clip_model_brset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b93017c1",
   "metadata": {},
   "source": [
    "### BRSET\n",
    "\n",
    "* [Dataset](https://physionet.org/content/brazilian-ophthalmological/1.0.0/)\n",
    "\n",
    "* [Original Paper](https://www.medrxiv.org/content/10.1101/2024.01.23.24301660v1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a42f04",
   "metadata": {},
   "source": [
    "### Setup Environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb1ea8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from src.classifiers import preprocess_data, process_labels,split_data\n",
    "\n",
    "from src.classifiers import VQADataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from src.classifiers_cpu_metrics import train_early_fusion, train_late_fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "462c6ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = 'Embeddings_vlm/brset/'\n",
    "FILE = 'embeddings_clip.csv'\n",
    "\n",
    "FILE_PATH = os.path.join(PATH, FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8406fd76",
   "metadata": {},
   "source": [
    "## Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33f23860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DR_ICDR</th>\n",
       "      <th>DR_2</th>\n",
       "      <th>DR_3</th>\n",
       "      <th>split</th>\n",
       "      <th>image_embedding_0</th>\n",
       "      <th>image_embedding_1</th>\n",
       "      <th>image_embedding_2</th>\n",
       "      <th>image_embedding_3</th>\n",
       "      <th>image_embedding_4</th>\n",
       "      <th>image_embedding_5</th>\n",
       "      <th>...</th>\n",
       "      <th>text_embedding_502</th>\n",
       "      <th>text_embedding_503</th>\n",
       "      <th>text_embedding_504</th>\n",
       "      <th>text_embedding_505</th>\n",
       "      <th>text_embedding_506</th>\n",
       "      <th>text_embedding_507</th>\n",
       "      <th>text_embedding_508</th>\n",
       "      <th>text_embedding_509</th>\n",
       "      <th>text_embedding_510</th>\n",
       "      <th>text_embedding_511</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0.012998</td>\n",
       "      <td>-0.012632</td>\n",
       "      <td>0.010281</td>\n",
       "      <td>0.016375</td>\n",
       "      <td>-0.040752</td>\n",
       "      <td>-0.018974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054316</td>\n",
       "      <td>0.026362</td>\n",
       "      <td>0.062661</td>\n",
       "      <td>0.023066</td>\n",
       "      <td>-0.023967</td>\n",
       "      <td>-0.013421</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>-0.058343</td>\n",
       "      <td>0.027308</td>\n",
       "      <td>0.011322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>0.013524</td>\n",
       "      <td>-0.017406</td>\n",
       "      <td>0.012482</td>\n",
       "      <td>0.012262</td>\n",
       "      <td>-0.039934</td>\n",
       "      <td>-0.022396</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054454</td>\n",
       "      <td>0.018202</td>\n",
       "      <td>0.061717</td>\n",
       "      <td>0.022467</td>\n",
       "      <td>-0.011161</td>\n",
       "      <td>-0.011376</td>\n",
       "      <td>0.030134</td>\n",
       "      <td>-0.049021</td>\n",
       "      <td>0.021231</td>\n",
       "      <td>0.010651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0.036357</td>\n",
       "      <td>-0.018087</td>\n",
       "      <td>-0.001801</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>-0.036854</td>\n",
       "      <td>-0.019405</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.055949</td>\n",
       "      <td>0.011358</td>\n",
       "      <td>0.072995</td>\n",
       "      <td>0.030307</td>\n",
       "      <td>-0.011789</td>\n",
       "      <td>-0.011042</td>\n",
       "      <td>0.042255</td>\n",
       "      <td>-0.057926</td>\n",
       "      <td>0.033326</td>\n",
       "      <td>0.018503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0.022021</td>\n",
       "      <td>-0.011668</td>\n",
       "      <td>0.010509</td>\n",
       "      <td>0.022121</td>\n",
       "      <td>-0.035989</td>\n",
       "      <td>-0.023524</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.055314</td>\n",
       "      <td>0.002594</td>\n",
       "      <td>0.074490</td>\n",
       "      <td>0.034430</td>\n",
       "      <td>-0.000350</td>\n",
       "      <td>-0.007918</td>\n",
       "      <td>0.042707</td>\n",
       "      <td>-0.047150</td>\n",
       "      <td>0.027270</td>\n",
       "      <td>0.020940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>0.018684</td>\n",
       "      <td>-0.010326</td>\n",
       "      <td>0.004721</td>\n",
       "      <td>0.006435</td>\n",
       "      <td>-0.045767</td>\n",
       "      <td>-0.025992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052848</td>\n",
       "      <td>0.021930</td>\n",
       "      <td>0.068045</td>\n",
       "      <td>0.027663</td>\n",
       "      <td>-0.020903</td>\n",
       "      <td>-0.012616</td>\n",
       "      <td>0.037164</td>\n",
       "      <td>-0.051632</td>\n",
       "      <td>0.033358</td>\n",
       "      <td>0.013019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1028 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DR_ICDR  DR_2  DR_3  split  image_embedding_0  image_embedding_1  \\\n",
       "0        0     0     0  train           0.012998          -0.012632   \n",
       "1        0     0     0   test           0.013524          -0.017406   \n",
       "2        0     0     0  train           0.036357          -0.018087   \n",
       "3        0     0     0  train           0.022021          -0.011668   \n",
       "4        0     0     0   test           0.018684          -0.010326   \n",
       "\n",
       "   image_embedding_2  image_embedding_3  image_embedding_4  image_embedding_5  \\\n",
       "0           0.010281           0.016375          -0.040752          -0.018974   \n",
       "1           0.012482           0.012262          -0.039934          -0.022396   \n",
       "2          -0.001801           0.010425          -0.036854          -0.019405   \n",
       "3           0.010509           0.022121          -0.035989          -0.023524   \n",
       "4           0.004721           0.006435          -0.045767          -0.025992   \n",
       "\n",
       "   ...  text_embedding_502  text_embedding_503  text_embedding_504  \\\n",
       "0  ...           -0.054316            0.026362            0.062661   \n",
       "1  ...           -0.054454            0.018202            0.061717   \n",
       "2  ...           -0.055949            0.011358            0.072995   \n",
       "3  ...           -0.055314            0.002594            0.074490   \n",
       "4  ...           -0.052848            0.021930            0.068045   \n",
       "\n",
       "   text_embedding_505  text_embedding_506  text_embedding_507  \\\n",
       "0            0.023066           -0.023967           -0.013421   \n",
       "1            0.022467           -0.011161           -0.011376   \n",
       "2            0.030307           -0.011789           -0.011042   \n",
       "3            0.034430           -0.000350           -0.007918   \n",
       "4            0.027663           -0.020903           -0.012616   \n",
       "\n",
       "   text_embedding_508  text_embedding_509  text_embedding_510  \\\n",
       "0            0.033898           -0.058343            0.027308   \n",
       "1            0.030134           -0.049021            0.021231   \n",
       "2            0.042255           -0.057926            0.033326   \n",
       "3            0.042707           -0.047150            0.027270   \n",
       "4            0.037164           -0.051632            0.033358   \n",
       "\n",
       "   text_embedding_511  \n",
       "0            0.011322  \n",
       "1            0.010651  \n",
       "2            0.018503  \n",
       "3            0.020940  \n",
       "4            0.013019  \n",
       "\n",
       "[5 rows x 1028 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(FILE_PATH)\n",
    "df.drop(columns=['text', 'image_id'], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a84798",
   "metadata": {},
   "source": [
    "## Data Perparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32d85b6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape: (13012, 1028)\n",
      "Test Shape: (3254, 1028)\n"
     ]
    }
   ],
   "source": [
    "# Split the data\n",
    "train_df, test_df = split_data(df)\n",
    "\n",
    "# Select features and labels vectors\n",
    "text_columns = [column for column in df.columns if 'text' in column] #[f'text_{i}']\n",
    "image_columns = [column for column in df.columns if 'image' in column] #[f'image_{i}']\n",
    "label_columns = 'DR_2'\n",
    "\n",
    "\n",
    "# Process and one-hot encode labels for training set\n",
    "train_labels, mlb, train_columns = process_labels(train_df, col=label_columns)\n",
    "test_labels = process_labels(test_df, col=label_columns, train_columns=train_columns)\n",
    "\n",
    "\n",
    "train_dataset = VQADataset(train_df, text_columns, image_columns, label_columns, mlb, train_columns)\n",
    "test_dataset = VQADataset(test_df, text_columns, image_columns, label_columns, mlb, train_columns)\n",
    "\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=2)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97dbcda1",
   "metadata": {},
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95a9e597",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_input_size = len(text_columns)\n",
    "image_input_size = len(image_columns)\n",
    "if label_columns == 'DR_2':\n",
    "    output_size = 1\n",
    "else:\n",
    "    output_size = len(pd.unique(train_df[label_columns]))\n",
    "multilabel = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "593acf78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Early Fusion Model:\n",
      "Model Memory Usage: 0.50 MB\n",
      "The number of parameters of the model are: 131585\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-09 05:10:19 44475:44475 ActivityProfilerController.cpp:312] Completed Stage: Warm Up\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30 - Test Accuracy: 0.9557, macro-f1: 0.7418\n",
      "Epoch 1/30 - Training Time: 1.12 seconds | Inference Time: 0.23 seconds\n",
      "Epoch 2/30 - Test Accuracy: 0.9674, macro-f1: 0.8337\n",
      "Epoch 2/30 - Training Time: 1.13 seconds | Inference Time: 0.23 seconds\n",
      "Epoch 3/30 - Test Accuracy: 0.9671, macro-f1: 0.8463\n",
      "Epoch 3/30 - Training Time: 0.87 seconds | Inference Time: 0.25 seconds\n",
      "Epoch 4/30 - Test Accuracy: 0.9650, macro-f1: 0.8138\n",
      "Epoch 4/30 - Training Time: 0.95 seconds | Inference Time: 0.25 seconds\n",
      "Epoch 5/30 - Test Accuracy: 0.9708, macro-f1: 0.8682\n",
      "Epoch 5/30 - Training Time: 0.93 seconds | Inference Time: 0.26 seconds\n",
      "Epoch 6/30 - Test Accuracy: 0.9720, macro-f1: 0.8750\n",
      "Epoch 6/30 - Training Time: 0.92 seconds | Inference Time: 0.24 seconds\n",
      "Epoch 7/30 - Test Accuracy: 0.9696, macro-f1: 0.8460\n",
      "Epoch 7/30 - Training Time: 0.89 seconds | Inference Time: 0.28 seconds\n",
      "Epoch 8/30 - Test Accuracy: 0.9708, macro-f1: 0.8695\n",
      "Epoch 8/30 - Training Time: 0.91 seconds | Inference Time: 0.26 seconds\n",
      "Epoch 9/30 - Test Accuracy: 0.9705, macro-f1: 0.8581\n",
      "Epoch 9/30 - Training Time: 0.84 seconds | Inference Time: 0.30 seconds\n",
      "Epoch 10/30 - Test Accuracy: 0.9570, macro-f1: 0.8301\n",
      "Epoch 10/30 - Training Time: 0.84 seconds | Inference Time: 0.32 seconds\n",
      "Epoch 11/30 - Test Accuracy: 0.9690, macro-f1: 0.8487\n",
      "Epoch 11/30 - Training Time: 0.99 seconds | Inference Time: 0.27 seconds\n",
      "Epoch 12/30 - Test Accuracy: 0.9696, macro-f1: 0.8501\n",
      "Epoch 12/30 - Training Time: 0.92 seconds | Inference Time: 0.26 seconds\n",
      "Epoch 13/30 - Test Accuracy: 0.9708, macro-f1: 0.8749\n",
      "Epoch 13/30 - Training Time: 0.97 seconds | Inference Time: 0.27 seconds\n",
      "Epoch 14/30 - Test Accuracy: 0.9677, macro-f1: 0.8661\n",
      "Epoch 14/30 - Training Time: 0.96 seconds | Inference Time: 0.26 seconds\n",
      "Epoch 15/30 - Test Accuracy: 0.9717, macro-f1: 0.8640\n",
      "Epoch 15/30 - Training Time: 0.95 seconds | Inference Time: 0.26 seconds\n",
      "Epoch 16/30 - Test Accuracy: 0.9693, macro-f1: 0.8582\n",
      "Epoch 16/30 - Training Time: 0.96 seconds | Inference Time: 0.28 seconds\n",
      "Epoch 17/30 - Test Accuracy: 0.9720, macro-f1: 0.8673\n",
      "Epoch 17/30 - Training Time: 0.88 seconds | Inference Time: 0.29 seconds\n",
      "Epoch 18/30 - Test Accuracy: 0.9696, macro-f1: 0.8672\n",
      "Epoch 18/30 - Training Time: 0.96 seconds | Inference Time: 0.26 seconds\n",
      "Epoch 19/30 - Test Accuracy: 0.9736, macro-f1: 0.8855\n",
      "Epoch 19/30 - Training Time: 0.90 seconds | Inference Time: 0.27 seconds\n",
      "Epoch 20/30 - Test Accuracy: 0.9711, macro-f1: 0.8596\n",
      "Epoch 20/30 - Training Time: 0.95 seconds | Inference Time: 0.31 seconds\n",
      "Epoch 21/30 - Test Accuracy: 0.9665, macro-f1: 0.8610\n",
      "Epoch 21/30 - Training Time: 0.90 seconds | Inference Time: 0.30 seconds\n",
      "Epoch 22/30 - Test Accuracy: 0.9690, macro-f1: 0.8437\n",
      "Epoch 22/30 - Training Time: 0.97 seconds | Inference Time: 0.34 seconds\n",
      "Epoch 23/30 - Test Accuracy: 0.9687, macro-f1: 0.8602\n",
      "Epoch 23/30 - Training Time: 0.98 seconds | Inference Time: 0.31 seconds\n",
      "Epoch 24/30 - Test Accuracy: 0.9699, macro-f1: 0.8552\n",
      "Epoch 24/30 - Training Time: 0.99 seconds | Inference Time: 0.33 seconds\n",
      "Epoch 25/30 - Test Accuracy: 0.9708, macro-f1: 0.8777\n",
      "Epoch 25/30 - Training Time: 0.96 seconds | Inference Time: 0.30 seconds\n",
      "Epoch 26/30 - Test Accuracy: 0.9705, macro-f1: 0.8551\n",
      "Epoch 26/30 - Training Time: 0.94 seconds | Inference Time: 0.31 seconds\n",
      "Epoch 27/30 - Test Accuracy: 0.9674, macro-f1: 0.8289\n",
      "Epoch 27/30 - Training Time: 1.03 seconds | Inference Time: 0.32 seconds\n",
      "Epoch 28/30 - Test Accuracy: 0.9714, macro-f1: 0.8600\n",
      "Epoch 28/30 - Training Time: 0.97 seconds | Inference Time: 0.33 seconds\n",
      "Epoch 29/30 - Test Accuracy: 0.9711, macro-f1: 0.8667\n",
      "Epoch 29/30 - Training Time: 1.01 seconds | Inference Time: 0.30 seconds\n",
      "Epoch 30/30 - Test Accuracy: 0.9705, macro-f1: 0.8739\n",
      "Epoch 30/30 - Training Time: 0.91 seconds | Inference Time: 0.30 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-09 05:11:02 44475:44475 ActivityProfilerController.cpp:318] Completed Stage: Collection\n",
      "STAGE:2024-02-09 05:11:02 44475:44475 ActivityProfilerController.cpp:322] Completed Stage: Post Processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg       CPU Mem  Self CPU Mem    # of Calls  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                         training_batch         9.93%        3.323s        52.56%       17.590s       2.874ms      -1.49 Gb      -1.50 Gb          6120  \n",
      "enumerate(DataLoader)#_MultiProcessingDataLoaderIter...        39.38%       13.177s        40.01%       13.387s       1.736ms       1.86 Gb       1.86 Gb          7710  \n",
      "                              Optimizer.step#AdamW.step         6.91%        2.311s        21.27%        7.116s       1.163ms       1.00 Mb      -5.96 Gb          6120  \n",
      "                                   DataParallel.forward         3.56%        1.193s        15.83%        5.297s     692.373us       1.87 Gb    -723.93 Mb          7650  \n",
      "                                        inference_batch         2.19%     734.489ms         7.39%        2.473s       1.616ms    -376.98 Mb    -377.72 Mb          1530  \n",
      "                                             aten::mul_         3.01%        1.008s         4.14%        1.386s      14.152us    -774.64 Kb      -1.09 Mb         97920  \n",
      "    autograd::engine::evaluate_function: AddmmBackward0         0.41%     137.280ms         4.00%        1.340s     109.477us       1.32 Gb      -1.86 Gb         12240  \n",
      "                                           aten::linear         0.23%      77.053ms         3.97%        1.329s      86.887us     240.13 Mb     665.70 Kb         15300  \n",
      "                                              aten::cat         3.67%        1.227s         3.94%        1.319s     172.453us       1.86 Gb       1.86 Gb          7650  \n",
      "                                              aten::div         2.91%     975.295ms         3.62%        1.211s      32.980us       3.00 Gb       3.00 Gb         36720  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "Self CPU time total: 33.463s\n",
      "\n",
      "Average Training Time per Epoch: 0.95 seconds\n",
      "Total Training Time per Epoch: 28.51 seconds\n",
      "Average Inference Time per Epoch: 0.28 seconds\n",
      "Total Inference Time per Epoch: 8.47 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.98      0.99      0.98      3043\n",
      "         1.0       0.79      0.73      0.76       211\n",
      "\n",
      "    accuracy                           0.97      3254\n",
      "   macro avg       0.89      0.86      0.87      3254\n",
      "weighted avg       0.97      0.97      0.97      3254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train early fusion model\n",
    "print(\"Training Early Fusion Model:\")\n",
    "train_early_fusion(train_loader, test_loader, text_input_size, image_input_size, output_size, num_epochs=30, multilabel=multilabel, report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "031fb779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Late Fusion Model:\n",
      "Model Memory Usage: 0.25 MB\n",
      "The number of parameters of the model are: 66049\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-09 05:15:16 44475:44475 ActivityProfilerController.cpp:312] Completed Stage: Warm Up\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30 - Test Accuracy: 0.9665, macro-f1: 0.8265\n",
      "Epoch 1/30 - Training Time: 1.82 seconds | Inference Time: 0.53 seconds\n",
      "Epoch 2/30 - Test Accuracy: 0.9647, macro-f1: 0.8201\n",
      "Epoch 2/30 - Training Time: 1.73 seconds | Inference Time: 0.48 seconds\n",
      "Epoch 3/30 - Test Accuracy: 0.9613, macro-f1: 0.8404\n",
      "Epoch 3/30 - Training Time: 1.63 seconds | Inference Time: 0.52 seconds\n",
      "Epoch 4/30 - Test Accuracy: 0.9699, macro-f1: 0.8559\n",
      "Epoch 4/30 - Training Time: 1.68 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 5/30 - Test Accuracy: 0.9693, macro-f1: 0.8482\n",
      "Epoch 5/30 - Training Time: 1.63 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 6/30 - Test Accuracy: 0.9662, macro-f1: 0.8224\n",
      "Epoch 6/30 - Training Time: 1.68 seconds | Inference Time: 0.48 seconds\n",
      "Epoch 7/30 - Test Accuracy: 0.9677, macro-f1: 0.8310\n",
      "Epoch 7/30 - Training Time: 1.76 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 8/30 - Test Accuracy: 0.9683, macro-f1: 0.8379\n",
      "Epoch 8/30 - Training Time: 1.66 seconds | Inference Time: 0.46 seconds\n",
      "Epoch 9/30 - Test Accuracy: 0.9705, macro-f1: 0.8527\n",
      "Epoch 9/30 - Training Time: 1.72 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 10/30 - Test Accuracy: 0.9671, macro-f1: 0.8463\n",
      "Epoch 10/30 - Training Time: 1.71 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 11/30 - Test Accuracy: 0.9708, macro-f1: 0.8538\n",
      "Epoch 11/30 - Training Time: 1.61 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 12/30 - Test Accuracy: 0.9671, macro-f1: 0.8659\n",
      "Epoch 12/30 - Training Time: 1.54 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 13/30 - Test Accuracy: 0.9723, macro-f1: 0.8690\n",
      "Epoch 13/30 - Training Time: 1.68 seconds | Inference Time: 0.51 seconds\n",
      "Epoch 14/30 - Test Accuracy: 0.9745, macro-f1: 0.8849\n",
      "Epoch 14/30 - Training Time: 1.66 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 15/30 - Test Accuracy: 0.9714, macro-f1: 0.8697\n",
      "Epoch 15/30 - Training Time: 1.76 seconds | Inference Time: 0.52 seconds\n",
      "Epoch 16/30 - Test Accuracy: 0.9665, macro-f1: 0.8657\n",
      "Epoch 16/30 - Training Time: 1.78 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 17/30 - Test Accuracy: 0.9680, macro-f1: 0.8386\n",
      "Epoch 17/30 - Training Time: 1.78 seconds | Inference Time: 0.51 seconds\n",
      "Epoch 18/30 - Test Accuracy: 0.9680, macro-f1: 0.8359\n",
      "Epoch 18/30 - Training Time: 1.80 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 19/30 - Test Accuracy: 0.9702, macro-f1: 0.8491\n",
      "Epoch 19/30 - Training Time: 1.65 seconds | Inference Time: 0.48 seconds\n",
      "Epoch 20/30 - Test Accuracy: 0.9711, macro-f1: 0.8718\n",
      "Epoch 20/30 - Training Time: 1.75 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 21/30 - Test Accuracy: 0.9665, macro-f1: 0.8591\n",
      "Epoch 21/30 - Training Time: 1.70 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 22/30 - Test Accuracy: 0.9723, macro-f1: 0.8684\n",
      "Epoch 22/30 - Training Time: 1.76 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 23/30 - Test Accuracy: 0.9714, macro-f1: 0.8657\n",
      "Epoch 23/30 - Training Time: 1.82 seconds | Inference Time: 0.53 seconds\n",
      "Epoch 24/30 - Test Accuracy: 0.9723, macro-f1: 0.8755\n",
      "Epoch 24/30 - Training Time: 1.78 seconds | Inference Time: 0.49 seconds\n",
      "Epoch 25/30 - Test Accuracy: 0.9683, macro-f1: 0.8709\n",
      "Epoch 25/30 - Training Time: 1.53 seconds | Inference Time: 0.51 seconds\n",
      "Epoch 26/30 - Test Accuracy: 0.9668, macro-f1: 0.8667\n",
      "Epoch 26/30 - Training Time: 1.39 seconds | Inference Time: 0.51 seconds\n",
      "Epoch 27/30 - Test Accuracy: 0.9717, macro-f1: 0.8797\n",
      "Epoch 27/30 - Training Time: 1.26 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 28/30 - Test Accuracy: 0.9736, macro-f1: 0.8798\n",
      "Epoch 28/30 - Training Time: 1.25 seconds | Inference Time: 0.50 seconds\n",
      "Epoch 29/30 - Test Accuracy: 0.9711, macro-f1: 0.8639\n",
      "Epoch 29/30 - Training Time: 1.29 seconds | Inference Time: 0.54 seconds\n",
      "Epoch 30/30 - Test Accuracy: 0.9579, macro-f1: 0.8432\n",
      "Epoch 30/30 - Training Time: 1.32 seconds | Inference Time: 0.52 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-09 05:16:29 44475:44475 ActivityProfilerController.cpp:318] Completed Stage: Collection\n",
      "STAGE:2024-02-09 05:16:29 44475:44475 ActivityProfilerController.cpp:322] Completed Stage: Post Processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg       CPU Mem  Self CPU Mem    # of Calls  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                         training_batch         8.98%        4.369s        67.11%       32.638s       4.266ms      -1.86 Gb      -1.87 Gb          7650  \n",
      "enumerate(DataLoader)#_MultiProcessingDataLoaderIter...        32.42%       15.766s        32.85%       15.974s       2.072ms       1.86 Gb       1.86 Gb          7710  \n",
      "                              Optimizer.step#AdamW.step         6.72%        3.269s        32.85%       15.974s       2.610ms     516.05 Kb      -2.98 Gb          6120  \n",
      "                                              aten::div        12.10%        5.884s        12.82%        6.233s     101.843us       1.51 Gb       1.51 Gb         61200  \n",
      "                                   DataParallel.forward         3.60%        1.752s        12.39%        6.025s     787.599us     389.05 Mb    -585.60 Mb          7650  \n",
      "                                             aten::sqrt         6.22%        3.023s         6.22%        3.023s      49.398us       1.51 Gb       1.51 Gb         61200  \n",
      "    autograd::engine::evaluate_function: AddmmBackward0         0.32%     156.334ms         5.83%        2.835s     154.392us       1.31 Gb    -378.90 Mb         18360  \n",
      "                                         AddmmBackward0         0.26%     124.184ms         5.18%        2.518s     137.168us       1.68 Gb           0 b         18360  \n",
      "                                               aten::mm         4.49%        2.185s         4.49%        2.185s      89.270us       1.68 Gb       1.68 Gb         24480  \n",
      "                                           aten::linear         0.21%     100.208ms         3.98%        1.934s      84.267us     240.13 Mb       1.25 Mb         22950  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "Self CPU time total: 48.630s\n",
      "\n",
      "Average Training Time per Epoch: 1.64 seconds\n",
      "Total Training Time per Epoch: 49.11 seconds\n",
      "Average Inference Time per Epoch: 0.50 seconds\n",
      "Total Inference Time per Epoch: 15.03 seconds\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.97      0.98      3043\n",
      "         1.0       0.64      0.79      0.71       211\n",
      "\n",
      "    accuracy                           0.96      3254\n",
      "   macro avg       0.81      0.88      0.84      3254\n",
      "weighted avg       0.96      0.96      0.96      3254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train late fusion model\n",
    "print(\"Training Late Fusion Model:\")\n",
    "train_late_fusion(train_loader, test_loader, text_input_size, image_input_size, output_size, num_epochs=30, multilabel=multilabel, report=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b54ec416",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:retina_embeddings_v0_0_1]",
   "language": "python",
   "name": "conda-env-retina_embeddings_v0_0_1-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}