CosFace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06aabb63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:10:06.900075Z",
     "start_time": "2021-09-20T16:10:03.557394Z"
    },
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import pretrainedmodels\n",
    "\n",
    "from utils.train import train\n",
    "from utils.evaluation import evaluation\n",
    "from utils.loader import get_loader, get_image_list\n",
    "from utils.losses import CostSensitiveRegularizedLoss\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb64b0db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:10:06.916076Z",
     "start_time": "2021-09-20T16:10:06.901076Z"
    },
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "# hyper params\n",
    "num_of_class = 102\n",
    "learning_rate = 1e-04\n",
    "batch_size = 128\n",
    "input_size = 224\n",
    "device = torch.device(\"cuda\")\n",
    "test_size = 0.2\n",
    "train_size = 0.7\n",
    "val_size = 0.1\n",
    "directoryAgeDB = 'AgeDB/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d920459",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:10:06.932076Z",
     "start_time": "2021-09-20T16:10:06.918078Z"
    },
    "code_folding": [],
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "def findAge(image_list):\n",
    "    listofzeros = [0] * 102\n",
    "    same_age = [i for i in range(0, 102)]\n",
    "    age_number = dict(zip(same_age, listofzeros))\n",
    "    sum = 0\n",
    "\n",
    "    for i in range(len(image_list)):\n",
    "\n",
    "        if image_list[i]['age'] in age_number:\n",
    "            age_number[image_list[i]['age']] += 1\n",
    "            sum += 1\n",
    "\n",
    "    print(sum)\n",
    "    return dict(age_number)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8ae2b44",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:11:44.871886Z",
     "start_time": "2021-09-20T16:10:06.933075Z"
    },
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80797b1fa5e74d58a189cead7f1cf058",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11541 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Length:  34623\n",
      "Test Set Length:  3298\n",
      "Validation Set Length:  1649\n",
      "Total:  39570\n"
     ]
    }
   ],
   "source": [
    "train_image_list, test_image_list, validate_image_list = get_image_list(\n",
    "    train_size=train_size, \n",
    "    test_size=test_size, \n",
    "    val_size=val_size, \n",
    "    directory=directoryAgeDB,\n",
    ")\n",
    "\n",
    "train_loader, test_loader, validation_loader = get_loader(\n",
    "    input_size=input_size,\n",
    "    train_image_list=train_image_list,\n",
    "    test_image_list=test_image_list, \n",
    "    validate_image_list=validate_image_list, \n",
    "    batch_size=batch_size,\n",
    "    train_augment=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5641b0b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:11:48.045884Z",
     "start_time": "2021-09-20T16:11:44.874886Z"
    },
    "code_folding": [],
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "resnet1 = pretrainedmodels.resnet18(pretrained='imagenet')\n",
    "resnet1.last_linear = nn.Linear(\n",
    "    in_features=resnet1.last_linear.in_features, \n",
    "    out_features=num_of_class, \n",
    "    bias=False\n",
    ")\n",
    "resnet2 = pretrainedmodels.resnet18(pretrained='imagenet')\n",
    "resnet2.last_linear = nn.Linear(\n",
    "    in_features=resnet2.last_linear.in_features, \n",
    "    out_features=num_of_class, \n",
    "    bias=False\n",
    ")\n",
    "resnet1 = resnet1.to(device)\n",
    "resnet2 = resnet2.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ca9b2a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:11:48.093885Z",
     "start_time": "2021-09-20T16:11:48.046887Z"
    },
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from pytorch_metric_learning.losses import CosFaceLoss\n",
    "criteria = CosFaceLoss(num_classes=num_of_class, embedding_size=num_of_class)\n",
    "optimizer = torch.optim.Adam(resnet1.parameters(), lr=learning_rate, weight_decay=1e-1)\n",
    "model_name = 'cosface-aug.pth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ed4bb3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:19:41.125218Z",
     "start_time": "2021-09-20T16:11:48.095887Z"
    },
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/5, Train Loss: 27.590712719737823,  Validation Loss: 26.579018812913162\n",
      "Validation Loss Decreased(inf ---> 26.579018812913162)\n",
      "Epoch: 2/5, Train Loss: 25.504153051059625,  Validation Loss: 26.909641559307392\n",
      "Epoch: 3/5, Train Loss: 24.592334008304835,  Validation Loss: 27.91579422583947\n",
      "Epoch: 4/5, Train Loss: 23.487289372405442,  Validation Loss: 30.819516988900993\n",
      "Epoch: 5/5, Train Loss: 21.00188089912668,  Validation Loss: 35.536054171048676\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(resnet1, optimizer, criteria, train_loader, validation_loader, num_of_epoch=5, model_name=model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10b735ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:20:29.598228Z",
     "start_time": "2021-09-20T16:19:41.128221Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 29.669496536254883\n",
      "Minimum: 0.0, Maximum: 96.0, Median: 29.0\n",
      "Mean Absolute Error: 29.79450035095215\n",
      "Minimum: 0.0, Maximum: 96.0, Median: 30.0\n"
     ]
    }
   ],
   "source": [
    "resnet1.load_state_dict(torch.load(model_name))\n",
    "loss_per_class = evaluation(resnet1, test_loader)\n",
    "_ = evaluation(resnet1, train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6452b440",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:20:29.661227Z",
     "start_time": "2021-09-20T16:20:29.601235Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3298\n",
      "Age: Test Sample: Loss\n",
      "1 : 1 : 76.00\n",
      "2  No Sample\n",
      "3 : 2 : 96.00\n",
      "4 : 1 : 95.00\n",
      "5 : 1 : 45.00\n",
      "6 : 2 : 68.50\n",
      "7 : 2 : 61.00\n",
      "8 : 2 : 42.00\n",
      "9 : 1 : 59.00\n",
      "10 : 3 : 46.00\n",
      "11 : 4 : 64.75\n",
      "12 : 3 : 56.00\n",
      "13 : 4 : 62.75\n",
      "14 : 5 : 56.60\n",
      "15 : 9 : 49.00\n",
      "16 : 10 : 53.30\n",
      "17 : 16 : 54.44\n",
      "18 : 24 : 46.04\n",
      "19 : 22 : 45.95\n",
      "20 : 32 : 45.97\n",
      "21 : 33 : 46.48\n",
      "22 : 37 : 42.65\n",
      "23 : 42 : 41.45\n",
      "24 : 52 : 39.48\n",
      "25 : 57 : 40.14\n",
      "26 : 55 : 39.24\n",
      "27 : 61 : 38.80\n",
      "28 : 69 : 37.23\n",
      "29 : 62 : 37.29\n",
      "30 : 77 : 38.48\n",
      "31 : 63 : 34.48\n",
      "32 : 75 : 34.84\n",
      "33 : 67 : 34.27\n",
      "34 : 72 : 34.75\n",
      "35 : 82 : 32.13\n",
      "36 : 69 : 35.57\n",
      "37 : 74 : 31.23\n",
      "38 : 76 : 31.08\n",
      "39 : 64 : 29.84\n",
      "40 : 77 : 29.21\n",
      "41 : 64 : 28.08\n",
      "42 : 71 : 29.34\n",
      "43 : 65 : 27.75\n",
      "44 : 56 : 28.00\n",
      "45 : 68 : 29.13\n",
      "46 : 57 : 24.44\n",
      "47 : 58 : 24.38\n",
      "48 : 62 : 25.11\n",
      "49 : 53 : 24.62\n",
      "50 : 63 : 26.62\n",
      "51 : 57 : 25.68\n",
      "52 : 62 : 25.37\n",
      "53 : 50 : 24.90\n",
      "54 : 51 : 24.57\n",
      "55 : 59 : 23.39\n",
      "56 : 45 : 24.53\n",
      "57 : 49 : 24.02\n",
      "58 : 49 : 22.20\n",
      "59 : 43 : 24.30\n",
      "60 : 49 : 21.20\n",
      "61 : 41 : 21.51\n",
      "62 : 48 : 23.90\n",
      "63 : 44 : 20.48\n",
      "64 : 39 : 23.87\n",
      "65 : 46 : 22.30\n",
      "66 : 35 : 21.74\n",
      "67 : 35 : 19.54\n",
      "68 : 43 : 21.14\n",
      "69 : 30 : 19.43\n",
      "70 : 43 : 19.56\n",
      "71 : 29 : 25.07\n",
      "72 : 35 : 21.40\n",
      "73 : 31 : 15.84\n",
      "74 : 28 : 16.68\n",
      "75 : 32 : 16.81\n",
      "76 : 22 : 20.86\n",
      "77 : 21 : 17.00\n",
      "78 : 21 : 18.67\n",
      "79 : 15 : 27.73\n",
      "80 : 18 : 14.00\n",
      "81 : 15 : 15.60\n",
      "82 : 14 : 34.43\n",
      "83 : 11 : 29.09\n",
      "84 : 9 : 29.44\n",
      "85 : 12 : 22.00\n",
      "86 : 7 : 51.86\n",
      "87 : 6 : 25.33\n",
      "88 : 4 : 22.00\n",
      "89 : 4 : 8.00\n",
      "90 : 4 : 23.00\n",
      "91 : 3 : 26.67\n",
      "92 : 2 : 40.00\n",
      "93 : 3 : 49.33\n",
      "94 : 1 : 67.00\n",
      "95 : 2 : 18.00\n",
      "96 : 1 : 69.00\n",
      "97 : 1 : 20.00\n",
      "98 : 1 : 71.00\n",
      "99 : 1 : 22.00\n",
      "100 : 1 : 23.00\n",
      "101 : 1 : 74.00\n"
     ]
    }
   ],
   "source": [
    "sample_per_class = findAge(test_image_list)\n",
    "print('Age: Test Sample: Loss')\n",
    "for i in range(1, 102):\n",
    "    if sample_per_class[i] == 0:\n",
    "        print(i,' No Sample')\n",
    "    else:\n",
    "        print(i,':', sample_per_class[i],':', format(loss_per_class[i].item()/sample_per_class[i],'.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38bb6666",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:23:53.232290Z",
     "start_time": "2021-09-20T16:20:29.662228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70ba94b0edf94858b9223a7d6e21b569",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11541 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Length:  11541\n",
      "Test Set Length:  3298\n",
      "Validation Set Length:  1649\n",
      "Total:  16488\n",
      "Epoch: 1/5, Train Loss: 29.32995506957337,  Validation Loss: 27.268490131084736\n",
      "Validation Loss Decreased(inf ---> 27.268490131084736)\n",
      "Epoch: 2/5, Train Loss: 26.09680683010227,  Validation Loss: 27.040675823505108\n",
      "Validation Loss Decreased(27.268490131084736 ---> 27.040675823505108)\n",
      "Epoch: 3/5, Train Loss: 24.877657418722634,  Validation Loss: 27.57297603900616\n",
      "Epoch: 4/5, Train Loss: 23.278796772380453,  Validation Loss: 29.789808860191933\n",
      "Epoch: 5/5, Train Loss: 18.935852711017315,  Validation Loss: 37.337444892296425\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_loader, test_loader, validation_loader = get_loader(\n",
    "    input_size=input_size,\n",
    "    train_image_list=train_image_list,\n",
    "    test_image_list=test_image_list, \n",
    "    validate_image_list=validate_image_list, \n",
    "    batch_size=batch_size,\n",
    "    train_augment=1,\n",
    ")\n",
    "criteria = CosFaceLoss(num_classes=num_of_class, embedding_size=num_of_class)\n",
    "optimizer = torch.optim.Adam(resnet2.parameters(), lr=learning_rate, weight_decay=1e-1)\n",
    "model_name = 'cosface.pth'\n",
    "train(resnet2, optimizer, criteria, train_loader, validation_loader, num_of_epoch=5, model_name=model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "647a89b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:24:20.208329Z",
     "start_time": "2021-09-20T16:23:53.234290Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 20.90448760986328\n",
      "Minimum: 0.0, Maximum: 81.0, Median: 18.0\n",
      "Mean Absolute Error: 20.7838134765625\n",
      "Minimum: 0.0, Maximum: 83.0, Median: 18.0\n"
     ]
    }
   ],
   "source": [
    "resnet2.load_state_dict(torch.load(model_name))\n",
    "loss_per_class = evaluation(resnet2, test_loader)\n",
    "_ = evaluation(resnet2, train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4cac931a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T16:24:20.336328Z",
     "start_time": "2021-09-20T16:24:20.210327Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3298\n",
      "Age: Test Sample: Loss\n",
      "1 : 1 : 56.00\n",
      "2  No Sample\n",
      "3 : 2 : 68.00\n",
      "4 : 1 : 53.00\n",
      "5 : 1 : 66.00\n",
      "6 : 2 : 58.00\n",
      "7 : 2 : 64.00\n",
      "8 : 2 : 63.00\n",
      "9 : 1 : 62.00\n",
      "10 : 3 : 56.33\n",
      "11 : 4 : 58.25\n",
      "12 : 3 : 56.67\n",
      "13 : 4 : 49.25\n",
      "14 : 5 : 43.80\n",
      "15 : 9 : 50.56\n",
      "16 : 10 : 50.10\n",
      "17 : 16 : 48.75\n",
      "18 : 24 : 46.62\n",
      "19 : 22 : 45.64\n",
      "20 : 32 : 47.09\n",
      "21 : 33 : 42.58\n",
      "22 : 37 : 45.97\n",
      "23 : 42 : 43.33\n",
      "24 : 52 : 39.96\n",
      "25 : 57 : 37.91\n",
      "26 : 55 : 39.40\n",
      "27 : 61 : 36.39\n",
      "28 : 69 : 36.87\n",
      "29 : 62 : 34.32\n",
      "30 : 77 : 32.75\n",
      "31 : 63 : 34.32\n",
      "32 : 75 : 31.72\n",
      "33 : 67 : 30.76\n",
      "34 : 72 : 27.67\n",
      "35 : 82 : 27.55\n",
      "36 : 69 : 24.58\n",
      "37 : 74 : 25.97\n",
      "38 : 76 : 24.22\n",
      "39 : 64 : 22.30\n",
      "40 : 77 : 22.14\n",
      "41 : 64 : 19.12\n",
      "42 : 71 : 19.17\n",
      "43 : 65 : 16.66\n",
      "44 : 56 : 15.38\n",
      "45 : 68 : 14.47\n",
      "46 : 57 : 13.05\n",
      "47 : 58 : 12.66\n",
      "48 : 62 : 12.10\n",
      "49 : 53 : 10.25\n",
      "50 : 63 : 8.56\n",
      "51 : 57 : 7.11\n",
      "52 : 62 : 5.90\n",
      "53 : 50 : 6.26\n",
      "54 : 51 : 3.27\n",
      "55 : 59 : 3.49\n",
      "56 : 45 : 3.24\n",
      "57 : 49 : 0.71\n",
      "58 : 49 : 1.98\n",
      "59 : 43 : 2.00\n",
      "60 : 49 : 3.02\n",
      "61 : 41 : 4.22\n",
      "62 : 48 : 5.08\n",
      "63 : 44 : 5.89\n",
      "64 : 39 : 6.82\n",
      "65 : 46 : 8.22\n",
      "66 : 35 : 9.00\n",
      "67 : 35 : 10.00\n",
      "68 : 43 : 10.84\n",
      "69 : 30 : 12.00\n",
      "70 : 43 : 12.72\n",
      "71 : 29 : 14.00\n",
      "72 : 35 : 14.80\n",
      "73 : 31 : 16.00\n",
      "74 : 28 : 18.71\n",
      "75 : 32 : 17.56\n",
      "76 : 22 : 19.00\n",
      "77 : 21 : 20.00\n",
      "78 : 21 : 21.00\n",
      "79 : 15 : 22.00\n",
      "80 : 18 : 23.00\n",
      "81 : 15 : 24.00\n",
      "82 : 14 : 25.00\n",
      "83 : 11 : 26.00\n",
      "84 : 9 : 27.00\n",
      "85 : 12 : 27.42\n",
      "86 : 7 : 29.00\n",
      "87 : 6 : 30.00\n",
      "88 : 4 : 31.00\n",
      "89 : 4 : 32.00\n",
      "90 : 4 : 33.00\n",
      "91 : 3 : 34.00\n",
      "92 : 2 : 35.00\n",
      "93 : 3 : 36.00\n",
      "94 : 1 : 37.00\n",
      "95 : 2 : 38.00\n",
      "96 : 1 : 39.00\n",
      "97 : 1 : 40.00\n",
      "98 : 1 : 41.00\n",
      "99 : 1 : 42.00\n",
      "100 : 1 : 43.00\n",
      "101 : 1 : 44.00\n"
     ]
    }
   ],
   "source": [
    "sample_per_class = findAge(test_image_list)\n",
    "print('Age: Test Sample: Loss')\n",
    "for i in range(1, 102):\n",
    "    if sample_per_class[i] == 0:\n",
    "        print(i,' No Sample')\n",
    "    else:\n",
    "        print(i,':', sample_per_class[i],':', format(loss_per_class[i].item()/sample_per_class[i],'.2f'))"
   ]
  }
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