dev_nb/snapshot/002_images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "sys.path.append('../docs')\n",
    "from gen_doc.nbdoc import show_doc as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from nb_001b import *\n",
    "import sys, PIL, matplotlib.pyplot as plt, itertools, math, random, collections, torch\n",
    "import scipy.stats, scipy.special\n",
    "\n",
    "from enum import Enum, IntEnum\n",
    "from torch import tensor, Tensor, FloatTensor, LongTensor, ByteTensor, DoubleTensor, HalfTensor, ShortTensor\n",
    "from operator import itemgetter, attrgetter\n",
    "from numpy import cos, sin, tan, tanh, log, exp\n",
    "from dataclasses import field\n",
    "from functools import reduce\n",
    "from collections import defaultdict, abc, namedtuple, Iterable\n",
    "from typing import Tuple, Hashable, Mapping, Dict\n",
    "\n",
    "import mimetypes\n",
    "import abc\n",
    "from abc import abstractmethod, abstractproperty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR subset data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we want to view our data to check if everything is how we expect it to be."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = Path('data')\n",
    "PATH = DATA_PATH/'cifar10_dog_air'\n",
    "TRAIN_PATH = PATH/'train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6X391tpLxAvbv9uAXVGJiWb/QnHT88vJCxvcnnV+fzxf6bWFOYAP++luYT5hPdN1jeu+uPs9G1zc2EN9DnYHy92TPU9JeXn344EIA2krRC0A3cG6Bzu5fABMaC8CExgIwobEATGgsABMaC8CExgIwoUlZrffaLs503/xyDfl+XCcAfdi0YBbo4Dw0t9DCftym0XuLDxu9l5fy6Kcj7CuAfn3KQ1Pefb7UdYnJYibj+y3sab65geNh/wDcn6H+/WQ7hPso4GiaEyAKLjTo4wed3ZhfGRaACY0FYEJjAZjQWAAmNBaACY0FYEKT1g/uyRfmc+2vnwo9P0D+/bSn9heMY+CFgftlIR9PfvnHk85/0z5d7C+n/cS4f0BD+e8S7v9iruc0ZlPt10R1DJpzuAX/pdOR6gYD9xN/MN8efd9oH4J/AUxoLAATGgvAhMYCMKGxAExoLAATGgvAhOb/k4XP3/+1jA4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x7F29909197B8>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_fn = list((TRAIN_PATH/'dog').iterdir())[0]\n",
    "dog_image = PIL.Image.open(dog_fn)\n",
    "dog_image.resize((256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x7F2A1045BF28>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_fn = list((TRAIN_PATH/'airplane').iterdir())[1]\n",
    "air_image = PIL.Image.open(air_fn)\n",
    "air_image.resize((256,256))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Dataset/Dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will build a Dataset class for our image files. A Dataset class needs to have two functions: `__len__` and `__getitem__`. Our `ImageDataset` class additionally gets image files from their respective directories and transforms them to tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def image2np(image:Tensor)->np.ndarray:\n",
    "    \"convert from torch style `image` to numpy/matplot style\"\n",
    "    res = image.cpu().permute(1,2,0).numpy()\n",
    "    return res[...,0] if res.shape[2]==1 else res\n",
    "\n",
    "def show_image(img:Tensor, ax:plt.Axes=None, figsize:tuple=(3,3), hide_axis:bool=True, \n",
    "               title:Optional[str]=None, cmap:str='binary', alpha:Optional[float]=None)->plt.Axes:\n",
    "    \"plot tensor `img` using matplotlib axis `ax`.  `figsize`,`axis`,`title`,`cmap` and `alpha` pass to `ax.imshow`\"\n",
    "    if ax is None: fig,ax = plt.subplots(figsize=figsize)\n",
    "    ax.imshow(image2np(img), cmap=cmap, alpha=alpha)\n",
    "    if hide_axis: ax.axis('off')\n",
    "    if title: ax.set_title(title)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Image():\n",
    "    def __init__(self, px): self.px = px\n",
    "    def show(self, ax=None, **kwargs): return show_image(self.px, ax=ax, **kwargs)\n",
    "    @property\n",
    "    def data(self): return self.px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "FilePathList = Collection[Path]\n",
    "TensorImage = Tensor\n",
    "NPImage = np.ndarray\n",
    "\n",
    "def find_classes(folder:Path)->FilePathList:\n",
    "    \"return class subdirectories in imagenet style train `folder`\"\n",
    "    classes = [d for d in folder.iterdir()\n",
    "               if d.is_dir() and not d.name.startswith('.')]\n",
    "    assert(len(classes)>0)\n",
    "    return sorted(classes, key=lambda d: d.name)\n",
    "\n",
    "image_extensions = set(k for k,v in mimetypes.types_map.items() if v.startswith('image/'))\n",
    "\n",
    "def get_image_files(c:Path, check_ext:bool=True)->FilePathList:\n",
    "    \"return list of files in `c` that are images. `check_ext` will filter to `image_extensions`.\"\n",
    "    return [o for o in list(c.iterdir())\n",
    "            if not o.name.startswith('.') and not o.is_dir()\n",
    "            and (not check_ext or (o.suffix in image_extensions))]\n",
    "\n",
    "def pil2tensor(image:NPImage)->TensorImage:\n",
    "    \"convert PIL style `image` array to torch style image tensor `get_image_files`\"\n",
    "    arr = torch.ByteTensor(torch.ByteStorage.from_buffer(image.tobytes()))\n",
    "    arr = arr.view(image.size[1], image.size[0], -1)\n",
    "    return arr.permute(2,0,1)\n",
    "\n",
    "PathOrStr = Union[Path,str]\n",
    "def open_image(fn:PathOrStr):\n",
    "    \"return `Image` object created from image in file `fn`\"\n",
    "    x = PIL.Image.open(fn).convert('RGB')\n",
    "    return Image(pil2tensor(x).float().div_(255))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "NPArrayableList = Collection[Union[np.ndarray, list]]\n",
    "NPArrayMask = np.ndarray\n",
    "SplitArrayList = List[Tuple[np.ndarray,np.ndarray]]\n",
    "\n",
    "def arrays_split(mask:NPArrayMask, *arrs:NPArrayableList)->SplitArrayList:\n",
    "    \"given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...]\"\n",
    "    mask = array(mask)\n",
    "    return list(zip(*[(a[mask],a[~mask]) for a in map(np.array, arrs)]))\n",
    "\n",
    "def random_split(valid_pct:float, *arrs:NPArrayableList)->SplitArrayList:\n",
    "    \"randomly `array_split` with `valid_pct` ratio. good for creating validation set.\"\n",
    "    is_train = np.random.uniform(size=(len(arrs[0]),)) > valid_pct\n",
    "    return arrays_split(is_train, *arrs)\n",
    "\n",
    "class DatasetBase(Dataset):\n",
    "    \"base class for all fastai datasets\"\n",
    "    def __len__(self): return len(self.x)\n",
    "    @property\n",
    "    def c(self): \n",
    "        \"number of classes expressed by dataset y variable\"\n",
    "        return self.y.shape[-1] if len(self.y.shape)>1 else 1\n",
    "    def __repr__(self): return f'{type(self).__name__} of len {len(self)}'\n",
    "\n",
    "class LabelDataset(DatasetBase):\n",
    "    \"base class for fastai datasets that do classification\"\n",
    "    @property\n",
    "    def c(self): \n",
    "        \"number of classes expressed by dataset y variable\"\n",
    "        return len(self.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "ImgLabel = str\n",
    "ImgLabels = Collection[ImgLabel]\n",
    "Classes = Collection[Any]\n",
    "\n",
    "class ImageDataset(LabelDataset):\n",
    "    \"Dataset for folders of images in style {folder}/{class}/{images}\"\n",
    "    def __init__(self, fns:FilePathList, labels:ImgLabels, classes:Optional[Classes]=None):\n",
    "        self.classes = ifnone(classes, list(set(labels)))\n",
    "        self.class2idx = {v:k for k,v in enumerate(self.classes)}\n",
    "        self.x = np.array(fns)\n",
    "        self.y = np.array([self.class2idx[o] for o in labels], dtype=np.int64)\n",
    "    \n",
    "    def __getitem__(self,i): return open_image(self.x[i]),self.y[i]\n",
    "    \n",
    "    @staticmethod\n",
    "    def _folder_files(folder:Path, label:ImgLabel, check_ext=True)->Tuple[FilePathList,ImgLabels]:\n",
    "        \"from `folder` return image files and labels. The labels are all `label`. `check_ext` means only image files\"\n",
    "        fnames = get_image_files(folder, check_ext=check_ext)\n",
    "        return fnames,[label]*len(fnames)\n",
    "    \n",
    "    @classmethod\n",
    "    def from_single_folder(cls, folder:PathOrStr, classes:Classes, check_ext=True):\n",
    "        \"typically used for test set. label all images in `folder` with `classes[0]`\"\n",
    "        fns,labels = cls._folder_files(folder, classes[0], check_ext=check_ext)\n",
    "        return cls(fns, labels, classes=classes)\n",
    "\n",
    "    @classmethod\n",
    "    def from_folder(cls, folder:Path, classes:Optional[Classes]=None, \n",
    "                    valid_pct:float=0., check_ext:bool=True) -> Union['ImageDataset', List['ImageDataset']]:\n",
    "        \"\"\"dataset of `classes` labeled images in `folder`. Optional `valid_pct` split validation set.\"\"\"\n",
    "        if classes is None: classes = [cls.name for cls in find_classes(folder)]\n",
    "            \n",
    "        fns,labels = [],[]\n",
    "        for cl in classes:\n",
    "            f,l = cls._folder_files(folder/cl, cl, check_ext=check_ext)\n",
    "            fns+=f; labels+=l\n",
    "            \n",
    "        if valid_pct==0.: return cls(fns, labels, classes=classes)\n",
    "        return [cls(*a, classes=classes) for a in random_split(valid_pct, fns, labels)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<a id=from_folder></a>**from_folder**(<em>cls</em>, <em>folder</em>: Path, <em>classes</em>: typing.Union[typing.Collection[typing.Any], NoneType]=None, <em>valid_pct</em>: float=0.0, <em>check_ext</em>: bool=True) -> typing.Union[_ForwardRef('ImageDataset'), typing.List[_ForwardRef('ImageDataset')]]\n",
       "\n",
       "\n",
       "dataset of `classes` labeled images in `folder`. Optional `valid_pct` split validation set."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd(ImageDataset.from_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to augment our data to increase the size of our training set with artificial images. These new images are basically \"free\" data that we can use in our training to help our model generalize better (reduce overfitting)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lighting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by changing the **brightness** and **contrast** of our images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Brightness**\n",
    "\n",
    "Brightness refers to where does our image stand on the dark-light spectrum. Brightness is applied by adding a positive constant to each of the image's channels. This works because each of the channels in an image goes from 0 (darkest) to 255 (brightest) in a dark-light continum. (0, 0, 0) is black (total abscence of light) and (255, 255, 255) is white (pure light). You can check how this works by experimenting by yourself [here](https://www.w3schools.com/colors/colors_rgb.asp).\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Change** How much brightness do we want to add to (or take from) the image.\n",
    "\n",
    "    Domain: Real numbers\n",
    "    \n",
    "**Contrast**\n",
    "\n",
    "Contrast refers to how sharp a distinction there is between brighter and darker sections of our image. To increase contrast we need darker pixels to be darker and lighter pixels to be lighter. In other words, we would like channels with a value smaller than 128 to decrease and channels with a value of greater than 128 to increase.\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Scale** How much contrast do we want to add to (or remove from) the image.\n",
    "\n",
    "    Domain: [0, +inf]\n",
    "    \n",
    "***On logit and sigmoid***\n",
    "\n",
    "Notice that for both transformations we first apply the logit to our tensor, then apply the transformation and finally take the sigmoid. This is important for two reasons. \n",
    "\n",
    "First, we don't want to overflow our tensor values. In other words, we need our final tensor values to be between [0,1].  Imagine, for instance, a tensor value at 0.99. We want to increase its brightness, but we can’t go over 1.0. By doing logit first, which first moves our space to -inf to +inf, this works fine. The same applies to contrast if we have a scale S > 1 (might make some of our tensor values greater than one).\n",
    "\n",
    "Second, when we apply contrast, we need to affect the dispersion of values around the middle value. Say we want to increase contrast. Then we need the bright values (>0.5) to get brighter and dark values (<0.5) to get darker. We must first transform our tensor values so our values which were originally <0.5 are now negative and our values which were originally >0.5 are now positive. This way, when we multiply by a constant, the dispersion around 0 will increase. The logit function does exactly this and allows us to increase or decrease dispersion around a mid value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def logit(x:Tensor)->Tensor:  return -(1/x-1).log()\n",
    "def logit_(x:Tensor)->Tensor: return (x.reciprocal_().sub_(1)).log_().neg_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def contrast(x:Tensor, scale:float)->Tensor: return x.mul_(scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "FlowField = Tensor\n",
    "LogitTensorImage = TensorImage\n",
    "AffineMatrix = Tensor\n",
    "KWArgs = Dict[str,Any]\n",
    "ArgStar = Collection[Any]\n",
    "CoordSize = Tuple[int,int,int]\n",
    "\n",
    "LightingFunc = Callable[[LogitTensorImage, ArgStar, KWArgs], LogitTensorImage]\n",
    "PixelFunc = Callable[[TensorImage, ArgStar, KWArgs], TensorImage]\n",
    "CoordFunc = Callable[[FlowField, CoordSize, ArgStar, KWArgs], LogitTensorImage]\n",
    "AffineFunc = Callable[[KWArgs], AffineMatrix]\n",
    "\n",
    "\n",
    "class ItemBase():\n",
    "    \"All tranformable dataset items use this type\"\n",
    "    @property\n",
    "    @abstractmethod\n",
    "    def device(self): pass\n",
    "    @property\n",
    "    @abstractmethod\n",
    "    def data(self): pass\n",
    "\n",
    "class ImageBase(ItemBase):\n",
    "    \"Img based `Dataset` items dervie from this. Subclass to handle lighting, pixel, etc\"\n",
    "    def lighting(self, func:LightingFunc, *args, **kwargs)->'ImageBase': return self\n",
    "    def pixel(self, func:PixelFunc, *args, **kwargs)->'ImageBase': return self\n",
    "    def coord(self, func:CoordFunc, *args, **kwargs)->'ImageBase': return self\n",
    "    def affine(self, func:AffineFunc, *args, **kwargs)->'ImageBase': return self\n",
    "\n",
    "    def set_sample(self, **kwargs)->'ImageBase':\n",
    "        \"set parameters that control how we `grid_sample` the image after transforms are applied\"\n",
    "        self.sample_kwargs = kwargs\n",
    "        return self\n",
    "    \n",
    "    def clone(self)->'ImageBase': \n",
    "        \"clones this item and its `data`\"\n",
    "        return self.__class__(self.data.clone())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Image(ImageBase):\n",
    "    \"supports appying transforms to image data\"\n",
    "    def __init__(self, px)->'Image':\n",
    "        \"create from raw tensor image data `px`\"\n",
    "        self._px = px\n",
    "        self._logit_px=None\n",
    "        self._flow=None\n",
    "        self._affine_mat=None\n",
    "        self.sample_kwargs = {}\n",
    "\n",
    "    @property\n",
    "    def shape(self)->Tuple[int,int,int]: \n",
    "        \"returns (ch, h, w) for this image\"\n",
    "        return self._px.shape\n",
    "    @property\n",
    "    def size(self)->Tuple[int,int,int]: \n",
    "        \"returns (h, w) for this image\"\n",
    "        return self.shape[-2:]\n",
    "    @property\n",
    "    def device(self)->torch.device: return self._px.device\n",
    "    \n",
    "    def __repr__(self): return f'{self.__class__.__name__} ({self.shape})'\n",
    "\n",
    "    def refresh(self)->None:\n",
    "        \"applies any logit or affine transfers that have been \"\n",
    "        if self._logit_px is not None:\n",
    "            self._px = self._logit_px.sigmoid_()\n",
    "            self._logit_px = None\n",
    "        if self._affine_mat is not None or self._flow is not None:\n",
    "            self._px = grid_sample(self._px, self.flow, **self.sample_kwargs)\n",
    "            self.sample_kwargs = {}\n",
    "            self._flow = None\n",
    "        return self\n",
    "\n",
    "    @property\n",
    "    def px(self)->TensorImage:\n",
    "        \"get the tensor pixel buffer\"\n",
    "        self.refresh()\n",
    "        return self._px\n",
    "    @px.setter\n",
    "    def px(self,v:TensorImage)->None: \n",
    "        \"set the pixel buffer to `v`\"\n",
    "        self._px=v\n",
    "\n",
    "    @property\n",
    "    def flow(self)->FlowField:\n",
    "        \"access the flow-field grid after applying queued affine transforms\"\n",
    "        if self._flow is None:\n",
    "            self._flow = affine_grid(self.shape)\n",
    "        if self._affine_mat is not None:\n",
    "            self._flow = affine_mult(self._flow,self._affine_mat)\n",
    "            self._affine_mat = None\n",
    "        return self._flow\n",
    "    \n",
    "    @flow.setter\n",
    "    def flow(self,v:FlowField): self._flow=v\n",
    "\n",
    "    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any)->'Image':\n",
    "        \"equivalent to `image = sigmoid(func(logit(image)))`\"\n",
    "        self.logit_px = func(self.logit_px, *args, **kwargs)\n",
    "        return self\n",
    "\n",
    "    def pixel(self, func:PixelFunc, *args, **kwargs)->'Image':\n",
    "        \"equivalent to `image.px = func(image.px)`\"\n",
    "        self.px = func(self.px, *args, **kwargs)\n",
    "        return self\n",
    "\n",
    "    def coord(self, func:CoordFunc, *args, **kwargs)->'Image':\n",
    "        \"equivalent to `image.flow = func(image.flow, image.size)`\"        \n",
    "        self.flow = func(self.flow, self.shape, *args, **kwargs)\n",
    "        return self\n",
    "\n",
    "    def affine(self, func:AffineFunc, *args, **kwargs)->'Image':\n",
    "        \"equivalent to `image.affine_mat = image.affine_mat @ func()`\"        \n",
    "        m = tensor(func(*args, **kwargs)).to(self.device)\n",
    "        self.affine_mat = self.affine_mat @ m\n",
    "        return self\n",
    "\n",
    "    def resize(self, size:Union[int,CoordSize])->'Image':\n",
    "        \"resize the image to `size`, size can be a single int\"\n",
    "        assert self._flow is None\n",
    "        if isinstance(size, int): size=(self.shape[0], size, size)\n",
    "        self.flow = affine_grid(size)\n",
    "        return self\n",
    "\n",
    "    @property\n",
    "    def affine_mat(self)->AffineMatrix:\n",
    "        \"get the affine matrix that will be applied by `refresh`\"\n",
    "        if self._affine_mat is None:\n",
    "            self._affine_mat = torch.eye(3).to(self.device)\n",
    "        return self._affine_mat\n",
    "    @affine_mat.setter\n",
    "    def affine_mat(self,v)->None: self._affine_mat=v\n",
    "\n",
    "    @property\n",
    "    def logit_px(self)->LogitTensorImage:\n",
    "        \"get logit(image.px)\"\n",
    "        if self._logit_px is None: self._logit_px = logit_(self.px)\n",
    "        return self._logit_px\n",
    "    @logit_px.setter\n",
    "    def logit_px(self,v:LogitTensorImage)->None: self._logit_px=v\n",
    "    \n",
    "    def show(self, ax:plt.Axes=None, **kwargs:Any)->None: \n",
    "        \"plots the image into `ax`\"\n",
    "        show_image(self.px, ax=ax, **kwargs)\n",
    "    \n",
    "    @property\n",
    "    def data(self)->TensorImage: \n",
    "        \"returns this images pixels as a tensor\"\n",
    "        return self.px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = ImageDataset.from_folder(PATH/'train')\n",
    "valid_ds = ImageDataset.from_folder(PATH/'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = lambda: train_ds[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0cxxhUfhujX2yCcXVYHUGlXZQQ4tERwSsIKEP0qRBpnHHnhbwvfSJPQZpPBU1B7lh7wu9QYQIkECECJBAhAiQQIQIGDeT3sPecbSXHR1LsUR2lAw5O05q5U/XwSkCF8GHmTCHFW51QM0moNKAT+nPh88Hni6s3fjXgPy18Zw32/8vdSSvG0mdADLx18i6X4XeIEIESCBCBEggQgRIIEIEFDctAxbi/wG9QYQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECPgH4UXiwJ9JU2YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = x()\n",
    "img.logit_px = contrast(img.logit_px, 0.5)\n",
    "img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0cxxhUfhujX2yCcXVYHUGlXZQQ4tERwSsIKEP0qRBpnHHnhbwvfSJPQZpPBU1B7lh7wu9QYQIkECECJBAhAiQQIQIGDeT3sPecbSXHR1LsUR2lAw5O05q5U/XwSkCF8GHmTCHFW51QM0moNKAT+nPh88Hni6s3fjXgPy18Zw32/8vdSSvG0mdADLx18i6X4XeIEIESCBCBEggQgRIIEIEFDctAxbi/wG9QYQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECJBAhAiQQIQIkECECPgH4UXiwJ9JU2YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x().lighting(contrast, 0.5).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Transform():\n",
    "    _wrap=None\n",
    "    def __init__(self, func): self.func=func\n",
    "    def __call__(self, x, *args, **kwargs):\n",
    "        if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)\n",
    "        else:          return self.func(x, *args, **kwargs)\n",
    "    \n",
    "class TfmLighting(Transform): _wrap='lighting'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@TfmLighting\n",
    "def brightness(x, change): return x.add_(scipy.special.logit(change))\n",
    "@TfmLighting\n",
    "def contrast(x, scale): return x.mul_(scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "\n",
    "x().show(axes[0])\n",
    "contrast(x(), 1.0).show(axes[1])\n",
    "contrast(x(), 0.5).show(axes[2])\n",
    "contrast(x(), 2.0).show(axes[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "\n",
    "x().show(axes[0])\n",
    "brightness(x(), 0.8).show(axes[1])\n",
    "brightness(x(), 0.5).show(axes[2])\n",
    "brightness(x(), 0.2).show(axes[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def brightness_contrast(x, scale_contrast, change_brightness):\n",
    "    return brightness(contrast(x, scale=scale_contrast), change=change_brightness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "\n",
    "brightness_contrast(x(), 0.75, 0.7).show(axes[0])\n",
    "brightness_contrast(x(), 2.0,  0.3).show(axes[1])\n",
    "brightness_contrast(x(), 2.0,  0.7).show(axes[2])\n",
    "brightness_contrast(x(), 0.75, 0.3).show(axes[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random lighting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will make our previous transforms random since we are interested in automatizing the pipeline. We will achieve this by making our parameters stochastic with a specific distribution. \n",
    "\n",
    "We will use a <a href=\"https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)\">uniform</a> distribution for brightness change since its domain is the real numbers and the impact varies linearly with the scale. For contrast change we use [log_uniform](https://www.vosesoftware.com/riskwiki/LogUniformdistribution.php) for two reasons. First, contrast scale has a domain of [0, inf]. Second, the impact of the scale in the transformation is non-linear (i.e. 0.5 is as extreme as 2.0, 0.2 is as extreme as 5). The log_uniform function is appropriate because it has the same domain and correctly represents the non-linearity of the transform, P(0.5) = P(2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def uniform(low:Number, high:Number, size:List[int]=None)->float:\n",
    "    \"draw 1 or shape=`size` random floats from uniform dist: min=`low`, max=`high`\"\n",
    "    return random.uniform(low,high) if size is None else torch.FloatTensor(*listify(size)).uniform_(low,high)\n",
    "\n",
    "def log_uniform(low, high, size=None):\n",
    "    \"draw 1 or shape=`size` random floats from uniform dist: min=log(`low`), max=log(`high`)\"\n",
    "    res = uniform(log(low), log(high), size)\n",
    "    return exp(res) if size is None else res.exp_()\n",
    "\n",
    "def rand_bool(p:float, size=None): \n",
    "    \"draw 1 or shape=`size` random booleans (True occuring probability p)\"\n",
    "    return uniform(0,1,size)<p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9986421989560922"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.gmean([log_uniform(0.5,2.0) for _ in range(1000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import inspect\n",
    "from copy import copy,deepcopy\n",
    "\n",
    "def get_default_args(func):\n",
    "    return {k: v.default\n",
    "            for k, v in inspect.signature(func).parameters.items()\n",
    "            if v.default is not inspect.Parameter.empty}\n",
    "\n",
    "def listify(p=None, q=None):\n",
    "    \"Makes `p` same length as `q`\"\n",
    "    if p is None: p=[]\n",
    "    elif not isinstance(p, Iterable): p=[p]\n",
    "    n = q if type(q)==int else len(p) if q is None else len(q)\n",
    "    if len(p)==1: p = p * n\n",
    "    assert len(p)==n, f'List len mismatch ({len(p)} vs {n})'\n",
    "    return list(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Transform():\n",
    "    _wrap=None\n",
    "    order=0\n",
    "    def __init__(self, func, order=None):\n",
    "        if order is not None: self.order=order\n",
    "        self.func=func\n",
    "        self.params = copy(func.__annotations__)\n",
    "        self.def_args = get_default_args(func)\n",
    "        setattr(Image, func.__name__,\n",
    "                lambda x, *args, **kwargs: self.calc(x, *args, **kwargs))\n",
    "        \n",
    "    def __call__(self, *args, p=1., is_random=True, **kwargs):\n",
    "        if args: return self.calc(*args, **kwargs)\n",
    "        else: return RandTransform(self, kwargs=kwargs, is_random=is_random, p=p)\n",
    "        \n",
    "    def calc(tfm, x, *args, **kwargs):\n",
    "        if tfm._wrap: return getattr(x, tfm._wrap)(tfm.func, *args, **kwargs)\n",
    "        else:          return tfm.func(x, *args, **kwargs)\n",
    "\n",
    "    @property\n",
    "    def name(self): return self.__class__.__name__\n",
    "    \n",
    "    def __repr__(self): return f'{self.name} ({self.func.__name__})'\n",
    "\n",
    "class TfmLighting(Transform): order,_wrap = 8,'lighting'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "@dataclass\n",
    "class RandTransform():\n",
    "    tfm:Transform\n",
    "    kwargs:dict\n",
    "    p:int=1.0\n",
    "    resolved:dict = field(default_factory=dict)\n",
    "    do_run:bool = True\n",
    "    is_random:bool = True\n",
    "    \n",
    "    def resolve(self):\n",
    "        if not self.is_random:\n",
    "            self.resolved = {**self.tfm.def_args, **self.kwargs}\n",
    "            return\n",
    "\n",
    "        self.resolved = {}\n",
    "        # for each param passed to tfm...\n",
    "        for k,v in self.kwargs.items():\n",
    "            # ...if it's annotated, call that fn...\n",
    "            if k in self.tfm.params:\n",
    "                rand_func = self.tfm.params[k]\n",
    "                self.resolved[k] = rand_func(*listify(v))\n",
    "            # ...otherwise use the value directly\n",
    "            else: self.resolved[k] = v\n",
    "        # use defaults for any args not filled in yet\n",
    "        for k,v in self.tfm.def_args.items():\n",
    "            if k not in self.resolved: self.resolved[k]=v\n",
    "        # anything left over must be callable without params\n",
    "        for k,v in self.tfm.params.items():\n",
    "            if k not in self.resolved: self.resolved[k]=v()\n",
    "\n",
    "        self.do_run = rand_bool(self.p)\n",
    "\n",
    "    @property\n",
    "    def order(self): return self.tfm.order\n",
    "\n",
    "    def __call__(self, x, *args, **kwargs):\n",
    "        return self.tfm(x, *args, **{**self.resolved, **kwargs}) if self.do_run else x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "@TfmLighting\n",
    "def brightness(x, change:uniform): return x.add_(scipy.special.logit(change))\n",
    "\n",
    "@TfmLighting\n",
    "def contrast(x, scale:log_uniform): return x.mul_(scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x().contrast(scale=2).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x().contrast(scale=2).brightness(0.8).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(RandTransform(tfm=TfmLighting (contrast), kwargs={'scale': (0.3, 3)}, p=1.0, resolved={'scale': 0.9796685184549353}, do_run=True, is_random=True),\n",
       " {'scale': 0.9796685184549353},\n",
       " True)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfm = contrast(scale=(0.3,3))\n",
    "tfm.resolve()\n",
    "tfm,tfm.resolved,tfm.do_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all the same\n",
    "tfm.resolve()\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: tfm(x()).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfm = contrast(scale=(0.3,3))\n",
    "\n",
    "# different\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes:\n",
    "    tfm.resolve()\n",
    "    tfm(x()).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfm = contrast(scale=2, is_random=False)\n",
    "tfm.resolve()\n",
    "tfm(x()).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Composition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in composing the transform functions so as to apply them all at once. We will try to feed a list of transforms to our pipeline for it to apply all of them.\n",
    "\n",
    "Applying a function to our transforms before calling them in Python is easiest if we use a decorator. You can find more about decorators [here](https://www.thecodeship.com/patterns/guide-to-python-function-decorators/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def resolve_tfms(tfms):\n",
    "    for f in listify(tfms): f.resolve()\n",
    "\n",
    "def apply_tfms(tfms, x, do_resolve=True):\n",
    "    if not tfms: return x\n",
    "    tfms = listify(tfms)\n",
    "    if do_resolve: resolve_tfms(tfms)\n",
    "    x = x.clone()\n",
    "    for tfm in tfms: x = tfm(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_ds[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms = [contrast(scale=(0.3,3.0), p=0.9),\n",
    "        brightness(change=(0.35,0.65), p=0.9)]\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfms,x).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(2,4, figsize=(12,6))\n",
    "for i in range(4):\n",
    "    apply_tfms(tfms,x).show(axes[0,i])\n",
    "    apply_tfms(tfms,x,do_resolve=False).show(axes[1,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply_tfms([],x).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DatasetTfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class DatasetTfm(Dataset):\n",
    "    def __init__(self, ds:Dataset, tfms:Collection[Callable]=None, **kwargs):\n",
    "        self.ds,self.tfms,self.kwargs = ds,tfms,kwargs\n",
    "        \n",
    "    def __len__(self): return len(self.ds)\n",
    "    \n",
    "    def __getitem__(self,idx):\n",
    "        x,y = self.ds[idx]\n",
    "        return apply_tfms(self.tfms, x, **self.kwargs), y\n",
    "    \n",
    "    def __getattr__(self,k): return getattr(self.ds, k)\n",
    "\n",
    "import nb_001b\n",
    "nb_001b.DatasetTfm = DatasetTfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs=64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def to_data(b):\n",
    "    if is_listy(b): return [to_data(o) for o in b]\n",
    "    return b.data if isinstance(b,ItemBase) else b\n",
    "\n",
    "def data_collate(batch):\n",
    "    return torch.utils.data.dataloader.default_collate(to_data(batch))\n",
    "\n",
    "@dataclass\n",
    "class DeviceDataLoader():\n",
    "    dl: DataLoader\n",
    "    device: torch.device\n",
    "    def __post_init__(self): self.dl.collate_fn=data_collate\n",
    "\n",
    "    def __len__(self): return len(self.dl)\n",
    "    def __getattr__(self,k): return getattr(self.dl, k)\n",
    "    def proc_batch(self,b): return to_device(b, self.device)\n",
    "\n",
    "    def __iter__(self):\n",
    "        self.gen = map(self.proc_batch, self.dl)\n",
    "        return iter(self.gen)\n",
    "\n",
    "    @classmethod\n",
    "    def create(cls, *args, device=default_device, **kwargs):\n",
    "        return cls(DataLoader(*args, **kwargs), device=device)\n",
    "    \n",
    "nb_001b.DeviceDataLoader = DeviceDataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 16, 2)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataBunch.create(train_ds, valid_ds, bs=bs, num_workers=4)\n",
    "len(data.train_dl), len(data.valid_dl), data.train_dl.dataset.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def show_image_batch(dl, classes, rows=None, figsize=(12,15)):\n",
    "    x,y = next(iter(dl))\n",
    "    if rows is None: rows = int(math.sqrt(len(x)))\n",
    "    show_images(x[:rows*rows],y[:rows*rows],rows, classes)\n",
    "\n",
    "def show_images(x,y,rows, classes, figsize=(9,9)):\n",
    "    fig, axs = plt.subplots(rows,rows,figsize=figsize)\n",
    "    for i, ax in enumerate(axs.flatten()):\n",
    "        show_image(x[i], ax)\n",
    "        ax.set_title(classes[y[i]])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image_batch(data.train_dl, train_ds.classes, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = DataBunch.create(train_ds, valid_ds, bs=bs, train_tfm=tfms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image_batch(data.train_dl, train_ds.classes, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now add affine transforms that operate on the coordinates instead of pixels like the lighting transforms we just saw. An [affine transformation](https://en.wikipedia.org/wiki/Affine_transformation) is a function \"(...) between affine spaces which preserves points, straight lines and planes.\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our implementation first creates a grid of coordinates for the original image. The grid is normalized to a [-1, 1] range with (-1, -1) representing the top left corner, (1, 1) the bottom right corner and (0, 0) the center. Next, we build an affine matrix representing our desired transform and we multiply it by our original grid coordinates. The result will be a set of x, y coordinates which references where in the input image will each of the pixels in the output image be mapped. It has a size of w \\* h \\* 2 since it needs two coordinates for each of the h * w pixels of the output image. \n",
    "\n",
    "This is clearest if we see it graphically. We will build an affine matrix of the following form:\n",
    "\n",
    "`[[a, b, e],\n",
    " [c, d, f]]`\n",
    "\n",
    "\n",
    "with which we will transform each pair of x, y coordinates in our original grid into our transformation grid:\n",
    "\n",
    "\n",
    "`[[a, b],       [[x],       [[e],       [[x'],\n",
    "  [c, d]]   x    [y]]    +   [f]]    =   [y']]`   \n",
    "\n",
    "So after the transform we will get a new grid with which to map our input image into our output image. This will be our **map of where from exactly does our transformation source each pixel in the output image**.\n",
    "\n",
    "**Enter problems**\n",
    "\n",
    "Affine transforms face two problems that must be solved independently:\n",
    "1. **The interpolation problem**: The result of our transformation gives us float coordinates, and we need to decide, for each (i,j), how to assign these coordinates to pixels in the input image.\n",
    "2. **The missing pixel problem**: The result of our transformation may have coordinates which exceed the [-1, 1] range of our original grid and thus fall outside of our original grid.\n",
    "\n",
    "**Solutions to problems**\n",
    "\n",
    "1.  **The interpolation problem**: We will perform a [bilinear interpolation](https://en.wikipedia.org/wiki/Bilinear_interpolation). This takes an average of the values of the pixels corresponding to the four points in the grid surrounding the result of our transformation, with weights depending on how close we are to each of those points. \n",
    "2. **The missing pixel problem**: For these values we need padding, and we face a few options:\n",
    "\n",
    "    1. Adding zeros on the side (so the pixels that fall out will be black)\n",
    "    2. Replacing them by the value at the border\n",
    "    3. Mirroring the content of the picture on the other side (reflect padding).\n",
    "    \n",
    "    \n",
    "### Transformation Method\n",
    "\n",
    "**Zoom**\n",
    "\n",
    "Zoom changes the focus of the image according to a scale. If a scale of >1 is applied, grid pixels will be mapped to coordinates that are more central than the pixel's coordinates (closer to 0,0) while if a scale of <1 is applied, grid pixels will be mapped to more perispheric coordinates (closer to the borders) in the input image.\n",
    "\n",
    "We can also translate our transform to zoom into a non-centrical area of the image. For this we use $col_c$ which displaces the x axis and $row_c$ which displaces the y axis.\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Scale** How much do we want to zoom in or out to our image.\n",
    "\n",
    "    Domain:  Real numbers\n",
    "        \n",
    "2. **Col_pct** How much do we want to displace our zoom along the x axis.\n",
    "\n",
    "    Domain: Real numbers between 0 and 1\n",
    "    \n",
    "    \n",
    "3. **Row_pct** How much do we want to displace our zoom along the y axis.\n",
    "\n",
    "    Domain: Real numbers between 0 and 1\n",
    "    \n",
    "\n",
    "<u>Affine matrix</u>\n",
    "\n",
    "`[[1/scale, 0,       col_c],\n",
    "  [0,       1/scale, row_c]]`\n",
    "\n",
    "\n",
    "**Rotate**\n",
    "\n",
    "Rotate shifts the image around its center in a given angle theta. The rotation is counterclockwise if theta is positive and clockwise if theta is negative. If you are curious about the derivation of the rotation matrix you can find it [here](https://matthew-brett.github.io/teaching/rotation_2d.html).\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Degrees** By which angle do we want to rotate our image.\n",
    "\n",
    "    Domain: Real numbers\n",
    "        \n",
    "<u>Affine matrix</u>\n",
    "\n",
    "`[[cos(theta), -sin(theta), 0],\n",
    "  [sin(theta),  cos(theta), 0]]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deterministic affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def grid_sample_nearest(input, coords, padding_mode='zeros'):\n",
    "    if padding_mode=='border': coords.clamp(-1,1)\n",
    "    bs,ch,h,w = input.size()\n",
    "    sz = tensor([w,h]).float()[None,None]\n",
    "    coords.add_(1).mul_(sz/2)\n",
    "    coords = coords[0].round_().long()\n",
    "    if padding_mode=='zeros':\n",
    "        mask = (coords[...,0] < 0) + (coords[...,1] < 0) + (coords[...,0] >= w) + (coords[...,1] >= h)\n",
    "        mask.clamp_(0,1)\n",
    "    coords[...,0].clamp_(0,w-1)\n",
    "    coords[...,1].clamp_(0,h-1)\n",
    "    result = input[...,coords[...,1],coords[...,0]]\n",
    "    if padding_mode=='zeros': result[...,mask] = result[...,mask].zero_()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def grid_sample(x, coords, mode='bilinear', padding_mode='reflect'):\n",
    "    if padding_mode=='reflect': padding_mode='reflection'\n",
    "    if mode=='nearest': return grid_sample_nearest(x[None], coords, padding_mode)[0]\n",
    "    return F.grid_sample(x[None], coords, mode=mode, padding_mode=padding_mode)[0]\n",
    "\n",
    "def affine_grid(size):\n",
    "    size = ((1,)+size)\n",
    "    N, C, H, W = size\n",
    "    grid = FloatTensor(N, H, W, 2)\n",
    "    linear_points = torch.linspace(-1, 1, W) if W > 1 else tensor([-1])\n",
    "    grid[:, :, :, 0] = torch.ger(torch.ones(H), linear_points).expand_as(grid[:, :, :, 0])\n",
    "    linear_points = torch.linspace(-1, 1, H) if H > 1 else tensor([-1])\n",
    "    grid[:, :, :, 1] = torch.ger(linear_points, torch.ones(W)).expand_as(grid[:, :, :, 1])\n",
    "    return grid\n",
    "\n",
    "def affine_mult(c,m):\n",
    "    if m is None: return c\n",
    "    size = c.size()\n",
    "    c = c.view(-1,2)\n",
    "    c = torch.addmm(m[:2,2], c,  m[:2,:2].t()) \n",
    "    return c.view(size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotate(degrees):\n",
    "    angle = degrees * math.pi / 180\n",
    "    return [[cos(angle), -sin(angle), 0.],\n",
    "            [sin(angle),  cos(angle), 0.],\n",
    "            [0.        ,  0.        , 1.]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xi(): return train_ds[1][0]\n",
    "x = xi().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = affine_grid(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.8660, -0.5000,  0.0000],\n",
       "        [ 0.5000,  0.8660,  0.0000],\n",
       "        [ 0.0000,  0.0000,  1.0000]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = rotate(30)\n",
    "m = x.new_tensor(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.0000, -0.9355, -0.8710,  ...,  0.8710,  0.9355,  1.0000],\n",
       "        [-1.0000, -0.9355, -0.8710,  ...,  0.8710,  0.9355,  1.0000],\n",
       "        [-1.0000, -0.9355, -0.8710,  ...,  0.8710,  0.9355,  1.0000],\n",
       "        ...,\n",
       "        [-1.0000, -0.9355, -0.8710,  ...,  0.8710,  0.9355,  1.0000],\n",
       "        [-1.0000, -0.9355, -0.8710,  ...,  0.8710,  0.9355,  1.0000],\n",
       "        [-1.0000, -0.9355, -0.8710,  ...,  0.8710,  0.9355,  1.0000]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,...,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.0000, -1.0000, -1.0000,  ..., -1.0000, -1.0000, -1.0000],\n",
       "        [-0.9355, -0.9355, -0.9355,  ..., -0.9355, -0.9355, -0.9355],\n",
       "        [-0.8710, -0.8710, -0.8710,  ..., -0.8710, -0.8710, -0.8710],\n",
       "        ...,\n",
       "        [ 0.8710,  0.8710,  0.8710,  ...,  0.8710,  0.8710,  0.8710],\n",
       "        [ 0.9355,  0.9355,  0.9355,  ...,  0.9355,  0.9355,  0.9355],\n",
       "        [ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,...,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.8660, -0.5000,  0.0000],\n",
       "        [ 0.5000,  0.8660,  0.0000],\n",
       "        [ 0.0000,  0.0000,  1.0000]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = affine_mult(c,m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.3660, -0.3102, -0.2543,  ...,  1.2543,  1.3102,  1.3660],\n",
       "        [-0.3983, -0.3424, -0.2865,  ...,  1.2220,  1.2779,  1.3338],\n",
       "        [-0.4305, -0.3747, -0.3188,  ...,  1.1898,  1.2456,  1.3015],\n",
       "        ...,\n",
       "        [-1.3015, -1.2456, -1.1898,  ...,  0.3188,  0.3747,  0.4305],\n",
       "        [-1.3338, -1.2779, -1.2220,  ...,  0.2865,  0.3424,  0.3983],\n",
       "        [-1.3660, -1.3102, -1.2543,  ...,  0.2543,  0.3102,  0.3660]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,...,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.3660, -1.3338, -1.3015,  ..., -0.4305, -0.3983, -0.3660],\n",
       "        [-1.3102, -1.2779, -1.2456,  ..., -0.3747, -0.3424, -0.3102],\n",
       "        [-1.2543, -1.2220, -1.1898,  ..., -0.3188, -0.2865, -0.2543],\n",
       "        ...,\n",
       "        [ 0.2543,  0.2865,  0.3188,  ...,  1.1898,  1.2220,  1.2543],\n",
       "        [ 0.3102,  0.3424,  0.3747,  ...,  1.2456,  1.2779,  1.3102],\n",
       "        [ 0.3660,  0.3983,  0.4305,  ...,  1.3015,  1.3338,  1.3660]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,...,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img2 = grid_sample(x, c, padding_mode='zeros')\n",
    "show_image(img2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xi().affine(rotate, 30).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Affine transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class TfmAffine(Transform): order,_wrap = 5,'affine'\n",
    "class TfmPixel(Transform): order,_wrap = 10,'pixel'\n",
    "\n",
    "@TfmAffine\n",
    "def rotate(degrees:uniform):\n",
    "    angle = degrees * math.pi / 180\n",
    "    return [[cos(angle), -sin(angle), 0.],\n",
    "            [sin(angle),  cos(angle), 0.],\n",
    "            [0.        ,  0.        , 1.]]\n",
    "\n",
    "def get_zoom_mat(sw, sh, c, r):\n",
    "    return [[sw, 0,  c],\n",
    "            [0, sh,  r],\n",
    "            [0,  0, 1.]]\n",
    "\n",
    "@TfmAffine\n",
    "def zoom(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):\n",
    "    s = 1-1/scale\n",
    "    col_c = s * (2*col_pct - 1)\n",
    "    row_c = s * (2*row_pct - 1)\n",
    "    return get_zoom_mat(1/scale, 1/scale, col_c, row_c)\n",
    "\n",
    "@TfmAffine\n",
    "def squish(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):\n",
    "    if scale <= 1: \n",
    "        col_c = (1-scale) * (2*col_pct - 1)\n",
    "        return get_zoom_mat(scale, 1, col_c, 0.)\n",
    "    else:          \n",
    "        row_c = (1-1/scale) * (2*row_pct - 1)\n",
    "        return get_zoom_mat(1, 1/scale, 0., row_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotate(xi(), 30).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom(xi(), 0.6).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom(xi(), 0.6).set_sample(padding_mode='zeros').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom(xi(), 2, 0.2, 0.2).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scales = [0.75,0.9,1.1,1.33]\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for i, ax in enumerate(axes): squish(xi(), scales[i]).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes=plt.subplots(1,3,figsize=(9,3))\n",
    "xi().show(axes[0])\n",
    "\n",
    "img2 = rotate(xi(), 30).refresh()\n",
    "img2 = zoom(img2, 1.6)\n",
    "img2.show(axes[1])\n",
    "\n",
    "zoom(rotate(xi(), 30), 1.6).show(axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xi().resize(48).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img2 = zoom(xi().resize(48), 1.6, 0.8, 0.2)\n",
    "rotate(img2, 30).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lx1Kmffts3haAv3L3+wH8LoCvm9lRAN8A8JK7HwHwUvdvIT5R9NJh6qK7v979vQrgbQCHADwO4Nnu3Z4F8KUbNUkhhsXH+ibdzO4A8JsAXgZwy5UWCO5+0cz2kTFPAngSAIoj+sgjbi56fsWa2RiA/wDwl+6+3Ou4zR2m8gUJRNxc9PSKNbMCNsTxL+7+n93Ns2Z2oBs/AGDuxkxRiOHRi4tl2OgH8ra7//2m0PMAvgbg6e7tj6/7WDDkO+ldtlt8LXizk46VK5FTxZ2eXODa5INMusj4YGucs+DxiiP8OY8EXZhGRrizxJyqiCwYUygEa8tpIiDQCro+RWvq4emYBwv4PViT7vSf1puL1ctnkEcA/BmAN8zsZHfbX2NDGP9uZk8A+ADAH/W0RyFuInrpMPW/4Inen9/e6Qixs9CnZiECJBAhAiQQIQIkECECBrsm3XNoNNKlLKP1zZOT6S5HI0HSXlS6M7L4tppcmCedkYpB8mBk10bj2L4AwDuB5UkT/oJEwFa6hgAAtIJ90SRBAJ0g8ZDZ5ZF5za3cqMSoSo8K0TcSiBABEogQARKIEAESiBABEogQAQO1ebNchtGJ6WRsdJx3b6IZtoFT124Ha8uDRM7Iyi0HHZVY+c7Yrg1s42AeFtjU7vx5N9h673aNjomW4bcDm9c6qzSWZTxTOU/ahrWdv5db8D7POl312GBKZxAhIiQQIQIkECECJBAhAiQQIQIkECECLMqE3PadmX0E4Gz3zz0ALg1s5xzN42o+LfO43d15q64uAxXIVTs2e9Xdjw9l55qH5tEjusQSIkACESJgmAI5McR9b0bzuBrNYxND+wwixM2ALrGECJBAhAgYikDM7FEz+6WZnTazoXWmMrMzZvaGmZ00s1cHuN9nzGzOzN7ctG3azF40s3e6t7uGNI9vmtn57jE5aWaPDWAeh83sf8zsbTN7y8z+ort94MfkWgYuEDPLAHwXwB8AOArgq92WbsPic+5+bMCe+w8APHrNtmG0tEvNAwC+3T0mx9z9hQHMY8e2+RvGGeRhAKfd/T13bwD4ETbauX1qcPefApi/ZvPAW9qReQycndzmbxgCOQTg3Ka/Z7rbhoED+ImZvdZtFTdMrmppByDZ0m5APGVmp7qXYAO9rIna/GEIx2QYAkktdhyW1/yIu/8WNi73vm5mvz+keewkvgfgbgDHAFwE8K1B7Xirbf5uJMMQyAyAw5v+vhXAhSHMA+5+oXs7B+A5bFz+DYsd0dLO3Wfdve0bbZu+jwEdk53a5m8YAnkFwBEzu9PMigC+go12bgPFzEbNbPzK7wC+CODNeNQN5UpLO6DHlnY3gisvyC5fxgCOSQ9t/oBhHRN3H/gPgMcA/ArAuwD+ZkhzuAvAz7o/bw1yHgB+iI3LlyY2zqhPANiNDafmne7t9JDm8c8A3gBwChsv0AMDmMfvYeMy+xSAk92fx4ZxTK79UaqJEAH6Jl2IAAlEiAAJRIgACUSIAAlEiAAJRIgACUSIgP8H12iIqaddxugAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img2 = zoom(xi().resize(24), 1.6, 0.8, 0.2)\n",
    "rotate(img2, 30).show(hide_axis=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img2 = zoom(xi().resize(48), 1.6, 0.8, 0.2)\n",
    "rotate(img2, 30).set_sample(mode='nearest').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random affine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did with the Lighting transform, we now want to build randomness into our pipeline so we can increase the automatization of the transform process. \n",
    "\n",
    "We will use a uniform distribution for both our transforms since their impact is linear and their domain is the real numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Apply all transforms**\n",
    "\n",
    "We will make all transforms try to do as little calculations as possible.\n",
    "\n",
    "We do only one affine transformation by multiplying all the affine matrices of the transforms, then we apply to the coords any non-affine transformation we might want (jitter, elastic distorsion). Next, we crop the coordinates we want to keep and, by doing it before the interpolation, we don't need to compute pixel values that won't be used afterwards. Finally we perform the interpolation and we apply all the transforms that operate pixelwise (brightness, contrast)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandTransform(tfm=TfmAffine (rotate), kwargs={'degrees': (-45, 45.0)}, p=0.75, resolved={}, do_run=True, is_random=True)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfm = rotate(degrees=(-45,45.), p=0.75); tfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandTransform(tfm=TfmAffine (rotate), kwargs={'degrees': (-45, 45.0)}, p=0.75, resolved={'degrees': 2.034913047687624}, do_run=False, is_random=True)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfm.resolve(); tfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = xi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfm, x).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms = [rotate(degrees=(-45,45.), p=0.75),\n",
    "        zoom(scale=(0.5,2.0), p=0.75)]\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfms,x).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def apply_tfms(tfms, x, do_resolve=True, xtra=None, size=None, **kwargs):\n",
    "    if not (tfms or size): return x\n",
    "    if not xtra: xtra={}\n",
    "    tfms = sorted(listify(tfms), key=lambda o: o.tfm.order)\n",
    "    if do_resolve: resolve_tfms(tfms)\n",
    "    x = x.clone()\n",
    "    if kwargs: x.set_sample(**kwargs)\n",
    "    if size: x.resize(size)\n",
    "    for tfm in tfms:\n",
    "        if tfm.tfm in xtra: x = tfm(x, **xtra[tfm.tfm])\n",
    "        else:               x = tfm(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms = [rotate(degrees=(-45,45.), p=0.75),\n",
    "        zoom(scale=(1.0,2.0), row_pct=(0,1.), col_pct=(0,1.))]\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfms,x, padding_mode='zeros', size=64).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms = [squish(scale=(0.5,2), row_pct=(0,1.), col_pct=(0,1.))]\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfms,x).show(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coord and pixel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jitter / flip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last two transforms we will use are **jitter** and **flip**. \n",
    "\n",
    "**Jitter**\n",
    "\n",
    "Jitter is a transform which adds a random value to each of the pixels to make them somewhat different than the original ones. In our implementation we first get a random number between (-1, 1) and we multiply it by a constant M which scales it.\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Magnitude** How much random noise do we want to add to each of the pixels in our image.\n",
    "\n",
    "    Domain: Real numbers between 0 and 1.\n",
    "    \n",
    "**Flip**\n",
    "\n",
    "Flip is a transform that reflects the image on a given axis.\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **P** Probability of applying the transformation to an input.\n",
    "\n",
    "    Domain: Real numbers between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class TfmCoord(Transform): order,_wrap = 4,'coord'\n",
    "\n",
    "@TfmCoord\n",
    "def jitter(c, size, magnitude:uniform):\n",
    "    return c.add_((torch.rand_like(c)-0.5)*magnitude*2)\n",
    "\n",
    "@TfmPixel\n",
    "def flip_lr(x): return x.flip(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfm = jitter(magnitude=(0,0.1))\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes:\n",
    "    tfm.resolve()\n",
    "    tfm(xi()).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfm = flip_lr(p=0.5)\n",
    "\n",
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes:\n",
    "    tfm.resolve()\n",
    "    tfm(xi()).show(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crop/pad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Crop**\n",
    "\n",
    "Crop is a transform that cuts a series of pixels from an image. It does this by removing rows and columns from the input image.\n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Size** What is the target size of each side in pixels. If only one number *s* is specified, image is made square with dimensions *s* \\* *s*.\n",
    "\n",
    "    Domain: Positive integers.\n",
    "    \n",
    "2. **Row_pct** Determines where to cut our image vertically on the bottom and top (which rows are left out). If <0.5, more rows will be cut in the top than in the bottom and viceversa (varies linearly).\n",
    "\n",
    "    Domain: Real numbers between 0 and 1.\n",
    "    \n",
    "3. **Col_pct** Determines where to cut our image horizontally on the left and right (which columns are left out). If <0.5, more rows will be cut in the left than in the right and viceversa (varies linearly).\n",
    "\n",
    "    Domain: Real numbers between 0 and 1.\n",
    "    \n",
    "Our three parameters are related with the following equations:\n",
    "\n",
    "1. output_rows = [**row_pct***(input_rows-**size**):**size**+**row_pct***(input_rows-**size**)]\n",
    "\n",
    "2. output_cols = [**col_pct***(input_cols-**size**):**size**+**col_pct***(input_cols-**size**)]\n",
    "\n",
    "**Pad**\n",
    "\n",
    "\n",
    "Pads each of the four borders of our image with a certain amount of pixels. Can pad with reflection (reflects border pixels to fill new pixels) or zero (adds black pixels). \n",
    "\n",
    "_Parameters_\n",
    "\n",
    "1. **Padding** Amount of pixels to add to each border. [More details](https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad)\n",
    "\n",
    "    Domain: Positive integers.\n",
    "    \n",
    "2. **Mode** How to fill new pixels. For more detail see the Pytorch subfunctions for padding.\n",
    "\n",
    "    Domain: \n",
    "    - Reflect (default): reflects opposite pixels to fill new pixels. [More details](https://pytorch.org/docs/stable/nn.html#torch.nn.ReflectionPad2d)\n",
    "    - Constant: adds pixels with specified value (default is 0, black pixels) [More details](https://pytorch.org/docs/stable/nn.html#torch.nn.ConstantPad2d)\n",
    "    - Replicate: replicates border row or column pixels to fill new pixels [More details](https://pytorch.org/docs/stable/nn.html#torch.nn.ReplicationPad2d)\n",
    "    \n",
    "    \n",
    "***On using padding and crop***\n",
    "\n",
    "A nice way to use these two functions is to combine them into one transform. We can add padding to the image and then crop some of it out. This way, we can create a new image to augment our training set without losing image information by cropping. Furthermore, this can be done in several ways (modifying the amount and type of padding and the crop style) so it gives us great flexibility to add images to our training set. You can find an example of this in the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Transform', 0),\n",
       " ('TfmCoord', 4),\n",
       " ('TfmAffine', 5),\n",
       " ('TfmLighting', 8),\n",
       " ('TfmPixel', 10)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(o.__name__,o.order) for o in\n",
    "    sorted((Transform,TfmAffine,TfmCoord,TfmLighting,TfmPixel),key=attrgetter('order'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "@partial(TfmPixel, order=-10)\n",
    "def pad(x, padding, mode='reflect'):\n",
    "    return F.pad(x[None], (padding,)*4, mode=mode)[0]\n",
    "\n",
    "@TfmPixel\n",
    "def crop(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):\n",
    "    size = listify(size,2)\n",
    "    rows,cols = size\n",
    "    row = int((x.size(1)-rows+1) * row_pct)\n",
    "    col = int((x.size(2)-cols+1) * col_pct)\n",
    "    return x[:, row:row+rows, col:col+cols].contiguous()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pad(xi(), 4, 'constant').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crop(pad(xi(), 4, 'constant'), 32, 0.25, 0.75).show(hide_axis=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crop(pad(xi(), 4), 32, 0.25, 0.75).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = [flip_lr(p=0.5),\n",
    "        pad(padding=4, mode='constant'),\n",
    "        crop(size=32, row_pct=(0,1.), col_pct=(0,1.))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfms, x).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = [\n",
    "    flip_lr(p=0.5),\n",
    "    contrast(scale=(0.5,2.0)),\n",
    "    brightness(change=(0.3,0.7)),\n",
    "    rotate(degrees=(-45,45.), p=0.5),\n",
    "    zoom(scale=(0.5,1.2), p=0.8)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,4, figsize=(12,3))\n",
    "for ax in axes: apply_tfms(tfms, x).show(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(2,4, figsize=(12,6))\n",
    "\n",
    "for i in range(4):\n",
    "    apply_tfms(tfms, x, padding_mode='zeros', size=48).show(axes[0][i], hide_axis=False)\n",
    "    apply_tfms(tfms, x, mode='nearest', do_resolve=False).show(axes[1][i], hide_axis=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomResizedCrop (Torchvision version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def compute_zs_mat(sz, scale, squish, invert, row_pct, col_pct):\n",
    "    orig_ratio = math.sqrt(sz[2]/sz[1])\n",
    "    for s,r,i in zip(scale,squish, invert):\n",
    "        s,r = math.sqrt(s),math.sqrt(r)\n",
    "        if s * r <= 1 and s / r <= 1: #Test if we are completely inside the picture\n",
    "            w,h = (s/r, s*r) if i else (s*r,s/r)\n",
    "            w /= orig_ratio\n",
    "            h *= orig_ratio\n",
    "            col_c = (1-w) * (2*col_pct - 1)\n",
    "            row_c = (1-h) * (2*row_pct - 1)\n",
    "            return get_zoom_mat(w, h, col_c, row_c)\n",
    "        \n",
    "    #Fallback, hack to emulate a center crop without cropping anything yet.\n",
    "    if orig_ratio > 1: return get_zoom_mat(1/orig_ratio**2, 1, 0, 0.)\n",
    "    else:              return get_zoom_mat(1, orig_ratio**2, 0, 0.)\n",
    "\n",
    "@TfmCoord\n",
    "def zoom_squish(c, size, scale:uniform=1.0, squish:uniform=1.0, invert:rand_bool=False, \n",
    "                row_pct:uniform=0.5, col_pct:uniform=0.5):\n",
    "    #This is intended for scale, squish and invert to be of size 10 (or whatever) so that the transform\n",
    "    #can try a few zoom/squishes before falling back to center crop (like torchvision.RandomResizedCrop)\n",
    "    m = compute_zs_mat(size, scale, squish, invert, row_pct, col_pct)\n",
    "    return affine_mult(c, FloatTensor(m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rrc = zoom_squish(scale=(0.25,1.0,10), squish=(0.5,1.0,10), invert=(0.5,10),\n",
    "                                  row_pct=(0,1.), col_pct=(0,1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(2,4, figsize=(12,6))\n",
    "for i in range(4):\n",
    "    apply_tfms(rrc, x, size=48).show(axes[0][i])\n",
    "    apply_tfms(rrc, x, do_resolve=False, mode='nearest').show(axes[1][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}