Relation_Network.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSJcJ3GD6EGL"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gXdCtnjq1PK",
        "outputId": "b25a1eb6-ad4c-4129-e6b1-0f1f2ad1311b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!git clone https://github.com/H-TayyarMadabushi/SemEval_2022_Task2-idiomaticity.git\n",
        "!git clone https://github.com/H-TayyarMadabushi/AStitchInLanguageModels.git\n",
        "!git clone https://github.com/huggingface/transformers.git\n",
        "%cd transformers/\n",
        "!pip install --editable .\n",
        "%cd /content/ \n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDt2xeA-6OxU"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jj_uaj3aq8Od"
      },
      "outputs": [],
      "source": [
        "import site\n",
        "site.main()\n",
        "import os\n",
        "import csv\n",
        "import os\n",
        "import sys\n",
        "import random\n",
        "import pickle\n",
        "import logging\n",
        "import tqdm\n",
        "from pathlib import Path\n",
        "\n",
        "from typing          import Optional\n",
        "from dataclasses     import dataclass, field\n",
        "\n",
        "import numpy as np\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "from datasets        import load_dataset, load_metric\n",
        "\n",
        "import transformers\n",
        "from transformers import (\n",
        "    AutoConfig,\n",
        "    AutoModel,\n",
        "    AutoModelForSequenceClassification,\n",
        "    AutoTokenizer,\n",
        "    DataCollatorWithPadding,\n",
        "    EvalPrediction,\n",
        "    HfArgumentParser,\n",
        "    PretrainedConfig,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    default_data_collator,\n",
        "    set_seed,\n",
        ")\n",
        "from transformers.utils         import check_min_version\n",
        "from transformers.trainer_utils import get_last_checkpoint, is_main_process\n",
        "\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMIFusj86ZIo"
      },
      "source": [
        "# Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jst76TcOq_kX"
      },
      "outputs": [],
      "source": [
        "def load_csv(path, delimiter=',') : \n",
        "  header = None\n",
        "  data   = list()\n",
        "  with open(path, encoding='utf-8') as csvfile:\n",
        "    reader = csv.reader(csvfile, delimiter=delimiter) \n",
        "    for row in reader : \n",
        "      if header is None : \n",
        "        header = row\n",
        "        continue\n",
        "      data.append(row) \n",
        "  return header, data\n",
        "\n",
        "class Node():\n",
        "  def __init__(self, sentence, label):\n",
        "    self.sentence = sentence\n",
        "    self.label = label\n",
        "\n",
        "def create_idiom_dict_train(directory, file_name) :\n",
        "    idiom_dict = {}\n",
        "    file_name = os.path.join(directory, file_name) \n",
        "    header, data = load_csv(file_name)\n",
        "    for elem in data:\n",
        "        label     = elem[header.index('Label')]\n",
        "        sentence = elem[header.index('Target')]\n",
        "        idiom = elem[header.index('MWE')]\n",
        "        if idiom in idiom_dict:\n",
        "          idiom_dict[idiom].append(Node(sentence, label))\n",
        "        else:\n",
        "          idiom_dict[idiom] = [Node(sentence, label)]\n",
        "    return idiom_dict\n",
        "d1 = create_idiom_dict_train('SemEval_2022_Task2-idiomaticity/SubTaskA/Data/', 'train_zero_shot.csv')\n",
        "d2 = create_idiom_dict_train('SemEval_2022_Task2-idiomaticity/SubTaskA/Data/', 'train_one_shot.csv')\n",
        "for key, value in d2.items():\n",
        "  if key in d1:\n",
        "    d1[key].append(value)\n",
        "  else:\n",
        "    d1[key] = value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "JvkkIptrrDsB"
      },
      "outputs": [],
      "source": [
        "def _get_train_data(directory, file_name, include_context, include_idiom):\n",
        "    file_name = os.path.join(directory, file_name) \n",
        "    header, data = load_csv(file_name)\n",
        "    out_header = ['label1', 'label2', 'sentence1', 'sentence3']\n",
        "    \n",
        "    if include_idiom :\n",
        "        out_header = [ 'label1', 'label2', 'sentence1', 'sentence2', 'sentence3', 'sentence4' ]\n",
        "        \n",
        "    # ['DataID', 'Language', 'MWE', 'Setting', 'Previous', 'Target', 'Next', 'Label']\n",
        "    out_data = list()\n",
        "    for elem1 in data :\n",
        "        label     = elem1[header.index('Label')]\n",
        "        sentence1 = elem1[header.index('Target')]\n",
        "        if include_context :\n",
        "            sentence1 = ' '.join([elem1[header.index('Previous')], elem1[header.index('Target')], elem1[header.index('Next')]])\n",
        "        for elem2 in d1[elem1[header.index('MWE')]]:\n",
        "          if elem2.sentence != sentence1:\n",
        "              label2 = elem2.label\n",
        "              sentence2 = elem2.sentence\n",
        "              this_row = None\n",
        "              if not include_idiom :\n",
        "                  this_row = [label, label2, sentence1, sentence2]\n",
        "              else :\n",
        "                  sentence3 = elem1[header.index('MWE')]\n",
        "                  sentence4 = sentence3\n",
        "                  this_row = [label, label2, sentence1, sentence3, sentence2, sentence4]\n",
        "              out_data.append( this_row )\n",
        "              assert len(out_header) == len(this_row)\n",
        "    \n",
        "    return [out_header] + out_data\n",
        "\n",
        "def _get_dev_data(directory, input_file_name, gold_file_name, include_context, include_idiom) :\n",
        "    input_headers, input_data = load_csv(os.path.join(directory, input_file_name)) \n",
        "    gold_header, gold_data  = load_csv( os.path.join(directory, gold_file_name))\n",
        "    assert len(input_data) == len(gold_data)\n",
        "    # ['ID', 'Language', 'MWE', 'Previous', 'Target', 'Next']\n",
        "    # ['ID', 'DataID', 'Language', 'Label']\n",
        "    out_header = ['label1', 'label2', 'sentence1', 'sentence3']\n",
        "    if include_idiom :\n",
        "        out_header = ['label1', 'label2', 'label3', 'sentence1', 'sentence2', 'sentence3', 'sentence4', 'sentence5', 'sentence6', 'language']\n",
        "\n",
        "    out_data = list()\n",
        "    for index in range(len(input_data)) :\n",
        "        this_input_id = input_data[index][input_headers.index('ID')]\n",
        "        this_gold_id = gold_data[index][gold_header.index('ID')]\n",
        "        assert this_input_id == this_gold_id\n",
        "        label = gold_data[index][gold_header.index('Label')]\n",
        "        language = gold_data[index][gold_header.index('Language')]\n",
        "        elem = input_data[index]\n",
        "        sentence1 = elem[input_headers.index('Target')]\n",
        "        if include_context :\n",
        "            sentence1 = ' '.join([elem[input_headers.index('Previous')], elem[input_headers.index('Target')], elem[input_headers.index('Next')]])\n",
        "        this_row = None\n",
        "        if not include_idiom:\n",
        "            this_row = [label, sentence1]\n",
        "        else :\n",
        "            sentence2 = elem[input_headers.index('MWE')]\n",
        "            this_row = [label, sentence1, sentence2]\n",
        "        idiom = elem[input_headers.index('MWE')]\n",
        "        other_nodes = d1[idiom]\n",
        "        if(len(other_nodes)==1):\n",
        "            if not include_idiom:\n",
        "                this_row = [label, other_nodes[0].label, sentence1, other_nodes[0].sentence] \n",
        "            else :\n",
        "                sentence2 = elem[input_headers.index('MWE')]\n",
        "                this_row = [label, other_nodes[0].label, other_nodes[0].label, sentence1, sentence2, other_nodes[0].sentence, sentence2, other_nodes[0].sentence, sentence2, language]\n",
        "        else:\n",
        "            if not include_idiom:\n",
        "                this_row = [label, other_nodes[0].label, sentence1, other_nodes[0].sentence] \n",
        "            else :\n",
        "                sentence2 = elem[input_headers.index('MWE')]\n",
        "                this_row = [label, other_nodes[0].label, other_nodes[1].label, sentence1, sentence2, other_nodes[0].sentence, sentence2, other_nodes[1].sentence, sentence2, language]    \n",
        "        # print(this_row)\n",
        "        assert len(out_header) == len(this_row) \n",
        "        out_data.append(this_row)\n",
        "    return [out_header] + out_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Ehjp8-ij9rbA"
      },
      "outputs": [],
      "source": [
        "def preprocess(input, tokenizer):\n",
        "    input1 = []\n",
        "    input2 = []\n",
        "    label1 = []\n",
        "    label2 = []\n",
        "    for i in input:\n",
        "      \"\"\"if(i[1]!='1' and i[1]!='0'):\n",
        "        continue\"\"\"\n",
        "      label1.append(int(i[0]))\n",
        "      label2.append(int(i[1]))\n",
        "      args = (\n",
        "            (i[2], i[3])\n",
        "      )\n",
        "      input1.append(args)\n",
        "      args = (\n",
        "            (i[4], i[5])\n",
        "      )\n",
        "      input2.append(args)\n",
        "    encoded_input1 = tokenizer(input1, padding=True, return_tensors=\"pt\")\n",
        "    encoded_input2 = tokenizer(input2, padding=True, return_tensors=\"pt\")\n",
        "    input_ids1 = encoded_input1['input_ids']\n",
        "    attention_mask1 = encoded_input1['attention_mask']\n",
        "    labels1 = torch.tensor(label1)\n",
        "    # print(input_ids1.size(), attention_mask1.size(), labels1.size())\n",
        "    input_ids2 = encoded_input2['input_ids']\n",
        "    attention_mask2 = encoded_input2['attention_mask']\n",
        "    labels2 = torch.tensor(label2)\n",
        "    # print(input_ids2.size(), attention_mask2.size(), labels2.size())\n",
        "    return input_ids1, attention_mask1, labels1, input_ids2, attention_mask2, labels2\n",
        "\n",
        "def preprocess_dev(input, tokenizer):\n",
        "    input1 = []\n",
        "    input2 = []\n",
        "    input3 = []\n",
        "    label1 = []\n",
        "    label2 = []\n",
        "    label3 = []\n",
        "    language = []\n",
        "    for i in input:\n",
        "      \"\"\"if(i[1]!='1' and i[1]!='0'):\n",
        "        continue\"\"\"\n",
        "      label1.append(int(i[0]))\n",
        "      label2.append(int(i[1]))\n",
        "      label3.append(int(i[2]))\n",
        "      args = (\n",
        "            (i[3], i[4])\n",
        "      )\n",
        "      input1.append(args)\n",
        "      args = (\n",
        "            (i[5], i[6])\n",
        "      )\n",
        "      input2.append(args)\n",
        "      args = (\n",
        "          (i[7], i[8])\n",
        "      )\n",
        "      input3.append(args)\n",
        "      language.append(i[9])\n",
        "    encoded_input1 = tokenizer(input1, padding=True, return_tensors=\"pt\")\n",
        "    encoded_input2 = tokenizer(input2, padding=True, return_tensors=\"pt\")\n",
        "    encoded_input3 = tokenizer(input3, padding=True, return_tensors=\"pt\")\n",
        "    input_ids1 = encoded_input1['input_ids']\n",
        "    attention_mask1 = encoded_input1['attention_mask']\n",
        "    labels1 = torch.tensor(label1)\n",
        "    input_ids2 = encoded_input2['input_ids']\n",
        "    attention_mask2 = encoded_input2['attention_mask']\n",
        "    labels2 = torch.tensor(label2)\n",
        "    input_ids3 = encoded_input3['input_ids']\n",
        "    attention_mask3 = encoded_input3['attention_mask']\n",
        "    labels3 = torch.tensor(label3)\n",
        "    return input_ids1, attention_mask1, labels1, input_ids2, attention_mask2, labels2, input_ids3, attention_mask3, labels3, language"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UYlG104qDP4Q"
      },
      "outputs": [],
      "source": [
        "def shuffle_data(data):\n",
        "    indices = list(range(len(data)))\n",
        "    random.shuffle(indices)\n",
        "    shuffled_data = []\n",
        "    for i in indices:\n",
        "        shuffled_data.append(data[i])\n",
        "    return shuffled_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "np7YcOhL4IWQ"
      },
      "outputs": [],
      "source": [
        "def train(model, train_data, tokenizer, lr, n_epoch, batch_size):\n",
        "    print(\"Start Training!\")\n",
        "    model.train()\n",
        "    optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
        "    num_classes = 2\n",
        "    for epoch in range(n_epoch):\n",
        "        shuffled_train_data = shuffle_data(train_data)\n",
        "        torch.cuda.empty_cache()\n",
        "        print(f\"\\nEpoch {epoch}\")\n",
        "        total_loss = 0.0\n",
        "        total_steps = 0\n",
        "        for step in tqdm.notebook.tqdm(range(0, len(train_data), batch_size), leave=False):\n",
        "            batch = preprocess(shuffled_train_data[step:(step + batch_size)], tokenizer)\n",
        "            batch = tuple(t.to(device) for t in batch)\n",
        "            model.zero_grad()\n",
        "            logits = model(batch[0], batch[1], batch[3], batch[4])\n",
        "            target = torch.where(batch[2]==batch[5], 1.0, 0.0)\n",
        "            loss = nn.functional.mse_loss(logits, target.reshape(-1, 1))\n",
        "            loss.backward()\n",
        "            total_loss += loss.item()\n",
        "            total_steps += 1\n",
        "            nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "            optimizer.step()\n",
        "        print(\"Train loss on epoch {}: {}\\n\".format(epoch, total_loss / total_steps))\n",
        "\n",
        "def eval(model, eval_data, tokenizer, batch_size):\n",
        "    model.eval()\n",
        "    predictions, true_labels = [], []\n",
        "    en_predictions, en_true_labels = [], []\n",
        "    pt_predictions, pt_true_labels = [], []\n",
        "    num_correct = 0\n",
        "    for step in tqdm.notebook.tqdm(range(0, len(eval_data), batch_size), leave=False):\n",
        "        batch = preprocess_dev(eval_data[step:(step + batch_size)], tokenizer)\n",
        "        batch, language = batch[:-1], batch[-1]\n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "        with torch.no_grad():\n",
        "            logits1 = model(batch[0], batch[1], batch[3], batch[4]) #B, 1\n",
        "        with torch.no_grad():\n",
        "            logits2 = model(batch[0], batch[1], batch[6], batch[7]) #B, 1\n",
        "        logits = torch.cat((logits1, logits2), dim=1) #B, 2\n",
        "        shifted_logits = torch.abs(logits - 1)\n",
        "        max_arg = torch.argmax(shifted_logits, dim=1) #B - can be 0, 1.\n",
        "        batch_predictions = []\n",
        "        batch_true_labels = batch[2]\n",
        "        first_sentence_labels = batch[5]\n",
        "        second_sentence_labels = batch[8]\n",
        "        sentence_labels = [first_sentence_labels, second_sentence_labels]\n",
        "        for idx in range(len(logits)):\n",
        "          if logits[idx][max_arg[idx]] < 0.5:\n",
        "            batch_predictions.append((sentence_labels[max_arg[idx]][idx] - 1) * -1) # 0, 1 toggle\n",
        "          else:\n",
        "            batch_predictions.append(sentence_labels[max_arg[idx]][idx])       \n",
        "        batch_en_predictions, batch_pt_predictions = [], []\n",
        "        batch_en_true_labels, batch_pt_true_labels = [], []\n",
        "        for idx, pred in enumerate(batch_predictions):\n",
        "          if language[idx]== 'EN':\n",
        "            batch_en_predictions.append(pred)\n",
        "            batch_en_true_labels.append(batch_true_labels[idx])\n",
        "          else:\n",
        "            batch_pt_predictions.append(pred)\n",
        "            batch_pt_true_labels.append(batch_true_labels[idx])\n",
        "        predictions += batch_predictions\n",
        "        true_labels += batch_true_labels\n",
        "        en_predictions += batch_en_predictions\n",
        "        en_true_labels += batch_en_true_labels\n",
        "        pt_predictions += batch_pt_predictions\n",
        "        pt_true_labels += batch_pt_true_labels\n",
        "    predictions, true_labels = list(map(int, predictions)), list(map(int, true_labels))\n",
        "    en_predictions, en_true_labels = list(map(int, en_predictions)), list(map(int, en_true_labels))\n",
        "    pt_predictions, pt_true_labels = list(map(int, pt_predictions)), list(map(int, pt_true_labels))\n",
        "    print(\"F1 Score: {:.2f}\".format(f1_score(true_labels, predictions, average='macro')))\n",
        "    print(\"EN F1 Score: {:.2f}\".format(f1_score(en_true_labels, en_predictions, average='macro')))\n",
        "    print(\"PT F1 Score: {:.2f}\".format(f1_score(pt_true_labels, pt_predictions, average='macro')))\n",
        "    print(\"Accuracy: {:.2f}\".format(accuracy_score(true_labels, predictions)))\n",
        "    print(\"EN Accuracy: {:.2f}\".format(accuracy_score(en_true_labels, en_predictions)))\n",
        "    print(\"PT Accuracy: {:.2f}\".format(accuracy_score(pt_true_labels, pt_predictions)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1Hu09yk90Lw"
      },
      "source": [
        "#Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "7qaNrZtXONvW"
      },
      "outputs": [],
      "source": [
        "train_zero_data = _get_train_data(\n",
        "        directory   = 'SemEval_2022_Task2-idiomaticity/SubTaskA/Data/',\n",
        "        file_name       = 'train_zero_shot.csv',\n",
        "        include_context = False,\n",
        "        include_idiom   = True\n",
        "    )\n",
        "train_one_data = _get_train_data(\n",
        "        directory   = 'SemEval_2022_Task2-idiomaticity/SubTaskA/Data/',\n",
        "        file_name       = 'train_one_shot.csv',\n",
        "        include_context = False,\n",
        "        include_idiom   = True\n",
        "    )\n",
        "\n",
        "assert train_zero_data[0] == train_one_data[0] ## Headers\n",
        "train_data = train_one_data + train_zero_data[1:]\n",
        "\n",
        "dev_data = _get_dev_data(\n",
        "        directory    = 'SemEval_2022_Task2-idiomaticity/SubTaskA/Data/',\n",
        "        input_file_name  = 'dev.csv',\n",
        "        gold_file_name   = 'dev_gold.csv', \n",
        "        include_context  = False,\n",
        "        include_idiom    = True\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfEAlb8FykIq"
      },
      "source": [
        "# Config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "a0_lZDObyWbQ"
      },
      "outputs": [],
      "source": [
        "batch_size = 16\n",
        "learning_rate = 0.00002\n",
        "num_epoch = 2\n",
        "dropout_rate = 0.5\n",
        "hf_model = 'bert-base-multilingual-cased'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AthsRKPOOs2x"
      },
      "source": [
        "#Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "GoS9OP9e0IcN"
      },
      "outputs": [],
      "source": [
        "class RelationNetwork(nn.Module):\n",
        "    def __init__(self, hf_model, dropout_rate=0.25):\n",
        "        super(RelationNetwork, self).__init__()\n",
        "        self.base_model = AutoModel.from_pretrained(hf_model)\n",
        "        self.dropout = nn.Dropout(dropout_rate)\n",
        "        self.feedforward_1 = nn.Linear(768*2, 300)\n",
        "        self.non_lin_1 = nn.PReLU()\n",
        "        self.feedforward_2 = nn.Linear(300, 300)\n",
        "        self.non_lin_2 = nn.PReLU()\n",
        "        self.feedforward_3 = nn.Linear(300, 1)\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "        \n",
        "    def forward(self, input_ids1, attn_mask1, input_ids2, attn_mask2):\n",
        "        outputs1 = self.base_model(input_ids1, attention_mask=attn_mask1).last_hidden_state[:, 0]\n",
        "        outputs2 = self.base_model(input_ids2, attention_mask=attn_mask2).last_hidden_state[:, 0]\n",
        "        concatenated_output = torch.cat((outputs1, outputs2), axis=1)\n",
        "        f1 = self.dropout(self.non_lin_1(self.feedforward_1(concatenated_output)))\n",
        "        f2 = self.dropout(self.non_lin_2(self.feedforward_2(f1)))\n",
        "        return self.sigmoid(self.feedforward_3(f2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
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          "output_type": "display_data",
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        {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(hf_model)\n",
        "model = RelationNetwork(hf_model, dropout_rate)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "if n_gpu > 1:\n",
        "    model.to(device)\n",
        "    model = torch.nn.DataParallel(model)\n",
        "else:\n",
        "    model.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZQA-G4gOxST"
      },
      "source": [
        "#Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
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