mask_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import random\n",
    "import sys\n",
    "from glob import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch.nn.functional as F\n",
    "from tqdm import tqdm\n",
    "from time import time\n",
    "import torch\n",
    "import torch.utils.data as data\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "from efficientnet_pytorch import EfficientNet\n",
    "import copy\n",
    "from adamp import AdamP\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "print (\"PyTorch version:[%s].\"%(torch.__version__))\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print (\"device:[%s].\"%(device))\n",
    "\n",
    "def seed_everything(seed):\n",
    "    \"\"\"\n",
    "    동일한 조건으로 학습을 할 때, 동일한 결과를 얻기 위해 seed를 고정시킵니다.\n",
    "    \n",
    "    Args:\n",
    "        seed: seed 정수값\n",
    "    \"\"\"\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)  # if use multi-GPU\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "\n",
    "seed_everything(42)\n",
    "\n",
    "\n",
    "IMG_EXTENSIONS = [\n",
    "    \".jpg\", \".JPG\", \".jpeg\", \".JPEG\", \".png\",\n",
    "    \".PNG\", \".ppm\", \".PPM\", \".bmp\", \".BMP\",\n",
    "]\n",
    "\n",
    "\n",
    "def is_image_file(filename):\n",
    "    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)\n",
    "\n",
    "class MaskLabels:\n",
    "    mask = 0\n",
    "    incorrect = 1\n",
    "    normal = 2\n",
    "\n",
    "class GenderLabels:\n",
    "    male = 0\n",
    "    female = 1\n",
    "\n",
    "\n",
    "class AgeGroup:\n",
    "    map_label = lambda x: 0 if int(x) < 30 else 1 if int(x) < 58 else 2\n",
    "\n",
    "\n",
    "class MaskBaseDataset(data.Dataset):\n",
    "    num_classes = 3 * 2 * 3\n",
    "\n",
    "    _file_names = {\n",
    "        \"mask1\": MaskLabels.mask,\n",
    "        \"mask2\": MaskLabels.mask,\n",
    "        \"mask3\": MaskLabels.mask,\n",
    "        \"mask4\": MaskLabels.mask,\n",
    "        \"mask5\": MaskLabels.mask,\n",
    "        \"incorrect_mask\": MaskLabels.incorrect,\n",
    "        \"normal\": MaskLabels.normal\n",
    "    }\n",
    "\n",
    "    image_paths = []\n",
    "    mask_labels = []\n",
    "    gender_labels = []\n",
    "    age_labels = []\n",
    "\n",
    "    def __init__(self, img_dir, transform=None):\n",
    "        \"\"\"\n",
    "        MaskBaseDataset을 initialize 합니다.\n",
    "\n",
    "        Args:\n",
    "            img_dir: 학습 이미지 폴더의 root directory 입니다.\n",
    "            transform: Augmentation을 하는 함수입니다.\n",
    "        \"\"\"\n",
    "        self.img_dir = img_dir\n",
    "        self.transform = transform\n",
    "\n",
    "        self.setup()\n",
    "\n",
    "    def set_transform(self, transform):\n",
    "        \"\"\"\n",
    "        transform 함수를 설정하는 함수입니다.\n",
    "        \"\"\"\n",
    "        self.transform = transform\n",
    "        \n",
    "    def setup(self):\n",
    "        \"\"\"\n",
    "        image의 경로와 각 이미지들의 label을 계산하여 저장해두는 함수입니다.\n",
    "        \"\"\"\n",
    "        profiles = os.listdir(self.img_dir)\n",
    "        for profile in profiles:\n",
    "            if profile.startswith(\".\"):  # \".\" 로 시작하는 파일은 무시합니다\n",
    "                continue\n",
    "            img_folder = os.path.join(self.img_dir, profile)\n",
    "            for file_name in os.listdir(img_folder):\n",
    "                _file_name, ext = os.path.splitext(file_name)\n",
    "                if _file_name not in self._file_names:  # \".\" 로 시작하는 파일 및 invalid 한 파일들은 무시합니다\n",
    "                    continue\n",
    "                img_path = os.path.join(self.img_dir, profile, file_name)  # (resized_data, 000004_male_Asian_54, mask1.jpg)\n",
    "                self.image_paths.append(img_path)\n",
    "                mask_label = self._file_names[_file_name]\n",
    "                self.mask_labels.append(mask_label)\n",
    "                id, gender, race, age = profile.split(\"_\")\n",
    "                gender_label = getattr(GenderLabels, gender)\n",
    "                age_label = AgeGroup.map_label(age)\n",
    "\n",
    "                self.gender_labels.append(gender_label)\n",
    "                self.age_labels.append(age_label)\n",
    "        '''\n",
    "        추가 데이터를 불러오는 라인이지만, 외부데이터 추가시 f1 score가 더 안나오는 현상이 발생하여 뺌\n",
    "        profiles = os.listdir('./input/data/train/addition')\n",
    "        for profile in profiles:\n",
    "            img_folder = os.path.join('./input/data/train/addition', profile)\n",
    "            for file_name in os.listdir(img_folder):\n",
    "                if file_name.startswith(\".\"):\n",
    "                    continue\n",
    "                img_path = os.path.join('./input/data/train/addition', profile, file_name)\n",
    "                self.image_paths.append(img_path)\n",
    "                id,gender,race,age,mask_label = profile.split(\"_\")\n",
    "                gender_label = getattr(GenderLabels, gender)\n",
    "                age_label = AgeGroup.map_label(age)\n",
    "                self.mask_labels.append(int(mask_label))\n",
    "                self.gender_labels.append(gender_label)\n",
    "                self.age_labels.append(age_label)\n",
    "        '''\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"\n",
    "        데이터를 불러오는 함수입니다. \n",
    "        데이터셋 class에 데이터 정보가 저장되어 있고, index를 통해 해당 위치에 있는 데이터 정보를 불러옵니다.\n",
    "        Args:\n",
    "            index: 불러올 데이터의 인덱스값입니다.\n",
    "        \"\"\"\n",
    "        # 이미지를 불러옵니다.\n",
    "        image_path = self.image_paths[index]\n",
    "        image = Image.open(image_path)\n",
    "        \n",
    "        # 레이블을 불러옵니다.\n",
    "        mask_label = self.mask_labels[index]\n",
    "        gender_label = self.gender_labels[index]\n",
    "        age_label = self.age_labels[index]\n",
    "        # 각각의 label들을 모두 return해주어 각각의 loss를 따로 구한다.\n",
    "        # 이미지를 Augmentation 시킵니다.\n",
    "        image_transform = self.transform(image)\n",
    "        return image_transform, mask_label,gender_label,age_label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_paths)\n",
    "\n",
    "\n",
    "class cfg:\n",
    "    data_dir = '../input/data/train'\n",
    "    img_dir = f'{data_dir}/images'\n",
    "    df_path = f'{data_dir}/train.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.init as init\n",
    "\n",
    "def initialize_weights(model):\n",
    "    \"\"\"\n",
    "    Initialize all weights using xavier uniform. \n",
    "    For more weight initialization methods, check https://pytorch.org/docs/stable/nn.init.html\n",
    "    \"\"\"\n",
    "    for m in model.modules():\n",
    "        if isinstance(m, nn.Conv2d):\n",
    "            init.xavier_uniform_(m.weight.data)\n",
    "            if m.bias is not None:\n",
    "                m.bias.data.zero_()\n",
    "        elif isinstance(m, nn.BatchNorm2d):\n",
    "            m.weight.data.fill_(1)\n",
    "            m.bias.data.zero_()\n",
    "        elif isinstance(m, nn.Linear):\n",
    "            m.weight.data.normal_(0, 0.01)\n",
    "            m.bias.data.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import torch.optim.lr_scheduler as lr_scheduler\n",
    "\n",
    "class CosineAnnealingWarmUpRestart(lr_scheduler._LRScheduler):\n",
    "    def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):\n",
    "        if T_0 <= 0 or not isinstance(T_0, int):\n",
    "            raise ValueError(\"Expected positive integer T_0, but got {}\".format(T_0))\n",
    "        if T_mult < 1 or not isinstance(T_mult, int):\n",
    "            raise ValueError(\"Expected integer T_mult >= 1, but got {}\".format(T_mult))\n",
    "        if T_up < 0 or not isinstance(T_up, int):\n",
    "            raise ValueError(\"Expected positive integer T_up, but got {}\".format(T_up))\n",
    "        self.T_0 = T_0\n",
    "        self.T_mult = T_mult\n",
    "        self.base_eta_max = eta_max\n",
    "        self.eta_max = eta_max\n",
    "        self.T_up = T_up\n",
    "        self.T_i = T_0\n",
    "        self.gamma = gamma\n",
    "        self.cycle = 0\n",
    "        self.T_cur = last_epoch\n",
    "        super(CosineAnnealingWarmUpRestart, self).__init__(optimizer, last_epoch)\n",
    "    \n",
    "    def get_lr(self):\n",
    "        if self.T_cur == -1:\n",
    "            return self.base_lrs\n",
    "        elif self.T_cur < self.T_up:\n",
    "            return [(self.eta_max - base_lr)*self.T_cur / self.T_up + base_lr for base_lr in self.base_lrs]\n",
    "        else:\n",
    "            return [base_lr + (self.eta_max - base_lr) * (1 + math.cos(math.pi * (self.T_cur-self.T_up) / (self.T_i - self.T_up))) / 2\n",
    "                    for base_lr in self.base_lrs]\n",
    "\n",
    "    def step(self, epoch=None):\n",
    "        if epoch is None:\n",
    "            epoch = self.last_epoch + 1\n",
    "            self.T_cur = self.T_cur + 1\n",
    "            if self.T_cur >= self.T_i:\n",
    "                self.cycle += 1\n",
    "                self.T_cur = self.T_cur - self.T_i\n",
    "                self.T_i = (self.T_i - self.T_up) * self.T_mult + self.T_up\n",
    "        else:\n",
    "            if epoch >= self.T_0:\n",
    "                if self.T_mult == 1:\n",
    "                    self.T_cur = epoch % self.T_0\n",
    "                    self.cycle = epoch // self.T_0\n",
    "                else:\n",
    "                    n = int(math.log((epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))\n",
    "                    self.cycle = n\n",
    "                    self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)\n",
    "                    self.T_i = self.T_0 * self.T_mult ** (n)\n",
    "            else:\n",
    "                self.T_i = self.T_0\n",
    "                self.T_cur = epoch\n",
    "                \n",
    "        self.eta_max = self.base_eta_max * (self.gamma**self.cycle)\n",
    "        self.last_epoch = math.floor(epoch)\n",
    "        for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n",
    "            param_group['lr'] = lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version:[1.6.0].\n",
      "device:[cuda:0].\n",
      "Loaded pretrained weights for efficientnet-b4\n"
     ]
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      "Epoch[0/10](2780/2835) || training loss 0.6661 || training accuracy 82.50% || lr 3.0530055907364446e-05\n"
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      "Calculating validation results...\n"
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      "New best model for val accuracy : 83.57%! saving the best model..\n"
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      "New best model for val accuracy : 88.28%! saving the best model..\n"
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      "Calculating validation results...\n"
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      "New best model for val accuracy : 91.51%! saving the best model..\n"
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      "Epoch[4/10](2600/2835) || training loss 0.5987 || training accuracy 94.17% || lr 3.4643830634544315e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 2610/2835 [1:15:38<06:29,  1.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2610/2835) || training loss 0.6197 || training accuracy 90.00% || lr 3.4162617193631234e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 2620/2835 [1:15:56<06:15,  1.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2620/2835) || training loss 0.603 || training accuracy 95.00% || lr 3.368080847552639e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 2630/2835 [1:16:13<05:56,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2630/2835) || training loss 0.5923 || training accuracy 95.83% || lr 3.319852335932151e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 2640/2835 [1:16:30<05:39,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2640/2835) || training loss 0.5811 || training accuracy 93.33% || lr 3.2715880841652446e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 2650/2835 [1:16:48<05:21,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2650/2835) || training loss 0.6107 || training accuracy 95.00% || lr 3.2233000007338485e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 2660/2835 [1:17:05<05:04,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2660/2835) || training loss 0.5843 || training accuracy 95.00% || lr 3.175e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 2670/2835 [1:17:23<04:45,  1.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2670/2835) || training loss 0.6496 || training accuracy 89.17% || lr 3.126699999266152e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▍| 2680/2835 [1:17:40<04:34,  1.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2680/2835) || training loss 0.6384 || training accuracy 94.17% || lr 3.078411915834756e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▍| 2690/2835 [1:17:57<04:12,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2690/2835) || training loss 0.617 || training accuracy 95.83% || lr 3.0301476640678485e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▌| 2700/2835 [1:18:15<03:55,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2700/2835) || training loss 0.6587 || training accuracy 93.33% || lr 2.9819191524473622e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 2710/2835 [1:18:32<03:37,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2710/2835) || training loss 0.5938 || training accuracy 94.17% || lr 2.9337382806368775e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 2720/2835 [1:18:49<03:20,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2720/2835) || training loss 0.5973 || training accuracy 94.17% || lr 2.8856169365455687e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▋| 2730/2835 [1:19:07<03:06,  1.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2730/2835) || training loss 0.6439 || training accuracy 91.67% || lr 2.837566993395036e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 2740/2835 [1:19:24<02:45,  1.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2740/2835) || training loss 0.63 || training accuracy 93.33% || lr 2.789600306789765e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 2750/2835 [1:19:41<02:26,  1.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2750/2835) || training loss 0.6198 || training accuracy 90.83% || lr 2.7417287117919336e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 2760/2835 [1:19:59<02:10,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2760/2835) || training loss 0.6424 || training accuracy 93.33% || lr 2.6939640200012903e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 2770/2835 [1:20:16<01:51,  1.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2770/2835) || training loss 0.6201 || training accuracy 94.17% || lr 2.6463180166408157e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 2780/2835 [1:20:33<01:32,  1.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2780/2835) || training loss 0.6208 || training accuracy 95.00% || lr 2.5988024576488972e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 2790/2835 [1:20:50<01:15,  1.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2790/2835) || training loss 0.6023 || training accuracy 95.83% || lr 2.5514290667787247e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 2800/2835 [1:21:07<00:59,  1.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2800/2835) || training loss 0.6098 || training accuracy 91.67% || lr 2.504209532705632e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 2810/2835 [1:21:24<00:43,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2810/2835) || training loss 0.5811 || training accuracy 94.17% || lr 2.4571555061430913e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 2820/2835 [1:21:41<00:26,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2820/2835) || training loss 0.5969 || training accuracy 95.00% || lr 2.410278596968072e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 2830/2835 [1:21:59<00:08,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[4/10](2830/2835) || training loss 0.6057 || training accuracy 93.33% || lr 2.3635903713564787e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2835/2835 [1:22:08<00:00,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating validation results...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New best model for val accuracy : 94.97%! saving the best model..\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  0%|          | 0/2835 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Val] acc : 94.97%, loss: 0.62 || best acc : 94.97%, best loss: 0.62\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  0%|          | 10/2835 [00:17<1:22:06,  1.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[5/10](10/2835) || training loss 0.6017 || training accuracy 92.50% || lr 2.2939370003620206e-05\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  0%|          | 14/2835 [00:26<1:28:02,  1.87s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-e88a795e6564>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m             \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0mloss_value\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/optim/lr_scheduler.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m                 \u001b[0mwrapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# Note that the returned function here is no longer a bound method,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/adamp/adamp.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0mwd_ratio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m                     \u001b[0mperturb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd_ratio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperturb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'delta'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wd_ratio'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m                 \u001b[0;31m# Weight decay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/adamp/adamp.py\u001b[0m in \u001b[0;36m_projection\u001b[0;34m(self, p, grad, perturb, delta, wd_ratio, eps)\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mcosine_sim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cosine_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mcosine_sim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mdelta\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mview_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m                 \u001b[0mp_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mview_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpand_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m                 \u001b[0mperturb\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mp_n\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mview_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp_n\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mperturb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpand_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from torchvision import datasets, models, transforms\n",
    "from torchvision.transforms import Resize, ToTensor, Normalize,CenterCrop\n",
    "\n",
    "#원래는 transform으로 아주 다양한 방법들을 모두 적용해 보았지만 결국에는 대부분을 뺐을때 성능이 가장 잘 나왔다.\n",
    "transform = transforms.Compose([\n",
    "            ToTensor(),\n",
    "    Normalize(mean=(0.548, 0.504, 0.479), std=(0.237, 0.247, 0.246))\n",
    "        ])\n",
    "dataset = MaskBaseDataset(\n",
    "    img_dir=cfg.img_dir,transform=transform\n",
    ")\n",
    "\n",
    "\n",
    "n_val = int(len(dataset) * 0.2)\n",
    "n_train = len(dataset) - n_val\n",
    "train_dataset, val_dataset = data.random_split(dataset, [n_train, n_val])\n",
    "\n",
    "\n",
    "train_loader = data.DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=16,\n",
    "    num_workers=4,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "val_loader = data.DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=16,\n",
    "    num_workers=4,\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "save_dir = os.path.join('model', 'efficient-b4')\n",
    "model = EfficientNet.from_pretrained('efficientnet-b4')\n",
    "model._fc = nn.Linear(1792, 18)\n",
    "initialize_weights(model._fc)\n",
    "model_ft = model.to(device)\n",
    "criterion1 = nn.CrossEntropyLoss()\n",
    "criterion2 = FocalLoss()\n",
    "optimizer = AdamP(model.parameters(), lr=0.000001, betas=(0.9, 0.999), weight_decay=1e-4)\n",
    "#scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=5000, T_mult=2, eta_min=0.00005)\n",
    "#scheduler = CosineAnnealingWarmUpRestart(optimizer, T_0=3000, T_mult=1, eta_max=0.001,  T_up=1000, gamma=0.5)\n",
    "\n",
    "\n",
    "for epoch in range(5):\n",
    "    model_ft.train()\n",
    "    avg_loss = 0\n",
    "    for inputs, mask_label,gender_label,age_label in tqdm(train_loader):\n",
    "        inputs = inputs.to(device)\n",
    "        mask_label = mask_label.to(device)\n",
    "        gender_label = gender_label.to(device)\n",
    "        age_label = age_label.to(device)\n",
    "        outputs = model_ft(inputs)\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        loss1 = criterion1(outputs[:,:3],mask_label)\n",
    "        #loss2 = criterion2(outputs[:,:3],mask_label)\n",
    "        loss3 = criterion1(outputs[:,3:5],gender_label)\n",
    "        #loss4 = criterion2(outputs[:,3:5],gender_label)\n",
    "        loss5 = criterion1(outputs[:,5:8],age_label)\n",
    "        #loss6 = criterion2(outputs[:,5:8],age_label)\n",
    "        #loss = ((0.5*loss1)+(0.5*loss2)+(0.5*loss3)+(0.5*loss4)+(0.5*loss5)+(0.5*loss6))/3\n",
    "        loss = (loss1+loss3+loss5)/3\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "        avg_loss+=loss.item()\n",
    "    print(avg_loss/len(train_loader))\n",
    "\n",
    "\n",
    "'''\n",
    "        # val loop\n",
    "        with torch.no_grad():\n",
    "            print(\"Calculating validation results...\")\n",
    "            model.eval()\n",
    "            val_loss_items = []\n",
    "            val_acc_items = []\n",
    "            figure = None\n",
    "            for val_batch in val_loader:\n",
    "                inputs, labels = val_batch\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "                outs = model(inputs)\n",
    "                preds = torch.argmax(outs, dim=-1)\n",
    "                loss_item = criterion(outs, labels).item()\n",
    "                acc_item = (labels == preds).sum().item()\n",
    "                val_loss_items.append(loss_item)\n",
    "                val_acc_items.append(acc_item)\n",
    "            val_loss = np.sum(val_loss_items) / len(val_loader)\n",
    "            val_acc = np.sum(val_acc_items) / len(val_dataset)\n",
    "            best_val_loss = min(best_val_loss, val_loss)\n",
    "            if val_acc > best_val_acc:\n",
    "                print(f\"New best model for val accuracy : {val_acc:4.2%}! saving the best model..\")\n",
    "                torch.save(model.state_dict(), f\"{save_dir}/best.pth\")\n",
    "                best_val_acc = val_acc\n",
    "                counter = 0\n",
    "            else:\n",
    "                counter+=1\n",
    "            torch.save(model.state_dict(), f\"{save_dir}/last.pth\")\n",
    "            print(\n",
    "                f\"[Val] acc : {val_acc:4.2%}, loss: {val_loss:4.2} || \"\n",
    "                f\"best acc : {best_val_acc:4.2%}, best loss: {best_val_loss:4.2}\"\n",
    "            )\n",
    "            if counter > 2:\n",
    "                print(\"Early Stopping...\")\n",
    "                break\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12600/12600 [14:53<00:00, 14.10it/s]\n"
     ]
    }
   ],
   "source": [
    "test_dir = '../input/data/eval'\n",
    "\n",
    "\n",
    "from torchvision import transforms\n",
    "from torchvision.transforms import Resize, ToTensor, Normalize,CenterCrop\n",
    "class TestDataset(data.Dataset):\n",
    "    def __init__(self, img_paths, transform):\n",
    "        self.img_paths = img_paths\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        image = Image.open(self.img_paths[index])\n",
    "\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return image\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.img_paths)\n",
    "\n",
    "\n",
    "# meta 데이터와 이미지 경로를 불러옵니다.\n",
    "submission = pd.read_csv(os.path.join(test_dir, 'info.csv'))\n",
    "image_dir = os.path.join(test_dir, 'images')\n",
    "\n",
    "\n",
    "# Test Dataset 클래스 객체를 생성하고 DataLoader를 만듭니다.\n",
    "image_paths = [os.path.join(image_dir, img_id) for img_id in submission.ImageID]\n",
    "'''\n",
    "transform = Compose([\n",
    "    Resize((512, 384), Image.BILINEAR),\n",
    "    Normalize(mean=(0.548, 0.504, 0.479), std=(0.237, 0.247, 0.246),max_pixel_value=255.0,p=1.0),\n",
    "    ToTensorV2(p=1.0)\n",
    "])\n",
    "'''\n",
    "transform = transforms.Compose([\n",
    "    ToTensor(),\n",
    "    Normalize(mean=(0.548, 0.504, 0.479), std=(0.237, 0.247, 0.246)),\n",
    "])\n",
    "dataset = TestDataset(image_paths, transformer)\n",
    "\n",
    "loader = data.DataLoader(\n",
    "    dataset,\n",
    "    shuffle=False\n",
    ")\n",
    "# 모델을 정의합니다. (학습한 모델이 있다면 torch.load로 모델을 불러주세요!)\n",
    "\n",
    "\n",
    "model_ft.eval()\n",
    "\n",
    "\n",
    "# 모델이 테스트 데이터셋을 예측하고 결과를 저장합니다.\n",
    "all_predictions = []\n",
    "for images in tqdm(loader):\n",
    "    with torch.no_grad():\n",
    "        images = images.to(device)\n",
    "        pred = model_ft(images)\n",
    "        mask_pred = pred[:,:3].argmax(dim=-1)\n",
    "        gender_pred = pred[:,3:5].argmax(dim=-1)\n",
    "        age_pred = pred[:,5:].argmax(dim=-1)\n",
    "        pred = (mask_pred * 6 + gender_pred * 3 + age_pred).cpu().numpy()\n",
    "        all_predictions.extend(pred)\n",
    "submission['ans'] = all_predictions\n",
    "\n",
    "\n",
    "\n",
    "# 제출할 파일을 저장합니다.\n",
    "submission.to_csv(os.path.join(test_dir, 'submission.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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