ConcatBERT_Movies.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ConcatBERT Movies.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/IsaacRodgz/ConcatBERT/blob/master/ConcatBERT_Movies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fkKu1K7n-QA"
      },
      "source": [
        "# ConcatBERT Training\n",
        "### Classifying movie genres from plot and movie poster"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-jQF6dvIz3g"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuMAiZTuc-VC"
      },
      "source": [
        "Gated Multimodal Units for Information Fusion (Arevalo et al.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWlf1Q1i0qoS"
      },
      "source": [
        "!pip install transformers jsonlines"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQF9SDSNx-4z"
      },
      "source": [
        "import json\n",
        "import jsonlines\n",
        "import numpy as np\n",
        "import os\n",
        "from PIL import Image\n",
        "Image.MAX_IMAGE_PIXELS = None\n",
        "import functools\n",
        "from collections import Counter\n",
        "from argparse import Namespace\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNWTxSLYoBAR"
      },
      "source": [
        "!wget https://archive.org/download/mmimdb/mmimdb.tar.gz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhqfwNSXxlBw"
      },
      "source": [
        "!tar -zxf mmimdb.tar.gz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKQJtaaVz6CT"
      },
      "source": [
        "Vocabulary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Pc-OGfly6ry"
      },
      "source": [
        "class Vocab(object):\n",
        "    def __init__(self, emptyInit=False):\n",
        "        if emptyInit:\n",
        "            self.stoi, self.itos, self.vocab_sz = {}, [], 0\n",
        "        else:\n",
        "            self.stoi = {\n",
        "                w: i\n",
        "                for i, w in enumerate([\"[PAD]\", \"[UNK]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"])\n",
        "            }\n",
        "            self.itos = [w for w in self.stoi]\n",
        "            self.vocab_sz = len(self.itos)\n",
        "\n",
        "    def add(self, words):\n",
        "        cnt = len(self.itos)\n",
        "        for w in words:\n",
        "            if w in self.stoi:\n",
        "                continue\n",
        "            self.stoi[w] = cnt\n",
        "            self.itos.append(w)\n",
        "            cnt += 1\n",
        "        self.vocab_sz = len(self.itos)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljGW5KiXtTbd"
      },
      "source": [
        "args = Namespace()\n",
        "args.savedir = os.path.join('model_save', 'concat_bert')\n",
        "os.makedirs(args.savedir, exist_ok=True)\n",
        "args.data_path = \"mmimdb\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate split files (train.jsonl, dev.jsonl and test.jsonl)"
      ],
      "metadata": {
        "id": "Dekxiwx8VNEF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "idxs = json.load(open(\"mmimdb/split.json\"))\n",
        "train_labels = set()\n",
        "\n",
        "train = []\n",
        "for idx in idxs['train']:\n",
        "    data = json.load(open(f\"mmimdb/dataset/{idx}.json\"))\n",
        "    train.append(\n",
        "        {\n",
        "            \"label\": data['genres'],\n",
        "            \"img\": f\"dataset/{idx}.jpeg\",\n",
        "            \"text\": data['plot'][0]\n",
        "        }\n",
        "    )\n",
        "    for label in data['genres']:\n",
        "        train_labels.add(label)\n",
        "\n",
        "with jsonlines.open('mmimdb/train.jsonl', 'w') as writer:\n",
        "    writer.write_all(train)"
      ],
      "metadata": {
        "id": "EbOk8Hp4UTpi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dev = []\n",
        "for idx in idxs['dev']:\n",
        "    data = json.load(open(f\"mmimdb/dataset/{idx}.json\"))\n",
        "    label = [label for label in data['genres'] if label in train_labels]\n",
        "    if len(label)>0:\n",
        "        dev.append(\n",
        "            {\n",
        "                \"label\": label,\n",
        "                \"img\": f\"dataset/{idx}.jpeg\",\n",
        "                \"text\": data['plot'][0]\n",
        "            }\n",
        "        )\n",
        "\n",
        "with jsonlines.open('mmimdb/dev.jsonl', 'w') as writer:\n",
        "    writer.write_all(dev)"
      ],
      "metadata": {
        "id": "g2ddrmxoaMXC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = []\n",
        "for idx in idxs['test']:\n",
        "    data = json.load(open(f\"mmimdb/dataset/{idx}.json\"))\n",
        "    label = [label for label in data['genres'] if label in train_labels]\n",
        "    if len(label)>0:\n",
        "        test.append(\n",
        "            {\n",
        "                \"label\": label,\n",
        "                \"img\": f\"dataset/{idx}.jpeg\",\n",
        "                \"text\": data['plot'][0]\n",
        "            }\n",
        "        )\n",
        "\n",
        "with jsonlines.open('mmimdb/test.jsonl', 'w') as writer:\n",
        "    writer.write_all(test)"
      ],
      "metadata": {
        "id": "ipue9OHkaMt1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfBhL35Cynxf"
      },
      "source": [
        "Load dataset partitions, set of labels (Movie genres) and frequencies\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQH0PIsqyTo5"
      },
      "source": [
        "label_freqs = Counter()\n",
        "data_labels = [json.loads(line)[\"label\"] for line in open(os.path.join(args.data_path, \"train.jsonl\"))]\n",
        "\n",
        "if type(data_labels[0]) == list:\n",
        "    for label_row in data_labels:\n",
        "        label_freqs.update(label_row)\n",
        "else:\n",
        "    label_freqs.update(data_labels)\n",
        "\n",
        "args.labels = list(label_freqs.keys())\n",
        "args.label_freqs = label_freqs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxbGDEH137Al"
      },
      "source": [
        "args.labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OrwZtxIziNv"
      },
      "source": [
        "Create vocabulary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhc9b_Y1zje_"
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\", do_lower_case=True)\n",
        "vocab = Vocab()\n",
        "vocab.stoi = tokenizer.vocab\n",
        "vocab.itos = tokenizer.ids_to_tokens\n",
        "vocab.vocab_sz = len(vocab.itos)\n",
        "args.vocab = vocab\n",
        "args.vocab_sz = vocab.vocab_sz\n",
        "args.n_classes = len(args.labels)\n",
        "tokenizer = tokenizer.tokenize"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG0MrWkOG_K7"
      },
      "source": [
        "args.n_classes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjyWQ49c1CA4"
      },
      "source": [
        "Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-szZL8iE1DWz"
      },
      "source": [
        "class JsonlDataset(Dataset):\n",
        "    def __init__(self, data_path, tokenizer, transforms, vocab, args):\n",
        "        self.data = [json.loads(l) for l in open(data_path)]\n",
        "        self.data_dir = os.path.dirname(data_path)\n",
        "        self.tokenizer = tokenizer\n",
        "        self.args = args\n",
        "        self.vocab = vocab\n",
        "        self.n_classes = len(args.labels)\n",
        "        self.text_start_token = [\"[CLS]\"]\n",
        "\n",
        "        self.max_seq_len = args.max_seq_len\n",
        "        self.transforms = transforms\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        sentence = (\n",
        "            self.text_start_token\n",
        "            + self.tokenizer(self.data[index][\"text\"])[:(self.args.max_seq_len - 1)]\n",
        "        )\n",
        "        segment = torch.zeros(len(sentence))\n",
        "\n",
        "        sentence = torch.LongTensor(\n",
        "            [\n",
        "                self.vocab.stoi[w] if w in self.vocab.stoi else self.vocab.stoi[\"[UNK]\"]\n",
        "                for w in sentence\n",
        "            ]\n",
        "        )\n",
        "\n",
        "        label = torch.zeros(self.n_classes)\n",
        "        label[\n",
        "            [self.args.labels.index(tgt) for tgt in self.data[index][\"label\"]]\n",
        "        ] = 1\n",
        "\n",
        "        if self.data[index][\"img\"]:\n",
        "            image = Image.open(\n",
        "                os.path.join(self.data_dir, self.data[index][\"img\"])\n",
        "            ).convert(\"RGB\")\n",
        "            \n",
        "        image = self.transforms(image)\n",
        "\n",
        "        return sentence, segment, image, label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EbeNwGaZ8l3i"
      },
      "source": [
        "Collate function to process batch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2K80iDY8ovv"
      },
      "source": [
        "def collate_fn(batch, args):\n",
        "    lens = [len(row[0]) for row in batch]\n",
        "    bsz, max_seq_len = len(batch), max(lens)\n",
        "\n",
        "    mask_tensor = torch.zeros(bsz, max_seq_len).long()\n",
        "    text_tensor = torch.zeros(bsz, max_seq_len).long()\n",
        "    segment_tensor = torch.zeros(bsz, max_seq_len).long()\n",
        "\n",
        "    img_tensor = torch.stack([row[2] for row in batch])\n",
        "\n",
        "    tgt_tensor = torch.stack([row[3] for row in batch])\n",
        "\n",
        "    for i_batch, (input_row, length) in enumerate(zip(batch, lens)):\n",
        "        tokens, segment = input_row[:2]\n",
        "        text_tensor[i_batch, :length] = tokens\n",
        "        segment_tensor[i_batch, :length] = segment\n",
        "        mask_tensor[i_batch, :length] = 1\n",
        "\n",
        "    return text_tensor, segment_tensor, mask_tensor, img_tensor, tgt_tensor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9dknwP-8exE"
      },
      "source": [
        "Configure Dataset parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7_EeHGM8hS7"
      },
      "source": [
        "args.max_seq_len = 512\n",
        "args.batch_sz = 4\n",
        "args.n_workers = 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0khpAahzqQb"
      },
      "source": [
        "Create DataLoaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C95NuKW0zsEv"
      },
      "source": [
        "model_transforms = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(\n",
        "            mean=[0.46777044, 0.44531429, 0.40661017],\n",
        "            std=[0.12221994, 0.12145835, 0.14380469],\n",
        "        ),\n",
        "    ]\n",
        ")\n",
        "\n",
        "train = JsonlDataset(\n",
        "    os.path.join(args.data_path, \"train.jsonl\"),\n",
        "    tokenizer,\n",
        "    model_transforms,\n",
        "    vocab,\n",
        "    args,\n",
        ")\n",
        "\n",
        "args.train_data_len = len(train)\n",
        "\n",
        "dev = JsonlDataset(\n",
        "    os.path.join(args.data_path, \"dev.jsonl\"),\n",
        "    tokenizer,\n",
        "    model_transforms,\n",
        "    vocab,\n",
        "    args,\n",
        ")\n",
        "\n",
        "collate = functools.partial(collate_fn, args=args)\n",
        "\n",
        "train_loader = DataLoader(\n",
        "    train,\n",
        "    batch_size=args.batch_sz,\n",
        "    shuffle=True,\n",
        "    num_workers=args.n_workers,\n",
        "    collate_fn=collate,\n",
        "    drop_last=True,\n",
        ")\n",
        "\n",
        "val_loader = DataLoader(\n",
        "    dev,\n",
        "    batch_size=args.batch_sz,\n",
        "    shuffle=False,\n",
        "    num_workers=args.n_workers,\n",
        "    collate_fn=collate,\n",
        ")\n",
        "\n",
        "test_set = JsonlDataset(\n",
        "    os.path.join(args.data_path, \"test.jsonl\"),\n",
        "    tokenizer,\n",
        "    model_transforms,\n",
        "    vocab,\n",
        "    args,\n",
        ")\n",
        "\n",
        "test_loader = DataLoader(\n",
        "    test_set,\n",
        "    batch_size=args.batch_sz,\n",
        "    shuffle=False,\n",
        "    num_workers=args.n_workers,\n",
        "    collate_fn=collate,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhHGWGyO87-n"
      },
      "source": [
        "Define model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1L5VCn89mJb"
      },
      "source": [
        "class BertEncoder(nn.Module):\n",
        "    def __init__(self, args):\n",
        "        super(BertEncoder, self).__init__()\n",
        "        self.args = args\n",
        "        self.bert = BertModel.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "    def forward(self, txt, mask, segment):\n",
        "        out = self.bert(\n",
        "            txt,\n",
        "            token_type_ids=segment,\n",
        "            attention_mask=mask,\n",
        "            output_hidden_states=False,\n",
        "        )\n",
        "        return out.pooler_output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qhhdUqg9nMY"
      },
      "source": [
        "class ImageEncoder(nn.Module):\n",
        "    def __init__(self, args):\n",
        "        super(ImageEncoder, self).__init__()\n",
        "        self.args = args\n",
        "        model = torchvision.models.resnet152(pretrained=True)\n",
        "        modules = list(model.children())[:-2]\n",
        "        self.model = nn.Sequential(*modules)\n",
        "\n",
        "        pool_func = (\n",
        "            nn.AdaptiveAvgPool2d\n",
        "            if args.img_embed_pool_type == \"avg\"\n",
        "            else nn.AdaptiveMaxPool2d\n",
        "        )\n",
        "\n",
        "        if args.num_image_embeds in [1, 2, 3, 5, 7]:\n",
        "            self.pool = pool_func((args.num_image_embeds, 1))\n",
        "        elif args.num_image_embeds == 4:\n",
        "            self.pool = pool_func((2, 2))\n",
        "        elif args.num_image_embeds == 6:\n",
        "            self.pool = pool_func((3, 2))\n",
        "        elif args.num_image_embeds == 8:\n",
        "            self.pool = pool_func((4, 2))\n",
        "        elif args.num_image_embeds == 9:\n",
        "            self.pool = pool_func((3, 3))\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048\n",
        "        out = self.pool(self.model(x))\n",
        "        out = torch.flatten(out, start_dim=2)\n",
        "        out = out.transpose(1, 2).contiguous()\n",
        "        return out  # BxNx2048"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qydcbOw90Vx"
      },
      "source": [
        "class MultimodalConcatBertClf(nn.Module):\n",
        "    def __init__(self, args):\n",
        "        super(MultimodalConcatBertClf, self).__init__()\n",
        "        self.args = args\n",
        "        self.txtenc = BertEncoder(args)\n",
        "        self.imgenc = ImageEncoder(args)\n",
        "\n",
        "        last_size = args.hidden_sz + (args.img_hidden_sz * args.num_image_embeds)\n",
        "        self.clf = nn.ModuleList()\n",
        "\n",
        "        self.clf.append(nn.Linear(last_size, args.n_classes))\n",
        "\n",
        "    def forward(self, txt, mask, segment, img):\n",
        "        txt = self.txtenc(txt, mask, segment)\n",
        "        img = self.imgenc(img)\n",
        "        img = torch.flatten(img, start_dim=1)\n",
        "        out = torch.cat([txt, img], -1)\n",
        "        for layer in self.clf:\n",
        "            out = layer(out)\n",
        "        return out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ID_8-dVq96id"
      },
      "source": [
        "Define model hyperparameters and instantiate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31vhdH2c-fYM"
      },
      "source": [
        "args.img_embed_pool_type = \"avg\"\n",
        "args.num_image_embeds = 1\n",
        "args.hidden_sz = 768\n",
        "args.img_hidden_sz = 2048"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRgUo95s-BdR"
      },
      "source": [
        "model = MultimodalConcatBertClf(args)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dODbg-56-TY-"
      },
      "source": [
        "Create loss criterion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZvJqISM-Uwk"
      },
      "source": [
        "freqs = [args.label_freqs[l] for l in args.labels]\n",
        "label_weights = (torch.FloatTensor(freqs) / args.train_data_len) ** -1\n",
        "criterion = nn.BCEWithLogitsLoss(pos_weight=label_weights.cuda())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FcUReXzE_U8S"
      },
      "source": [
        "Create optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulcVQdbe_b44"
      },
      "source": [
        "args.lr = 1e-5\n",
        "optimizer = optim.AdamW(model.parameters(), lr=args.lr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbRLlubT_y9A"
      },
      "source": [
        "Create scheduler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAXAvSGf_5_x"
      },
      "source": [
        "args.lr_patience = 2\n",
        "args.lr_factor = 0.5\n",
        "scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
        "        optimizer, \"max\", patience=args.lr_patience, verbose=True, factor=args.lr_factor\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_rLAOuTAFw5"
      },
      "source": [
        "Setup training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q54aii2EAM45"
      },
      "source": [
        "model.cuda();\n",
        "torch.save(args, os.path.join(args.savedir, \"args.pt\"))\n",
        "start_epoch, global_step, n_no_improve, best_metric = 0, 0, 0, -np.inf\n",
        "\n",
        "if os.path.exists(os.path.join(args.savedir, \"checkpoint.pt\")):\n",
        "        checkpoint = torch.load(os.path.join(args.savedir, \"checkpoint.pt\"))\n",
        "        start_epoch = checkpoint[\"epoch\"]\n",
        "        n_no_improve = checkpoint[\"n_no_improve\"]\n",
        "        best_metric = checkpoint[\"best_metric\"]\n",
        "        model.load_state_dict(checkpoint[\"state_dict\"])\n",
        "        optimizer.load_state_dict(checkpoint[\"optimizer\"])\n",
        "        scheduler.load_state_dict(checkpoint[\"scheduler\"])\n",
        "\n",
        "args.max_epochs = 100\n",
        "args.gradient_accumulation_steps = 16\n",
        "args.patience = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzvXiJVCFnp2"
      },
      "source": [
        "Model evaluation function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ij6aN7OiFn3d"
      },
      "source": [
        "def model_eval(i_epoch, data, model, args, criterion):\n",
        "    with torch.no_grad():\n",
        "        losses, preds, tgts = [], [], []\n",
        "        for batch in data:\n",
        "            txt, segment, mask, img, tgt = batch\n",
        "\n",
        "            txt, img = txt.cuda(), img.cuda()\n",
        "            mask, segment = mask.cuda(), segment.cuda()\n",
        "            tgt = tgt.cuda()\n",
        "\n",
        "            out = model(txt, mask, segment, img)\n",
        "            loss = criterion(out, tgt)\n",
        "            losses.append(loss.item())\n",
        "\n",
        "            pred = torch.sigmoid(out).cpu().detach().numpy() > 0.5\n",
        "\n",
        "            preds.append(pred.tolist())\n",
        "            tgt = tgt.cpu().detach().numpy().tolist()\n",
        "            tgts.extend(tgt)\n",
        "\n",
        "    metrics = {\"loss\": np.mean(losses)}\n",
        "    tgts = np.vstack(tgts)\n",
        "    preds = np.vstack(preds)\n",
        "    metrics[\"macro_f1\"] = f1_score(tgts, preds, average=\"macro\")\n",
        "    metrics[\"micro_f1\"] = f1_score(tgts, preds, average=\"micro\")\n",
        "\n",
        "    return metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0ORcg6Tau0n"
      },
      "source": [
        "Function for saving and loading model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XT6p5JdavBY"
      },
      "source": [
        "import shutil\n",
        "\n",
        "def save_checkpoint(state, is_best, checkpoint_path, filename=\"checkpoint.pt\"):\n",
        "    filename = os.path.join(checkpoint_path, filename)\n",
        "    torch.save(state, filename)\n",
        "    if is_best:\n",
        "        shutil.copyfile(filename, os.path.join(checkpoint_path, \"model_best.pt\"))\n",
        "\n",
        "def load_checkpoint(model, path):\n",
        "    best_checkpoint = torch.load(path)\n",
        "    model.load_state_dict(best_checkpoint[\"state_dict\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ecWC8wxAjKQ"
      },
      "source": [
        "Start training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3A8UNPcEAkqi"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "for i_epoch in range(start_epoch, args.max_epochs):\n",
        "    train_losses = []\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    for batch in tqdm(train_loader, total=len(train_loader)):\n",
        "        txt, segment, mask, img, tgt = batch\n",
        "\n",
        "        txt, img = txt.cuda(), img.cuda()\n",
        "        mask, segment = mask.cuda(), segment.cuda()\n",
        "        tgt = tgt.cuda()\n",
        "        out = model(txt, mask, segment, img)\n",
        "        loss = criterion(out, tgt)\n",
        "\n",
        "        if args.gradient_accumulation_steps > 1:\n",
        "            loss = loss / args.gradient_accumulation_steps\n",
        "\n",
        "        train_losses.append(loss.item())\n",
        "        loss.backward()\n",
        "        global_step += 1\n",
        "        if global_step % args.gradient_accumulation_steps == 0:\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "    model.eval()\n",
        "    metrics = model_eval(i_epoch, val_loader, model, args, criterion)\n",
        "    print(\"Train Loss: {:.4f}\".format(np.mean(train_losses)))\n",
        "    print(\n",
        "            \"{}: Loss: {:.5f} | Macro F1 {:.5f} | Micro F1: {:.5f}\".format(\n",
        "                \"Val\", metrics[\"loss\"], metrics[\"macro_f1\"], metrics[\"micro_f1\"]\n",
        "            )\n",
        "        )\n",
        "\n",
        "    tuning_metric = metrics[\"micro_f1\"]\n",
        "    scheduler.step(tuning_metric)\n",
        "    is_improvement = tuning_metric > best_metric\n",
        "    if is_improvement:\n",
        "        best_metric = tuning_metric\n",
        "        n_no_improve = 0\n",
        "    else:\n",
        "        n_no_improve += 1\n",
        "\n",
        "    save_checkpoint(\n",
        "        {\n",
        "            \"epoch\": i_epoch + 1,\n",
        "            \"state_dict\": model.state_dict(),\n",
        "            \"optimizer\": optimizer.state_dict(),\n",
        "            \"scheduler\": scheduler.state_dict(),\n",
        "            \"n_no_improve\": n_no_improve,\n",
        "            \"best_metric\": best_metric,\n",
        "        },\n",
        "        is_improvement,\n",
        "        args.savedir,\n",
        "    )\n",
        "\n",
        "    if n_no_improve >= args.patience:\n",
        "        print(\"No improvement. Breaking out of loop.\")\n",
        "        break\n",
        "\n",
        "load_checkpoint(model, os.path.join(args.savedir, \"model_best.pt\"))\n",
        "model.eval()\n",
        "for test_name, test_loader in test_loader.items():\n",
        "    test_metrics = model_eval(np.inf, test_loader, model, args, criterion)\n",
        "    print(\n",
        "        \"{}: Loss: {:.5f} | Macro F1 {:.5f} | Micro F1: {:.5f}\".format(\n",
        "            \"Test\", test_metrics[\"loss\"], test_metrics[\"macro_f1\"], test_metrics[\"micro_f1\"]\n",
        "        )\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qi74YfFRftnb"
      },
      "source": [
        "Original results:\n",
        "\n",
        "Macro F1: 0.549\n",
        "\n",
        "Micro F1: 0.636"
      ]
    }
  ]
}