Blood Cell Identification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.imports import *\n",
    "from fastai.torch_imports import *\n",
    "from fastai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fastai.model import *\n",
    "from fastai.dataset import *\n",
    "from fastai.sgdr import *\n",
    "from fastai.plots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.set_device(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"data/bloodcell/dataset2-master/\"\n",
    "sz=224\n",
    "arch = resnext101_64\n",
    "bs = 58"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_csv = f'{PATH}labels.csv'\n",
    "n = len(list(open(label_csv))) - 1\n",
    "val_idxs = get_cv_idxs(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_df = pd.read_csv(label_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NEUTROPHIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NEUTROPHIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NEUTROPHIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NEUTROPHIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NEUTROPHIL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Image    Category\n",
       "0      0  NEUTROPHIL\n",
       "1      1  NEUTROPHIL\n",
       "2      2  NEUTROPHIL\n",
       "3      3  NEUTROPHIL\n",
       "4      4  NEUTROPHIL"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL</th>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EOSINOPHIL</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LYMPHOCYTE</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONOCYTE</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASOPHIL</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL, NEUTROPHIL</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL, EOSINOPHIL</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL, LYMPHOCYTE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL,EOSINOPHIL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL,BASOPHIL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL, MONOCYTE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONOCYTE, LYMPHOCYTE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LYMPHOCYTE, LYMPHOCYTE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EOSINOPHIL,EOSINOPHIL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EOSINOPHIL, LYMPHOCYTE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EOSINOPHIL, EOSINOPHIL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROPHIL,NEUTROPHIL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Image\n",
       "Category                     \n",
       "NEUTROPHIL                207\n",
       "EOSINOPHIL                 88\n",
       "LYMPHOCYTE                 33\n",
       "MONOCYTE                   21\n",
       "BASOPHIL                    3\n",
       "NEUTROPHIL, NEUTROPHIL      3\n",
       "NEUTROPHIL, EOSINOPHIL      2\n",
       "NEUTROPHIL, LYMPHOCYTE      1\n",
       "NEUTROPHIL,EOSINOPHIL       1\n",
       "NEUTROPHIL,BASOPHIL         1\n",
       "NEUTROPHIL, MONOCYTE        1\n",
       "MONOCYTE, LYMPHOCYTE        1\n",
       "LYMPHOCYTE, LYMPHOCYTE      1\n",
       "EOSINOPHIL,EOSINOPHIL       1\n",
       "EOSINOPHIL, LYMPHOCYTE      1\n",
       "EOSINOPHIL, EOSINOPHIL      1\n",
       "NEUTROPHIL,NEUTROPHIL       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_df.pivot_table(index = 'Category', aggfunc = len).sort_values('Image', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-c80e4edf2160>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtfms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtfms_from_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0march\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maug_tfms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransforms_top_down\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_zoom\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImageClassifierData\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPATH\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TRAIN'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mf'{PATH}labels.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_idxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval_idxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'.jpg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtfms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtfms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Documents\\fastai\\courses\\dl1\\fastai\\dataset.py\u001b[0m in \u001b[0;36mfrom_csv\u001b[1;34m(cls, path, folder, csv_fname, bs, tfms, val_idxs, suffix, test_name, continuous, skip_header, num_workers)\u001b[0m\n\u001b[0;32m    370\u001b[0m             \u001b[0mImageClassifierData\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    371\u001b[0m         \"\"\"\n\u001b[1;32m--> 372\u001b[1;33m         \u001b[0mfnames\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mclasses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcsv_source\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcsv_fname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskip_header\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontinuous\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcontinuous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    373\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m         \u001b[0mval_idxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_cv_idxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mval_idxs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mval_idxs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\fastai\\courses\\dl1\\fastai\\dataset.py\u001b[0m in \u001b[0;36mcsv_source\u001b[1;34m(folder, csv_file, skip_header, suffix, continuous)\u001b[0m\n\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcsv_source\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcsv_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskip_header\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontinuous\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m     \u001b[0mfnames\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcsv_labels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel2idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_csv_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcsv_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskip_header\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m     \u001b[0mfull_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0msuffix\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfnames\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcontinuous\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\fastai\\courses\\dl1\\fastai\\dataset.py\u001b[0m in \u001b[0;36mparse_csv_labels\u001b[1;34m(fn, skip_header)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[0mskip\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mskip_header\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[0mcsv_lines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mskip\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m     \u001b[0mfnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfname\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcsv_lines\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m     \u001b[0mcsv_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcsv_lines\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m     \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcsv_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\fastai\\courses\\dl1\\fastai\\dataset.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[0mskip\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mskip_header\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[0mcsv_lines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mskip\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m     \u001b[0mfnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfname\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcsv_lines\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m     \u001b[0mcsv_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcsv_lines\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m     \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcsv_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "tfms = tfms_from_model(arch, sz, aug_tfms = transforms_top_down, max_zoom = 1.1)\n",
    "data = ImageClassifierData.from_csv(PATH, 'TRAIN', f'{PATH}labels.csv', val_idxs = val_idxs, suffix = '.jpg', tfms = tfms, bs = bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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