Training-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afc16f1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import torchio as tio\n",
    "import torch\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "from pytorch_lightning.loggers import NeptuneLogger\n",
    "from torchmetrics.functional import dice_score \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nibabel as nib\n",
    "from pytorch_lightning import seed_everything\n",
    "import os\n",
    "#from pytorch_lightning.plugins import DDPPlugin\n",
    "\n",
    "from model import UNet\n",
    "seed_everything(seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "440752b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_loss(inputs, target):\n",
    "    smooth = 1.\n",
    "\n",
    "    iflat = inputs.view(-1)\n",
    "    tflat = target.view(-1)\n",
    "    intersection = (iflat * tflat).sum()\n",
    "    \n",
    "    return 1 - ((2. * intersection + smooth) /\n",
    "              (iflat.sum() + tflat.sum() + smooth))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1cb8a3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12c6f01d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4d6433f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cfc89ccc",
   "metadata": {},
   "source": [
    "# Load in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19e49c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('train.csv')\n",
    "val=pd.read_csv('val.csv')\n",
    "trainlist=tuple(zip(train['input_file_path'].values,train['Parcellation'].values))\n",
    "vallist=tuple(zip(val['input_file_path'].values,val['Parcellation'].values))\n",
    "train_subjects=[]\n",
    "val_subjects=[]\n",
    "\n",
    "#Scalar Image\n",
    "\n",
    "\n",
    "for subject_path in trainlist:\n",
    "    subject = tio.Subject({\"MRI\":tio.ScalarImage(subject_path[0]), \"Label\":tio.LabelMap(subject_path[1])})\n",
    "    train_subjects.append(subject)\n",
    "\n",
    "for subject_path in vallist:\n",
    "    subject = tio.Subject({\"MRI\":tio.ScalarImage(subject_path[0]), \"Label\":tio.LabelMap(subject_path[1])})\n",
    "    val_subjects.append(subject)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4060920a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " Subject(Keys: ('MRI', 'Label'); images: 2),\n",
       " ...]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79c591e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([   0.,    4.,    6.,    7.,   10.,   11.,   12.,   13.,   14.,   15.,\n",
       "          16.,   17.,   18.,   24.,   28.,   43.,   45.,   46.,   49.,   50.,\n",
       "          51.,   52.,   53.,   54.,   60.,  630.,  631.,  632., 1002., 1003.,\n",
       "        1005., 1006., 1007., 1008., 1009., 1010., 1011., 1012., 1013., 1014.,\n",
       "        1015., 1016., 1017., 1018., 1019., 1020., 1021., 1022., 1023., 1024.,\n",
       "        1025., 1026., 1027., 1028., 1029., 1030., 1031., 1034., 1035., 2002.,\n",
       "        2003., 2005., 2006., 2007., 2008., 2009., 2010., 2011., 2012., 2013.,\n",
       "        2014., 2015., 2016., 2017., 2018., 2019., 2020., 2021., 2022., 2023.,\n",
       "        2024., 2025., 2026., 2027., 2028., 2029., 2030., 2031., 2034., 2035.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject['Label'].data.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ddada96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "abe6f5bf",
   "metadata": {},
   "source": [
    "# Setup Preprocessing augmentations \n",
    "\n",
    "In this section there are alot of options i could rotate, flip, defrom, change the ratio at which I do that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "824909d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "remapp=pd.read_pickle('mapping.pkl')\n",
    "\n",
    "process = tio.Compose([\n",
    "            tio.CropOrPad((192, 192, 192)), #This crops it to the desired metirc\n",
    "            tio.RescaleIntensity((-1, 1)), # This normalizes the data\n",
    "            tio.RemapLabels(remapp)\n",
    "            ])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#augmentation = tio.RandomAffine(scales=(0.9, 1.1), degrees=(-10, 10)) #original augmentation \n",
    "\n",
    "spatial_transforms = {\n",
    "    tio.RandomElasticDeformation(): 0.2,\n",
    "    tio.RandomAffine(scales=(0.9, 1.1), degrees=(-20, 20)): 0.8,\n",
    "}\n",
    "augmentation = tio.Compose([\n",
    "    tio.OneOf(spatial_transforms, p=0.5),\n",
    "   ])\n",
    "\n",
    "\n",
    "\n",
    "##Actual Augmentation process\n",
    "val_transform = process\n",
    "train_transform = tio.Compose([process, augmentation])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a5e0a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ccb49d7f",
   "metadata": {},
   "source": [
    "# So when I am using cross entropy loss I need to have conitnous 0-n-1 classes since there are 90 classes with labels up to 2000+ this is throwing an error I had to fix it with remap labels library in tio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fdf13f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = tio.SubjectsDataset(train_subjects, transform=train_transform)\n",
    "val_dataset = tio.SubjectsDataset(val_subjects, transform=val_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "615994cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1158"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c945f876",
   "metadata": {},
   "source": [
    "## I would like to get a large batch size but I may have to contend with patching and sampling from the patches Ill impliment this later to see if it increases results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b1fd934",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c16d1f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScalarImage(shape: (1, 182, 218, 182); spacing: (1.00, 1.00, 1.00); orientation: LAS+; path: \"/home2/wamri/brainlabs/Brainwork/input_mri/input_files/sub-ADNI137S1426_ses-M12_T1w.nii\")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject['MRI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b72315d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = subject[\"MRI\"][\"data\"]\n",
    "mask = subject[\"Label\"][\"data\"][:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea45f6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.dice_loss(inputs, target)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79f5e0e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Segmenter(pl.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.model = UNet()\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-4,weight_decay=1e-6)\n",
    "        self.loss_fn = torch.nn.CrossEntropyLoss()\n",
    "    \n",
    "    def forward(self, data):\n",
    "        pred = self.model(data)\n",
    "        return pred\n",
    "    \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        img = batch[\"MRI\"][\"data\"]\n",
    "        mask = batch[\"Label\"][\"data\"][:,0]  # Remove single channel as CrossEntropyLoss expects NxHxW\n",
    "        mask = mask.long()\n",
    "        pred = self(img)\n",
    "    #    preds = torch.argmax(pred, 1)\n",
    "     #   print('Dice loss',dice_loss(preds,mask))\n",
    "        print('Cross Entropy',self.loss_fn(pred, mask))\n",
    "        loss = self.loss_fn(pred, mask\n",
    "        #Got here\n",
    "        dice_sc = dice_score(pred,mask)\n",
    "        print(f'Dice Score: {dice_sc}')\n",
    "        print(f'train loss: {loss.item()}')\n",
    "        self.log('Dice loss',dice_sc)\n",
    "        # Logs\n",
    "        self.log(\"Train Loss\", loss.item())\n",
    "        if batch_idx % 50 == 0:\n",
    "            names=[i.split('/')[-1] for i in batch['MRI']['path']]\n",
    "            self.log_images(img.cpu(), pred.cpu(), mask.cpu(), \"Train\",names)\n",
    "        return loss\n",
    "    \n",
    "        \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        img = batch[\"MRI\"][\"data\"]\n",
    "        mask = batch[\"Label\"][\"data\"][:,0]  # Remove single channel as CrossEntropyLoss expects NxHxW\n",
    "        mask = mask.long()\n",
    "        \n",
    "        pred = self(img)\n",
    "        loss = self.loss_fn(pred, mask)\n",
    "        \n",
    "        # Logs\n",
    "        self.log(\"Val Loss\", loss.item())\n",
    "        names=[i.split('/')[-1] for i in batch['MRI']['path']]\n",
    "        self.log_images(img.cpu(), pred.cpu(), mask.cpu(), \"Val\",names)\n",
    "        print(f'val loss: {loss.item()}')\n",
    "        \n",
    "        return loss\n",
    "\n",
    "    \n",
    "    def log_images(self, img, pred, mask, name, file_names):\n",
    "        \n",
    "        pred = torch.argmax(pred, 1) # Take the output with the highest value\n",
    "        axial_slice = 100  # Always plot slice 50 of the 96 slices\n",
    "        \n",
    "        fig, axis = plt.subplots(1, 2)\n",
    "        axis[0].imshow(img[0][0][:,:,axial_slice], cmap=\"bone\")\n",
    "        mask_ = np.ma.masked_where(mask[0][:,:,axial_slice]==0, mask[0][:,:,axial_slice])\n",
    "        axis[0].imshow(mask_, alpha=0.6)\n",
    "        axis[0].set_title(\"Ground Truth \\n\"+file_names[0],wrap=True)\n",
    "        \n",
    "        axis[1].imshow(img[0][0][:,:,axial_slice], cmap=\"bone\")\n",
    "        mask_ = np.ma.masked_where(pred[0][:,:,axial_slice]==0, pred[0][:,:,axial_slice])\n",
    "        axis[1].imshow(mask_, alpha=0.6, cmap=\"autumn\")\n",
    "        axis[1].set_title(\"Prediction\")\n",
    "        self.logger.experiment[f\"{name} Prediction vs Label\"].log(File.as_image(fig))\n",
    "        #plt.close('all')\n",
    "    \n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        #Caution! You always need to return a list here (just pack your optimizer into one :))\n",
    "        return [self.optimizer]\n",
    "    \n",
    "#Possible learning rate schedulars \n",
    "\n",
    "#     def configure_optimizers(self):\n",
    "#         self.opt1 = torch.optim.Adam(self.model.parameters(), lr=1e-4)\n",
    "#         self.opt2 = torch.optim.SGD(self.model.parameters(), lr=1e-4, momentum=0.9)\n",
    "#         optimizers = [opt1, opt2]\n",
    "#         lr_schedulers = {\"scheduler\": ReduceLROnPlateau(opt1, 'min'), \"monitor\": \"metric_to_track\"}\n",
    "#         return optimizers, lr_schedulers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ef0bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Segmenter()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bef3c98",
   "metadata": {},
   "source": [
    "# Test if you brok your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96bf865f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features= next(iter(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac11469d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.training_step(train_features,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a943c19b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'experiment'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_480341/295084149.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidation_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_480341/1863858005.py\u001b[0m in \u001b[0;36mvalidation_step\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Val Loss\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MRI'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'path'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Val\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'val loss: {loss.item()}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_480341/1863858005.py\u001b[0m in \u001b[0;36mlog_images\u001b[0;34m(self, img, pred, mask, name, file_names)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0maxis\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"autumn\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0maxis\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Prediction\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperiment\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"{name} Prediction vs Label\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;31m#plt.close('all')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'experiment'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.validation_step(train_features,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3535558c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "y=[1,2,3]\n",
    "plt.plot(x,y)\n",
    "plt.title('yeeT\\n beet',wrap=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b77b3b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_callback = ModelCheckpoint(\n",
    "    monitor='Val Loss',\n",
    "    save_top_k=10,\n",
    "    mode='min')\n",
    "\n",
    "\n",
    "#check this cell\n",
    "#Ssave name of brains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39aac0bc",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected EOF while parsing (45777448.py, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_3162994/45777448.py\"\u001b[0;36m, line \u001b[0;32m15\u001b[0m\n\u001b[0;31m    # )#\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
     ]
    }
   ],
   "source": [
    "# trainer = pl.Trainer(\n",
    "#     fast_dev_run = True,\n",
    "#     gpus=[3], \n",
    "#     #auto_select_gpus=True,\n",
    "#     #auto_lr_find=True,\n",
    "#     #strategy='dp',\n",
    "#     log_every_n_steps=1,\n",
    "#     precision=16,\n",
    "#     #deterministic=True,\n",
    "#     #plugins=DDPPlugin(find_unused_parameters=False),\n",
    "#     callbacks=[checkpoint_callback],\n",
    "#     min_epochs= 50,\n",
    "#     max_epochs = 100,\n",
    "#     logger=TensorBoardLogger(save_dir=\"./logs\")\n",
    "# )# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4bf4a910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home2/wamri/brainlabs/Brainwork/workfiles\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b42a9677",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    }
   ],
   "source": [
    "gpus =1  #\n",
    "logger=CSVLogger(\"logs\", name=\"my_exp_name\")\n",
    "trainer = pl.Trainer(gpus=gpus, logger=logger, log_every_n_steps=1,\n",
    "                     callbacks=checkpoint_callback,\n",
    "                     max_epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "15bcb07c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6]\n",
      "\n",
      "  | Name    | Type             | Params\n",
      "---------------------------------------------\n",
      "0 | model   | UNet             | 5.8 M \n",
      "1 | loss_fn | CrossEntropyLoss | 0     \n",
      "---------------------------------------------\n",
      "5.8 M     Trainable params\n",
      "0         Non-trainable params\n",
      "5.8 M     Total params\n",
      "23.356    Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jott2/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are moving forward?\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jott2/anaconda3/lib/python3.9/site-packages/pytorch_lightning/utilities/data.py:59: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
      "  warning_cache.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are moving forward?\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jott2/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3eecb3b28d4048ac921c9fce610920b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are training?\n",
      "we are moving forward?\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 2.37 GiB (GPU 0; 23.65 GiB total capacity; 10.70 GiB already allocated; 2.33 GiB free; 11.85 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3162994/4098901670.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, train_dataloader, ckpt_path)\u001b[0m\n\u001b[1;32m    735\u001b[0m             )\n\u001b[1;32m    736\u001b[0m             \u001b[0mtrain_dataloaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m         self._call_and_handle_interrupt(\n\u001b[0m\u001b[1;32m    738\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_impl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataloaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatamodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mckpt_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m         )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(self, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    680\u001b[0m         \"\"\"\n\u001b[1;32m    681\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 682\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mtrainer_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    683\u001b[0m         \u001b[0;31m# TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m_fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    770\u001b[0m         \u001b[0;31m# TODO: ckpt_path only in v1.7\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    771\u001b[0m         \u001b[0mckpt_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mckpt_path\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 772\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mckpt_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mckpt_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    774\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m         \u001b[0;31m# dispatch `start_training` or `start_evaluating` or `start_predicting`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1195\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m         \u001b[0;31m# plugin will finalized fitting (e.g. ddp_spawn will load trained model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1272\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_predicting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1273\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1274\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun_stage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py\u001b[0m in \u001b[0;36mstart_training\u001b[0;34m(self, trainer)\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstart_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"pl.Trainer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0;31m# double dispatch to initiate the training loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_stage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstart_evaluating\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"pl.Trainer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1282\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicting\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1283\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1284\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1286\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_pre_training_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m_run_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1312\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_detect_anomaly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_detect_anomaly\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0m_EVALUATE_OUTPUT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestarting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py\u001b[0m in \u001b[0;36madvance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"run_training_epoch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_fetcher\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;31m# the global step is manually decreased here due to backwards compatibility with existing loggers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestarting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py\u001b[0m in \u001b[0;36madvance\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"run_training_batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m                 \u001b[0mbatch_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_progress\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mincrement_processed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestarting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py\u001b[0m in \u001b[0;36madvance\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlightning_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautomatic_optimization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0moptimizers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_active_optimizers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer_frequencies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanual_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    143\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madvance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_advance_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestarting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py\u001b[0m in \u001b[0;36madvance\u001b[0;34m(self, batch, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madvance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore[override]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         result = self._run_optimization(\n\u001b[0m\u001b[1;32m    216\u001b[0m             \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py\u001b[0m in \u001b[0;36m_run_optimization\u001b[0;34m(self, split_batch, batch_idx, optimizer, opt_idx)\u001b[0m\n\u001b[1;32m    264\u001b[0m         \u001b[0;31m# gradient update with accumulated gradients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconsume_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py\u001b[0m in \u001b[0;36m_optimizer_step\u001b[0;34m(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;31m# model hook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m         lightning_module.optimizer_step(\n\u001b[0m\u001b[1;32m    379\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/core/lightning.py\u001b[0m in \u001b[0;36moptimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs)\u001b[0m\n\u001b[1;32m   1662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \"\"\"\n\u001b[0;32m-> 1664\u001b[0;31m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer_closure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1666\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0moptimizer_zero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer_idx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofiler_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py\u001b[0m in \u001b[0;36moptimizer_step\u001b[0;34m(self, optimizer, opt_idx, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m    334\u001b[0m         \"\"\"\n\u001b[1;32m    335\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlightning_module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecision_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0moptimizer_zero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_epoch\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py\u001b[0m in \u001b[0;36moptimizer_step\u001b[0;34m(self, model, optimizer, optimizer_idx, closure, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLightningModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0mclosure\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_closure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_track_grad_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"pl.Trainer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m                 \u001b[0mprofile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mclosure\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py\u001b[0m in \u001b[0;36m_wrap_closure\u001b[0;34m(self, model, optimizer, optimizer_idx, closure)\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0mconsistent\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mthe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mPrecisionPlugin\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0msubclasses\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mcannot\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mdirectly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \"\"\"\n\u001b[0;32m--> 148\u001b[0;31m         \u001b[0mclosure_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_after_closure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mclosure_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py\u001b[0m in \u001b[0;36mclosure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mClosureResult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_profiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"training_step_and_backward\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0mstep_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstep_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosure_loss\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py\u001b[0m in \u001b[0;36m_training_step\u001b[0;34m(self, split_batch, batch_idx, opt_idx)\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0mlightning_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_fx_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"training_step\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"training_step\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m                 \u001b[0mtraining_step_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_training_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, step_kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \"\"\"\n\u001b[1;32m    215\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecision_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_step_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mstep_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpost_training_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpost_training_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_3162994/1815386354.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m   1148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         return F.cross_entropy(input, target, weight=self.weight,\n\u001b[0m\u001b[1;32m   1151\u001b[0m                                \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m                                label_smoothing=self.label_smoothing)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m   2844\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2845\u001b[0m         \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2846\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_smoothing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2848\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 2.37 GiB (GPU 0; 23.65 GiB total capacity; 10.70 GiB already allocated; 2.33 GiB free; 11.85 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.fit(model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40f7b820",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01025387",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}