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  {
   "cell_type": "code",
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   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install FastAI\n",
    "\n",
    "https://github.com/fastai/fastai\n",
    "\n",
    "In short, `conda activate` the python environement, and run:  \n",
    "```bash\n",
    "conda install -c fastai -c pytorch -c anaconda fastai gh anaconda\n",
    "```\n",
    "\n",
    "Note: For more details about the modelling approach, please refer to:  \n",
    "https://github.com/fastai/fastbook/blob/master/01_intro.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.CAMVID_TINY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acc_camvid(inp, targ, void_code=0):\n",
    "    targ = targ.squeeze(1)\n",
    "    mask = targ != void_code\n",
    "    return (inp.argmax(dim=1)[mask] == targ[mask]).float().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_y(o, path=path):\n",
    "    return path / \"labels\" / f\"{o.stem}_P{o.suffix}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = SegmentationDataLoaders.from_label_func(\n",
    "    path,\n",
    "    bs=8,\n",
    "    fnames=get_image_files(path / \"images\"),\n",
    "    label_func=get_y,\n",
    "    codes=np.loadtxt(path / \"codes.txt\", dtype=str),\n",
    ")\n",
    "dls.one_batch()[0].shape[-2:], get_c(dls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = unet_learner(dls, resnet50, metrics=acc_camvid)\n",
    "learn.fine_tune(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.show_results(max_n=6, figsize=(7,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export(\"./fastai_unet.pkl\")"
   ]
  }
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