{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example of single image prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "import fastai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = get_transforms()\n",
    "bs = 64\n",
    "path = untar_data(URLs.PETS)/'images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (ImageFileList.from_folder(path)\n",
    "        .label_from_re(r'^(.*)_\\d+.jpg$')\n",
    "        .random_split_by_pct(0.2)\n",
    "        .datasets(ImageClassificationDataset)\n",
    "        .transform(tfms, size=224)\n",
    "        .databunch(bs=bs)\n",
    "        .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = open_image(get_image_files(path)[0])\n",
    "img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.show_batch(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = create_cnn(data, models.resnet34, metrics=accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this once to create your model\n",
    "# learn.fit_one_cycle(1)\n",
    "# learn.save('one-epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('one-epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_class,pred_idx,outputs = learn.predict(img)\n",
    "pred_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single image prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastai.defaults.device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You wouldn't use data.classes really\n",
    "# - instead load your classes list from somewhere else\n",
    "data2 = ImageDataBunch.single_from_classes(\n",
    "    path, data.classes, tfms=tfms, size=224).normalize(imagenet_stats)\n",
    "learn = create_cnn(data2, models.resnet34)\n",
    "learn.load('one-epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_class,pred_idx,outputs = learn.predict(img)\n",
    "pred_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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