finetune_main-vgg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prelims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x105dbbf70>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import pandas as pd\n",
    "from skimage import io, transform\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "# Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import logging\n",
    "\n",
    "debug = logging.getLogger(\"Debug\")\n",
    "info  = print\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running MPS Mode: mps\n"
     ]
    }
   ],
   "source": [
    "#check GPU\n",
    "device = None\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    print(\"Running CUDA Mode:\", device, torch.cuda.get_device_name(0))\n",
    "elif torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "    print(\"Running MPS Mode:\", device)\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "    print(\"Running CPU Mode:\", device)\n",
    "\n",
    "import time\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "plt.switch_backend('agg')\n",
    "def asMinutes(s):\n",
    "    m = math.floor(s / 60)\n",
    "    s -= m * 60\n",
    "    return '%dm %ds' % (m, s)\n",
    "def timeSince(since, percent):\n",
    "    now = time.time()\n",
    "    s = now - since\n",
    "    es = s / (percent)\n",
    "    rs = es - s\n",
    "    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))\n",
    "    \n",
    "def saveModel(save_path, model_state, optimiser_state, loss):\n",
    "    torch.save({\n",
    "            'model_state_dict': model_state,\n",
    "            'optimizer_state_dict':optimiser_state,\n",
    "            'loss': loss,  \n",
    "    }, save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class Definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vocab and Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB = None\n",
    "MAX_SEQ_LEN = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "START_TOKEN = \"<START>\"\n",
    "END_TOKEN = \"<END>\"\n",
    "UNK_TOKEN = \"<UNK>\"\n",
    "PAD_TOKEN = \"<PAD>\"\n",
    "\n",
    "class Vocabulary:\n",
    "    def __init__(self, freq_dict, wd_to_id, id_to_wd):\n",
    "        self.freq_dict = freq_dict\n",
    "        self.wd_to_id = wd_to_id\n",
    "        self.id_to_wd = id_to_wd\n",
    "        self.N = len(freq_dict)\n",
    "    \n",
    "    def get_id(self, word):\n",
    "        if word in self.wd_to_id:\n",
    "            return self.wd_to_id[word]\n",
    "        else:\n",
    "            return self.wd_to_id[UNK_TOKEN]\n",
    "\n",
    "class LatexFormulaDataset(Dataset):\n",
    "    \"\"\"Latex Formula Dataset: Image and Text\"\"\"\n",
    "    \n",
    "    def __init__(self, csv_file, root_dir, transform = None, max_examples = None):\n",
    "        \"\"\"\n",
    "        Arguments:\n",
    "            csv_file (string): Path to the csv file with image name and text\n",
    "            root_dir (string): Directory with all the images.\n",
    "            transform (callable, optional): Optional transform to be applied\n",
    "                on a sample.\n",
    "        \"\"\"\n",
    "        info(\"Loading Dataset...\")\n",
    "        self.df = pd.read_csv(csv_file)\n",
    "        \n",
    "        #info(\"Loaded Dataset\", self.df.info)\n",
    "        \n",
    "        #Slice the dataset if max_examples is not None\n",
    "        if max_examples is not None:\n",
    "            self.df = self.df.iloc[:max_examples, :]\n",
    "\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "\n",
    "        self.df['formula'] = self.df['formula'].apply(lambda x: x.split())\n",
    "        self.df['formula'] = self.df['formula'].apply(lambda x: [START_TOKEN] + x + [END_TOKEN])\n",
    "\n",
    "        self.maxlen = 0\n",
    "        for formula in self.df['formula']:\n",
    "            if len(formula) > self.maxlen:\n",
    "                self.maxlen = len(formula)\n",
    "     \n",
    "        \n",
    "        self.df['formula'] = self.df['formula'].apply(lambda x: x +[PAD_TOKEN]*(max(self.maxlen, MAX_SEQ_LEN) - len(x)))\n",
    "        self.vocab= self.construct_vocab() \n",
    "        # self.df['formula'] = self.df['formula'].apply(convert_to_ids)\n",
    "        info(\"Loaded.\")\n",
    "        \n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \"\"\"\n",
    "        Returns sample of type image, textformula\n",
    "        \"\"\"\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "\n",
    "        img_name = os.path.join(self.root_dir,\n",
    "                                self.df.iloc[idx, 0])\n",
    "        image = io.imread(img_name)\n",
    "        formula = self.df.iloc[idx, 1]\n",
    "\n",
    "        # formula = np.array([formula], dtype=str).reshape(-1, 1)\n",
    "        # formula = [self.vocab.get_id(wd[0]) for wd in formula]\n",
    "        \n",
    "        def convert_to_ids(formula):\n",
    "            form2 = [VOCAB.get_id(wd) for wd in formula]\n",
    "            return torch.tensor(form2, dtype=torch.int64)[0:MAX_SEQ_LEN]\n",
    "        \n",
    "        sample = {'image': image, 'formula': convert_to_ids(formula)}\n",
    "\n",
    "        if self.transform:\n",
    "            sample['image'] = self.transform(sample['image'])\n",
    "            \n",
    "        return sample \n",
    "    \n",
    "    def construct_vocab(self):\n",
    "        \"\"\"\n",
    "        Constructs vocabulary from the dataset formulas\n",
    "        \"\"\"\n",
    "        #Split on spaces to tokenize\n",
    "        freq_dict = {}\n",
    "        for formula in self.df['formula']:\n",
    "            for wd in formula:\n",
    "                if wd not in freq_dict:\n",
    "                    freq_dict[wd] = 1\n",
    "                else:\n",
    "                    freq_dict[wd] += 1\n",
    "        freq_dict[UNK_TOKEN] = 1\n",
    "        N = len(freq_dict)\n",
    "        wd_to_id = {}\n",
    "        for i, wd in enumerate(freq_dict):\n",
    "            wd_to_id[wd] = i\n",
    "        id_to_wd = {v: k for k, v in wd_to_id.items()}\n",
    "    \n",
    "        #pad the formulas with \n",
    "        return Vocabulary(freq_dict, wd_to_id, id_to_wd)      \n",
    "\n",
    "def get_dataloader(csv_path, image_root, batch_size, transform = None, max_examples = None):\n",
    "    \"\"\"\n",
    "    Returns dataloader,dataset for the dataset\n",
    "    \"\"\"\n",
    "    dataset = LatexFormulaDataset(csv_path, image_root, max_examples=max_examples,transform=transform) #checked\n",
    "    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "    return dataloader, dataset\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import vgg16 from torch\n",
    "from torchvision.models import vgg16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLSTM(nn.Module):\n",
    "    \"\"\"\n",
    "    Inputs:\n",
    "    (here M is whatever the batch size is passed)\n",
    "\n",
    "    context_size : size of the context vector [shape: (1,M,context_size)]\n",
    "    n_layers: number of layers [for our purposes, defaults to 1]\n",
    "    hidden_size : size of the hidden state vectors [shape: (n_layers,M,hidden_size)]\n",
    "    embed_size : size of the embedding vectors [shape: (1,M,embed_size)]\n",
    "    vocab_size : size of the vocabulary\n",
    "    max_length : maximum length of the formula\n",
    "    \"\"\"\n",
    "    def __init__(self, context_size, vocab, max_seq_len, n_layers = 1, hidden_size = 512, embed_size = 512):\n",
    "        super().__init__()\n",
    "        self.context_size = context_size\n",
    "        self.vocab = vocab\n",
    "        self.vocab_size = vocab.N\n",
    "        self.n_layers = n_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        self.embed_size = embed_size\n",
    "        self.input_size = context_size + embed_size\n",
    "        self.max_seq_len = max_seq_len\n",
    "\n",
    "        self.embed = nn.Embedding(self.vocab_size, embed_size)\n",
    "        self.lstm = nn.LSTMCell(self.input_size, self.hidden_size)\n",
    "        self.linear = nn.Linear(hidden_size, self.vocab_size)\n",
    "    \n",
    "    def forward(self, context, target_tensor = None):\n",
    "        \"\"\"\n",
    "        M: batch_size\n",
    "        context is the context vector from the encoder [shape: (M,context_size)]\n",
    "        target_tensor is the formula in tensor form [shape: (M,max_length)] (in the second dimension, it is sequence of indices of formula tokens)\n",
    "            if target_tensor is not None, then we are in Teacher Forcing mode\n",
    "            else normal jo bhi (last prediction ka embed is concatenated)\n",
    "        \"\"\"\n",
    "        context.to(device)\n",
    "        batch_size = context.shape[0]\n",
    "\n",
    "        #initialize hidden state and cell state\n",
    "        hidden = context\n",
    "        cell = torch.zeros(batch_size, self.hidden_size).to(device)\n",
    "\n",
    "        #initialize the input with embedding of the start token. Expand for batch size.\n",
    "        init_embed = self.embed(torch.tensor([self.vocab.wd_to_id[START_TOKEN]]).to(device).expand(batch_size, -1)).squeeze()\n",
    "        \n",
    "        #initialize the output_history and init_output\n",
    "        outputs = []\n",
    "        output = torch.zeros((batch_size, self.vocab_size)).to(device)\n",
    "        \n",
    "        \n",
    "        for i in range(self.max_seq_len):\n",
    "            #teacher forcing: 50% times\n",
    "            r = torch.rand(1)\n",
    "            if r>0.5 and target_tensor is not None:\n",
    "                if i==0 :\n",
    "                    embedding = init_embed\n",
    "                else: \n",
    "                    embedding = self.embed(target_tensor[:, i-1]).reshape((batch_size, self.embed_size)).to(device)            \n",
    "            else:\n",
    "                if i==0 :\n",
    "                    embedding = init_embed\n",
    "\n",
    "                else:\n",
    "                    #create embedding from previous input\n",
    "                    embedding = self.embed(torch.argmax(output, dim = 1))\n",
    "\n",
    "            lstm_input = torch.cat([context, embedding], dim = 1).to(device)\n",
    "    \n",
    "            hidden, cell = self.lstm(lstm_input, (hidden, cell))\n",
    "            output = self.linear(hidden)\n",
    "            outputs.append(output)\n",
    "            \n",
    "        output_tensor = torch.stack(outputs).permute(1,0,2) #LBV - > BLV\n",
    "\n",
    "        return output_tensor, hidden, cell\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HandwritingToLatexModel(nn.Module):\n",
    "    def __init__(self, context_size, vocab, max_seq_len, n_layers, hidden_size, embed_size):\n",
    "        super().__init__()\n",
    "        self.encoder = vgg16(weights=\"IMAGENET1K_V1\")\n",
    "        #Freeze weights of the encoder and unfreeze last layer\n",
    "        self.encoder.classifier[-1] = nn.Linear(4096, 512)\n",
    "        for param in self.encoder.parameters():\n",
    "            param.requires_grad = False\n",
    "        for param in self.encoder.classifier[-1].parameters():\n",
    "            param.requires_grad = True\n",
    "        self.decoder = DecoderLSTM(context_size, vocab, max_seq_len, n_layers, hidden_size, embed_size)\n",
    "    \n",
    "    def forward(self, image, target_tensor = None):\n",
    "        context = self.encoder(image)\n",
    "        outputs, _, _ = self.decoder(context, target_tensor)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_BASE_PATH = \"data/\"\n",
    "SAVE_BASE_PATH = \"checkpoints/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(dataloader,model, optimizer, criterion):\n",
    "    total_loss = 0\n",
    "    idx = 0\n",
    "    pb = tqdm(dataloader, desc=\"Batch\")\n",
    "    for data in pb:\n",
    "        idx+=1\n",
    "        input_tensor, target_tensor = data['image'].to(device), data['formula'].to(device)\n",
    "        outputs = model(input_tensor, target_tensor)\n",
    "        train_dataset = dataloader.dataset \n",
    "        if(train_dataset and idx%100==0):\n",
    "            generated_formula = [VOCAB.id_to_wd[token.item()] for token in torch.argmax(outputs, dim=2)[0]]\n",
    "            required_formula = [VOCAB.id_to_wd[token.item()] for token in target_tensor[0]]\n",
    "            print(f\"Generated: {' '.join(generated_formula)}\")\n",
    "            print(f\"Actual: {' '.join(required_formula)}\")\n",
    "\n",
    "        output_logits = outputs.permute(0,2,1)\n",
    "        \n",
    "        loss = criterion(\n",
    "            output_logits,\n",
    "            target_tensor\n",
    "        )\n",
    "        \n",
    "        #backprop\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step() \n",
    "\n",
    "        total_loss += loss.item()\n",
    "        pb.set_description(f\"Loss: {loss.item()}\")\n",
    "\n",
    "    return total_loss / len(dataloader)\n",
    "\n",
    "def train(train_dataloader, model, n_epochs, optimizer = None, learning_rate=0.001, print_every=1, save_interval=2, save_prefix = 'model'):\n",
    "    start = time.time()\n",
    "    plot_losses = []\n",
    "    print_loss_total = 0  # Reset every print_every\n",
    "\n",
    "    if not optimizer: optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "    criterion = nn.CrossEntropyLoss(ignore_index=VOCAB.wd_to_id[PAD_TOKEN]).to(device) #as stated in assignment\n",
    "\n",
    "    model.train()\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        info(f\"Epoch {epoch}\")\n",
    "        loss = train_epoch(train_dataloader, model, optimizer, criterion)\n",
    "        print_loss_total += loss\n",
    "\n",
    "        if epoch % print_every == 0:\n",
    "            print_loss_avg = print_loss_total / print_every\n",
    "            print_loss_total = 0\n",
    "    \n",
    "        if epoch % save_interval == 0:\n",
    "            saveModel(f'{SAVE_BASE_PATH}{save_prefix}_epoch_{epoch}.pt', model.state_dict(), optimizer.state_dict(), loss)    \n",
    "                \n",
    "        info('%s (%d %d%%) %.4f' % (timeSince(start, epoch / n_epochs), epoch, epoch / n_epochs * 100, print_loss_avg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Dataset...\n",
      "Loaded.\n",
      "Loading Dataset...\n",
      "Loaded.\n"
     ]
    }
   ],
   "source": [
    "#part a\n",
    "batch_size = 32\n",
    "vocab_size = 1000\n",
    "CONTEXT_SIZE = 512\n",
    "HIDDEN_SIZE = 512\n",
    "EMBED_SIZE = 512\n",
    "MAX_EXAMPLES = 1000\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Resize((224, 224)),\n",
    "])\n",
    "\n",
    "transform_hw = transforms.Compose([\n",
    "    transforms.ToPILImage(),\n",
    "    transforms.Grayscale(num_output_channels=3),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Resize((224, 224)),\n",
    "])\n",
    "\n",
    "train_csv_path = f\"{DATA_BASE_PATH}/SyntheticData/train.csv\"\n",
    "image_root_path = f\"{DATA_BASE_PATH}/SyntheticData/images/\"\n",
    "train_dataloader, train_dataset = get_dataloader(train_csv_path, image_root_path, batch_size, transform, max_examples=None)\n",
    "\n",
    "hw_train_csv_path = f\"{DATA_BASE_PATH}/HandwrittenData/train_hw.csv\"\n",
    "hw_image_root_path = f\"{DATA_BASE_PATH}/HandwrittenData/images/train/\"\n",
    "hw_train_dataloader, hw_train_dataset = get_dataloader(hw_train_csv_path, hw_image_root_path, batch_size, transform_hw, max_examples=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create vocabulary connsisting of both vocabs of synthetic and handwritten datasets\n",
    "def combine_vocabs(v1, v2):\n",
    "    freq_dict = {}\n",
    "    for wd in v1.freq_dict:\n",
    "        freq_dict[wd] = v1.freq_dict[wd]\n",
    "    for wd in v2.freq_dict:\n",
    "        if wd not in freq_dict:\n",
    "            freq_dict[wd] = v2.freq_dict[wd]\n",
    "        else:\n",
    "            freq_dict[wd] += v2.freq_dict[wd]\n",
    "    freq_dict[UNK_TOKEN] = 1\n",
    "    N = len(freq_dict)\n",
    "    wd_to_id = {}\n",
    "    for i, wd in enumerate(freq_dict):\n",
    "        wd_to_id[wd] = i\n",
    "    id_to_wd = {v: k for k, v in wd_to_id.items()}\n",
    "    return Vocabulary(freq_dict, wd_to_id, id_to_wd)\n",
    "\n",
    "VOCAB = combine_vocabs(train_dataset.vocab, hw_train_dataset.vocab)\n",
    "\n",
    "# model = HandwritingToLatexModel(CONTEXT_SIZE, VOCAB, n_layers=1, hidden_size= HIDDEN_SIZE, embed_size=EMBED_SIZE, max_seq_len=MAX_SEQ_LEN).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_from_checkpoint(checkpoint_path):\n",
    "    model_dicts = torch.load(checkpoint_path, map_location=device)\n",
    "    \n",
    "    model = HandwritingToLatexModel(CONTEXT_SIZE, VOCAB, n_layers=1, hidden_size= HIDDEN_SIZE, embed_size=EMBED_SIZE, max_seq_len=MAX_SEQ_LEN).to(device)\n",
    "    model.load_state_dict(model_dicts['model_state_dict'])\n",
    "    \n",
    "    optims = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "    optims.load_state_dict(model_dicts['optimizer_state_dict'])\n",
    "\n",
    "    return model, optims\n",
    "\n",
    "model_load, optims_load = load_from_checkpoint(f\"{SAVE_BASE_PATH}model4.0_epoch_10.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 3.564805030822754:   3%|▎         | 8/282 [00:13<07:35,  1.66s/it] \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb Cell 27\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m train(hw_train_dataloader, model_load, \u001b[39m25\u001b[39;49m, optims_load, save_interval\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m, save_prefix \u001b[39m=\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mFT4.0\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "\u001b[1;32m/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb Cell 27\u001b[0m line \u001b[0;36m4\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=41'>42</a>\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m, n_epochs \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m):\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=42'>43</a>\u001b[0m     info(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mEpoch \u001b[39m\u001b[39m{\u001b[39;00mepoch\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=43'>44</a>\u001b[0m     loss \u001b[39m=\u001b[39m train_epoch(train_dataloader, model, optimizer, criterion)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m     print_loss_total \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m loss\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=46'>47</a>\u001b[0m     \u001b[39mif\u001b[39;00m epoch \u001b[39m%\u001b[39m print_every \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
      "\u001b[1;32m/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb Cell 27\u001b[0m line \u001b[0;36m5\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m idx \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pb \u001b[39m=\u001b[39m tqdm(dataloader, desc\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mBatch\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39mfor\u001b[39;00m data \u001b[39min\u001b[39;00m pb:\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     idx\u001b[39m+\u001b[39m\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     input_tensor, target_tensor \u001b[39m=\u001b[39m data[\u001b[39m'\u001b[39m\u001b[39mimage\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mto(device), data[\u001b[39m'\u001b[39m\u001b[39mformula\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mto(device)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/tqdm/std.py:1182\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1179\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[1;32m   1181\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1182\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[1;32m   1183\u001b[0m         \u001b[39myield\u001b[39;00m obj\n\u001b[1;32m   1184\u001b[0m         \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1185\u001b[0m         \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    628\u001b[0m     \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    629\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset()  \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 630\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[1;32m    631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    633\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    634\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    672\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    673\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index()  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 674\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index)  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    675\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[1;32m    676\u001b[0m         data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     49\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "\u001b[1;32m/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb Cell 27\u001b[0m line \u001b[0;36m8\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=78'>79</a>\u001b[0m sample \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mimage\u001b[39m\u001b[39m'\u001b[39m: image, \u001b[39m'\u001b[39m\u001b[39mformula\u001b[39m\u001b[39m'\u001b[39m: convert_to_ids(formula)}\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=80'>81</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform:\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=81'>82</a>\u001b[0m     sample[\u001b[39m'\u001b[39m\u001b[39mimage\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransform(sample[\u001b[39m'\u001b[39;49m\u001b[39mimage\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/samarth/Library/CloudStorage/OneDrive-IITDelhi/Sem5/COL774/assgns/assgn4/Handwriting-to-Latex-ML/finetune_main-vgg.ipynb#X34sZmlsZQ%3D%3D?line=83'>84</a>\u001b[0m \u001b[39mreturn\u001b[39;00m sample\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torchvision/transforms/transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[1;32m     94\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[0;32m---> 95\u001b[0m         img \u001b[39m=\u001b[39m t(img)\n\u001b[1;32m     96\u001b[0m     \u001b[39mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torchvision/transforms/transforms.py:1580\u001b[0m, in \u001b[0;36mGrayscale.forward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m   1572\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[1;32m   1573\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1574\u001b[0m \u001b[39m    Args:\u001b[39;00m\n\u001b[1;32m   1575\u001b[0m \u001b[39m        img (PIL Image or Tensor): Image to be converted to grayscale.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1578\u001b[0m \u001b[39m        PIL Image or Tensor: Grayscaled image.\u001b[39;00m\n\u001b[1;32m   1579\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1580\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mrgb_to_grayscale(img, num_output_channels\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnum_output_channels)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torchvision/transforms/functional.py:1297\u001b[0m, in \u001b[0;36mrgb_to_grayscale\u001b[0;34m(img, num_output_channels)\u001b[0m\n\u001b[1;32m   1295\u001b[0m     _log_api_usage_once(rgb_to_grayscale)\n\u001b[1;32m   1296\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(img, torch\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m-> 1297\u001b[0m     \u001b[39mreturn\u001b[39;00m F_pil\u001b[39m.\u001b[39;49mto_grayscale(img, num_output_channels)\n\u001b[1;32m   1299\u001b[0m \u001b[39mreturn\u001b[39;00m F_t\u001b[39m.\u001b[39mrgb_to_grayscale(img, num_output_channels)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/torchvision/transforms/_functional_pil.py:343\u001b[0m, in \u001b[0;36mto_grayscale\u001b[0;34m(img, num_output_channels)\u001b[0m\n\u001b[1;32m    341\u001b[0m     img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mconvert(\u001b[39m\"\u001b[39m\u001b[39mL\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    342\u001b[0m     np_img \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(img, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39muint8)\n\u001b[0;32m--> 343\u001b[0m     np_img \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mdstack([np_img, np_img, np_img])\n\u001b[1;32m    344\u001b[0m     img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(np_img, \u001b[39m\"\u001b[39m\u001b[39mRGB\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    345\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mdstack\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.4/lib/python3.10/site-packages/numpy/lib/shape_base.py:723\u001b[0m, in \u001b[0;36mdstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(arrs, \u001b[39mlist\u001b[39m):\n\u001b[1;32m    722\u001b[0m     arrs \u001b[39m=\u001b[39m [arrs]\n\u001b[0;32m--> 723\u001b[0m \u001b[39mreturn\u001b[39;00m _nx\u001b[39m.\u001b[39;49mconcatenate(arrs, \u001b[39m2\u001b[39;49m)\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train(hw_train_dataloader, model_load, 25, optims_load, save_interval=2, save_prefix = 'FT4.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}