ContrastiveLoss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06aabb63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:12:36.106493Z",
     "start_time": "2021-09-20T14:12:33.864496Z"
    },
    "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",
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     "end_time": "2021-09-20T14:12:36.122492Z",
     "start_time": "2021-09-20T14:12:36.107492Z"
    },
    "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": "2021-09-20T14:12:36.138492Z",
     "start_time": "2021-09-20T14:12:36.123492Z"
    },
    "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": "2021-09-20T14:13:55.796719Z",
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   },
   "outputs": [
    {
     "data": {
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       "model_id": "05ac51ed6c374ecfb94e9270b1d1ba49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11541 [00:00<?, ?it/s]"
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     "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": "2021-09-20T14:13:57.827279Z",
     "start_time": "2021-09-20T14:13:55.798720Z"
    },
    "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": 7,
   "id": "3ca9b2a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:15:41.881682Z",
     "start_time": "2021-09-20T14:15:41.863682Z"
    },
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from pytorch_metric_learning.losses import ContrastiveLoss\n",
    "criteria = ContrastiveLoss()\n",
    "optimizer = torch.optim.Adam(resnet1.parameters(), lr=learning_rate)\n",
    "model_name = 'contrast-aug.pth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ed4bb3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:20:33.355710Z",
     "start_time": "2021-09-20T14:15:42.503682Z"
    },
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/5, Train Loss: 1.0089165150459403,  Validation Loss: 1.032126206618089\n",
      "Validation Loss Decreased(inf ---> 1.032126206618089)\n",
      "Epoch: 2/5, Train Loss: 1.0058821393554942,  Validation Loss: 1.046208422917586\n",
      "Epoch: 3/5, Train Loss: 1.004900832457736,  Validation Loss: 1.0356595516204834\n",
      "Epoch: 4/5, Train Loss: 1.0037781556593977,  Validation Loss: 1.0797595427586482\n",
      "Epoch: 5/5, Train Loss: 1.00092295900922,  Validation Loss: 1.1341925309254572\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": 9,
   "id": "10b735ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:21:41.925820Z",
     "start_time": "2021-09-20T14:21:13.065822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 29.03972053527832\n",
      "Minimum: 0.0, Maximum: 90.0, Median: 29.0\n",
      "Mean Absolute Error: 29.48236656188965\n",
      "Minimum: 0.0, Maximum: 93.0, 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": 10,
   "id": "6452b440",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:22:14.359278Z",
     "start_time": "2021-09-20T14:22:14.289280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3298\n",
      "Age: Test Sample: Loss\n",
      "1 : 1 : 84.00\n",
      "2  No Sample\n",
      "3 : 2 : 51.00\n",
      "4 : 1 : 76.00\n",
      "5 : 1 : 31.00\n",
      "6 : 2 : 30.50\n",
      "7 : 2 : 41.00\n",
      "8 : 2 : 32.00\n",
      "9 : 1 : 27.00\n",
      "10 : 3 : 50.33\n",
      "11 : 4 : 31.00\n",
      "12 : 3 : 48.00\n",
      "13 : 4 : 39.00\n",
      "14 : 5 : 30.00\n",
      "15 : 9 : 36.00\n",
      "16 : 10 : 41.70\n",
      "17 : 16 : 36.88\n",
      "18 : 24 : 39.50\n",
      "19 : 22 : 40.64\n",
      "20 : 32 : 36.22\n",
      "21 : 33 : 34.76\n",
      "22 : 37 : 31.11\n",
      "23 : 42 : 32.48\n",
      "24 : 52 : 34.88\n",
      "25 : 57 : 31.28\n",
      "26 : 55 : 29.56\n",
      "27 : 61 : 31.90\n",
      "28 : 69 : 31.93\n",
      "29 : 62 : 29.21\n",
      "30 : 77 : 30.55\n",
      "31 : 63 : 29.48\n",
      "32 : 75 : 29.72\n",
      "33 : 67 : 30.03\n",
      "34 : 72 : 31.15\n",
      "35 : 82 : 26.83\n",
      "36 : 69 : 28.94\n",
      "37 : 74 : 27.49\n",
      "38 : 76 : 29.09\n",
      "39 : 64 : 27.14\n",
      "40 : 77 : 28.13\n",
      "41 : 64 : 28.73\n",
      "42 : 71 : 30.92\n",
      "43 : 65 : 28.55\n",
      "44 : 56 : 29.91\n",
      "45 : 68 : 28.50\n",
      "46 : 57 : 25.35\n",
      "47 : 58 : 23.52\n",
      "48 : 62 : 27.05\n",
      "49 : 53 : 25.11\n",
      "50 : 63 : 24.57\n",
      "51 : 57 : 21.42\n",
      "52 : 62 : 23.89\n",
      "53 : 50 : 26.92\n",
      "54 : 51 : 24.75\n",
      "55 : 59 : 21.42\n",
      "56 : 45 : 24.00\n",
      "57 : 49 : 22.18\n",
      "58 : 49 : 22.47\n",
      "59 : 43 : 23.28\n",
      "60 : 49 : 25.88\n",
      "61 : 41 : 22.68\n",
      "62 : 48 : 20.35\n",
      "63 : 44 : 22.70\n",
      "64 : 39 : 25.23\n",
      "65 : 46 : 28.35\n",
      "66 : 35 : 22.29\n",
      "67 : 35 : 28.83\n",
      "68 : 43 : 32.12\n",
      "69 : 30 : 30.93\n",
      "70 : 43 : 29.79\n",
      "71 : 29 : 32.41\n",
      "72 : 35 : 25.23\n",
      "73 : 31 : 31.71\n",
      "74 : 28 : 30.32\n",
      "75 : 32 : 32.00\n",
      "76 : 22 : 36.95\n",
      "77 : 21 : 39.43\n",
      "78 : 21 : 35.81\n",
      "79 : 15 : 29.33\n",
      "80 : 18 : 44.56\n",
      "81 : 15 : 32.60\n",
      "82 : 14 : 29.21\n",
      "83 : 11 : 39.91\n",
      "84 : 9 : 34.33\n",
      "85 : 12 : 49.42\n",
      "86 : 7 : 46.14\n",
      "87 : 6 : 42.50\n",
      "88 : 4 : 51.50\n",
      "89 : 4 : 60.25\n",
      "90 : 4 : 47.25\n",
      "91 : 3 : 69.33\n",
      "92 : 2 : 48.50\n",
      "93 : 3 : 60.33\n",
      "94 : 1 : 83.00\n",
      "95 : 2 : 37.00\n",
      "96 : 1 : 48.00\n",
      "97 : 1 : 4.00\n",
      "98 : 1 : 87.00\n",
      "99 : 1 : 88.00\n",
      "100 : 1 : 27.00\n",
      "101 : 1 : 90.00\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": 11,
   "id": "38bb6666",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:24:30.617317Z",
     "start_time": "2021-09-20T14:22:19.487424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f386c3c6cedb41c7b0e46b5028301a69",
       "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: 1.012089776468801,  Validation Loss: 1.003298268868373\n",
      "Validation Loss Decreased(inf ---> 1.003298268868373)\n",
      "Epoch: 2/5, Train Loss: 1.006551029917958,  Validation Loss: 1.0266311168670654\n",
      "Epoch: 3/5, Train Loss: 1.0030446746847133,  Validation Loss: 1.0154147102282598\n",
      "Epoch: 4/5, Train Loss: 0.9963049017466031,  Validation Loss: 1.064068262393658\n",
      "Epoch: 5/5, Train Loss: 1.0063538957428146,  Validation Loss: 1.0702399015426636\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 = ContrastiveLoss()\n",
    "optimizer = torch.optim.Adam(resnet2.parameters(), lr=learning_rate)\n",
    "model_name = 'contrast.pth'\n",
    "train(resnet2, optimizer, criteria, train_loader, validation_loader, num_of_epoch=5, model_name=model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "647a89b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:26:30.821286Z",
     "start_time": "2021-09-20T14:26:13.906880Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 26.379623413085938\n",
      "Minimum: 0.0, Maximum: 91.0, Median: 22.0\n",
      "Mean Absolute Error: 25.92790985107422\n",
      "Minimum: 0.0, Maximum: 92.0, Median: 21.0\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": 13,
   "id": "4cac931a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-20T14:27:07.288275Z",
     "start_time": "2021-09-20T14:27:07.182286Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3298\n",
      "Age: Test Sample: Loss\n",
      "1 : 1 : 31.00\n",
      "2  No Sample\n",
      "3 : 2 : 29.50\n",
      "4 : 1 : 78.00\n",
      "5 : 1 : 10.00\n",
      "6 : 2 : 26.50\n",
      "7 : 2 : 82.50\n",
      "8 : 2 : 27.00\n",
      "9 : 1 : 84.00\n",
      "10 : 3 : 13.33\n",
      "11 : 4 : 11.50\n",
      "12 : 3 : 72.33\n",
      "13 : 4 : 46.50\n",
      "14 : 5 : 30.20\n",
      "15 : 9 : 42.89\n",
      "16 : 10 : 36.60\n",
      "17 : 16 : 14.12\n",
      "18 : 24 : 20.96\n",
      "19 : 22 : 25.45\n",
      "20 : 32 : 32.00\n",
      "21 : 33 : 22.73\n",
      "22 : 37 : 24.05\n",
      "23 : 42 : 33.52\n",
      "24 : 52 : 30.58\n",
      "25 : 57 : 28.32\n",
      "26 : 55 : 29.60\n",
      "27 : 61 : 31.48\n",
      "28 : 69 : 29.25\n",
      "29 : 62 : 29.13\n",
      "30 : 77 : 27.95\n",
      "31 : 63 : 32.03\n",
      "32 : 75 : 28.97\n",
      "33 : 67 : 29.40\n",
      "34 : 72 : 30.01\n",
      "35 : 82 : 26.80\n",
      "36 : 69 : 27.61\n",
      "37 : 74 : 24.61\n",
      "38 : 76 : 24.70\n",
      "39 : 64 : 25.02\n",
      "40 : 77 : 26.17\n",
      "41 : 64 : 26.84\n",
      "42 : 71 : 24.87\n",
      "43 : 65 : 26.95\n",
      "44 : 56 : 26.12\n",
      "45 : 68 : 22.97\n",
      "46 : 57 : 29.12\n",
      "47 : 58 : 23.76\n",
      "48 : 62 : 21.85\n",
      "49 : 53 : 21.23\n",
      "50 : 63 : 24.70\n",
      "51 : 57 : 22.68\n",
      "52 : 62 : 22.55\n",
      "53 : 50 : 21.76\n",
      "54 : 51 : 22.25\n",
      "55 : 59 : 23.32\n",
      "56 : 45 : 21.42\n",
      "57 : 49 : 23.22\n",
      "58 : 49 : 24.94\n",
      "59 : 43 : 24.09\n",
      "60 : 49 : 22.51\n",
      "61 : 41 : 24.56\n",
      "62 : 48 : 22.04\n",
      "63 : 44 : 22.80\n",
      "64 : 39 : 22.95\n",
      "65 : 46 : 27.87\n",
      "66 : 35 : 25.49\n",
      "67 : 35 : 21.40\n",
      "68 : 43 : 22.70\n",
      "69 : 30 : 22.30\n",
      "70 : 43 : 23.07\n",
      "71 : 29 : 22.79\n",
      "72 : 35 : 23.51\n",
      "73 : 31 : 26.23\n",
      "74 : 28 : 25.14\n",
      "75 : 32 : 28.59\n",
      "76 : 22 : 36.77\n",
      "77 : 21 : 31.90\n",
      "78 : 21 : 33.29\n",
      "79 : 15 : 32.13\n",
      "80 : 18 : 26.61\n",
      "81 : 15 : 28.40\n",
      "82 : 14 : 31.21\n",
      "83 : 11 : 36.45\n",
      "84 : 9 : 27.56\n",
      "85 : 12 : 33.92\n",
      "86 : 7 : 38.00\n",
      "87 : 6 : 39.67\n",
      "88 : 4 : 48.75\n",
      "89 : 4 : 27.75\n",
      "90 : 4 : 28.00\n",
      "91 : 3 : 35.00\n",
      "92 : 2 : 35.50\n",
      "93 : 3 : 53.00\n",
      "94 : 1 : 40.00\n",
      "95 : 2 : 32.00\n",
      "96 : 1 : 90.00\n",
      "97 : 1 : 91.00\n",
      "98 : 1 : 12.00\n",
      "99 : 1 : 5.00\n",
      "100 : 1 : 60.00\n",
      "101 : 1 : 42.00\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": null,
   "id": "3e2de18d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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