinate Systems\n",
    "\n",
    "By default all functions operate in this coordinate system:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "This corresponds to what COLMAP uses.\n",
    "\n",
    "By default all functions assume the viewer is looking in the direction of Z; The direction up is -Y and the direction to the right is X. (When you're working with absolute coordinates or rotations, you're operating in this coordinate system.)\n",
    "\n",
    "##### Warning\n",
    "\n",
    "Geometry works refactoring it will be painful. Don't touch geometry for the time being."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb254bb",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Misc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c934279b",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Geometry Stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bda52c8",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a05e1cb",
   "metadata": {
    "code_folding": [
     0,
     22,
     33,
     39,
     53,
     71
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_raw_view_vecs(sizex, sizey, scalingcoef = None, scalingcoefx = 1.0, scalingcoefy = 1.0):\n",
    "    \"\"\"\n",
    "    Returns a numpy array of size [sizex, sizey, 3].\n",
    "    Represents view vectors in direction of z axis.\n",
    "    \n",
    "    Contains vectors of the form [x, y, 1].\n",
    "    x and y increase with index.\n",
    "    \"\"\"\n",
    "    \n",
    "    if scalingcoef is not None:\n",
    "        scalingcoefx = scalingcoef\n",
    "        scalingcoefy = scalingcoef\n",
    "    \n",
    "    res = np.zeros([sizex, sizey, 3], dtype = np.float32)\n",
    "    compx = np.linspace(-1, 1, sizex) * scalingcoefx\n",
    "    compy = np.linspace(1, -1, sizey) * sizey / sizex * scalingcoefy\n",
    "    res[:, :, 0] = compx.reshape([sizex, -1])\n",
    "    res[:, :, 1] = compy.reshape([-1, sizey])\n",
    "    res[:, :, 2] = 1\n",
    "    \n",
    "    return res\n",
    "\n",
    "def norm_view_vecs(view_vecs):\n",
    "    \"\"\"\n",
    "    Normalizes vectors to unit vectors.\n",
    "    \n",
    "    Input and output are numpy arrays.\n",
    "    \"\"\"\n",
    "    \n",
    "    res = view_vecs / np.linalg.norm(view_vecs, axis = -1, keepdims = True)\n",
    "    \n",
    "    return res\n",
    "\n",
    "def get_view_vecs(sizex, sizey, scalingcoef = None, scalingcoefx = 1.0, scalingcoefy = 1.0):\n",
    "    vecs = get_raw_view_vecs(sizex, sizey, scalingcoef, scalingcoefx, scalingcoefy)\n",
    "    vecs = norm_view_vecs(vecs)\n",
    "    \n",
    "    return vecs\n",
    "\n",
    "def negate_y(data):\n",
    "    \"\"\"\n",
    "    Assumes data is numpy array of 3D vectors of shape [d1, d2..., 3].\n",
    "    Negates y coordinate sign.\n",
    "    \"\"\"\n",
    "    \n",
    "    res = data.copy()\n",
    "    temp_shape = res.shape\n",
    "    res = res.reshape([-1, 3])\n",
    "    res[:, 1]*= -1\n",
    "    res = res.reshape(temp_shape)\n",
    "    \n",
    "    return res\n",
    "\n",
    "def mpl_format(data):\n",
    "    \"\"\"\n",
    "    Reformats image from default format, to format that can be rendered by matplotlib.\n",
    "    data - array [image_count, sizex, sizey, 3] or [sizex, sizey, 3] in default format.\n",
    "    \n",
    "    Result is in format - \n",
    "      [sizey, sizex, 3]. \n",
    "      Increase in y coordinates means \"go down in image\".\n",
    "      \n",
    "    #CoordVerified\n",
    "    \"\"\"\n",
    "    if len(data.shape) == 3:\n",
    "        return np.flip(data.swapaxes(0, 1), 0)\n",
    "    elif len(data.shape) == 4:\n",
    "        return np.flip(data.swapaxes(1, 2), 1)\n",
    "    else:\n",
    "        raise ValueError('Wrong number of dimensions passed to mpl_format ' + str(data.shape))\n",
    "\n",
    "def mpl_format_inverse(data):\n",
    "    \"\"\"\n",
    "    Reverses mpl_format.\n",
    "    \"\"\"\n",
    "    if len(data.shape) == 3:\n",
    "        return np.flip(data, 0).swapaxes(0, 1)\n",
    "    elif len(data.shape) == 4:\n",
    "        return np.flip(data, 1).swapaxes(1, 2)\n",
    "    else:\n",
    "        raise ValueError('Wrong number of dimensions passed to mpl_format ' + str(data.shape))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1faf50a",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Rotation Hangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "330344c1",
   "metadata": {
    "code_folding": [
     19,
     35,
     49,
     63,
     77,
     91
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def rotate_vecs(rot, vecs):\n",
    "    \"\"\"\n",
    "    Rotates an array of vectors.\n",
    "    Analogous to computing np.matmul(rot, vec) for each vector.\n",
    "    \n",
    "    rot - np array, rotation matrix of size [3, 3].\n",
    "    vecs - np array, of vectors of size [d1, d2..., 3]\n",
    "    \"\"\"\n",
    "    tmp_shape = vecs.shape\n",
    "    vecs = vecs.reshape(-1, 3).swapaxes(0, 1)\n",
    "    vecs = np.matmul(rot, vecs)\n",
    "    vecs = vecs.swapaxes(0, 1).reshape(tmp_shape)\n",
    "    \n",
    "    return vecs\n",
    "\n",
    "class Viewer():\n",
    "    \"\"\"\n",
    "    Represents a viewer.\n",
    "    The viewer can rotate in certain directions.\n",
    "    Can transform vectors between absolute and viewer reference frames.\n",
    "    \n",
    "    In absolute coordinates:\n",
    "    Assumes x direction is to right.\n",
    "    Assumes y direction is down.\n",
    "    Assumes z direction is forward.\n",
    "    \n",
    "    In viewer reference frame:\n",
    "    Assumes x direction is to right.\n",
    "    Assumes y direction is down.\n",
    "    Assumes z direction is forward.\n",
    "    \n",
    "    They're the same, don't ask questions.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        Initializes to identity matrix.\n",
    "        Initially looks in direction of z axis.\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        s.reset()\n",
    "    \n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Makes viewer look in direction of z axis,\n",
    "        such that the viewer is up right.\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        s.data = np.zeros([3, 3], dtype = np.float32)\n",
    "        for i in range(3):\n",
    "            s.data[i, i] = 1\n",
    "    \n",
    "    def rot_xz(self, angle):\n",
    "        \"\"\"\n",
    "        Rotates viewer leftward.\n",
    "        \"\"\"\n",
    "        sin = math.sin(angle)\n",
    "        cos = math.cos(angle)\n",
    "        rot = np.array([\n",
    "            [cos, 0, sin],\n",
    "            [0, 1, 0],\n",
    "            [-sin, 0, cos]\n",
    "        ])\n",
    "        s = self\n",
    "        s.data = np.matmul(s.data, rot)\n",
    "    \n",
    "    def rot_zy(self, angle):\n",
    "        \"\"\"\n",
    "        Rotates viewer downward.\n",
    "        \"\"\"\n",
    "        sin = math.sin(angle)\n",
    "        cos = math.cos(angle)\n",
    "        rot = np.array([\n",
    "            [1, 0, 0],\n",
    "            [0, cos, -sin],\n",
    "            [0, sin, cos]\n",
    "        ])\n",
    "        s = self\n",
    "        s.data = np.matmul(rot, s.data)\n",
    "    \n",
    "    def to_absolute(self, vecs):\n",
    "        \"\"\"\n",
    "        Rotates numpy array of vectors from viewer reference frame to absolute reference frame.\n",
    "        Assumes vecs has shape [d1, d2..., 3]\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        shape = vecs.shape\n",
    "        vecs = vecs.reshape([-1, 3])\n",
    "        vecs = np.matmul(vecs, s.data)\n",
    "        vecs = vecs.reshape(shape)\n",
    "        \n",
    "        return vecs\n",
    "    \n",
    "    def to_viewer(self, vecs):\n",
    "        \"\"\"\n",
    "        Rotates numpy array of vectors from absolute reference frame to viewer reference frame.\n",
    "        Assums vecs has shape [d1, d2..., 3]\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        shape = vecs.shape\n",
    "        vecs = vecs.reshape([-1, 3])\n",
    "        vecs = np.matmul(vecs, np.linalg.inv(s.data))\n",
    "        vecs = vecs.reshape(shape)\n",
    "        \n",
    "        return vecs\n",
    "    \n",
    "    def look_at(self, view_vec):\n",
    "        \"\"\"\n",
    "        Rotates camera so it looks along a given view vector.\n",
    "        Keeps up side up.\n",
    "        \"\"\"\n",
    "        self.reset()\n",
    "        \n",
    "        view_vec = view_vec / np.linalg.norm(view_vec)\n",
    "        \n",
    "        x = view_vec[0]\n",
    "        y = -view_vec[1]\n",
    "        z = view_vec[2]\n",
    "        \n",
    "        #Rotate horizontally\n",
    "        if x == 0 and z == 0:\n",
    "            xz_angle = 0\n",
    "        else:\n",
    "            xz_angle = math.atan2(-x, z)\n",
    "        self.rot_xz(xz_angle)\n",
    "\n",
    "        #Rotate vertically\n",
    "        horcomp = math.sqrt(x ** 2 + z ** 2)\n",
    "        zy_angle = -math.atan2(y, horcomp)\n",
    "        self.rot_zy(zy_angle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12234ca8",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Image Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffb6164a",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def load_image(path):\n",
    "    \"\"\"\n",
    "    Returns numpy array in format [sizex, sizey, 3].\n",
    "    \"\"\"\n",
    "    \n",
    "    rgb = np.array(PIL.Image.open(path)) / 255.0\n",
    "    rgb = mpl_format_inverse(rgb)\n",
    "    return rgb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74dceb6a",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Misc Misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f3626f2",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def beautify_number(num):\n",
    "    \"\"\"\n",
    "    Adds tickmarks to integer.\n",
    "    \"\"\"\n",
    "    if num == 0:\n",
    "        return '0'\n",
    "    \n",
    "    if num < 1000:\n",
    "        return str(num)\n",
    "    \n",
    "    res = str(num % 1000)\n",
    "    num//= 1000\n",
    "    while num != 0:\n",
    "        while len(res) % 4 != 3:\n",
    "            res = '0' + res\n",
    "        res = str(num % 1000) + '\\'' + res\n",
    "        num//= 1000\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f902978",
   "metadata": {
    "code_folding": [
     0,
     7
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def torchify(ray, requires_grad = False):\n",
    "    \"\"\"\n",
    "    Turns numpy array into float32 pytorch tensor.\n",
    "    \"\"\"\n",
    "    tens = torch.tensor(ray, device = def_device, dtype = torch.float32, requires_grad = requires_grad)\n",
    "    return tens\n",
    "    \n",
    "def numpyify(tens):\n",
    "    \"\"\"\n",
    "    Turns pytorch tensor into numpy array.\n",
    "    \"\"\"\n",
    "    if tens.requires_grad_:\n",
    "        tens = tens.detach()\n",
    "    \n",
    "    if torch.get_device(tens) != -1:\n",
    "        tens = tens.cpu()\n",
    "        \n",
    "    return tens.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06d2b64d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class TimeEstimator():\n",
    "    \"\"\"\n",
    "    Class that estimates remaining time to completion.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.start = time.time()\n",
    "    \n",
    "    def format_time(self, seconds):\n",
    "        \"\"\"\n",
    "        Formats amount of seconds in a nice readable way.\n",
    "        \"\"\"\n",
    "        sleft = int(seconds)\n",
    "        s = 'eta: '\n",
    "        if sleft > 59:\n",
    "            mleft = sleft // 60\n",
    "            sleft = sleft % 60\n",
    "            s+= str(mleft) + 'm '\n",
    "        s+= str(sleft) + ' s'\n",
    "        return s\n",
    "    \n",
    "    def get_estimate(self, progress, total):\n",
    "        \"\"\"\n",
    "        progress - completed work amount.\n",
    "        total - neccessary work amount.\n",
    "        \"\"\"\n",
    "        remaining = (total - progress) / progress * (time.time() - self.start)\n",
    "        return self.format_time(remaining)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e88fab4",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Model Parts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3ab756",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Abstract Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "532b2b6f",
   "metadata": {
    "code_folding": [
     3,
     7,
     14,
     17,
     23,
     33,
     36,
     45
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Model():\n",
    "    \"\"\"Just an abstract class, that keeps track of simple model parameters.\"\"\"\n",
    "    \n",
    "    def __init__(self, tag = 'noname'):\n",
    "        self.params = []\n",
    "        self.tag = tag\n",
    "    \n",
    "    def weights(self, ins, outs, coefficient = 1.0):\n",
    "        k = coefficient / math.sqrt(ins)\n",
    "        data = np.random.uniform(-k, k, size = [ins, outs])\n",
    "        tens = torch.tensor(data, requires_grad = True, device = def_device, dtype = torch.float32)\n",
    "        self.params.append(tens)\n",
    "        return tens\n",
    "\n",
    "    def biases(self, outs):\n",
    "        return self.zeros([outs])\n",
    "    \n",
    "    def zeros(self, dims):\n",
    "        data = np.zeros(dims, dtype = np.float32)\n",
    "        tens = torch.tensor(data, dtype = torch.float32, requires_grad = True, device = def_device)\n",
    "        self.params.append(tens)\n",
    "        return tens\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        \"\"\"\n",
    "        Prints useful information about model.\n",
    "        \"\"\"\n",
    "        class_name = str(type(self)).split('\\'')[1].split('.')[1]\n",
    "        self._iprint('Tag', self.tag, indent = indent)\n",
    "        self._iprint('Class', class_name, indent = indent + 1)\n",
    "        self._iprint('Tensor count:', len(self.params), indent = indent + 1)\n",
    "        self._iprint('Param count:', beautify_number(self._get_param_count()), indent = indent + 1)\n",
    "        \n",
    "    def forward(self, *args, **kw):\n",
    "        raise NotImplementedError()\n",
    "    \n",
    "    def _get_param_count(self):\n",
    "        \"\"\"\n",
    "        Computes the current amount of trainable parameters in the model.\n",
    "        \"\"\"\n",
    "        res = 0\n",
    "        for tens in self.params:\n",
    "            res+= torch.numel(tens)\n",
    "        return res\n",
    "    \n",
    "    def _iprint(self, *args, indent = 0, **kw):\n",
    "        \"\"\"\n",
    "        Function that works just like print function except adds an indentation\n",
    "        of size indent * 2 spaces before printing.\n",
    "        \"\"\"\n",
    "        res = ''\n",
    "        for i in range(indent):\n",
    "            res+= ' |'\n",
    "        print(res, *args, **kw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c165d914",
   "metadata": {
    "code_folding": [
     6,
     9,
     20,
     36,
     39
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class FeatureGrid(Model):\n",
    "    \"\"\"\n",
    "    Abstract class.\n",
    "    Datastructure that returns feature vectors given coordinates.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, tag = 'FeatureGrid'):\n",
    "        super().__init__(tag)\n",
    "    \n",
    "    def _get_rounded(self, coords):\n",
    "        s = self\n",
    "        \n",
    "        #Rescale [-1..1] -> [0..grid_size - 1]\n",
    "        coordsr = (coords + 1) / 2 * (s.grid_size - 1)\n",
    "        \n",
    "        #Round down\n",
    "        coordsd = torch.floor(coordsr).long()\n",
    "        \n",
    "        return (coordsr, coordsd)\n",
    "    \n",
    "    def _cubic_linear_interpolate(self, rem, feats):\n",
    "        \"\"\"\n",
    "        Assumes rem - [batch_size, 3]\n",
    "        Assumes feats - [batch_size, 2, 2, 2, feat_count]\n",
    "        \"\"\"\n",
    "        remx = rem[:, 0].reshape([-1, 1, 1, 1])\n",
    "        feats_ix = feats[:, 0] * (1 - remx) + feats[:, 1] * remx\n",
    "        \n",
    "        remy = rem[:, 1].reshape([-1, 1, 1])\n",
    "        feats_ixy = feats_ix[:, 0] * (1 - remy) + feats_ix[:, 1] * remy\n",
    "        \n",
    "        remz = rem[:, 2].reshape([-1, 1])\n",
    "        feats_ixyz = feats_ixy[:, 0] * (1 - remz) + feats_ixy[:, 1] * remz\n",
    "        \n",
    "        return feats_ixyz\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(self, coords):\n",
    "        \"\"\"\n",
    "        Gets feature vectors from coordinates.\n",
    "        \n",
    "        Assumes coords is a tensor of size [n, 3].\n",
    "        returns [n, feature_count].\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "055e2eba",
   "metadata": {
    "code_folding": [
     6,
     15,
     22
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Aggregator(FeatureGrid):\n",
    "    \"\"\"\n",
    "    Abstract class.\n",
    "    Class that aggregates together output feature vectors of multiple feature grids and returns feature vector.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, grids, tag = 'Aggregator'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        for grid in grids:\n",
    "            self.params+= grid.params\n",
    "            \n",
    "        self.grids = grids\n",
    "        self.grid_count = len(self.grids)\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Num. of Feature Grids: ', self.grid_count, indent = indent + 1)\n",
    "        self._iprint('Grids: ', indent = indent + 1)\n",
    "        for grid in self.grids:\n",
    "            grid.spam(indent = indent + 2)\n",
    "        \n",
    "    def forward(self, coords):\n",
    "        raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "daa042ed",
   "metadata": {
    "code_folding": [
     6,
     26,
     35
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class OpGraph(Model):\n",
    "    \"\"\"\n",
    "    Thing that takes inputs, and returns parameters of interest.\n",
    "    Performs an acyclic graph of operations using Model objects.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, operations, inputs, returnables, tag = 'OpGraph'):\n",
    "        \"\"\"\n",
    "        Operations should be a list of dict.\n",
    "        Each dict should be {'op', 'from', 'to'}.\n",
    "          'op' is the thing that will do processing.\n",
    "          'from' is list of labels of the data that are to be processed.\n",
    "          'to' is the list of labels of the output data.\n",
    "        \"\"\"\n",
    "        \n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s.operations = operations\n",
    "        s.inputs = inputs\n",
    "        s.returnables = returnables\n",
    "        \n",
    "        for op in s.operations:\n",
    "            s.params+= op['op'].params\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        s = self\n",
    "        \n",
    "        super().spam(indent)\n",
    "        self._iprint('Operation graph:', indent = indent + 1)\n",
    "        for op in s.operations:\n",
    "            self._iprint('Operation ' + str(op['from']) + ' -> ' + op['op'].tag + ' -> ' + str(op['to']) + '', indent = indent + 2)\n",
    "            op['op'].spam(indent + 3)\n",
    "    \n",
    "    def forward(self, *inputs):\n",
    "        s = self\n",
    "        \n",
    "        state = {}\n",
    "        for idx, i in enumerate(inputs):\n",
    "            state[s.inputs[idx]] = i\n",
    "                \n",
    "        for op in s.operations:\n",
    "            inputs = []\n",
    "            for name in op['from']:\n",
    "                inputs.append(state[name])\n",
    "            opres = op['op'].forward(*inputs)\n",
    "            if len(op['to']) == 1:\n",
    "                state[op['to'][0]] = opres\n",
    "            else:\n",
    "                for idx, name in enumerate(op['to']):\n",
    "                    state[name] = opres[idx]\n",
    "        \n",
    "        res = []\n",
    "        for ret in s.returnables:\n",
    "            res.append(state[ret])\n",
    "        \n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c360797",
   "metadata": {
    "code_folding": [
     6,
     11
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Decoder(OpGraph):\n",
    "    \"\"\"\n",
    "    Class that represents a NeRF decoder.\n",
    "    Computes pointwise rgb and density from optionally features, coordinates, view_vectors.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, operations, inputs = ['feats', 'coords', 'view_vecs'], returnables = ['rgb', 'density'], tag = 'Decoder'):\n",
    "        super().__init__(operations, inputs, returnables, tag)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af91d0e",
   "metadata": {
    "code_folding": [
     6,
     9,
     12
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class NeRF(Model):\n",
    "    \"\"\"\n",
    "    Class represents a NeRF.\n",
    "    Computes rgb and density from optionally coordinates, view_vectors.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, tag = 'NeRF'):\n",
    "        super().__init__(tag)\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(self, coords, view_vecs):\n",
    "        raise NotImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09a6570f",
   "metadata": {
    "code_folding": [
     6,
     9,
     12
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Tracer(Model):\n",
    "    \"\"\"\n",
    "    Abstract class.\n",
    "    That does 'rgb' integration over a NeRF that retuns 'rgb' and 'density' given coordinates and view directions.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, tag = 'Tracer'):\n",
    "        super().__init__(tag)\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(NeRF, coords, view_vecs):\n",
    "        raise notImplementedError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "139e5f6b",
   "metadata": {
    "code_folding": [
     9,
     12,
     15,
     18
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Pipe(Model):\n",
    "    \"\"\"\n",
    "    Abstract class.\n",
    "    Represents a full pipeline.\n",
    "    Contains a forward pass.\n",
    "    Contains loss.\n",
    "    \"\"\"\n",
    "    #@todo, rewrite so that inputs and outputs get reshaped automatically.\n",
    "    \n",
    "    def __init__(self, tag = 'Pipe'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        \n",
    "    def forward(self, coords, view_vecs):\n",
    "        raise notImplementedError()\n",
    "    \n",
    "    def loss(self, coords, view_vecs, gt_rgb):\n",
    "        guess = self.forward(coords, view_vecs)\n",
    "        loss = ((guess - gt_rgb) ** 2).mean()\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42331459",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Specific Parts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cbaa3c6",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Feature Grids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "188f3027",
   "metadata": {
    "code_folding": [
     6,
     16,
     21
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class DenseGrid(FeatureGrid):\n",
    "    \"\"\"\n",
    "    A 3D feature grid that uses trilinear interpolation.\n",
    "    Stores features for each voxel.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, grid_size = 10, feat_count = 8, tag = 'DenseGrid'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s.grid_size = grid_size\n",
    "        s.feat_count = feat_count\n",
    "        \n",
    "        s.grid = self.zeros([s.grid_size ** 3, s.feat_count])\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent = indent)\n",
    "        self._iprint('Grid size: ', self.grid_size, indent = indent + 1)\n",
    "        self._iprint('Feature count: ', beautify_number(self.feat_count), indent = indent + 1)\n",
    "    \n",
    "    def forward(self, coords):\n",
    "        s = self\n",
    "        \n",
    "        cr, cd = s._get_rounded(coords)\n",
    "        \n",
    "        rem = cr - cd\n",
    "        \n",
    "        #Reshape into 1D vector for selection.\n",
    "        indexes = cd[:, 0] + cd[:, 1] * s.grid_size + cd[:, 2] * s.grid_size ** 2\n",
    "        indexes = indexes.abs()\n",
    "        \n",
    "        #Get voxels where coordinates are rounded up.\n",
    "        indexes = torch.stack([indexes, indexes + 1], dim = 1) #Add voxels with ceil(x)\n",
    "        indexes = torch.stack([indexes, indexes + s.grid_size], dim = 2) #Add voxels with ceil(y)\n",
    "        indexes = torch.stack([indexes, indexes + s.grid_size ** 2], dim = 3) #Add voxels with ceil(z)\n",
    "        indexes = torch.fmod(indexes, s.grid_size ** 3)\n",
    "        \n",
    "        #Get features from indexes.\n",
    "        feats = torch.index_select(s.grid, dim = 0, index = indexes.reshape([-1]))\n",
    "        feats = feats.reshape([-1, 2, 2, 2, s.feat_count])\n",
    "        \n",
    "        #Interpolate\n",
    "        feats_ixyz = s._cubic_linear_interpolate(rem, feats)\n",
    "\n",
    "        #Masking situations where we're asked to predict outside bounds.\n",
    "        mask = (torch.where(coords < -1, 0, 1) * torch.where(coords >= 1, 0, 1)).prod(dim = 1)\n",
    "        res = feats_ixyz * mask.reshape([-1, 1])\n",
    "        \n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278a8a0e",
   "metadata": {
    "code_folding": [
     6,
     24,
     30
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class HashGrid(FeatureGrid):\n",
    "    \"\"\"\n",
    "    A 3D feature grid that uses trilinear interpolation.\n",
    "    Stores features for each voxel.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, grid_size = 10, feat_count = 8, table_size = None, primey = 19349663, primez = 83492791, tag = 'HashGrid'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s.grid_size = grid_size\n",
    "        s.feat_count = feat_count\n",
    "        \n",
    "        if table_size is not None:\n",
    "            s.table_size = table_size\n",
    "        else:\n",
    "            s.table_size = 6 * s.grid_size ** 2\n",
    "        \n",
    "        s.grid = self.zeros([s.table_size, s.feat_count])\n",
    "    \n",
    "        s.primey = primey\n",
    "        s.primez = primez\n",
    "\n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent = indent)\n",
    "        self._iprint('Grid size: ', self.grid_size, indent = indent + 1)\n",
    "        self._iprint('Feature count: ', beautify_number(self.feat_count), indent = indent + 1)\n",
    "        self._iprint('Hash table size: ', beautify_number(self.table_size), indent = indent + 1)\n",
    "        \n",
    "    def forward(self, coords):\n",
    "        s = self\n",
    "        \n",
    "        cr, cd = s._get_rounded(coords)\n",
    "        \n",
    "        rem = cr - cd\n",
    "        \n",
    "        #Multiply indexes by primes.\n",
    "        indexes = cd\n",
    "        indexes[:, 1]*= s.primey\n",
    "        indexes[:, 2]*= s.primez\n",
    "        indexes = indexes.abs()\n",
    "        \n",
    "        #Get voxels where coordinates are rounded up.\n",
    "        indexes = torch.stack([indexes, indexes], dim = 1) #Add voxels with ceil(x)\n",
    "        indexes[:, 1]+= 1\n",
    "        indexes = torch.stack([indexes, indexes], dim = 2) #Add voxels with ceil(y)\n",
    "        indexes[:, :, 1]+= s.primey\n",
    "        indexes = torch.stack([indexes, indexes], dim = 3) #Add voxels with ceil(z)\n",
    "        indexes[:, :, :, 1]+= s.primez\n",
    "        indexes = torch.bitwise_xor(torch.bitwise_xor(indexes[:, :, :, :, 0], indexes[:, :, :, :, 1]), indexes[:, :, :, :, 2])\n",
    "        indexes = torch.fmod(indexes, s.table_size)\n",
    "        \n",
    "        #Get features from indexes.\n",
    "        feats = torch.index_select(s.grid, dim = 0, index = indexes.reshape([-1]))\n",
    "        feats = feats.reshape([-1, 2, 2, 2, s.feat_count])\n",
    "        \n",
    "        #Interpolate\n",
    "        feats_ixyz = s._cubic_linear_interpolate(rem, feats)\n",
    "\n",
    "        #Masking situations where we're asked to predict outside bounds.\n",
    "        mask = (torch.where(coords < -1, 0, 1) * torch.where(coords >= 1, 0, 1)).prod(dim = 1)\n",
    "        res = feats_ixyz * mask.reshape([-1, 1])\n",
    "        \n",
    "        return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cebf3c8e",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Specific FeatureGrid Constructors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e854ff38",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def construct_INGP_grid(min_res = 10, \n",
    "                        max_res = 1000, \n",
    "                        levels = 8, \n",
    "                        table_size = 2 ** 14, \n",
    "                        level_feature_count = 2):\n",
    "    \"\"\"\n",
    "    Constructs a multi-resolution hash table feature grid.\n",
    "    Aggregation used is contatentation between different resolutions.\n",
    "    Output should correspond to how the feature grid works in InstantNGP.\n",
    "    Function takes care of the messy stuff, so you don't have to.\n",
    "        \n",
    "    min_res - resolution of coarsest feature grid.\n",
    "    max_res - resolution of finest feature grid.\n",
    "    levels - number of feature grids used.\n",
    "    table_size - size of hash table per level.\n",
    "    level_feature_count - number of features returned by each level.\n",
    "\n",
    "    Function returns Aggregator object that can be used as a FeatureGrid object.\n",
    "\n",
    "    Note:\n",
    "    The defaults of this function do not correspond to what is used in INGP.\n",
    "    INGP uses:\n",
    "        min_res = 16\n",
    "        max_res = (In paper this parameter varies between 512 and 524288)\n",
    "        levels = 16\n",
    "        table_size = (In paper this parameter varies between 2^14 and 2^24)\n",
    "        level_feature_count = 2\n",
    "    \"\"\"\n",
    "    \n",
    "    #Calculate feature grid sizes.\n",
    "    b = math.exp((math.log(max_res) - math.log(min_res)) / (levels - 1))\n",
    "    grid_sizes = []\n",
    "    for level in range(levels):\n",
    "        grid_size = math.floor(min_res * b ** level)\n",
    "        grid_sizes.append(grid_size)\n",
    "    \n",
    "    #Construct feature grids.\n",
    "    grids = []\n",
    "    for level in range(levels):\n",
    "        emulated_size =  grid_sizes[level] ** 3\n",
    "        if emulated_size <= table_size:\n",
    "            grids.append(DenseGrid(grid_sizes[level], level_feature_count))\n",
    "        else:\n",
    "            grids.append(HashGrid(grid_sizes[level], level_feature_count, table_size = table_size))\n",
    "    \n",
    "    #Aggregate\n",
    "    res = CatAggregator(grids, tag = 'INGPFeatureGrid')\n",
    "    \n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b44849",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Aggregators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bf493b2",
   "metadata": {
    "code_folding": [
     4,
     7,
     10
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class CatAggregator(Aggregator):\n",
    "    \"\"\"\n",
    "    Concatenates output features of multiple feature grids.\n",
    "    \"\"\"\n",
    "    def __init__(self, *args, tag = 'CatAggregator', **kw):\n",
    "        super().__init__(*args, tag = tag, **kw)\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(self, coords):\n",
    "        res = []\n",
    "        for grid in self.grids:\n",
    "            res.append(grid.forward(coords))\n",
    "        res = torch.cat(res, dim = 1)\n",
    "        \n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e0f2f0",
   "metadata": {
    "code_folding": [
     4,
     10,
     13
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class MeanAggregator(Aggregator):\n",
    "    \"\"\"\n",
    "    Takes the mean of the feature vectors of multiple feature grid outputs.\n",
    "    \"\"\"\n",
    "    def __init__(self, *args, tag = 'MeanAggregator', **kw):\n",
    "        \"\"\"\n",
    "        Requires that feature grids output the same amount of features per grid.\n",
    "        \"\"\"\n",
    "        super().__init__(*args, tag = tag, **kw)\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(self, coords):\n",
    "        res = 0\n",
    "        for grid in self.grids:\n",
    "            res+= grid.forward(coords)\n",
    "        res/= self.grid_count\n",
    "        \n",
    "        return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b1060a",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Decoder Parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0370d0fd",
   "metadata": {
    "code_folding": [
     5,
     26,
     34
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class FCNN(Model):\n",
    "    \"\"\"\n",
    "    A general purpouse fully connected neural network.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self,\n",
    "                 layer_sizes = [],\n",
    "                 activations = [None, 'relu', 'relu', 'relu'],\n",
    "                 tag = 'FCNN'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        s.tag = 'FCNN'\n",
    "        \n",
    "        s.layer_count = len(layer_sizes)\n",
    "        s.layer_sizes = layer_sizes.copy()\n",
    "        s.activations = activations.copy()\n",
    "        \n",
    "        #Initializing variables\n",
    "        s.w = [None]\n",
    "        s.b = [None]\n",
    "        for cur_layer in range(1, self.layer_count):\n",
    "            prev_layer = cur_layer - 1\n",
    "            s.w.append(s.weights(s.layer_sizes[prev_layer], s.layer_sizes[cur_layer]))\n",
    "            s.b.append(s.biases(s.layer_sizes[cur_layer]))\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Layer sizes: ', self.layer_sizes, indent = indent + 1)\n",
    "        architecture = str(self.layer_sizes[0])\n",
    "        for layer in range(1, self.layer_count):\n",
    "            architecture+= ' -> ' + str(self.layer_sizes[layer]) + '(' + self.activations[layer] + ')'\n",
    "        self._iprint('Architecture: ', architecture, indent = indent + 1)\n",
    "    \n",
    "    def forward(self, *inputs):\n",
    "        \"\"\"\n",
    "        Assumes *inputs is a list of tensors [batch_size, feats_i].\n",
    "        Concatenates them along dim 1.\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        layer = torch.cat(inputs, dim = 1)\n",
    "        for cur_layer in range(1, s.layer_count):\n",
    "            prev_layer = cur_layer - 1\n",
    "            \n",
    "            layer = torch.matmul(layer, s.w[cur_layer]) + s.b[cur_layer]\n",
    "            \n",
    "            activation = s.activations[cur_layer]\n",
    "            if activation == 'sigmoid':\n",
    "                layer = torch.sigmoid(layer)\n",
    "            elif activation == 'relu':\n",
    "                layer = torch.relu(layer)\n",
    "            elif activation == 'none' or activation is None:\n",
    "                pass\n",
    "        \n",
    "        return layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19f639c",
   "metadata": {
    "code_folding": [
     4,
     14,
     17
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class SinEncoder(Model):\n",
    "    \"\"\"\n",
    "    Object that takes in vectors of features and encodes them with sines of exponentially increasing frequency.\n",
    "    \"\"\"\n",
    "    def __init__(self, levels = 10, base_multiplier = 1.0, exponent = 2.0, tag = 'SinEncoder'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        s.tag = 'SinEnc'\n",
    "        \n",
    "        s.levels = levels\n",
    "        s.base_multiplier = base_multiplier\n",
    "        s.exponent = exponent\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Assumes x is a tensor of size [batch_size, feats].\n",
    "        Returns tensor of size [batch_size, feats * levels]\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        mult = s.base_multiplier * s.exponent ** torch.linspace(0, s.levels - 1, s.levels, device = def_device)\n",
    "        mult = mult.reshape([1, 1, -1])\n",
    "        \n",
    "        res = torch.sin(x.reshape([-1, x.shape[1], 1]) * mult).reshape([-1, x.shape[1] * s.levels])\n",
    "        \n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab81ee37",
   "metadata": {
    "code_folding": [
     5,
     13,
     16
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Splitter(Model):\n",
    "    \"\"\"\n",
    "    Literally splits a tensor of size [batch_size, n] into multiple bits.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, sizes, tag = 'Splitter'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        s.tag = 'Split'\n",
    "        \n",
    "        s.sizes = sizes\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        s = self\n",
    "        res = []\n",
    "        cum = 0\n",
    "        for size in s.sizes:\n",
    "            res.append(x[:, cum:cum+size])\n",
    "            cum+= size\n",
    "        return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e25c56b",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Specific Decoder Constructors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a26d50e5",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def construct_FCNNsingle_decoder(layers = [None, 64, 64, None], \n",
    "                                 activations = [None, 'relu', 'relu', 'relu'],\n",
    "                                 feat_count = 8, \n",
    "                                 feats = True, \n",
    "                                 coords = True, \n",
    "                                 view_vecs = True, \n",
    "                                 coords_sine = True, \n",
    "                                 encoding_levels = 10):\n",
    "    opchain = []\n",
    "    inputs = []\n",
    "    input_size = 0\n",
    "    if feats:\n",
    "        input_size+= feat_count\n",
    "        inputs.append('feats')\n",
    "    \n",
    "    if coords:\n",
    "        if coords_sine:\n",
    "            input_size+= 3 * encoding_levels\n",
    "        else:\n",
    "            input_size+= 3\n",
    "        inputs.append('coords')\n",
    "    \n",
    "    if view_vecs:\n",
    "        input_size+= 3\n",
    "        inputs.append('view_vecs')\n",
    "    \n",
    "    if coords:\n",
    "        if coords_sine:\n",
    "            encoder = SinEncoder(levels = encoding_levels)\n",
    "            opchain.append({'op': encoder, 'from': ['coords'], 'to': ['coords']})\n",
    "    \n",
    "    layers = layers.copy()\n",
    "    layers[0] = input_size\n",
    "    layers[-1] = 4\n",
    "    \n",
    "    decoder = FCNN(layers, activations)\n",
    "    \n",
    "    opchain.append({'op': decoder, 'from': inputs, 'to': ['rgba']})\n",
    "    \n",
    "    splitter = Splitter([3, 1])\n",
    "    \n",
    "    opchain.append({'op': splitter, 'from': ['rgba'], 'to': ['rgb', 'density']})\n",
    "    \n",
    "    return Decoder(opchain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab6b6d1d",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def construct_FCNNdouble_decoder(layers = [None, 64, 64, None], \n",
    "                                 activations = [None, 'relu', 'relu', 'relu'],\n",
    "                                 density_layers = [None, 64, 64, None],\n",
    "                                 density_activations = [None, 'relu', 'relu', 'relu'],\n",
    "                                 feat_count = 8, \n",
    "                                 feats = True, \n",
    "                                 coords = True, \n",
    "                                 view_vecs = True, \n",
    "                                 coords_sine = True, \n",
    "                                 encoding_levels = 10):\n",
    "    opchain = []\n",
    "    inputs = []\n",
    "    input_size = 0\n",
    "    if feats:\n",
    "        input_size+= feat_count\n",
    "        inputs.append('feats')\n",
    "    \n",
    "    if coords:\n",
    "        if coords_sine:\n",
    "            input_size+= 3 * encoding_levels\n",
    "        else:\n",
    "            input_size+= 3\n",
    "        inputs.append('coords')\n",
    "    \n",
    "    if view_vecs:\n",
    "        input_size+= 3\n",
    "        inputs.append('view_vecs')\n",
    "    \n",
    "    if coords:\n",
    "        if coords_sine:\n",
    "            encoder = SinEncoder(levels = encoding_levels)\n",
    "            opchain.append({'op': encoder, 'from': ['coords'], 'to': ['coords']})\n",
    "    \n",
    "    layers = layers.copy()\n",
    "    layers[0] = input_size\n",
    "    layers[-1] = 3\n",
    "    \n",
    "    rgb_decoder = FCNN(layers, activations)\n",
    "    \n",
    "    opchain.append({'op': rgb_decoder, 'from': inputs, 'to': ['rgb']})\n",
    "    \n",
    "    #Construct density decoder\n",
    "    density_layers[0] = input_size\n",
    "    if view_vecs:\n",
    "        density_layers[0]-= 3\n",
    "        inputs = inputs[:-1]\n",
    "    density_layers[-1] = 1\n",
    "    density_decoder = FCNN(density_layers, density_activations)\n",
    "    \n",
    "    opchain.append({'op': density_decoder, 'from': inputs, 'to': ['density']})\n",
    "    \n",
    "    return Decoder(opchain, returnables = ['rgb', 'density'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea6ce93",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### NeRFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e1357e6",
   "metadata": {
    "code_folding": [
     1,
     11,
     18
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class FeatureGridNeRF(NeRF):\n",
    "    def __init__(self, feature_grid, decoder, tag = 'FeatureGridNeRF'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s.feature_grid = feature_grid\n",
    "        s.decoder = decoder\n",
    "        \n",
    "        s.params = s.feature_grid.params + s.decoder.params\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Feature Grid: ', indent = indent + 1)\n",
    "        self.feature_grid.spam(indent + 2)\n",
    "        self._iprint('Decoder: ', indent = indent + 1)\n",
    "        self.decoder.spam(indent + 2)\n",
    "    \n",
    "    def forward(self, coords, view_vecs):\n",
    "        s = self\n",
    "        \n",
    "        features = s.feature_grid.forward(coords)\n",
    "        res = s.decoder.forward(features, coords, view_vecs)\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7401191f",
   "metadata": {
    "code_folding": [
     4,
     13,
     18
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class FeaturelessNeRF(NeRF):\n",
    "    \"\"\"\n",
    "    NeRF that doesn't have a feature grid.\n",
    "    \"\"\"\n",
    "    def __init__(self, decoder, tag = 'FeaturelessNeRF'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s.decoder = decoder\n",
    "        \n",
    "        s.params = s.decoder.params\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Decoder: ', indent = indent + 1)\n",
    "        self.decoder.spam(indent + 2)\n",
    "    \n",
    "    def forward(self, coords, view_vecs):\n",
    "        s = self\n",
    "        \n",
    "        res = s.decoder.forward(torch.zeros([coords.shape[0], 0], device = def_device, dtype = torch.float32), coords, view_vecs)\n",
    "        return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000ffb5e",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Tracers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47858cb5",
   "metadata": {
    "code_folding": [
     5,
     11,
     16
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class UniformTracer(Tracer):\n",
    "    \"\"\"\n",
    "    Samples density and rgb linearly along the view vector, given a NeRF.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, stacked = True, distance = math.sqrt(3) * 2.0, steps = 32, tag = 'UniformTracer'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        self.distance = distance\n",
    "        self.steps = steps\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Tracing distance: ', self.distance, indent = indent + 1)\n",
    "        self._iprint('Tracing steps: ', self.steps, indent = indent + 1)\n",
    "        \n",
    "    def forward(self, coords, view_vecs, nerf):\n",
    "        \"\"\"\n",
    "        Function that returns pixel color we see in nerf from certain location looking in a certain angle.\n",
    "        \n",
    "        Dimensions of coords are assumed to be [n, 3].\n",
    "        Dimensions of view_vecs are assumed to be [n, 3], a tensor of unit vectors.\n",
    "        nerf is assumed to be an instance of NeRF.\n",
    "        distance - how far to look along the view vector.\n",
    "        steps - how many samples to do along the view vector.\n",
    "        \n",
    "        Returns tensor [n, 3], which represents RGB values.\n",
    "        \"\"\"\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        #This code may look odd because I'm trying to stack all steps into a single nerf run.\n",
    "        #It would look more intuitive if it were written less optimally.\n",
    "        \n",
    "        #Calculating view vectors that will be passed to nerf\n",
    "        view_vecs = view_vecs.reshape([-1, 1, 3])\n",
    "        tmp_view_vecs = view_vecs #This is a quick and dirty bugfix.\n",
    "        view_vecs = torch.broadcast_to(view_vecs, [view_vecs.shape[0], s.steps, 3])\n",
    "        view_vecs = view_vecs.reshape([-1, 3])\n",
    "        \n",
    "        #calculating cooredinates that will be passed to nerf.\n",
    "        step_size = s.distance / s.steps\n",
    "        view_vec_step = tmp_view_vecs * step_size\n",
    "        \n",
    "        step_nums = [i + 1 for i in range(s.steps)]\n",
    "        step_nums = torch.tensor(step_nums, device = def_device, dtype = torch.float32, requires_grad = False)\n",
    "        step_nums = step_nums.reshape([1, s.steps, 1])\n",
    "\n",
    "        sample_coords = coords.reshape([-1, 1, 3])\n",
    "        sample_coords = sample_coords + view_vec_step * step_nums\n",
    "        sample_coords = sample_coords.reshape([-1, 3])\n",
    "        \n",
    "        #Running nerf.\n",
    "        rgb, density = nerf.forward(sample_coords, view_vecs)\n",
    "\n",
    "        rgb = rgb.reshape([-1, s.steps, 3])\n",
    "        density = density.reshape([-1, s.steps])\n",
    "\n",
    "        density = density * step_size\n",
    "        \n",
    "        cum_density = torch.cumsum(density, dim = 1)\n",
    "        visibility = torch.exp(-cum_density)\n",
    "        pad = torch.ones([visibility.shape[0], 1], dtype = torch.float32, device = def_device, requires_grad = False)\n",
    "        visibility = torch.cat([pad, visibility], dim = 1)\n",
    "        visibility = visibility[:, :-1] - visibility[:, 1:]\n",
    "        \n",
    "        result = (rgb * visibility.reshape([-1, s.steps, 1])).sum(dim = 1)\n",
    "        \n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb4cba2f",
   "metadata": {
    "code_folding": [
     5,
     11,
     16
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class UniformRandomTracer(Tracer):\n",
    "    \"\"\"\n",
    "    Samples density and rgb linearly along the view vector, given a NeRF.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, stacked = True, distance = math.sqrt(3) * 2.0, steps = 32, tag = 'UniformRandomTracer'):\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        self.distance = distance\n",
    "        self.steps = steps\n",
    "        \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Tracing distance: ', self.distance, indent = indent + 1)\n",
    "        self._iprint('Tracing steps: ', self.steps, indent = indent + 1)\n",
    "        \n",
    "    def forward(self, coords, view_vecs, nerf):\n",
    "        \"\"\"\n",
    "        Function that returns pixel color we see in nerf from certain location looking in a certain angle.\n",
    "        \n",
    "        Dimensions of coords are assumed to be [n, 3].\n",
    "        Dimensions of view_vecs are assumed to be [n, 3], a tensor of unit vectors.\n",
    "        nerf is assumed to be an instance of NeRF.\n",
    "        distance - how far to look along the view vector.\n",
    "        steps - how many samples to do along the view vector.\n",
    "        \n",
    "        Returns tensor [n, 3], which represents RGB values.\n",
    "        \"\"\"\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        #This code may look odd because I'm trying to stack all steps into a single nerf run.\n",
    "        #It would look more intuitive if it were written less optimally.\n",
    "        \n",
    "        #Calculating view vectors that will be passed to nerf\n",
    "        view_vecs = view_vecs.reshape([-1, 1, 3])\n",
    "        tmp_view_vecs = view_vecs #This is a quick and dirty bugfix.\n",
    "        view_vecs = torch.broadcast_to(view_vecs, [view_vecs.shape[0], s.steps, 3])\n",
    "        view_vecs = view_vecs.reshape([-1, 3])\n",
    "        \n",
    "        #calculating cooredinates that will be passed to nerf.\n",
    "        step_size = s.distance / s.steps\n",
    "        view_vec_step = tmp_view_vecs * step_size\n",
    "        \n",
    "        step_nums = [i + 1 for i in range(s.steps)]\n",
    "        step_nums = torch.tensor(step_nums, device = def_device, dtype = torch.float32, requires_grad = False)\n",
    "        step_nums = step_nums.reshape([1, s.steps, 1])\n",
    "        step_nums = step_nums - torch.rand(*step_nums.shape, device = def_device, dtype = torch.float32)\n",
    "\n",
    "        sample_coords = coords.reshape([-1, 1, 3])\n",
    "        sample_coords = sample_coords + view_vec_step * step_nums\n",
    "        sample_coords = sample_coords.reshape([-1, 3])\n",
    "        \n",
    "        #Running nerf.\n",
    "        rgb, density = nerf.forward(sample_coords, view_vecs)\n",
    "\n",
    "        rgb = rgb.reshape([-1, s.steps, 3])\n",
    "        density = density.reshape([-1, s.steps])\n",
    "\n",
    "        density = density * step_size\n",
    "        \n",
    "        cum_density = torch.cumsum(density, dim = 1)\n",
    "        visibility = torch.exp(-cum_density)\n",
    "        pad = torch.ones([visibility.shape[0], 1], dtype = torch.float32, device = def_device, requires_grad = False)\n",
    "        visibility = torch.cat([pad, visibility], dim = 1)\n",
    "        visibility = visibility[:, :-1] - visibility[:, 1:]\n",
    "        \n",
    "        result = (rgb * visibility.reshape([-1, s.steps, 1])).sum(dim = 1)\n",
    "        \n",
    "        return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9a5e06",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Pipes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cde1f7d3",
   "metadata": {
    "code_folding": [
     6,
     16,
     26
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class SimplePipe(Pipe):\n",
    "    \"\"\"\n",
    "    Just the standard pipeline.\n",
    "    A NeRF + tracer.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, nerf, tracer):\n",
    "        super().__init__()\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s.nerf = nerf\n",
    "        s.tracer = tracer\n",
    "        \n",
    "        s.params = s.nerf.params\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        \n",
    "        s = self\n",
    "        \n",
    "        s._iprint('NeRF: ', indent = indent + 1)\n",
    "        s.nerf.spam(indent + 2)\n",
    "        s._iprint('Tracer: ', indent = indent + 1)\n",
    "        s.tracer.spam(indent + 2)\n",
    "        \n",
    "    def forward(self, coords, view_vecs, batch_size = None):\n",
    "        s = self\n",
    "        if batch_size is None:\n",
    "            guess = s.tracer.forward(coords, view_vecs, s.nerf)\n",
    "        else:\n",
    "            res = []\n",
    "            offset = 0\n",
    "            while offset < coords.shape[0]:\n",
    "                coord_slice = coords[offset:offset + batch_size]\n",
    "                view_vec_slice = view_vecs[offset: offset + batch_size]\n",
    "                res.append(s.tracer.forward(coord_slice, view_vec_slice, s.nerf))\n",
    "                offset+= batch_size\n",
    "            guess = torch.cat(res, dim = 0)\n",
    "        return guess"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31b64a8",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649e0142",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Abstract Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "594d0c78",
   "metadata": {
    "code_folding": [
     4,
     23,
     31,
     41,
     44,
     60
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Image():\n",
    "    \"\"\"\n",
    "    Class that represents an image and it's relevant parameters.\n",
    "    \"\"\"\n",
    "    def __init__(self, coord, rot, rgb, fov, aberrations = {}, misc = {}):\n",
    "        self.coord = coord.copy()\n",
    "        \n",
    "        self.rot = rot.copy()\n",
    "        \n",
    "        self.rgb = rgb.copy() #Represents rgb relating to image.\n",
    "        self.sizex = self.rgb.shape[0]\n",
    "        self.sizey = self.rgb.shape[1]\n",
    "        self.pix_count = self.sizex * self.sizey\n",
    "        \n",
    "        self.fov = fov\n",
    "        \n",
    "        unit = np.array([0, 0, 1], dtype = np.float32)\n",
    "        self.forward = np.matmul(self.rot, unit) #Stores forward direction of image.\n",
    "        \n",
    "        self.aberrations = aberrations\n",
    "        \n",
    "        self.misc = misc\n",
    "    \n",
    "    def get_view_vecs(self):\n",
    "        \"\"\"\n",
    "        Returns view vectors of image.\n",
    "        \"\"\"\n",
    "        vecs = get_view_vecs(self.sizex, self.sizey, self.fov)\n",
    "        vecs = rotate_vecs(self.rot, vecs)\n",
    "        return vecs\n",
    "    \n",
    "    def downscale(self, factor = 2):\n",
    "        \"\"\"\n",
    "        Downsamples image.\n",
    "        Mucks around with image parameters so that they're still legit.\n",
    "        \"\"\"\n",
    "        self.rgb = self.rgb[::factor, ::factor]\n",
    "        self.sizex = self.rgb.shape[0]\n",
    "        self.sizey = self.rgb.shape[1]\n",
    "        self.pix_count = self.sizex * self.sizey\n",
    "    \n",
    "    def show_image(self):\n",
    "        plt.imshow(mpl_format(self.rgb))\n",
    "    \n",
    "    def spam(self):\n",
    "        print('Image')\n",
    "        \n",
    "        print('Size: ', self.sizex, 'x', self.sizey, '[', self.pix_count, ']')\n",
    "        \n",
    "        print('Coordinates:', self.coord)\n",
    "        \n",
    "        print('Rotation Matrix:')\n",
    "        print(self.rot)\n",
    "        \n",
    "        print('Forward Vector:', self.forwards)\n",
    "        \n",
    "        print('Aberrations: ', self.aberrations)\n",
    "        \n",
    "        print('Misc information: ', self.misc)\n",
    "        \n",
    "def norm_image_coords(images):\n",
    "    \"\"\"\n",
    "    Normalizes image coordinates, so that hopefully everything would fit into a [-1..1]^3 cube.\n",
    "    \"\"\"\n",
    "    \n",
    "    coords = []\n",
    "    for image in images:\n",
    "        coords.append(image.coord)\n",
    "    coords = np.array(coords)\n",
    "    mean = coords.mean(axis = 0)\n",
    "    for image in images:\n",
    "        image.coord-= mean\n",
    "        image.coord/= 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b86678d2",
   "metadata": {
    "code_folding": [
     7,
     21,
     27,
     33,
     57,
     63,
     94,
     99
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Dataset(Model):\n",
    "    \"\"\"\n",
    "    Abstract class for object that gives samples of camera positions, view vectors and ground truth rgb.\n",
    "    \n",
    "    #CoordVerified\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, images = None, tag = 'Dataset'):\n",
    "        \"\"\"\n",
    "        Assumes image - list of Image objects.\n",
    "        \"\"\"\n",
    "        super().__init__(tag)\n",
    "        \n",
    "        self.image_count = len(images)\n",
    "        \n",
    "        self.images = images\n",
    "        \n",
    "        self.pix_count = 0\n",
    "        for image in self.images:\n",
    "            self.pix_count+= image.pix_count\n",
    "    \n",
    "    def sample(self, batch_size = None):\n",
    "        \"\"\"\n",
    "        Function returns tuple of (coords, view_vecs, rgb)\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "        \n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Function that resets batch sampling.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "    \n",
    "    def permute(lists, seed = None):\n",
    "        \"\"\"\n",
    "        Function that permutes list of tensors of size [[n, d1], [n, d2], [n, d3]...].\n",
    "        Returns list of permuted tensors.\n",
    "        \"\"\"\n",
    "        #Data verification\n",
    "        element_count = lists[0].shape[0]\n",
    "        for ls in lists:\n",
    "            if ls.shape[0] != element_count:\n",
    "                raise ValueError('Dimension 0 of tensors are different. Cannot permute.')\n",
    "        \n",
    "        #Permute\n",
    "        if seed is not None:\n",
    "            np.random.seed(seed)\n",
    "        permutation = torch.tensor(np.random.permutation(element_count), \n",
    "                                   dtype = torch.long, \n",
    "                                   requires_grad = False, \n",
    "                                   device = def_device)\n",
    "        res = []\n",
    "        for ls in lists:\n",
    "            res.append(torch.index_select(ls, 0, permutation))\n",
    "        \n",
    "        return res\n",
    "    \n",
    "    def loss(s):\n",
    "        \"\"\"\n",
    "        Function that returns Dataset loss if it has trainable parameters.\n",
    "        \"\"\"\n",
    "        return 0\n",
    "    \n",
    "    def _coord_vec_rgb_constructor(s, images):\n",
    "        \"\"\"\n",
    "        The operation of getting coords, view_vecs and rgb, tensors seems to recur,\n",
    "        so I'm making a function for it.\n",
    "        \"\"\"\n",
    "        s.coords = []\n",
    "        for image in images:\n",
    "            s.coords.append(image.coord)\n",
    "        s.coords = np.array(s.coords)\n",
    "        s.coords = torchify(s.coords)\n",
    "        \n",
    "        s.view_vecs = []\n",
    "        for image in images:\n",
    "            s.view_vecs.append(image.get_view_vecs())\n",
    "        s.view_vecs = np.array(s.view_vecs)\n",
    "        s.view_vecs = torchify(s.view_vecs)\n",
    "        \n",
    "        s.rgb = []\n",
    "        for image in images:\n",
    "            s.rgb.append(image.rgb)\n",
    "        s.rgb = np.array(s.rgb)\n",
    "        s.rgb = torchify(s.rgb)\n",
    "        \n",
    "        #Broadcast coords\n",
    "        s.coords = torch.broadcast_to(s.coords.reshape([-1, 1, 1, 3]), s.view_vecs.shape)\n",
    "        \n",
    "        #Reshape to vector of vectors.\n",
    "        s.coords = s.coords.reshape([-1, 3])\n",
    "        s.view_vecs = s.view_vecs.reshape([-1, 3])\n",
    "        s.rgb = s.rgb.reshape([-1, 3])\n",
    "    \n",
    "    def spam(self, indent = 0):\n",
    "        super().spam(indent)\n",
    "        self._iprint('Image count: ', self.image_count, indent = indent + 1)\n",
    "        self._iprint('Pixel Count: ', beautify_number(self.pix_count), indent = indent + 1)\n",
    "    \n",
    "    def show_images(self, amt = -1):\n",
    "        if amt == -1:\n",
    "            amt = self.image_count\n",
    "        \n",
    "        sidelen = math.ceil(math.sqrt(amt))\n",
    "        plt.figure(figsize = (12, 12))\n",
    "        for img in range(self.image_count):\n",
    "            plt.subplot(sidelen, sidelen, img + 1)\n",
    "            plt.imshow(mpl_format(self.images[img].rgb))\n",
    "            plt.title(str(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499e807f",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Dataset Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70fda60c",
   "metadata": {
    "code_folding": [
     8,
     23,
     43
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class StaticDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Class represents just a regular dataset of images.\n",
    "    View vectors, coords and rgb do not change during training.\n",
    "    \n",
    "    #CoordVerified\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(s, images, tag = 'StaticDataset'):\n",
    "        \"\"\"\n",
    "        Assumes images - list of Image object.\n",
    "        Assumes camera - Camera object.\n",
    "        \"\"\"\n",
    "        super().__init__(images, tag)\n",
    "        \n",
    "        s._coord_vec_rgb_constructor(images)\n",
    "        \n",
    "        #Permute dataset\n",
    "        s.coords, s.view_vecs, s.rgb = Dataset.permute([s.coords, s.view_vecs, s.rgb])\n",
    "        \n",
    "        #Initialize offset\n",
    "        s.reset()\n",
    "\n",
    "    def sample(self, batch_size = 2048):\n",
    "        s = self\n",
    "        \n",
    "        ifrom = self.offset\n",
    "        ito = (self.offset + batch_size) % s.pix_count\n",
    "        \n",
    "        if self.offset + batch_size < s.pix_count:\n",
    "            coords = s.coords[ifrom: ito]\n",
    "            view_vecs = s.view_vecs[ifrom: ito]\n",
    "            rgb = s.rgb[ifrom: ito]\n",
    "        else:\n",
    "            coords = torch.cat([s.coords[ifrom:], s.coords[:ito]], dim = 0)\n",
    "            view_vecs = torch.cat([s.view_vecs[ifrom:], s.view_vecs[:ito]], dim = 0)\n",
    "            rgb = torch.cat([s.rgb[ifrom:], s.rgb[:ito]], dim = 0)\n",
    "            \n",
    "        self.offset+= batch_size\n",
    "        self.offset%= s.pix_count\n",
    "        \n",
    "        return (coords, view_vecs, rgb)\n",
    "        \n",
    "    def reset(self):\n",
    "        s = self\n",
    "        \n",
    "        s.offset = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f33d4cc3",
   "metadata": {
    "code_folding": [
     91,
     98,
     106,
     112,
     122,
     139,
     219,
     255,
     277,
     311
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class MorphingDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Dataset that contains some learnable camera or image parameters.\n",
    "    Takes colmap output as base to train on.\n",
    "    \n",
    "    Learnable parameters:\n",
    "        learn_radial - Whether to adjust scaling of image.\n",
    "        learn_position - Whether to adjust camera positions.\n",
    "        learn_xfov - Whether to adjust camera x scaling.\n",
    "        learn_yfoc - Whether to adjust camera y scaling.\n",
    "        learn_angle - Whether to adjust camera angle.\n",
    "    All default to False.\n",
    "    \n",
    "    L2 loss coefficeints:\n",
    "        l2_radialfov,\n",
    "        l2_position,\n",
    "        l2_xfov,\n",
    "        l2_yfov,\n",
    "        l2_angle\n",
    "    All default to 0.01.\n",
    "        \n",
    "    radial, xfov, yfov are considered to be the same for all images.\n",
    "    position and angle are adjusted seperately for each image.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 images,\n",
    "                 learn_radialfov = False,\n",
    "                 learn_position = False,\n",
    "                 learn_xfov = False,\n",
    "                 learn_yfov = False,\n",
    "                 learn_angle = False,\n",
    "                 l2_radialfov = 1e-2,\n",
    "                 l2_position = 1e-2,\n",
    "                 l2_xfov = 1e-2,\n",
    "                 l2_yfov = 1e-2,\n",
    "                 l2_angle = 1e-2,\n",
    "                 tag = 'MorphingDataset'):\n",
    "        \"\"\"\n",
    "        Assumes images is list of Image object.\n",
    "        \"\"\"\n",
    "        super().__init__(images, tag)\n",
    "        \n",
    "        s = self\n",
    "\n",
    "        s.images = images\n",
    "        \n",
    "        s._coord_vec_rgb_constructor(s.images)\n",
    "        \n",
    "        s.view_vecs = None\n",
    "        \n",
    "        #Create view vector components for scaling purpouses.\n",
    "        s.xcomp = []\n",
    "        s.ycomp = []\n",
    "        s.zcomp = []\n",
    "        for image in s.images:\n",
    "            xcomp = np.zeros([image.sizex, image.sizey, 3], dtype = np.float32)\n",
    "            xcomp[:, :, 0] = (np.linspace(-1, 1, image.sizex) * image.fov).reshape([image.sizex, 1])\n",
    "            xcomp = rotate_vecs(image.rot, xcomp)\n",
    "            s.xcomp.append(xcomp)\n",
    "            \n",
    "            ycomp = np.zeros([image.sizex, image.sizey, 3], dtype = np.float32)\n",
    "            ycomp[:, :, 1] = (np.linspace(1, -1, image.sizey) * image.fov / image.sizex * image.sizey).reshape([1, image.sizey])\n",
    "            ycomp = rotate_vecs(image.rot, ycomp)\n",
    "            s.ycomp.append(ycomp)\n",
    "            \n",
    "            zcomp = np.zeros([image.sizex, image.sizey, 3], dtype = np.float32)\n",
    "            zcomp[:, :, 2] = 1\n",
    "            zcomp = rotate_vecs(image.rot, zcomp)\n",
    "            s.zcomp.append(zcomp)\n",
    "        s.xcomp = np.array(s.xcomp)\n",
    "        s.xcomp = torchify(s.xcomp)\n",
    "        s.ycomp = np.array(s.ycomp)\n",
    "        s.ycomp = torchify(s.ycomp)\n",
    "        s.zcomp = np.array(s.zcomp)\n",
    "        s.zcomp = torchify(s.zcomp)\n",
    "        \n",
    "        s.coords = s.coords.reshape([-1, 3])\n",
    "        s.rgb = s.rgb.reshape([-1, 3])\n",
    "        s.xcomp = s.xcomp.reshape([-1, 3])\n",
    "        s.ycomp = s.ycomp.reshape([-1, 3])\n",
    "        s.zcomp = s.zcomp.reshape([-1, 3])\n",
    "        \n",
    "        s.coords = Dataset.permute([s.coords], seed = 42)[0]\n",
    "        s.rgb = Dataset.permute([s.rgb], seed = 42)[0]\n",
    "        s.xcomp = Dataset.permute([s.xcomp], seed = 42)[0]\n",
    "        s.ycomp = Dataset.permute([s.ycomp], seed = 42)[0]\n",
    "        s.zcomp = Dataset.permute([s.zcomp], seed = 42)[0]\n",
    "        \n",
    "        #Prep for learning radial fov.\n",
    "        s.learn_radialfov = learn_radialfov\n",
    "        if s.learn_radialfov:\n",
    "            s.l2_radialfov = l2_radialfov\n",
    "            s.radial_adjustment = torchify(1.0, requires_grad = True)\n",
    "            s.params.append(s.radial_adjustment)\n",
    "        \n",
    "        #Prep for learning camera positions.\n",
    "        s.learn_position = learn_position\n",
    "        if s.learn_position:\n",
    "            s.l2_position = l2_position\n",
    "            data = np.zeros([s.image_count, 3], dtype = np.float32)\n",
    "            s.position_adjustments = torchify(data, requires_grad = True)\n",
    "            s.params.append(s.position_adjustments)\n",
    "            \n",
    "        #Prep for component-wise fov adjustment.\n",
    "        s.learn_xfov = learn_xfov\n",
    "        if s.learn_xfov:\n",
    "            s.l2_xfov = l2_xfov\n",
    "            s.xfov_adjustment = torchify(1.0, requires_grad = True)\n",
    "            s.params.append(s.xfov_adjustment)\n",
    "        \n",
    "        s.learn_yfov = learn_yfov\n",
    "        if s.learn_yfov:\n",
    "            s.l2_yfov = l2_yfov\n",
    "            s.yfov_adjustment = torchify(1.0, requires_grad = True)\n",
    "            s.params.append(s.yfov_adjustment)\n",
    "        \n",
    "        #As of now these are not implemented:\n",
    "        #Prep for angle adjustment.\n",
    "        #Like no idea how I can actually implement this.\n",
    "        #I guess for small aberations I can model this as yaw, pitch and roll.\n",
    "        s.learn_angle = learn_angle\n",
    "        if s.learn_angle:\n",
    "            s.l2_angle = l2_angle\n",
    "            \n",
    "            s.yaw = torchify(np.zeros([s.image_count]), requires_grad = True)\n",
    "            s.pitch = torchify(np.zeros([s.image_count]), requires_grad = True)\n",
    "            s.roll = torchify(np.zeros([s.image_count]), requires_grad = True)\n",
    "            \n",
    "            s.params.append(s.yaw)\n",
    "            s.params.append(s.pitch)\n",
    "            s.params.append(s.roll)\n",
    "            \n",
    "            rots = np.zeros([s.image_count, 3, 3], np.float32)\n",
    "            rots[:, 0, 0] = 1\n",
    "            rots[:, 1, 1] = 1\n",
    "            rots[:, 2, 2] = 1\n",
    "            s.unit_matrixes = torchify(rots)\n",
    "\n",
    "        if s.learn_position or s.learn_angle:\n",
    "            data = np.zeros([s.image_count, s.images[0].sizex, s.images[0].sizey], dtype = np.int32)\n",
    "            for i in range(s.image_count):\n",
    "                data[i] = i\n",
    "\n",
    "            s.position_adjustment_idx = torch.tensor(data, dtype = torch.long, device = def_device)\n",
    "            s.position_adjustment_idx = s.position_adjustment_idx.reshape([-1])\n",
    "            s.position_adjustment_idx = Dataset.permute([s.position_adjustment_idx], seed = 42)[0]\n",
    "\n",
    "    def sample_slice(s, ifrom, ito):\n",
    "        coords = s.coords[ifrom:ito]\n",
    "        \n",
    "        if s.learn_position:\n",
    "            nudge_idx = s.position_adjustment_idx[ifrom:ito]\n",
    "            nudges = torch.index_select(s.position_adjustments, 0, nudge_idx)\n",
    "            coords = coords + nudges\n",
    "        \n",
    "        rgb = s.rgb[ifrom:ito]\n",
    "        \n",
    "        xcomp = s.xcomp[ifrom:ito]\n",
    "        ycomp = s.ycomp[ifrom:ito]\n",
    "        zcomp = s.zcomp[ifrom:ito]\n",
    "        \n",
    "        if s.learn_radialfov:\n",
    "            xcomp = xcomp * s.radial_adjustment\n",
    "            ycomp = ycomp * s.radial_adjustment\n",
    "        \n",
    "        if s.learn_xfov:\n",
    "            xcomp = xcomp * s.xfov_adjustment\n",
    "        \n",
    "        if s.learn_yfov:\n",
    "            ycomp = ycomp * s.yfov_adjustment\n",
    "        \n",
    "        #Modeling radial adjustment.\n",
    "        view_vecs = xcomp + ycomp + zcomp\n",
    "        view_vecs = view_vecs / torch.norm(view_vecs, dim = 1).reshape([-1, 1])\n",
    "        \n",
    "        if s.learn_angle:\n",
    "            #Disclaimer, I think this probably is a reasonable way to model aberations in angle,\n",
    "            #but I'm not certain.\n",
    "            \n",
    "            #Compute rotation matrices for each image.\n",
    "            sin = torch.sin(s.yaw)\n",
    "            cos = torch.cos(s.yaw)\n",
    "            mtxy = torch.zeros([s.image_count, 3, 3], dtype = torch.float32, requires_grad = False, device = def_device)\n",
    "            mtxy[:, 0, 0] = 1\n",
    "            mtxy[:, 1, 1] = cos\n",
    "            mtxy[:, 2, 2] = cos\n",
    "            mtxy[:, 1, 2] = -sin\n",
    "            mtxy[:, 2, 1] = sin\n",
    "\n",
    "            sin = torch.sin(s.pitch)\n",
    "            cos = torch.cos(s.pitch)\n",
    "            mtxp = torch.zeros([s.image_count, 3, 3], dtype = torch.float32, requires_grad = False, device = def_device)\n",
    "            mtxp[:, 1, 1] = 1\n",
    "            mtxp[:, 0, 0] = cos\n",
    "            mtxp[:, 2, 2] = cos\n",
    "            mtxp[:, 0, 2] = -sin\n",
    "            mtxp[:, 2, 0] = sin\n",
    "\n",
    "            sin = torch.sin(s.roll)\n",
    "            cos = torch.cos(s.roll)\n",
    "            mtxr = torch.zeros([s.image_count, 3, 3], dtype = torch.float32, requires_grad = False, device = def_device)\n",
    "            mtxr[:, 2, 2] = 1\n",
    "            mtxr[:, 1, 1] = cos\n",
    "            mtxr[:, 0, 0] = cos\n",
    "            mtxr[:, 1, 0] = -sin\n",
    "            mtxr[:, 0, 1] = sin\n",
    "            \n",
    "            mtx = torch.matmul(torch.matmul(mtxp, mtxy), mtxr)\n",
    "            \n",
    "            #Select rotation matrices\n",
    "            nudge_idx = s.position_adjustment_idx[ifrom:ito] #I probably need to rename this variable.\n",
    "            mtxes = torch.index_select(mtx, 0, nudge_idx)\n",
    "            \n",
    "            #Rotate view vectors\n",
    "            view_vecs = torch.matmul(mtxes, view_vecs.reshape(-1, view_vecs.shape[1], 1))[:, :, 0]\n",
    "        \n",
    "        return (coords, view_vecs, rgb)\n",
    "        \n",
    "    def sample(self, batch_size = 2048):\n",
    "        s = self\n",
    "        \n",
    "        ifrom = self.offset\n",
    "        ito = (self.offset + batch_size) % s.pix_count\n",
    "        \n",
    "        if self.offset + batch_size <= s.pix_count:\n",
    "            coords, view_vecs, rgb = self.sample_slice(ifrom, ito)\n",
    "        else:\n",
    "            coords1, view_vecs1, rgb1 = self.sample_slice(ifrom, s.pix_count)\n",
    "            coords2, view_vecs2, rgb2 = self.sample_slice(0, ito)\n",
    "            \n",
    "            coords = torch.cat([coords1, coords2], dim = 0)\n",
    "            view_vecs = torch.cat([view_vecs1, view_vecs2], dim = 0)\n",
    "            rgb = torch.cat([rgb1, rgb2], dim = 0)\n",
    "            \n",
    "        self.offset+= batch_size\n",
    "        self.offset%= s.pix_count\n",
    "        \n",
    "        return (coords, view_vecs, rgb)\n",
    "        \n",
    "    def _radial_loss(s):\n",
    "        return (s.radial_adjustment - 1) ** 2 * s.l2_radialfov\n",
    "        \n",
    "    def _position_loss(s):\n",
    "        return (s.position_adjustments ** 2).sum() * s.l2_position\n",
    "        \n",
    "    def _xfov_loss(s):\n",
    "        return (s.xfov_adjustment - 1) ** 2 * s.l2_xfov\n",
    "    \n",
    "    def _yfov_loss(s):\n",
    "        return (s.yfov_adjustment - 1) ** 2 * s.l2_yfov\n",
    "        \n",
    "    def _angle_loss(s):\n",
    "        return (s.yaw ** 2 + s.pitch ** 2 + s.roll ** 2).sum() * s.l2_angle\n",
    "        \n",
    "    def loss(s):\n",
    "        \"\"\"\n",
    "        Loss of dataset adjustments.\n",
    "        \"\"\"\n",
    "        res = 0\n",
    "        if s.learn_radialfov:\n",
    "            res+= s._radial_loss()\n",
    "        \n",
    "        if s.learn_position:\n",
    "            res+= s._position_loss()\n",
    "        \n",
    "        if s.learn_xfov:\n",
    "            res+= s._xfov_loss()\n",
    "        \n",
    "        if s.learn_yfov:\n",
    "            res+= s._yfov_loss()\n",
    "\n",
    "        if s.learn_angle:\n",
    "            res+= s._angle_loss()\n",
    "\n",
    "        return res\n",
    "        \n",
    "    def spam(s, indent = 0):\n",
    "        super().spam(indent)\n",
    "        if s.learn_radialfov:\n",
    "            s._iprint('Radial FOV Adjustment', indent = indent + 1)\n",
    "            s._iprint('Radial FOV Adjustment: ', s.radial_adjustment, indent = indent + 2)\n",
    "            s._iprint('Radial L2 coef: ', s.l2_radialfov, indent = indent + 2)\n",
    "            s._iprint('Radial Loss: ', s._radial_loss(), indent = indent + 2)\n",
    "            \n",
    "        if s.learn_position:\n",
    "            s._iprint('Positions Adjustment', indent = indent + 1)\n",
    "            s._iprint('Average Position Adjustment: ', s.position_adjustments.abs().mean(), indent = indent + 2)\n",
    "            s._iprint('Position L2 coef: ', s.l2_position, indent = indent + 2)\n",
    "            s._iprint('Position Loss: ', s._position_loss(), indent = indent + 2)\n",
    "            \n",
    "        if s.learn_xfov:\n",
    "            s._iprint('XFOV Adjustment', indent = indent + 1)\n",
    "            s._iprint('XFOV Adjustment: ', s.xfov_adjustment, indent = indent + 2)\n",
    "            s._iprint('XFOV L2 coef: ', s.l2_xfov, indent = indent + 2)\n",
    "            s._iprint('XFOV Loss: ', s._xfov_loss(), indent = indent + 2)\n",
    "            \n",
    "        if s.learn_yfov:\n",
    "            s._iprint('YFOV Adjustment', indent = indent + 1)\n",
    "            s._iprint('YFOV Adjustment: ', s.yfov_adjustment, indent = indent + 2)\n",
    "            s._iprint('YFOV L2 coef: ', s.l2_yfov, indent = indent + 2)\n",
    "            s._iprint('YFOV Loss: ', s._yfov_loss(), indent = indent + 2)\n",
    "\n",
    "        if s.learn_angle:\n",
    "            s._iprint('Angle Adjustment', indent = indent + 1)\n",
    "            s._iprint('Average angle adjustment: ', s.yaw.abs().mean() + s.pitch.abs().mean() + s.roll.abs().mean(), indent = indent + 2)\n",
    "            s._iprint('Angle L2 coef: ', s.l2_angle, indent = indent + 2)\n",
    "            s._iprint('Angle Loss: ', s._angle_loss(), indent = indent + 2)\n",
    "\n",
    "        s._iprint('Cumulative Loss: ', s.loss(), indent = indent + 1)\n",
    "             \n",
    "    def reset(self):\n",
    "        s = self\n",
    "        \n",
    "        s.offset = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f9b437",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Getting Synthetic datasets (This doesn't work currently)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be15b2f",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Rendering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb019beb",
   "metadata": {
    "code_folding": [
     21,
     29,
     44,
     59,
     87,
     96,
     107,
     118,
     121,
     124,
     132
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x_size = 64\n",
    "y_size = 64\n",
    "\n",
    "depth_buffer = np.zeros([x_size, y_size], dtype = np.float32) + 100000\n",
    "color_buffer = np.zeros([x_size, y_size, 3], dtype = np.float32)\n",
    "\n",
    "#CoordVerified\n",
    "\n",
    "camera_pos = np.zeros([3], dtype = np.float32)\n",
    "hor_angle = 0.0 #Angles turn camera counterclockwise.\n",
    "ver_angle = 0.0 #Positive angles turn camera down.\n",
    "#Angles in radians.\n",
    "pov_coef = 1.0 #Assumes this to be true for x and scales y analogously.\n",
    "\n",
    "#CoordVerified\n",
    "\n",
    "def reset_camera():\n",
    "    global camera_rot\n",
    "    camera_rot = np.zeros([3, 3], dtype = np.float32)\n",
    "    for i in range(3):\n",
    "        camera_rot[i, i] = 1\n",
    "\n",
    "reset_camera()\n",
    "\n",
    "def rot_xz(angle):\n",
    "    global camera_rot\n",
    "    #Turns camera counterclockwise.\n",
    "    rot = np.zeros([3, 3], dtype = np.float32)\n",
    "\n",
    "    rot[1, 1] = 1\n",
    "    rot[0, 0] = math.cos(angle)\n",
    "    rot[0, 2] = -math.sin(angle)\n",
    "    rot[2, 0] = math.sin(angle)\n",
    "    rot[2, 2] = math.cos(angle)\n",
    "\n",
    "    camera_rot = np.matmul(camera_rot, rot)\n",
    "\n",
    "    #CoordVerified.\n",
    "    \n",
    "def rot_yz(angle):\n",
    "    global camera_rot\n",
    "    #Turns camera downward.\n",
    "    rot = np.zeros([3, 3], dtype = np.float32)\n",
    "\n",
    "    rot[0, 0] = 1\n",
    "    rot[1, 1] = math.cos(angle)\n",
    "    rot[1, 2] = math.sin(angle)\n",
    "    rot[2, 1] = - math.sin(angle)\n",
    "    rot[2, 2] = math.cos(angle)\n",
    "\n",
    "    camera_rot = np.matmul(camera_rot, rot)\n",
    "    \n",
    "    #CoordVerified.\n",
    "\n",
    "def render_pix(pixpos, col = np.array([1.0, 1.0, 1.0], dtype = np.float32)):\n",
    "    global depth_buffer\n",
    "    global color_buffer\n",
    "    relpos = pixpos - camera_pos\n",
    "    relpos = np.matmul(relpos, camera_rot)\n",
    "    if relpos[2] <= 0:\n",
    "        return\n",
    "\n",
    "    scrpos = relpos.copy()\n",
    "    scrpos[0]/= relpos[2] / x_size / pov_coef\n",
    "    scrpos[1]/= relpos[2] / x_size / pov_coef\n",
    "    scrpos[0]+= x_size / 2.0\n",
    "    scrpos[1]*= -1\n",
    "    scrpos[1]+= y_size / 2.0\n",
    "\n",
    "    cx = math.floor(scrpos[0])\n",
    "    cy = math.floor(scrpos[1])\n",
    "\n",
    "    if cx < 0 or cy < 0:\n",
    "        return\n",
    "    if cx >= x_size or cy >= y_size:\n",
    "        return\n",
    "    if depth_buffer[cx, cy] > scrpos[2]:\n",
    "        depth_buffer[cx, cy] = scrpos[2]\n",
    "        color_buffer[cx, cy] = col\n",
    "        \n",
    "    #CoordVerified\n",
    "\n",
    "def render_pg(offset, v, u, vpoints = 100, upoints = 100, col = np.array([1.0, 1.0, 1.0], dtype = np.float32)):\n",
    "    vstep = v / (vpoints - 1)\n",
    "    ustep = u / (upoints - 1)\n",
    "    for vpoint in range(vpoints):\n",
    "        for upoint in range(upoints):\n",
    "            curpos = offset + vstep * vpoint + ustep * upoint\n",
    "            render_pix(curpos, col)\n",
    "    return\n",
    "\n",
    "def gen_view_vecs():\n",
    "    vecs = np.zeros([x_size, y_size, 3], dtype = np.float32)\n",
    "    for x in range(x_size):\n",
    "        for y in range(y_size):\n",
    "            vec = np.array([(x - x_size / 2) / x_size * pov_coef, -(y - y_size / 2) / x_size * pov_coef, 1], dtype = np.float32)\n",
    "            vecs[x, y] = vec / np.linalg.norm(vec)\n",
    "    vecs = np.matmul(vecs, np.linalg.inv(camera_rot))\n",
    "    return vecs\n",
    "\n",
    "    #CoordVerified\n",
    "\n",
    "def render_box(c1, c2):\n",
    "    xvec = np.array([c2[0] - c1[0], 0, 0])\n",
    "    yvec = np.array([0, c2[1] - c1[1], 0])\n",
    "    zvec = np.array([0, 0, c2[2] - c1[2]])\n",
    "    render_pg(c1, xvec, yvec, col = np.array([0.5, 0.7, 0.9]))\n",
    "    render_pg(c1, xvec, zvec, col = np.array([0.7, 0.5, 0.9]))\n",
    "    render_pg(c1, yvec, zvec, col = np.array([0.5, 0.9, 0.7]))\n",
    "    render_pg(c1 + yvec, xvec, zvec, col = np.array([0.7, 0.9, 0.5]))\n",
    "    render_pg(c1 + xvec, yvec, zvec, col = np.array([0.9, 0.5, 0.7]))\n",
    "    render_pg(c1 + zvec, xvec, yvec, col = np.array([0.9, 0.7, 0.5]))\n",
    "    \n",
    "def render_box_rel(c1, sizes):\n",
    "    render_box(c1, c1 + sizes)\n",
    "\n",
    "def vec(x, y, z):\n",
    "    return np.array([x, y, z])\n",
    "\n",
    "def cvp():\n",
    "    global color_buffer\n",
    "    global depth_buffer\n",
    "    color_buffer*= 0\n",
    "    #color_buffer+= 0.5\n",
    "    depth_buffer[:, :] = 1000000\n",
    "    reset_camera()\n",
    "\n",
    "def draw():\n",
    "    plt.figure()\n",
    "    tmp = np.flip(color_buffer.swapaxes(0, 1), 0)\n",
    "    plt.imshow(tmp)\n",
    "    \n",
    "    #CoordVerified"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5c207b",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Dataset Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "884ff62f",
   "metadata": {
    "code_folding": [
     5
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "img_data = []\n",
    "vec_data = []\n",
    "positions = []\n",
    "\n",
    "ang_c = 0\n",
    "for i in range(ang_c):\n",
    "    cvp()\n",
    "    angle = math.pi * i / ang_c * 2\n",
    "    pos = vec(math.sin(angle), -0.5, -math.cos(angle)) / 2\n",
    "    camera_pos = pos\n",
    "    rot_xz(angle)\n",
    "    rot_yz(0.4)\n",
    "    render_box_rel(vec(-0.25, -0.25, -0.25) / 2, vec(0.5, 0.5, 0.5)/ 2)\n",
    "    img_data.append(color_buffer.copy())\n",
    "    vec_data.append(gen_view_vecs())\n",
    "    positions.append(pos)\n",
    "    \n",
    "    #CoordVerified\n",
    "\n",
    "img_data = np.array(img_data)\n",
    "vec_data = np.array(vec_data)\n",
    "positions = np.array(positions)\n",
    "#DATASET_CUBE = StaticDataset(positions, vec_data, img_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a7a557",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#DATASET_CUBE.show_images()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66bd168",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Getting Real Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e2426cd",
   "metadata": {
    "code_folding": [
     2,
     51,
     125,
     152
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def get_colmap_cameras(path):\n",
    "    \"\"\"\n",
    "    Tries to decode colmap camera parameters.\n",
    "    Assumes the colmap output will contain exactly one camera.\n",
    "    Assumes the camera has colmap type SIMPLE_RADIAL.\n",
    "    \n",
    "    path - path to file cameras.txt\n",
    "    \n",
    "    Returns dictionary that contains camera description.\n",
    "    \n",
    "    #CoordVerified\n",
    "    \"\"\"\n",
    "    \n",
    "    file = open(path, 'r')\n",
    "    lines = file.readlines()\n",
    "    file.close()\n",
    "    \n",
    "    for line in lines:\n",
    "        #Skip comment lines\n",
    "        if line[0] == '#':\n",
    "            continue\n",
    "\n",
    "        res = {}\n",
    "\n",
    "        parameters = line.split(' ')\n",
    "        \n",
    "        if parameters[1] != 'SIMPLE_RADIAL':\n",
    "            raise ValueError('Colmap file ' + path + ' is type ' + parameters[1] + '; expected SIMPLE_RADIAL.')\n",
    "        \n",
    "        #Image size\n",
    "        res['sizex'] = int(parameters[2])\n",
    "        res['sizey'] = int(parameters[3])\n",
    "        \n",
    "        #Focal length in pixels.\n",
    "        #Connected to camera angle width.\n",
    "        res['fl'] = float(parameters[4])\n",
    "        res['scalingcoef'] = res['sizex'] / res['fl'] / 2\n",
    "        \n",
    "        #I think this is somehow related to the camera camera center offset.\n",
    "        res['cx'] = float(parameters[5])\n",
    "        res['cy'] = float(parameters[6])\n",
    "        \n",
    "        #Literally no clue what this is.\n",
    "        res['???'] = float(parameters[7])\n",
    "        \n",
    "        break\n",
    "    \n",
    "    return [res]\n",
    "\n",
    "def get_colmap_images(path):\n",
    "    \"\"\"\n",
    "    Tries to decode colmap image parameters.\n",
    "    Assumes there's exactly one camera\n",
    "    \n",
    "    Returns list of dictionaries describing images.\n",
    "    \n",
    "    path - path to images.txt file.\n",
    "    \n",
    "    #CoordVerified\n",
    "    \"\"\"\n",
    "    \n",
    "    file = open(path, 'r')\n",
    "    lines = file.readlines()\n",
    "    file.close()\n",
    "    \n",
    "    images = []\n",
    "    \n",
    "    for line in lines:\n",
    "        #Skip comments.\n",
    "        if line[0] == '#':\n",
    "            continue\n",
    "            \n",
    "        params = line.split(' ')\n",
    "        #Skip image feature lines\n",
    "        if len(params) > 30:\n",
    "            continue\n",
    "            \n",
    "        image = {}\n",
    "        \n",
    "        #Image ID\n",
    "        image['ID'] = int(params[0])\n",
    "        \n",
    "        #Rotation quaternion\n",
    "        qr = float(params[1])\n",
    "        qi = float(params[2])\n",
    "        qj = float(params[3])\n",
    "        qk = float(params[4])\n",
    "        image['QR'] = qr\n",
    "        image['QX'] = qi\n",
    "        image['QY'] = qj\n",
    "        image['QZ'] = qk\n",
    "        \n",
    "        #Get rotation matrix.\n",
    "        rot = [[1-2*(qj**2 + qk**2), 2*(qi*qj - qk*qr), 2*(qi*qk + qj*qr)],\n",
    "               [2*(qi*qj + qk*qr), 1-2*(qi**2 + qk**2), 2*(qj*qk - qi*qr)],\n",
    "               [2*(qi*qk - qj*qr), 2*(qj*qk + qi*qr), 1-2*(qi**2 + qj**2)]]\n",
    "        rot = np.array(rot)\n",
    "        rot = rot.transpose()\n",
    "        \n",
    "        #Rotation matrix such that np.matmul(rot, [x, y, z]) moves coordinates from viewer's rotation reference frame to absolute\n",
    "        #rotation reference frame.\n",
    "        image['rot'] = np.array(rot)\n",
    "        \n",
    "        #Image transposition\n",
    "        tx = float(params[5])\n",
    "        ty = float(params[6])\n",
    "        tz = float(params[7])\n",
    "        image['TX'] = tx\n",
    "        image['TY'] = ty\n",
    "        image['TZ'] = tz\n",
    "        \n",
    "        if params[8] != '1':\n",
    "            raise ValueError('Expected file ' + path + ' to contain one camera, but contains more.')\n",
    "            \n",
    "        #Absolute coordinates of camera.\n",
    "        image['coord'] = -np.matmul(rot, np.array([image['TX'], image['TY'], image['TZ']]))\n",
    "        \n",
    "        image['filename'] = params[-1][:-1]\n",
    "        \n",
    "        images.append(image)\n",
    "    \n",
    "    return images\n",
    "\n",
    "def construct_images_from_colmap(path, image_path = None, downscale = 1):\n",
    "    \"\"\"\n",
    "    Constructs list of Image objects from colmap outputs.\n",
    "    \n",
    "    path - folder containing images.txt and cameras.txt.\n",
    "    image_path - folder containing images.\n",
    "    \"\"\"\n",
    "    \n",
    "    if image_path is None:\n",
    "        image_path = path + '/Images'\n",
    "    \n",
    "    colmap_images = get_colmap_images(path + '/images.txt')\n",
    "    colmap_cameras = get_colmap_cameras(path + '/cameras.txt')\n",
    "    \n",
    "    res = []\n",
    "    for image in colmap_images:\n",
    "        rgb = load_image(image_path + '/' + image['filename'])\n",
    "        new_img = Image(coord = image['coord'], \n",
    "                        rot = image['rot'],\n",
    "                        fov = colmap_cameras[0]['scalingcoef'],\n",
    "                        rgb = rgb,\n",
    "                        misc = {'filename': image['filename']})\n",
    "        res.append(new_img)\n",
    "        res[-1].downscale(downscale)\n",
    "    \n",
    "    return res\n",
    "\n",
    "def quick_colmap_images(name, downscale = 2):\n",
    "    \"\"\"\n",
    "    Function that fetches a specific dataset just given the dataset name.\n",
    "    Uses the fact that I know where exactly the files are and in what format.\n",
    "    \n",
    "    Returns list of Image objects.\n",
    "    \n",
    "    It's likely this function will be deprecated in the future.\n",
    "    \"\"\"\n",
    "    \n",
    "    res = construct_images_from_colmap('Datasets/' + name, downscale = downscale)\n",
    "    norm_image_coords(res)\n",
    "    \n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25214bb",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62d3e696",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def train(pipe, dataset, batch_count = 1000, batch_size = 2048, spam_frequency = 100, lr = 0.001, lr_dataset = 0.00001, dataset_loss_start = 0):\n",
    "    \"\"\"\n",
    "    Function that trains pipeline on some dataset.\n",
    "    \"\"\"\n",
    "    \n",
    "    optim = torch.optim.Adam(pipe.params, lr = lr)\n",
    "    if len(dataset.params) != 0:\n",
    "        optim2 = torch.optim.Adam(dataset.params, lr = lr_dataset)\n",
    "        learn_dataset = True\n",
    "    else:\n",
    "        learn_dataset = False\n",
    "    \n",
    "    dataset.reset()\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    for batch_number in range(1, batch_count + 1):\n",
    "        batch_coords, batch_view_vecs, batch_rgb = dataset.sample(batch_size)\n",
    "        \n",
    "        loss = pipe.loss(batch_coords, batch_view_vecs, batch_rgb)\n",
    "        if learn_dataset and batch_number > dataset_loss_start:\n",
    "            loss = loss + dataset.loss()\n",
    "        \n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "        \n",
    "        if learn_dataset and batch_number > dataset_loss_start:\n",
    "            optim2.step()\n",
    "        \n",
    "        optim.zero_grad()\n",
    "        \n",
    "        if learn_dataset and batch_number > dataset_loss_start:\n",
    "            optim2.zero_grad()\n",
    "        \n",
    "        \n",
    "        if batch_number == 1 or batch_number % spam_frequency == 0:\n",
    "            if batch_number != 1:\n",
    "                cur_time = time.time()\n",
    "                sleft = int((batch_count - batch_number) * (cur_time - start_time) / batch_number)\n",
    "                s = 'eta: '\n",
    "                if sleft > 59:\n",
    "                    mleft = sleft // 60\n",
    "                    sleft = sleft % 60\n",
    "                    s+= str(mleft) + 'm '\n",
    "                s+= str(sleft) + ' s'\n",
    "            else:\n",
    "                s = ''\n",
    "            print('Loss at batch ' + str(batch_number) + '/' + str(batch_count) + ': ', loss, s)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f98f91",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f2cd65",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Rendering Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329ee441",
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def show_image(pipe, coords = [0, 0, -0.5], view_vec = [0, 0, 1], sizex = 128, sizey = 128, fovx = 2.0):\n",
    "    \"\"\"\n",
    "    Renders image using pipeline.\n",
    "    Assumes coords is a numpy array or list of size [3].\n",
    "    Assumes view_vec is a numpy array of list of size [3].\n",
    "    \"\"\"\n",
    "    \n",
    "    if type(coords) is list:\n",
    "        coords = np.array(coords)\n",
    "    if type(view_vec) is list:\n",
    "        view_vec = np.array(view_vec)\n",
    "    \n",
    "    viewer = Viewer()\n",
    "    viewer.look_at(view_vec)\n",
    "    \n",
    "    view_vecs = get_view_vecs(sizex, sizey, fovx)\n",
    "    #view_vecs = negate_y(view_vecs)\n",
    "    view_vecs = viewer.to_absolute(view_vecs)\n",
    "    \n",
    "    view_vecs = torchify(view_vecs)\n",
    "    coords = torchify(coords)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        rgb = pipe.forward(coords, view_vecs)\n",
    "    rgb = numpyify(rgb).reshape([sizex, sizey, 3])\n",
    "    \n",
    "    img = mpl_format(rgb)\n",
    "    plt.figure()\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e8a82c",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Rendering Videos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1107d72",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Video Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cb880b2",
   "metadata": {
    "code_folding": [
     6,
     22,
     51,
     60,
     73,
     85,
     88,
     111,
     141,
     160,
     172,
     180,
     209
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class VideoPath():\n",
    "    \"\"\"\n",
    "    Just a class for representing the path a camera takes in a video.\n",
    "    For rendering purpouses.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, coords, view_dirs):\n",
    "        \"\"\"\n",
    "        Assumes coords is numpy array of size [frame_count, 3].\n",
    "        Assumes view_dirs is numpy array of size [frame_count, 3].\n",
    "        \"\"\"\n",
    "        s = self\n",
    "        \n",
    "        if type(coords) is list:\n",
    "            coords = np.array(coords)\n",
    "        if type(view_dirs) is list:\n",
    "            view_dirs = np.array(view_dirs)\n",
    "        \n",
    "        s.coords = coords.copy()\n",
    "        s.view_dirs = view_dirs.copy()\n",
    "        s.frame_count = s.coords.shape[0]\n",
    "    \n",
    "    def get_pipe_inputs(self, sizex = 128, sizey = 128, fovx = 2.0):\n",
    "        \"\"\"\n",
    "        Returns tuple (coords, view_vecs).\n",
    "        Intended to be fed directly into pipe.\n",
    "        coords is a tensor of size [frame_count, size_x, size_y, 3].\n",
    "        view_vecs is a tensor of size [frame_count, size_x, size_y, 3].\n",
    "        \"\"\"\n",
    "\n",
    "        s = self\n",
    "        \n",
    "        viewer = Viewer()\n",
    "\n",
    "        res = []\n",
    "        \n",
    "        coords = torchify(s.coords).reshape([-1, 1, 1, 3])\n",
    "        coords = torch.broadcast_to(coords, [s.frame_count, sizex, sizey, 3])\n",
    "        \n",
    "        view_vecs = []\n",
    "        \n",
    "        tmp = get_view_vecs(sizex, sizey, fovx)\n",
    "        #tmp = negate_y(tmp)\n",
    "        for frame in range(s.frame_count):\n",
    "            viewer.look_at(s.view_dirs[frame])\n",
    "            view_vecs.append(viewer.to_absolute(tmp))\n",
    "        view_vecs = np.array(view_vecs)\n",
    "        view_vecs = torchify(view_vecs)\n",
    "        \n",
    "        return (coords, view_vecs)\n",
    "    \n",
    "    def downsample(self, step_size = 2):\n",
    "        \"\"\"\n",
    "        Downsamples video path frame count.\n",
    "        Makes skips of size step_size.\n",
    "        Returns new VideoPath object.\n",
    "        \"\"\"\n",
    "        \n",
    "        return VideoPath(self.coords[::step_size], self.view_dirs[::step_size])\n",
    "    \n",
    "    def __add__(self, coord):\n",
    "        \"\"\"\n",
    "        Returns new transposed video path.\n",
    "        coord - assumed to be a list or numpy array [x, y, z].\n",
    "        \"\"\"\n",
    "        if type(coord) is list:\n",
    "            coord = np.array(coord)\n",
    "            \n",
    "        new_coords = self.coords + coord.reshape([1, 3])\n",
    "        res = VideoPath(new_coords, self.view_dirs)\n",
    "        \n",
    "        return res\n",
    "    \n",
    "    def __mul__(self, scalar):\n",
    "        \"\"\"\n",
    "        Returns new scaled video path.\n",
    "        Scales towards coordinate (0, 0, 0) (center).\n",
    "        scalar - number to scale by.\n",
    "        \"\"\"\n",
    "        \n",
    "        new_coords = self.coords * scalar\n",
    "        res = VideoPath(new_coords, self.view_dirs)\n",
    "        \n",
    "        return res\n",
    "    \n",
    "    def __truediv__(self, divisor):\n",
    "        return self * (1 / divisor)\n",
    "\n",
    "def video_render_confirmation(path, sizex, sizey, size_limit = 2 ** 26):\n",
    "    \"\"\"\n",
    "    Confirms if person really wants to render a video,\n",
    "    in case the video will take up a lot of memory.\n",
    "    \n",
    "    path - VideoPath object\n",
    "    size_limit - size limit in bytes.\n",
    "    \"\"\"\n",
    "    \n",
    "    estimate = sizex * sizey * path.frame_count * 3 * 4\n",
    "    \n",
    "    if estimate <= size_limit:\n",
    "        return False\n",
    "    else:\n",
    "        print('My estimates say the output video size will be about', beautify_number(estimate), 'Bytes.')\n",
    "        print('This might bloat the notebook you\\'re working in.')\n",
    "        print('If you wish to proceed with the video render type \"y\"')\n",
    "        cmd = input()\n",
    "        if cmd == 'y':\n",
    "            return False\n",
    "    \n",
    "    return True\n",
    "    \n",
    "def embed_video(data):\n",
    "    \"\"\"\n",
    "    Embeds numpy array data as a video.\n",
    "    data - float array of dimensions [image_count, sizex, sizey, 3].\n",
    "    \"\"\"\n",
    "\n",
    "    from matplotlib import animation\n",
    "    from IPython.display import HTML, display\n",
    "\n",
    "    data = np.clip(data, 0, 1)\n",
    "    data = mpl_format(data)\n",
    "\n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    im = plt.imshow(data[0,:,:,:])\n",
    "    plt.close()\n",
    "\n",
    "    def init():\n",
    "        im.set_data(data[0,:,:,:])\n",
    "\n",
    "    def animate(i):\n",
    "        im.set_data(data[i,:,:,:])\n",
    "        return im\n",
    "\n",
    "    anim = animation.FuncAnimation(fig, animate, init_func=init, frames=data.shape[0],\n",
    "                                   interval=50)\n",
    "    display(HTML(anim.to_jshtml()))\n",
    "\n",
    "def make_video(pipe, path, sizex = 128, sizey = 128, fovx = 2.0):\n",
    "    \"\"\"\n",
    "    Renders a video using the Pipe object.\n",
    "    Returns numpy array with shape [frame_count, sizex, sizey, 3].\n",
    "    path - VideoPath object.\n",
    "    \"\"\"\n",
    "\n",
    "    video = []\n",
    "    coords, view_vecs = path.get_pipe_inputs(sizex, sizey, fovx)\n",
    "    testimator = TimeEstimator()\n",
    "    for frame in range(path.frame_count):\n",
    "        with torch.no_grad():\n",
    "            guess = pipe.forward(coords[frame].reshape([-1, 3]), view_vecs[frame].reshape([-1, 3]), batch_size = 2048)\n",
    "        video.append(numpyify(guess).reshape([sizex, sizey, 3]))\n",
    "        if (frame + 1) % (path.frame_count // 10) == 0:\n",
    "            print('Frames Rendered:', str(frame + 1) + '/' + str(path.frame_count), testimator.get_estimate(frame + 1, path.frame_count))\n",
    "    video = np.array(video)\n",
    "    return video\n",
    "    \n",
    "def show_video(pipe, path, sizex = 128, sizey = 128, fovx = 2.0):\n",
    "    \"\"\"\n",
    "    Renders and displays video in notebook.\n",
    "    \"\"\"\n",
    "    cancel = video_render_confirmation(path, sizex, sizey)\n",
    "    if cancel:\n",
    "        return\n",
    "    \n",
    "    video = make_video(pipe, path, sizex, sizey, fovx)\n",
    "    embed_video(video)\n",
    "    \n",
    "import torchvision\n",
    "def save_video(video, path):\n",
    "    \"\"\"\n",
    "    Saves a video.\n",
    "    video - Numpy array of shape [frame_count, sizex, sizey, 3].\n",
    "    \"\"\"\n",
    "    video = (mpl_format(video).copy() * 255).astype(np.uint8)\n",
    "    torchvision.io.write_video(path, video, 30)\n",
    "    \n",
    "def get_interpolation_path(coords, view_dirs, frame_count = 500):\n",
    "    \"\"\"\n",
    "    Gets a video path that's simply an interpolation between camera positions and view directions.\n",
    "    coords is assumed to be a numpy array of size [image_count, 3].\n",
    "    view_dirs is assumed to be a numpy array of size [image_count, 3].\n",
    "    \"\"\"\n",
    "    \n",
    "    coords = np.array(coords)\n",
    "    view_dirs = np.array(view_dirs)\n",
    "    \n",
    "    sections = coords.shape[0]\n",
    "    section_period = frame_count / sections\n",
    "    \n",
    "    res_coords = []\n",
    "    res_view_dirs = []\n",
    "    \n",
    "    for step in range(frame_count):\n",
    "        section = step // section_period\n",
    "        lamb = (step % section_period) / section_period\n",
    "        idx1 = int(section)\n",
    "        idx2 = int((section + 1) % sections)\n",
    "        \n",
    "        coord = coords[idx2] * lamb + coords[idx1] * (1 - lamb)\n",
    "        view_dir = view_dirs[idx2] * lamb + view_dirs[idx1] * (1 - lamb)\n",
    "        res_coords.append(coord)\n",
    "        res_view_dirs.append(view_dir)\n",
    "        \n",
    "    return VideoPath(res_coords, res_view_dirs)\n",
    "\n",
    "def get_path_from_images(images, frame_count = 500):\n",
    "    coords = []\n",
    "    view_dirs = []\n",
    "    for image in images:\n",
    "        coords.append(image.coord)\n",
    "        view_dirs.append(image.forward)\n",
    "    \n",
    "    return get_interpolation_path(coords, view_dirs, frame_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1ce7d8",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Specific Video Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ec3546",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "frame_count = 500\n",
    "coords = []\n",
    "view_dirs = []\n",
    "for frame in range(frame_count):\n",
    "    t = frame / frame_count\n",
    "    sine = math.sin(t * math.pi * 2)\n",
    "    cosine = math.cos(t * math.pi * 2)\n",
    "    \n",
    "    coords.append([sine, 0, cosine])\n",
    "    view_dirs.append([-sine, 0, -cosine])\n",
    "\n",
    "VIDEOPATH_XZCIRCLE = VideoPath(coords, view_dirs) \n",
    "#Path that circles around the center in the xz plane and stares at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77def478",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "frame_count = 500\n",
    "coords = []\n",
    "view_dirs = []\n",
    "for frame in range(frame_count):\n",
    "    t = frame / frame_count\n",
    "    sine = math.sin(t * math.pi * 2)\n",
    "    cosine = math.cos(t * math.pi * 2)\n",
    "    y_coord = math.sin(t * math.pi * 2 * 7) \n",
    "    \n",
    "    coords.append([sine, y_coord, cosine])\n",
    "    view_dirs.append([-sine, -y_coord, -cosine])\n",
    "    \n",
    "VIDEOPATH_WAVY_XZCIRCLE = VideoPath(coords, view_dirs)\n",
    "#Path that circles around the center in the xz plane and stares at it.\n",
    "#But also moves around in the y direction in a wavy fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4b380d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "frame_count = 500\n",
    "coords = []\n",
    "view_dirs = []\n",
    "for frame in range(frame_count):\n",
    "    t = frame / frame_count\n",
    "    x_coord = math.sin(t * math.pi * 2)\n",
    "    y_coord = math.cos(t * math.pi * 2 * 5)\n",
    "    z_coord = math.sin(t * math.pi * 2 * 7) \n",
    "    \n",
    "    coords.append([x_coord, y_coord, z_coord])\n",
    "    view_dirs.append([-x_coord, -y_coord, -z_coord])\n",
    "    \n",
    "VIDEOPATH_360 = VideoPath(coords, view_dirs)\n",
    "#Path that just kinda randomly moves around the center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b350e76",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "frame_count = 500\n",
    "coords = []\n",
    "view_dirs = []\n",
    "for frame in range(frame_count):\n",
    "    t = frame / frame_count\n",
    "    x_coord = math.sin(t * math.pi * 2 * 5)\n",
    "    y_coord = math.sin(t * math.pi * 2)\n",
    "    z_coord = math.cos(t * math.pi * 2 * 5) \n",
    "    \n",
    "    coords.append([x_coord, y_coord, z_coord])\n",
    "    view_dirs.append([-x_coord, -y_coord, -z_coord])\n",
    "    \n",
    "VIDEOPATH_XZSPIRAL = VideoPath(coords, view_dirs)\n",
    "#Path that moves in a spiral around the center."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0d9307",
   "metadata": {},
   "source": [
    "# User Manual"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28c05bd",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Class Diagrams\n",
    "\n",
    "It's probably best that you just look at the examples, and only read this part, if you find that necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac8fa796",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### NeRF Class Structure"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "id": "0a768088",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Actually the main structure is very similar to how it was in the previous notebook. The only change relating to NeRFs is that there's only one decoder class and decoders are constructed using functions.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Purple boxes represent abstract classes.\n",
    "\n",
    "Blue boxes represent instantiatable classes.\n",
    "\n",
    "Pink boxes represent functions that construct specific objects.\n",
    "\n",
    "Solid arrows pointing from purple/blue boxes represent inheritence.\n",
    "\n",
    "Partial arrows pointing from blue boxes represent that an object of that class would contain an object/s of the type that the arrow is pointing to.\n",
    "\n",
    "Solid arrows pointing from punk boxes represent what object type the function returns.\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "#### Short descriptions of each part\n",
    "- Model\n",
    "  - Represents something with (optionally)parameters and a forward function.\n",
    "- Pipe\n",
    "  - Represents a full pipeline that you can simply pass coordinates and view directions to, and it returns RGB values.\n",
    "  - Inheriting classes:\n",
    "    - SimplePipe\n",
    "      - Consists of a NeRF and a Tracer.\n",
    "- Tracer\n",
    "  - Represents an object that does numeric integration by using a NeRF to get RGB values from NeRF's RGBσ values, given coordinates and view directions.\n",
    "  - Inheriting classes:\n",
    "    - UniformTracer:\n",
    "      - Samples NeRF output in uniform steps along view vector.\n",
    "    - UniformRandomTracer:\n",
    "      - Samples NeRF output in uniform steps along view vectors, however adds random the size of a step.\n",
    "- NeRF\n",
    "  - Represents and object that takes coordinates and a view direction and returns RGB and density at that point.\n",
    "  - Inheriting classes:\n",
    "    - FeatureGridNeRF:\n",
    "      - NeRF that consists of some structure that gives features and a decoder.\n",
    "    - FeaturelessNeRF:\n",
    "      - NeRF that simply has a decoder and no feature structure.\n",
    "- OpGraph\n",
    "  - Represents an object that takes in some input tensors, performs some sequence of operations on them and returns some output tensors. (I'm not sure if it was wise to create a class like this)\n",
    "  - Inheriting classes:\n",
    "    - Decoder\n",
    "- Decoder\n",
    "  - Represents an object that takes in features, coordinates and view direction, and returns RGB and density values.\n",
    "  - Inheriting classes and special constructors:\n",
    "    - construct_FCNNSingle_decoder:\n",
    "      - Contains a single neural network that predicts both RGB and density from all inputs.\n",
    "    - construct_FCNNDouble_decoder:\n",
    "      - Contains two neural networks. One for RGB (that receives view direction information), and one for density (that doesn't receive direction information).\n",
    "- FeatureGrid:\n",
    "  - Represents an object that takes in coordinates in range [-1..1]^3 and returns feature vectors.\n",
    "  - Inheriting classes:\n",
    "    - DenseGrid:\n",
    "      - A grid that explicitly stores feature vectors at each voxel.\n",
    "    - HashGrid:\n",
    "      - Same implementation as a single resolution hashtable in InstantNGP.\n",
    "- Aggregator:\n",
    "  - Represents an object that takes outputs of multiple feature grids and converts them into a single tensor.\n",
    "  - Inheriting classes:\n",
    "    - CatAggregator:\n",
    "      - Concatenates features together. (This is what is used in InstantNGP)\n",
    "      - Special constructor:\n",
    "        - construct_INGP_grid\n",
    "          - Constructs and InstantNGP-like feature grid.\n",
    "    - MeanAggregator:\n",
    "      - Takes the mean value of feature vectors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5171937",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Datasets"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "fe7d1ba6",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "Notation used is same as in NeRF Class Structure.\n",
    "\n",
    "An image object describes an image and it's camera parameters.\n",
    "\n",
    "Basically there are two Dataset types:\n",
    "- StaticDataset\n",
    "  - Doesn't have any learnable parameters.\n",
    "- MorphingDataset\n",
    "  - Has learnable parameters that you set during construction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925e65c1",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Getting Dataset Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b17d343",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#First you get images\n",
    "images = quick_colmap_images('Plant') #This function returns a list of Image objects.\n",
    "#Available datasets are: 'Plant', 'Stikli', 'Blocks', 'Pillow', 'Vaze', 'Figurina'.\n",
    "\n",
    "#Then you construct a dataset form the images\n",
    "dataset = StaticDataset(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec583984",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#There's a nifty function for showing the images the dataset contains:\n",
    "dataset.show_images()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "942d3979",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#There's also a nifty function for spamming all kinds of \"perhaps useful\" information:\n",
    "dataset.spam()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a655d327",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here's an example on how you'd go about getting a dataset with learnable parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9374a541",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "images = quick_colmap_images('Plant', downscale = 4)\n",
    "#downscale is a parameter that just downsamples the image in each direction by a factor of 4.\n",
    "#This might be of use, because storing billions of floats on GPU seems to become a problem occasionally.\n",
    "\n",
    "#Here's the full list of parameters for a MorphingDataset constructor:\n",
    "dataset = MorphingDataset(images,\n",
    "                         learn_radialfov = False,\n",
    "                         learn_position = False,\n",
    "                         learn_xfov = False,\n",
    "                         learn_yfov = False,\n",
    "                         learn_angle = False,\n",
    "                         l2_radialfov = 1e-2,\n",
    "                         l2_position = 1e-2,\n",
    "                         l2_xfov = 1e-2,\n",
    "                         l2_yfov = 1e-2,\n",
    "                         l2_angle = 1e-2)\n",
    "#In order to learn one or more of these parameters you need to simply set learn_X to True.\n",
    "#If it's set to False, then the parameter is not learned.\n",
    "#  All default ot False\n",
    "#l2_X means L2 loss coefficient for that parameter. \n",
    "#  All default to 0.01\n",
    "#\n",
    "#There by default will be a pressure towards values that don't change the dataset.\n",
    "#  radialfov - Whether to streatch or shrink the picture in both directions simultaneously. (common for all images)\n",
    "#  position - Whether to nugde image coordinates. (different for each image)\n",
    "#  xfov & yfov - Whether to stretch or shring the picture in x or y direction. (Common for all images)\n",
    "#  angle - Whether to rotate the camera in 3D. (different for each image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7029498",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dataset.spam()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77cf0381",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Training Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67336ccb",
   "metadata": {
    "hidden": true
   },
   "source": [
    "After you have your dataset, You can train as per usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0da189df",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Define pipeline\n",
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 128, 128, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 128, 128, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "\n",
    "#Train\n",
    "train(pipe, dataset, batch_count = 2500, spam_frequency = 500, lr = 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df639f56",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dataset.spam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5aeee97",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#This is the full list of parameters and their defaults for the training function.\n",
    "#batch_size - number of pixels per gradient step.\n",
    "#lr_dataset - learning rate for dataset parameters. Note: The dataset parameters have a separate Adam optimizer.\n",
    "#dataset_loss_start - batch number at which to start training learnable parameters in dataset.\n",
    "\"\"\"\n",
    "train(pipe, \n",
    "      dataset, \n",
    "      batch_count = 1000, \n",
    "      batch_size = 2048, \n",
    "      spam_frequency = 100, \n",
    "      lr = 0.001, \n",
    "      lr_dataset = 0.00001, \n",
    "      dataset_loss_start = 0)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7caf1b",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Visualization Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "937637f6",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Get a path along which the camera in the video will be moving.\n",
    "path = get_path_from_images([images[0], images[10], images[20]], frame_count = 200)\n",
    "#This function simply interpolates between the positions and rotations of a list of Image objects that you give it.\n",
    "#Here I've chosen 3 images with numbers 0, 10 and 20 in the dataset.\n",
    "#I've also chosen the video to have 200 frames.\n",
    "#You can choose image numbers to interpolate through by looking at dataset.show_images()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7e54057",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "show_video(pipe, path, sizex = 96, sizey = 96, fovx = 1)\n",
    "#Function that displays video in notebook\n",
    "#sizex - pixel count in video, in x direction.\n",
    "#sizey - pixel count in video, in y direction.\n",
    "#The higher the fovx number, the wider the camera angle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2bd542",
   "metadata": {
    "hidden": true
   },
   "source": [
    "If you want to save a video to disk, rather than display it in the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56e1dfac",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "video = make_video(pipe, path, sizex = 256, sizey = 256, fovx = 1)\n",
    "#This function returns a np.float32 array of shape [frame_count, sizex, sizey, rgb].\n",
    "#The values are in the range [0..1].\n",
    "\n",
    "save_video(video, 'MyVideo.mp4')\n",
    "#This function saves the numpy array as an mp4 to disk."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e97e43a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Some Full Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89bc2b97",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Get dataset\n",
    "images = quick_colmap_images('Pillow', downscale = 4)\n",
    "dataset = MorphingDataset(images, learn_xfov = True, learn_yfov = True)\n",
    "\n",
    "#Make pipeline\n",
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     density_activations = [None, 'relu', 'relu', 'sigmoid'],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 384)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "\n",
    "#Train\n",
    "train(pipe, dataset, batch_count = 2500, spam_frequency = 500, lr = 0.002)\n",
    "\n",
    "#Visualize\n",
    "path = get_path_from_images([images[0], images[5], images[10], images[15]], frame_count = 500)\n",
    "video = make_video(pipe, path, sizex = 128, sizey = 128, fovx = 1)\n",
    "save_video(video, 'Example1.mp4')\n",
    "embed_video(video) #This function shows the video in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad05725f",
   "metadata": {},
   "source": [
    "# Random Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b90f197b",
   "metadata": {},
   "outputs": [],
   "source": [
    "raise Error()\n",
    "#This is only here so that you could click \"run all\", and it wouldn't run these experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c5767a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define pipeline\n",
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 384)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "\n",
    "#Train\n",
    "train(pipe, dataset, batch_count = 20000, spam_frequency = 1000, lr = 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd2fe61",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = get_path_from_images([images[0], images[10], images[20], images[25]], frame_count = 1000)\n",
    "\n",
    "vid = make_video(pipe, path, sizex = 256, sizey = 256, fovx = 1)\n",
    "save_video(vid, 'plant.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0c7e5d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06a0768",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "images = construct_images_from_colmap('Datasets/Figurina', 'Datasets/Figurina/Images', downscale = 2)\n",
    "norm_image_coords(images)\n",
    "stuff = MorphingDataset(images, learn_radialfov = True, learn_position = False, learn_xfov = True, learn_yfov = True, learn_angle = True)\n",
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2cd56a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14923eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = get_path_from_images(images, frame_count = 1000)\n",
    "\n",
    "vid = make_video(pipe, path, sizex = 256, sizey = 256, fovx = 1)\n",
    "save_video(vid, 'test.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dabaff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = get_path_from_images(images[25:], frame_count = 200)\n",
    "\n",
    "show_video(pipe, path, sizex = 128, sizey = 128, fovx = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87b2ddce",
   "metadata": {},
   "outputs": [],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59def4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "halt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e089e14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = get_path_from_images(images, frame_count = 1000)\n",
    "\n",
    "vid = make_video(pipe, path, sizex = 256, sizey = 256, fovx = 1)\n",
    "save_video(vid, 'test.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c87b2783",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.show_images()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "558744a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15ce645d",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = None\n",
    "gc.collect()\n",
    "stuff = construct_images_from_colmap('Datasets/Stikli', 'Datasets/Stikli/Images')\n",
    "stuff = MorphingDataset(stuff, learn_radialfov = False, learn_position = False, learn_xfov = False, learn_yfov = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c42d2af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73baec86",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_video(pipe, VIDEOPATH_XZCIRCLE.downsample(6) / 3 + [-0.1, 0.25, 0.4], sizex = 128, sizey = 128, fovx = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd50a57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb7aad36",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = None\n",
    "gc.collect()\n",
    "stuff = construct_images_from_colmap('Datasets/Stikli', 'Datasets/Stikli/Images')\n",
    "stuff = MorphingDataset(stuff, learn_radialfov = True, learn_position = False, learn_xfov = False, learn_yfov = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87850c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a218ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_video(pipe, VIDEOPATH_XZCIRCLE.downsample(6) / 3 + [-0.1, 0.25, 0.4], sizex = 128, sizey = 128, fovx = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f0ae6ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a38cebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5821bc2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.show_images()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f79a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = construct_colmap_dataset('Datasets/Stikli', 'Datasets/Stikli/Images')\n",
    "stuff = StaticDataset(stuff[0], stuff[1], stuff[2])\n",
    "stuff.show_images()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54e0f661",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = construct_FCNNsingle_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 4], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adebbeaa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "show_video(pipe, VIDEOPATH_XZCIRCLE.downsample(6) / 3 + [-0.1, 0.25, 0.4], sizex = 96, sizey = 96, fovx = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279bbe89",
   "metadata": {},
   "source": [
    "## Delay Learning Dataset Experiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a78ce42",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = None\n",
    "gc.collect()\n",
    "stuff = construct_images_from_colmap('Datasets/Stikli', 'Datasets/Stikli/Images')\n",
    "stuff = MorphingDataset(stuff, learn_radialfov = True, learn_position = False, learn_xfov = False, learn_yfov = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f49a5a",
   "metadata": {},
   "source": [
    "### With offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4d71726",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002, dataset_loss_start = 5000)\n",
    "show_video(pipe, VIDEOPATH_XZCIRCLE.downsample(6) / 3 + [-0.1, 0.25, 0.4], sizex = 128, sizey = 128, fovx = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "451d4f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a0f67b",
   "metadata": {},
   "source": [
    "### No learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2606f255",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = None\n",
    "gc.collect()\n",
    "stuff = construct_images_from_colmap('Datasets/Stikli', 'Datasets/Stikli/Images')\n",
    "stuff = StaticDataset(stuff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ca7bb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002, dataset_loss_start = 100000)\n",
    "show_video(pipe, VIDEOPATH_XZCIRCLE.downsample(6) / 3 + [-0.1, 0.25, 0.4], sizex = 128, sizey = 128, fovx = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74ab8608",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a016584",
   "metadata": {},
   "source": [
    "### Everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30116e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = None\n",
    "stuff = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e187df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff = None\n",
    "gc.collect()\n",
    "stuff = construct_images_from_colmap('Datasets/Stikli', 'Datasets/Stikli/Images')\n",
    "stuff = MorphingDataset(stuff, learn_radialfov = True, learn_position = True, learn_xfov = True, learn_yfov = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0552bfac",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder = construct_FCNNdouble_decoder(feat_count = 0, \n",
    "                     encoding_levels = 14, \n",
    "                     layers = [None, 256, 256, 3], \n",
    "                     activations = [None, 'relu', 'relu', 'sigmoid'], \n",
    "                     density_layers = [None, 256, 256, 1],\n",
    "                     feats = False,\n",
    "                     view_vecs = True)\n",
    "nerf = FeaturelessNeRF(decoder)\n",
    "tracer = UniformRandomTracer(distance = 2.5, steps = 256)\n",
    "pipe = SimplePipe(nerf, tracer)\n",
    "train(pipe, stuff, batch_count = 10000, spam_frequency = 1000, lr = 0.002, dataset_loss_start = 5000, lr_dataset = 3e-4)\n",
    "show_video(pipe, VIDEOPATH_XZCIRCLE.downsample(6) / 3 + [-0.1, 0.25, 0.4], sizex = 128, sizey = 128, fovx = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7c6c55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "stuff.spam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55d225e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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