{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from nb_006a import *\n",
    "from concurrent.futures import ProcessPoolExecutor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Camvid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('data/camvid')\n",
    "PATH_X = PATH/'701_StillsRaw_full'\n",
    "PATH_Y = PATH/'LabeledApproved_full'\n",
    "PATH_Y_PROCESSED = PATH/'LabelProcessed'\n",
    "label_csv = PATH/'label_colors.txt'\n",
    "\n",
    "PATH_Y_PROCESSED.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('data/camvid/LabeledApproved_full/0016E5_08109_L.png')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(PATH_Y.iterdir())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_y_fn(x_fn): return PATH_Y/f'{x_fn.name[:-4]}_L.png'\n",
    "def get_y_proc_fn(y_fn): return PATH_Y_PROCESSED/f'{y_fn.name[:-6]}_P.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([PosixPath('data/camvid/701_StillsRaw_full/Seq05VD_f02220.png'),\n",
       "  PosixPath('data/camvid/701_StillsRaw_full/0016E5_08550.png'),\n",
       "  PosixPath('data/camvid/701_StillsRaw_full/0001TP_007260.png')],\n",
       " [PosixPath('data/camvid/LabeledApproved_full/Seq05VD_f02220_L.png'),\n",
       "  PosixPath('data/camvid/LabeledApproved_full/0016E5_08550_L.png'),\n",
       "  PosixPath('data/camvid/LabeledApproved_full/0001TP_007260_L.png')],\n",
       " [PosixPath('data/camvid/LabelProcessed/Seq05VD_f02220_P.png'),\n",
       "  PosixPath('data/camvid/LabelProcessed/0016E5_08550_P.png'),\n",
       "  PosixPath('data/camvid/LabelProcessed/0001TP_007260_P.png')])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_fns = get_image_files(PATH_X)\n",
    "y_fns = [get_y_fn(o) for o in x_fns]\n",
    "y_proc_fns = [get_y_proc_fn(o) for o in y_fns]\n",
    "x_fns[:3],y_fns[:3],y_proc_fns[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_code(l):\n",
    "    a,b = [c for c in l.strip().split(\"\\t\") if c]\n",
    "    return tuple(int(o) for o in a.split(' ')), b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(((64, 128, 64), (192, 0, 128), (0, 128, 192), (0, 128, 64), (128, 0, 0)),\n",
       " ('Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building'),\n",
       " 32)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_codes,label_names = zip(*[parse_code(l) for l in open(PATH/\"label_colors.txt\")])\n",
    "label_t = tensor(label_codes)\n",
    "n_labels = len(label_codes)\n",
    "label_codes[:5],label_names[:5], n_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name2code = dict(zip(label_names, label_codes))\n",
    "name2id = {v:k for k,v in enumerate(label_names)}\n",
    "void_code = name2id['Void']\n",
    "\n",
    "code2id = ByteTensor(255,255,255).zero_()+void_code\n",
    "for i,code in enumerate(label_codes):\n",
    "    if not code == void_code: code2id[code]=i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colors_to_codes(color_data):\n",
    "    n = len(color_data)\n",
    "    idxs = tuple(color_data.reshape(n,-1).long())\n",
    "    return code2id[idxs].view(color_data.shape[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "x_img = open_image(x_fns[i])\n",
    "y_img_mask = open_mask(y_fns[i])\n",
    "y_img = Image(y_img_mask.data.int())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_code = colors_to_codes(y_img.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def codes_to_colors(label_data):\n",
    "    h,w = label_data.shape\n",
    "    idxs = label_data.flatten().long()\n",
    "    return Image(label_t.index_select(0, idxs).reshape(h,w,3).permute(2,0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_img2 = codes_to_colors(y_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANMAAACgCAYAAACWsVm2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADlVJREFUeJztnWuWqzoOhVW9el7tmZXuyNo9suofxMEY+S2DMftbJycJ+BXwRrJsqJ+/vz8CAPTzr7sbAMAqQEwAKAExAaAExASAEhATAEpATAAoATEBoATEBIASEBMASvz77gYQEf3z86OzDINZpRhtmPnwInKvQyrhc/gefk5texj+udM4j6VlFKT7/f39KSkKlmkw7J0sPp04jnyuqqExH9AGYhrIZolCEcU+H/PVpAdzMIWbtx6SiHJp9rSyBZPKSpW/MNJxncDFh2XqxHX80JpsX5lSIoiLLba9Nk1NuocQO2bblWl/3QDE1MlRTOHnfZuXQ9gW7ovtT7dBo6xbCNufEszEQEyq8PZ/9qRz8B7bn8tPh/rOY61UObk6JmNyIRFBTDfB3juf9hpz3pYuY3svjwYXJryLUkulVZeSe4gAxAWc55fcq6tUoZyysn2XdGruEL1U5+9vUVZYpos4R+2Y0h0/tj2WNiy3JM+k3BhE6AGWqQO2Nr3fmO09GZCg0zZrpTRBjq+1k8sA17OOZZrySsaUtxglaYRcybB6qoyy8kE964hpWjh41yiHhe25fdJ+oAncvEZyLp6Qw3vnaKoUe5SPKV49Rz6D0cAyDYcz3/M4Ebmx1C4kv6xcudxUdxFTutjXAzHdAlOJEDYR8SEg0Sak0jQdpASVmsdZSIhw826FKSWkkqheWR3++0BKhOHSLCQiByzTJXDhto16IaXScmb/TTx0LinFOmKa8MSwNf634nx5IXHwmYVtdXWCfuDmDcJ+JmyJzHH74RtXlspiHncTYizteYIXjGAdy1TC410LlrdyuJ8PaSGka1hLTLmI0lSwQr7wMyf2B6VMdzyezzpiylkdf9+NHam/E4f5mYicG8cki+m8vasZfqgbovyyjphyTHfSWSG/e7mft38/Cync11s/CHmPmETsje6OXG/ZjYEuf/gKy2UhLRjFy8VExGwojLFFEhIpRcVSZbRN1LLw2X9nUhPSdBZ+HtYKjXedaEvMeUvVK6Zjdqawk5dP2DLJItm/qw5pIKIsa4nJg41Jr+xmJmITbDIXdhq5nnLLxJF3LwUEcCnruHkXdpyeTuoCBbEiysdM3xIJY6E5WEdMF6I3buovZyjud8LCFbGkmL7PXjDm+5IT2quadKz2EMjgRMpsSadyWzAmkRdCKmY5MUWFE8EYQ6YyTw/yAyObSiItMVmr2a73spSYqoX0Gexbw5uVKrBUOi7e4dthX/2tF3t6LffTmM1agTqWEtMV6Fy5Ofi8f6+btD2WpWVVbHUQBBAtHBr3qX/4SUGZblz2Kdt+wuymehzGh2/lc0wD8MQIQdUDyzQCa8kIVuK4jYPXJ41CJ8b9S/fwCss0knCcxt6Aw9LZ9do7+nG7o+1prnKaKkaLz02Q3xRBvQJYpg5KOqzr+L4AjmHxfBleaad6pSjc1FYpWHWyEkuLia1Njpd6xgXGbFbE1LRHFJP/nnPzjvt8ozi1gF7CUmIKhZOcsKU9NF5XCe8ukfW2VRXB3rt71TZje6qrtQqhbAhRhVePmazhNkGl8DtmOF7ytvuC8q1K6ZOJ3HxQd6ASQlJjKcsUo3YyV69iPr787Ydk+8LXnJvni2+kkNQvMt867ZhyJ2A5MTlXT3L5Qro6jKXwKV5D2fTIJ0vWVWAC9XmmwhUmT2Y5Mfn4ghoxcZtD++p+lZCIBlqmhVlyzMTW3ufaBYQrJb7bt51BaksxXP9H1O4asjeXCixrmYZbInPeVOUa1c63MCFYcAG5CHCKdcU02DI54djWDs6WOBhDFBW1sKBm8SZaWVdMXiBCy0oZ5u/6OmlMUTvOYOEZFNZa2syety8sNiOoaJ8Mo4pKZG/CLCzDMct4rfb3LDlmctwRdJCItSO0TNu2/vrE6koKZnOOuCXW1LWMK0qJjTVzuItdq8dg2XxX/mPMdCHhGEn6njohHN2TTnfOx+Ie3qMWhRVZeVsipD2Tayat1L8SiGkgLe5KrD+w/xISzRLl813hUjQEqSUk/3602na9Rkyz+OEh3JrvcCsHiyKcRWA5NMe1RB1BIdpv8iSCm9fM4Y/8xdIwH06UE6imUGVRmCBYYcMUXtrg80BBaQmgN3hxQKkYIlimJpyQSgR1zMfKLUm4eZMuxbEHkeuhba1KObh5sEz1GHt3C3ZCMW3GxQrpTDS9WxDbw5XPgNASTY97t5dhmvMuHRr3ueMBIS11lnpmOUv1HU41YiwXt98Xg20URq+gNITkaG3LKyxTzZim1tXb8pSXr8EuJPv5rj80euvTiXp+92ssU4oWAfkwfR6eQl3GYCuL5c9iWrPVyp8fcFpR0dkWUMcrLFMtPeJirUaU1mf5IypDm6StSrmzTiXMDMQkcGdAosVds5bIEFMqLlwrDt/diV1ciqYTCtKsAty8B+CLO+yc5mQLgwTffMd0VrChYVnW9F1Y3iQkIlimW2HKu4W5zuyeTGQMERsm9kTjl11imXyBhal7hGFNXf67gh+9ru3rLZPGkhuOfB6Dpdw0Pwedka35ZLFERGQKxlVMWqOv9/B6MTXhOqt0BY1cVbnwqhdq+3hFt4d3tp+IXsYfY+PSm2iaWmZz4WrmxWL05oeYFAktgo/U112HdPuYmIwJF60KGcNCCgc2TlQaCw6chkvTzbTKZBQYM9UiCEbLxXfPzNuXENlkejZWvZdypM7eamazZBIYM2Vg2idUQyx9wsom2GGY6OJ5Fmvco47Dxshwbe+usWBkyT1FncnS55+339TVXd+EsvK8c2T0im1meTER6fzNI6KPX95YlE30oq+7Z9rKLsLzt6R6nOvHxhJb8xWRhLNexhNcicCe4uodRFrRd5YRU8w9qb6C0xYilg5iiZA0B/nROtxvqr3UJwZuxtvXErAIj78krtXHTuuIaeWz5HH4nRq/uSMKuK0Ql/P77qGpcMI0onI91P2B7iPLiKkI4dLoXB43+y+tDKhhRVGnRFUyxrPWkjHbY8wstY+5ZuddYsp0dGvse2cqC3ww/0JR68769zn5LuFKwnqXmBJ8AwTG6EzEPI3KCaGYBa4VWTjWsmS/254mtPXFtPqotxXpuLQGNjxK3dyY6Hy3MRZU+qQUpXbneGt9MZWc3IkepHgZqeNywbKFbtEJgYK0+PJYIT+Tpd/C/MuIia35zpEQ0elzmMY/mZa3Bye6d6ItEBF7nsGKQYYTk6wDCo+1O6c2vAAquebWGDKNZf38/f2pNKKHf35+mhrRfSX6RJl68zrR7WUZ8iMZTHR7p3wD4cXTbfMvrLH9fn6Xxn3/+/vPT0n9jxJTr3h8zgK4riwmOo5P/O9gOn7/+1skpundPE0BzQITHVZ+fm+jIDoux4bAHsW0q8bZC5GuzrYWjnbx+CKaYbl17a2yL2UKy/QW0VQhCYsoHRQYFTDoKXOCIMZVTGuZQIRcSHs2ZmzTIKawTADchu++hkGhSiAm8E5Oz0yz0Qni103a3olGeP0VE8EzES7aVQiwQEwT8FghPTyEr30jJwIQT2HG0LSxekKa8fdVAst0IyeLFE7cat9VOzMX/74RjxeAZZqRBa7SbwRiuomkVfLf3T7/Bepwxyyy2FWLKRa6/vz87/5GXIh4In23rmXVQExkq7uHpbhjGhzbEnevdNU4xkwz4J/QVstTIhphbuU1+ELyBBXentEDLNPFJK3SDKHmtwiuwkKVWiaMme5mtjGQC3dLYW9h7CHufxL+ndmdFw5YpgvJBh3uZJQQZvhtKYS1eaGVetSdtm8QE4fRuZmQ7vrNBTRyNzNKC0hnJdLWR962vrqYouMkIpXHa03JTOPAUiIRv9Lb1jFmGkyRHz6bpdIg7JRP+I3+HF9DexEaH0hUSG7QS4bYPVZK48pd4qaF+8OQcdgWzfmrJ1nfhnbCzRtITkyPcYN6XNJQqH45DwFu3s0UCWlGpCVLYZi8xg2SFu0+weVrAJZpAMVCmkFYV7fhgZPCyzw3b3lGdabSTntlZ5ZcvoWAm6dM1irFvmsilV1aX027an+DJKSFhAXLpEhRGPxqtyZXnxRMkDq4VI40HmrhSVG+BLBMVxAbK42iZrAfduLaW9FL3Tb/GKQmsR8MxKREsXvnc9V4qZeSeatcWyTRhtsffvMj3DwFqty7OzpLb50xNyznDubcN2nCuCTfpEBMVxAbbzydlDUudRdjS3geKCiIqZOkVXpa5CoViKhpe8st9058sx+jBBBTB0VCGu3ela7H89tSU65UtrbFiK2umGFSuwIEIBrpuitTs3PUXNF7BvhXLAdy7ZMs5AMsFizTCFJXVM3V4T35NeaGHFoXB2k1+4OAmBrofVZAFyUTqqnbJmIuVE4cqY5dOslbQmpB7eRBCbh5lWSFlFrKM8K9qy0zJrzPdiZzztNiIZxr1uqixY7ZxNYKYhrFiMCDhntHdBah1+GZLJ3QWCbVs47vIYKCmAphU/AoqJHRp9IxikYIu6QMqYP71lKymhoLY1vLugCIqYDmMdLdd5eOXnhbukC2JxKYWkw7maAgJi1Gz4nULj71Ph/GQakOH3tJpFxGbfxVEhO7fBBThiKrpDlZ2ouwgJQNj6tnVPqrylIEYkpQ5d5dcYVuxWuLGK1rLTMWrcv9do1jM+Fqc4gpQrGQUqFwoumuomK07ipGLkMiul1QEFMPE1wNL6N1zHY1N54TrIDQINZ5JrNKQ+iJdGocHyl8ftNKCVgmga6gw4TWSm2cNCuTRPkgpoBb192NQLNTacz1jAzS3BwEmuIhlACsACwTAEpATAAoATEBoATEBIASEBMASkBMACgBMQGgBMQEgBIQEwBKQEwAKAExAaAExASAEhATAEpATAAoATEBoATEBIASEBMASkBMACgBMQGgBMQEgBIQEwBKQEwAKAExAaDE/wEkW6pLebya2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_img.show(), y_img2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_file(fns):\n",
    "    yfn, pfn = fns\n",
    "    if not pfn.exists():\n",
    "        y_data = open_mask(yfn).px.long()\n",
    "        proc_data = colors_to_codes(y_data)\n",
    "        img = PIL.Image.fromarray(proc_data.numpy())\n",
    "        img.save(pfn)\n",
    "    return pfn\n",
    "\n",
    "def process_label_files(y_fns, y_proc_fns):\n",
    "    ex = ProcessPoolExecutor(16)\n",
    "    for pfn in ex.map(process_file, zip(y_fns, y_proc_fns)):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 396 ms, sys: 260 ms, total: 656 ms\n",
      "Wall time: 39.1 s\n"
     ]
    }
   ],
   "source": [
    "%time process_label_files(y_fns, y_proc_fns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_datasets(path, valid_pct=0.2):\n",
    "    x_fns = get_image_files(path)\n",
    "    y_fns = [get_y_fn(o) for o in x_fns]\n",
    "    y_proc_fns = [get_y_proc_fn(o) for o in y_fns]\n",
    "    total = len(x_fns)\n",
    "    \n",
    "    train, valid = random_split(valid_pct, x_fns, y_proc_fns)\n",
    "    return (MatchedImageDataset(*train), MatchedImageDataset(*valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tfm_datasets(size):\n",
    "    datasets = get_datasets(PATH_X)\n",
    "    tfms = get_transforms(do_flip=True, max_rotate=4, max_lighting=0.2)\n",
    "    return transform_datasets(*datasets, tfms=tfms, tfm_y=True, size=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_norm,default_denorm = normalize_funcs(*imagenet_stats)\n",
    "bs = 8\n",
    "size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = get_transforms(do_flip=True, max_rotate=4, max_lighting=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(size, bs):\n",
    "    return DataBunch.create(*get_tfm_datasets(size), bs=bs, tfms=default_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_data(size, bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([3, 512, 512]), torch.Size([1, 512, 512]), torch.int64)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = data.train_ds[0]\n",
    "x.shape, y.shape, y.data.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_no_void(input, target):\n",
    "    target = target.squeeze()\n",
    "    mask = target != void_code\n",
    "    return (input.argmax(dim=1)[mask]==target[mask]).float().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.7114, device='cuda:0')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_no_void(p,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics=[accuracy_no_void]\n",
    "lr = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "body = create_body(tvm.resnet34(True), 2)\n",
    "model = DynamicUnet(body, n_classes=len(label_codes)).cuda()\n",
    "learn = Learner(data, model, metrics=metrics, loss_fn=CrossEntropyFlat())\n",
    "learn.split([model[0][6], model[1]])\n",
    "learn.freeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c159584c71a441e583c8c3b2cb64c482",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=2), HTML(value=''))), HTML(value='epoch  train loss  va…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_find(learn)\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, max=6), HTML(value=''))), HTML(value='epoch  train loss  va…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 02:55\n",
      "epoch  train loss  valid loss\n",
      "0      1.144465    1.669415    (00:28)\n",
      "1      0.827026    0.767053    (00:29)\n",
      "2      0.702947    0.656885    (00:29)\n",
      "3      0.644283    0.570501    (00:29)\n",
      "4      0.598689    0.517745    (00:28)\n",
      "5      0.575997    0.510366    (00:29)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(6, slice(lr), pct_start=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('u0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('u0')\n",
    "x,y = next(iter(learn.data.valid_dl))\n",
    "py = learn.model(x).detach()\n",
    "py = py.softmax(dim=1).max(dim=1, keepdim=True)[1]\n",
    "x,y,py = x.cpu(),y.cpu(),py.cpu()\n",
    "x = default_denorm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 4\n",
    "fig, axs = plt.subplots(n,3,figsize=(10,10), sharey=True)\n",
    "for i in range(n):\n",
    "    Image(x[i]).show(ax=axs[i][0])\n",
    "    codes_to_image(y[i].numpy()).show(ax=axs[i][1])\n",
    "    codes_to_image(py[i].numpy()).show(ax=axs[i][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()\n",
    "lr=1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.fit_one_cycle(6, slice(lr/100,lr), pct_start=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "size=640\n",
    "bs = 4\n",
    "learn.data = get_data(size, bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.freeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.fit_one_cycle(6, slice(lr), pct_start=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}