muslim-hate-meme-train-test.ipynb

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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "%pip install tiktoken verovio accelerate -q\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import re\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader, ConcatDataset\n",
    "\n",
    "\n",
    "from datasets import Dataset as DS\n",
    "from datasets import load_metric\n",
    "\n",
    "import seaborn as sn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import random\n",
    "\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "\n",
    "import gc\n",
    "\n",
    "import inspect\n",
    "\n",
    "try:\n",
    "    from transformers import (\n",
    "        AutoTokenizer,\n",
    "        AutoModelForSeq2SeqLM,\n",
    "        DataCollatorForSeq2Seq,\n",
    "        Seq2SeqTrainer,\n",
    "        Seq2SeqTrainingArguments,\n",
    "        TrainingArguments,\n",
    "        Trainer,\n",
    "        pipeline,\n",
    "        AutoModelForSequenceClassification\n",
    "    )\n",
    "except:\n",
    "    %pip install transformers\n",
    "    from transformers import (\n",
    "        AutoTokenizer,\n",
    "        AutoModelForSeq2SeqLM,\n",
    "        DataCollatorForSeq2Seq,\n",
    "        Seq2SeqTrainer,\n",
    "        Seq2SeqTrainingArguments,\n",
    "        pipeline\n",
    "    )\n",
    "    \n",
    "from transformers import AutoConfig, AutoModel, AutoModelForSequenceClassification\n",
    "\n",
    "print(\"All libraries have been installed successfully!\", end=\"\\r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
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    "tags": [],
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   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
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   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
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   "cell_type": "code",
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     "status": "completed"
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   "source": [
    "data = pd.read_csv(\"dataset.csv\")"
   ]
  },
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   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
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    "execution": {
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   "outputs": [],
   "source": [
    "data.reset_index(drop=True, inplace=True)"
   ]
  },
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   "outputs": [],
   "source": [
    "data.columns"
   ]
  },
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    "execution": {
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   "source": [
    "def sanity_check():\n",
    "    for i, row in data.iterrows():\n",
    "        if not os.path.exists(f\"./images/{row['image']}\"):\n",
    "            print(row['image'],\"does not exist\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "execution": {
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   "source": [
    "# sanity_check()"
   ]
  },
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   "outputs": [],
   "source": [
    "train_df, val_df = train_test_split(data, test_size=0.2, shuffle=True)\n",
    "train_df = train_df.reset_index(drop=True)\n",
    "val_df = val_df.reset_index(drop=True)"
   ]
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   "source": [
    "val_df, test_df = train_test_split(val_df, test_size=0.5, shuffle=True)"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "test_df = test_df.reset_index(drop=True)"
   ]
  },
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   "cell_type": "code",
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    "execution": {
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   "source": [
    "len(train_df), len(val_df), len(test_df)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "execution": {
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     "status": "completed"
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    "tags": [],
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   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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    },
    "id": "IYXL9zFbt3ys",
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   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:12.270034Z",
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    },
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   "outputs": [],
   "source": [
    "from transformers import AutoModel, AutoTokenizer, BeitFeatureExtractor, BeitForMaskedImageModeling, AutoModelForPreTraining, BertTokenizerFast, VisualBertModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-11-14T18:10:12.298217Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "from transformers import ViltProcessor, ViltModel"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "OCR_MODEL_NAME = \"ucaslcl/GOT-OCR2_0\"\n",
    "MODEL_NAME = \"dandelin/vilt-b32-mlm\"\n",
    "\n",
    "ocr_tokenizer = AutoTokenizer.from_pretrained(OCR_MODEL_NAME, trust_remote_code=True)\n",
    "ocr_model = AutoModel.from_pretrained(OCR_MODEL_NAME, trust_remote_code=True, \n",
    "                                      low_cpu_mem_usage=True, device_map=device, use_safetensors=True, pad_token_id=ocr_tokenizer.eos_token_id)\n",
    "\n",
    "ocr_model = ocr_model.eval().cuda()\n",
    "ocr_model.generation_config.pad_token_id = ocr_tokenizer.pad_token_id\n",
    "\n",
    "processor = ViltProcessor.from_pretrained(MODEL_NAME)\n",
    "model = ViltModel.from_pretrained(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-11-14T18:10:58.709587Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.715905Z",
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    },
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   },
   "outputs": [],
   "source": [
    "# res = ocr_model.chat(ocr_tokenizer, \"/kaggle/input/muslim-hate-memes/images/0desqkyb24r51.jpeg\", ocr_type='ocr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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    },
    "id": "ApZPmqZgdiX1",
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   "outputs": [],
   "source": [
    "from datasets import Dataset as DS\n",
    "from datasets import load_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class MuslimHateMemes(Dataset):\n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        row = self.df.iloc[index]\n",
    "        image_path = f\"./images/{row['image']}\"\n",
    "        label = int(row['label'])\n",
    "        ocr_text = ocr_model.chat(ocr_tokenizer, image_path, ocr_type='ocr')\n",
    "        image = Image.open(image_path).resize((252,252)).convert(\"RGB\")\n",
    "        inputs = processor(image, ocr_text, max_length=40, padding='max_length', truncation=True, return_tensors=\"pt\")\n",
    "\n",
    "        for x in inputs:\n",
    "            inputs[x] = inputs[x].squeeze(0)\n",
    "            # print(x,\":\",inputs[x].shape)\n",
    "        \n",
    "        return inputs, label, image_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.826792Z",
     "iopub.status.busy": "2024-11-14T18:10:58.826437Z",
     "iopub.status.idle": "2024-11-14T18:10:58.836353Z",
     "shell.execute_reply": "2024-11-14T18:10:58.835612Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.826739Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "trainds = MuslimHateMemes(train_df)\n",
    "valds = MuslimHateMemes(val_df)\n",
    "testds = MuslimHateMemes(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.837651Z",
     "iopub.status.busy": "2024-11-14T18:10:58.837300Z",
     "iopub.status.idle": "2024-11-14T18:10:58.846585Z",
     "shell.execute_reply": "2024-11-14T18:10:58.845795Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.837620Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "trainloader = DataLoader(trainds, batch_size=16, shuffle=True)\n",
    "valloader = DataLoader(valds, batch_size=2, shuffle=False)\n",
    "testloader = DataLoader(testds, batch_size=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.858589Z",
     "iopub.status.busy": "2024-11-14T18:10:58.858304Z",
     "iopub.status.idle": "2024-11-14T18:10:58.872392Z",
     "shell.execute_reply": "2024-11-14T18:10:58.871602Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.858559Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# example = next(iter(trainloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.874218Z",
     "iopub.status.busy": "2024-11-14T18:10:58.873528Z",
     "iopub.status.idle": "2024-11-14T18:10:58.883145Z",
     "shell.execute_reply": "2024-11-14T18:10:58.882290Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.874135Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.884609Z",
     "iopub.status.busy": "2024-11-14T18:10:58.884245Z",
     "iopub.status.idle": "2024-11-14T18:10:58.895710Z",
     "shell.execute_reply": "2024-11-14T18:10:58.894884Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.884564Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "class MemeClassifer(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        finetune: str = \"limit\",\n",
    "        enc_finetune_limit: int = 2, \n",
    "        output: int = 1\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.vilt = model\n",
    "        self.clf = nn.Sequential(\n",
    "            nn.LayerNorm(768),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(768, 768, bias=True),\n",
    "            nn.LayerNorm(768),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(768, output, bias=True),\n",
    "        )\n",
    "        self.act = nn.Sigmoid()\n",
    "\n",
    "        if finetune == \"all\":\n",
    "            for child in self.vilt.encoder.layer.children():\n",
    "                for param in child.parameters():\n",
    "                    param.requires_grad = True\n",
    "        else:\n",
    "            count = 0\n",
    "            length = sum(1 for _ in self.vilt.encoder.layer.children())\n",
    "            for child in self.vilt.encoder.layer.children():\n",
    "                if count >= (length - enc_finetune_limit):\n",
    "                    for param in child.parameters():\n",
    "                        param.requires_grad = True\n",
    "                else:\n",
    "                    for param in child.parameters():\n",
    "                        param.requires_grad = False\n",
    "                count += 1\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        \n",
    "        outputs = self.vilt(**inputs)\n",
    "        outputs = self.act(self.clf(outputs.pooler_output))\n",
    "\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.897803Z",
     "iopub.status.busy": "2024-11-14T18:10:58.896987Z",
     "iopub.status.idle": "2024-11-14T18:10:58.915930Z",
     "shell.execute_reply": "2024-11-14T18:10:58.914901Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.897738Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "memeclsmodel = MemeClassifer(finetune=\"all\", enc_finetune_limit=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:58.917327Z",
     "iopub.status.busy": "2024-11-14T18:10:58.917033Z",
     "iopub.status.idle": "2024-11-14T18:10:59.067086Z",
     "shell.execute_reply": "2024-11-14T18:10:59.066087Z",
     "shell.execute_reply.started": "2024-11-14T18:10:58.917296Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "memeclsmodel = memeclsmodel.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:59.068639Z",
     "iopub.status.busy": "2024-11-14T18:10:59.068319Z",
     "iopub.status.idle": "2024-11-14T18:10:59.072925Z",
     "shell.execute_reply": "2024-11-14T18:10:59.071954Z",
     "shell.execute_reply.started": "2024-11-14T18:10:59.068606Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# memeclsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:59.074303Z",
     "iopub.status.busy": "2024-11-14T18:10:59.073983Z",
     "iopub.status.idle": "2024-11-14T18:10:59.083420Z",
     "shell.execute_reply": "2024-11-14T18:10:59.082601Z",
     "shell.execute_reply.started": "2024-11-14T18:10:59.074265Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "def reset_weights(m):\n",
    "    '''\n",
    "        Try resetting model weights to avoid\n",
    "        weight leakage.\n",
    "      '''\n",
    "    for layer in m.children():\n",
    "        if hasattr(layer, 'reset_parameters'):\n",
    "            print(f'Reset trainable parameters of layer = {layer}')\n",
    "            layer.reset_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:10:59.084861Z",
     "iopub.status.busy": "2024-11-14T18:10:59.084504Z",
     "iopub.status.idle": "2024-11-14T18:11:12.664697Z",
     "shell.execute_reply": "2024-11-14T18:11:12.663399Z",
     "shell.execute_reply.started": "2024-11-14T18:10:59.084829Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "%pip install evaluate -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:12.666898Z",
     "iopub.status.busy": "2024-11-14T18:11:12.666441Z",
     "iopub.status.idle": "2024-11-14T18:11:15.364256Z",
     "shell.execute_reply": "2024-11-14T18:11:15.363520Z",
     "shell.execute_reply.started": "2024-11-14T18:11:12.666848Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "accuracy = evaluate.load('accuracy')\n",
    "f1_metric = evaluate.load(\"f1\", \"binary\")\n",
    "precision_metric = evaluate.load(\"precision\", \"binary\")\n",
    "recall_metric = f1_macro = evaluate.load(\"recall\", \"binary\")\n",
    "# clf_metrics = evaluate.combine([\"accuracy\", \"f1\", \"precision\", \"recall\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.365570Z",
     "iopub.status.busy": "2024-11-14T18:11:15.365300Z",
     "iopub.status.idle": "2024-11-14T18:11:15.373664Z",
     "shell.execute_reply": "2024-11-14T18:11:15.372701Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.365539Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "def compute_metrics_v2(preds, labels):\n",
    "    preds = preds.detach().cpu().numpy()\n",
    "    labels = labels.detach().cpu().numpy()\n",
    "    \n",
    "    torch.cuda.empty_cache()\n",
    "    \n",
    "    acc = accuracy.compute(predictions=preds, references=labels)['accuracy']\n",
    "    f1_macro = f1_metric.compute(predictions=preds, references=labels, average=\"macro\")['f1']\n",
    "    f1_micro = f1_metric.compute(predictions=preds, references=labels, average=\"micro\")['f1']\n",
    "    f1_weighted = f1_metric.compute(predictions=preds, references=labels, average=\"weighted\")['f1']\n",
    "    \n",
    "    precision = precision_metric.compute(predictions=preds, references=labels, average='weighted', zero_division=1)['precision']\n",
    "    \n",
    "    recall = recall_metric.compute(predictions=preds, references=labels, average='weighted', zero_division=1)['recall']\n",
    "\n",
    "    return {\n",
    "        \"accuracy\": acc,\n",
    "        \"f1_macro\": f1_macro,\n",
    "        \"f1_micro\": f1_micro,\n",
    "        \"f1_weighted\": f1_weighted,\n",
    "        \"precision\": precision,\n",
    "        \"recall\": recall,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.375283Z",
     "iopub.status.busy": "2024-11-14T18:11:15.374998Z",
     "iopub.status.idle": "2024-11-14T18:11:15.387213Z",
     "shell.execute_reply": "2024-11-14T18:11:15.386440Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.375251Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(memeclsmodel.parameters(), lr=3e-4)\n",
    "loss_function = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.388613Z",
     "iopub.status.busy": "2024-11-14T18:11:15.388302Z",
     "iopub.status.idle": "2024-11-14T18:11:15.400420Z",
     "shell.execute_reply": "2024-11-14T18:11:15.399471Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.388580Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "def training(model, training_loader):\n",
    "    tr_loss = 0\n",
    "    nb_tr_steps = 0\n",
    "    nb_tr_examples = 0\n",
    "    example_ct = 0\n",
    "    \n",
    "    metrs = {\n",
    "        \"train/accuracy\": 0,\n",
    "        \"train/f1_macro\": 0,\n",
    "        \"train/f1_micro\": 0,\n",
    "        \"train/f1_weighted\": 0,\n",
    "        \"train/precision\": 0,\n",
    "        \"train/recall\": 0,\n",
    "    }\n",
    "    \n",
    "    model.train()\n",
    "\n",
    "    try:\n",
    "        wandb.watch(model, log=None, log_freq=10)\n",
    "    except:\n",
    "        print(\"WANDB not logging\")\n",
    "    \n",
    "    for _ , (data, labels, img_paths) in enumerate(training_loader):\n",
    "\n",
    "        data = data.to(device)\n",
    "        labels = labels.to(device, dtype = torch.float)\n",
    "\n",
    "        outputs = model(data).squeeze(-1).float()\n",
    "        loss = loss_function(outputs, labels)\n",
    "        tr_loss += loss.item()\n",
    "        big_idx = torch.round(outputs.data)\n",
    "        metrics = compute_metrics_v2(big_idx, labels)\n",
    "        \n",
    "        for k,v in metrics.items():\n",
    "            metrs[f\"train/{k}\"] += v\n",
    "        \n",
    "        example_ct += len(data)\n",
    "        \n",
    "        nb_tr_steps += 1\n",
    "        nb_tr_examples+=labels.size(0)\n",
    "        \n",
    "        if _%1000==0:\n",
    "            loss_step = tr_loss/len(training_loader)\n",
    "#             wandb.log( mergeDicts([{\"loss\": loss_step}, metrics]) , step=nb_tr_steps)\n",
    "            print(f\"Training Loss per 1000 steps: {loss_step}\")\n",
    "            print(metrics)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        # # When using GPU\n",
    "        optimizer.step()\n",
    "        \n",
    "        torch.cuda.empty_cache()\n",
    "\n",
    "    epoch_loss = tr_loss/len(training_loader)\n",
    "    \n",
    "    for k,v in metrs.items():\n",
    "        metrs[k] = v / len(training_loader)\n",
    "\n",
    "    print(f\"Total Training: {epoch_loss}\")\n",
    "    print(f\"Total Training Metrics:\")\n",
    "    \n",
    "    print(metrs)\n",
    "\n",
    "    return epoch_loss, metrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.402135Z",
     "iopub.status.busy": "2024-11-14T18:11:15.401726Z",
     "iopub.status.idle": "2024-11-14T18:11:15.415142Z",
     "shell.execute_reply": "2024-11-14T18:11:15.414342Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.402091Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "def testing(model, testing_loader):\n",
    "    model.eval()\n",
    "    \n",
    "    metrs = {\n",
    "        \"eval/accuracy\": 0,\n",
    "        \"eval/f1_macro\": 0,\n",
    "        \"eval/f1_micro\": 0,\n",
    "        \"eval/f1_weighted\": 0,\n",
    "        \"eval/precision\": 0,\n",
    "        \"eval/recall\": 0,\n",
    "    }\n",
    "    \n",
    "    try:\n",
    "        wandb.watch(model, log=None, log_freq=10)\n",
    "    except:\n",
    "        print(\"WANDB not logging\")\n",
    "    \n",
    "    n_correct = 0; n_wrong = 0; total = 0\n",
    "    tr_loss = 0\n",
    "    nb_tr_steps = 0\n",
    "    nb_tr_examples = 0\n",
    "    with torch.no_grad():\n",
    "        for _,(data, labels, img_paths) in enumerate(testing_loader):\n",
    "            data = data.to(device)\n",
    "            labels = labels.to(device, dtype = torch.float)\n",
    "            \n",
    "            outputs = model(data).squeeze(-1).float()\n",
    "            loss = loss_function(outputs, labels)\n",
    "            tr_loss += loss.item()\n",
    "            big_idx = torch.round(outputs.data)\n",
    "            metrics = compute_metrics_v2(big_idx, labels)\n",
    "    \n",
    "            for k,v in metrics.items():\n",
    "                metrs[f\"eval/{k}\"] += v\n",
    "            \n",
    "            nb_tr_steps += 1\n",
    "            nb_tr_examples+=labels.size(0)\n",
    "            \n",
    "            if _%1000==0:\n",
    "                loss_step = tr_loss/len(testing_loader)\n",
    "#                 wandb.log( mergeDicts([{\"loss\": loss_step}, metrs]) , step=nb_tr_steps)\n",
    "                print(f\"Validation Loss per 1000 steps: {loss_step}\")\n",
    "                print(metrics)\n",
    "                \n",
    "            torch.cuda.empty_cache()\n",
    "                \n",
    "    epoch_loss = tr_loss/len(testing_loader)\n",
    "    for k,v in metrs.items():\n",
    "        metrs[k] = v / len(testing_loader)\n",
    "    \n",
    "    print(f\"Total Eval Loss: {epoch_loss}\")\n",
    "    print(f\"Total Eval Metrics:\")\n",
    "    print(metrs)\n",
    "    \n",
    "    return epoch_loss, metrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.416724Z",
     "iopub.status.busy": "2024-11-14T18:11:15.416368Z",
     "iopub.status.idle": "2024-11-14T18:11:15.426858Z",
     "shell.execute_reply": "2024-11-14T18:11:15.425984Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.416659Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "best_loss = 1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.428302Z",
     "iopub.status.busy": "2024-11-14T18:11:15.427985Z",
     "iopub.status.idle": "2024-11-14T18:11:15.437381Z",
     "shell.execute_reply": "2024-11-14T18:11:15.436445Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.428263Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-14T18:11:15.439121Z",
     "iopub.status.busy": "2024-11-14T18:11:15.438439Z",
     "iopub.status.idle": "2024-11-14T18:11:15.447922Z",
     "shell.execute_reply": "2024-11-14T18:11:15.447025Z",
     "shell.execute_reply.started": "2024-11-14T18:11:15.439076Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "with wandb.init(project=\"muslimhatememes\", name='vilt run'):\n",
    "    \n",
    "    wandb.define_metric(\"epoch\")\n",
    "    wandb.define_metric(\"train/*\", step_metric=\"epoch\")\n",
    "    wandb.define_metric(\"eval/*\", step_metric=\"epoch\")\n",
    "    \n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        print(\"==============================================================================\")\n",
    "        print(\"Starting epoch\", epoch)\n",
    "        \n",
    "        loss, metrs = training(memeclsmodel, trainloader)\n",
    "        eval_loss, eval_metrs = testing(memeclsmodel, valloader)\n",
    "        \n",
    "        print(\"Logging to W&B\")\n",
    "        wandb.log({\"train/loss\": loss} | {\"eval/loss\": eval_loss} | {\"epoch\": epoch} | metrs | eval_metrs, step=epoch)\n",
    "        print(\"Done logging to W&B\")\n",
    "        \n",
    "        if eval_loss < best_loss:\n",
    "            best_loss = eval_loss\n",
    "            torch.save(memeclsmodel.state_dict(), f'best_weights.pth')\n",
    "        \n",
    "        torch.save(memeclsmodel.state_dict(), f'checkpoint.pth')\n",
    "\n",
    "    memeclsmodel.load_state_dict(torch.load(\"./best_weights.pth\"))\n",
    "    test_loss, test_metrs = testing(memeclsmodel, testloader)\n",
    "    print(\"Test Loss:\",test_loss)\n",
    "    print(\"Test Metrics:\")\n",
    "    print(test_metrs)"
   ]
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